diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..dde3895 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.DS_Store +*.pyc diff --git a/README.md b/README.md new file mode 100644 index 0000000..68c4a32 --- /dev/null +++ b/README.md @@ -0,0 +1,108 @@ +# Explanations Communicate Optimal Interventions + + +This repository contains the experiment, data, analyses and figured for the CogSci 2024 paper "Do as I explain" Explanations communicate optimal interventions" by Lara Kirfel, Jacqueline Harding, Jeong Shin, Cindy Wu, Thomas Icard and Tobias Gerstenberg. + +## Abstract + +People often select only a few events when explaining what happened. What drives people's explanation selection? Prior research argued that people's explanation choices are affected by event normality and causal structure. Here, we propose a new model of these existing findings and test its predictions in a novel experiment. The model predicts that speakers value accuracy and relevance. They choose explanations that are true, and that communicate useful information to the listener. We test the model's predictions empirically by manipulating what goals a listener has and what actions they can take. Across twelve experimental conditions, we find that our model accurately predicts that people like to choose explanations that communicate optimal interventions. + +## Pre-registrations + +The pre-registrations for all experiments may be accessed via the Open Science Framework [here](https://osf.io/fpyst/). + +Here are separate links for each experiment: + - [Condition "Hard Intervention / Negative Outcome"](https://osf.io/8k9sy) + - [Condition "Hard Intervention / Positive Outcome"](https://osf.io/7qzu9) + - [Condition "Soft Intervention / Negative Outcome"](https://osf.io/aw286) + - [Condition "Soft Intervention / Positive Outcome"](https://osf.io/dmgcw) + - [Condition "Fixed Intervention / Negative Outcome"](https://osf.io/49bfq) + - [Condition "Fixed Intervention / Positive Outcome"](https://osf.io/rbu7y) + +## Repository structure + +``` +. +├── code +│   └── R +├── data +├── docs +│   ├── analyses +│   ├── experiment_1 +│   ├── experiment_2 +│   └── experiment_3 +├── figures +│   └── plots +└── writeup + └── cogsci +``` + +### code + +This folder contains two types of R scripts. + +- The model predictions can be seen [here](https://cicl-stanford.github.io/explanation_intervention/analyses/model/index.html) +- The analyses and plots can be seen [here](https://cicl-stanford.github.io/explanation_intervention/analyses/experiments/index.html) + +- R/optimal_intervention_model: + - R/optimal_intervention.rmd creates the model predictions for the intervention model, the truth model and a combined model. + - explanation_predictions_truth_only.csv, for example, contains the predictions for a "Truth Only" model. +- R/experiments: This folder contains all raw data from all experimental conditions. + - [...]study_X-responses.csv contains the response data (i.e., intervention and explanation selection). + - [...]study_X-participants.csv contains demographic information and post-experiment feedback/comments from participants. + - Experiment.rmd reads in the response data from each experimental condition (e.g., fixed intervention / negative), calculates average responses and outputs these in a new data file (e.g., fixedint_negative.csv). + +### docs + +Contains all the experiment code. You can preview the experiments below: + +- [Condition "Hard Intervention / Positive Outcome"](https://cicl-stanford.github.io/explanation_intervention/experiment_1/index.html?condition=1) + +- [Condition "Hard Intervention / Negative Outcome"](https://cicl-stanford.github.io/explanation_intervention/experiment_1/index.html?condition=3) + +- [Condition "Soft Intervention / Positive Outcome"](https://cicl-stanford.github.io/explanation_intervention/experiment_2/index.html?condition=1) + +- [Condition "Soft Intervention / Negative Outcome"](https://cicl-stanford.github.io/explanation_intervention/experiment_2/index.html?condition=3) + +- [Condition "Fixed Intervention / Positive Outcome"](https://cicl-stanford.github.io/explanation_intervention/experiment_3/index.html?condition=1) + +- [Condition "Fixed Intervention / Negative Outcome"](https://cicl-stanford.github.io/explanation_intervention/experiment_3/index.html?condition=3) + + +### data + +Contains anonymized combined data for all experimental conditions (hard / soft / fixed intervention x positive / negative outcome) (for raw data and how these were computed, see code/R/). + +For example, fixedint_negative.csv contains the average percentage of choice selection (abnormal switch, normal switch, no preference) in the intervention and explanation task. + +data_int.csv combines all four dataframes (hardint_pos, hardint_neg, softint_pos, softint_neg, fixedint_pos, fixedint_neg) + + +### figures + +Contains all the figures from the paper (generated using the script in code/R/experiments). + +### writeup + +Contains a pdf of the CogSci 2024 paper. + +## CRediT + +Please see [here](https://www.elsevier.com/researcher/author/policies-and-guidelines/credit-author-statement) for definitions of the different terms. + +| | Lara | Jacqueline | Jeong | Cindy | Thomas | Tobias | +|----------------------------|------|------------|-------|-------|--------|--------| +| Conceptualization | X | | | | X | X | +| Methodology | X | X | X | | | X | +| Software | X | X | X | X | | X | +| Validation | X | | | | | X | +| Formal analysis | X | X | | | | X | +| Investigation | X | | X | | | | +| Resources | | | | | | | +| Data Curation | X | | | | | X | +| Writing - Original Draft | X | X | | | | | +| Writing - Review & Editing | X | X | X | X | X | X | +| Visualization | | | | | | X | +| Supervision | | | | | X | X | +| Project administration | | | | | | X | +| Funding acquisition | | | | | | X | \ No newline at end of file diff --git a/code/R/experiments-readme.md b/code/R/experiments-readme.md new file mode 100644 index 0000000..fa77071 --- /dev/null +++ b/code/R/experiments-readme.md @@ -0,0 +1,51 @@ +# Experiments readme + +Information about each of the experiment that is run as part of this project. + +Experiment 1: Missing --- was it turned into Experiment 1b? + +Experiment 1b: "Hard Interventions" +"The Influence of Outcome Valence on Explanation Selection in Positive / Negative Outcome Cases" +People select a hard intervention that turns the switch ON or OFF in Conjunctive and Disjunctive Structures with positive and negative outcomes. +Condition 1: Con/Pos , Dis/Pos +Condition 2: Dis/Pos , Con/Pos +Condition 3: Con/Neg , Dis/Neg +Condition 4: Dis/Neg , Con/Neg + +Positive Condition pre-reg: https://osf.io/7qzu9 +Negative Condition pre-reg: https://osf.io/8k9sy + +Experiment 2: "Probability Estimation" +"Estimating outcome probability in causal structures with positive and negative outcomes" +One Condition +Pre-reg: https://osf.io/dmgcw + +Experiment 3: "Soft interventions" +"The Influence of Normality on Explanation Selection in Soft Intervention Cases for Positive and Negative Outcomes" +People select a soft intervention of increasing or decreasing the probablity by 20%. +Condition 1: Con/Pos , Dis/Pos +Condition 2: Dis/Pos , Con/Pos +Condition 3: Con/Neg , Dis/Neg +Condition 4: Dis/Neg , Con/Neg + + +Positive Condition pre-reg: https://osf.io/dmgcw +Negative Condition pre-reg: https://osf.io/aw286 + +Experiment 4: Fixed Interventions +"The Influence of Normality on Explanation Selection in Fixed Intervention Cases for Positive and Negative Outcomes" +People select a fixed intervention of increasing or decreasing the probablity to 90%/10%, irrespective of the probability + +Condition 1: Con/Pos , Dis/Pos +Condition 2: Dis/Pos , Con/Pos +Condition 3: Con/Neg , Dis/Neg +Condition 4: Dis/Neg , Con/Neg + +Experiment 5: Cost of Interventions, with Intervention Task +"The Influence of Cost of Interventions on Explanation Selection" +People select an intervention that is either cheap or expensive. Includes Intervention Task + +Condition 1: Con/Pos +Condition 2: Dis/Pos + + diff --git a/code/R/experiments/Experiment.Rmd b/code/R/experiments/Experiment.Rmd new file mode 100644 index 0000000..cda070b --- /dev/null +++ b/code/R/experiments/Experiment.Rmd @@ -0,0 +1,1083 @@ +--- +title: "Explanation and Intervention Data" +author: "Tobias Gerstenberg & Lara Kirfel" +date: "`r format(Sys.Date(), '%B %d, %Y')`" +bibliography: grateful-refs.bib +output: + bookdown::html_document2: + toc: true + toc_depth: 4 + toc_float: true + theme: cosmo + highlight: tango +--- + +```{r, message=FALSE} +library("knitr") +library("modelr") # for bootstrapping +library("patchwork") # making figure panels +library("tidyverse") # for data wrangling, visualization, etc. +``` + +# Set options + +```{r} +theme_set(theme_classic() + + theme(text = element_text(size = 24))) + +opts_chunk$set(comment = "", + fig.show = "hold") + +# suppress grouping warning +options(dplyr.summarise.inform = F) +``` + +# Condition 1: Hard Intervention: Positive Outcome +## Read in Data +```{r} + +#### Read in Data +df.responses = read.csv(file = "explanation_selection_positive_outcome_study_2-responses.csv", stringsAsFactors = F, sep = ",") %>% + select(-error) + + +df.participants <- read.csv(file = "explanation_selection_positive_outcome_study_2-participants.csv", stringsAsFactors = F, sep = ",") %>% + select(-c(proliferate.condition, error)) + + +df.data1 <- merge(df.responses, df.participants, by="workerid") +``` + +## Wrangle Data + +```{r} +df.exp1 <- df.data1 %>% + gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% + mutate(CausalStructure = + case_when( + str_detect(proliferate.condition, "Conpos_") & str_detect(index, "response_1") ~ "Conjunctive", + str_detect(proliferate.condition, "Conpos_") & str_detect(index, "response_2") ~ "Disjunctive", + str_detect(proliferate.condition, "Dispos_") & str_detect(index, "response_1") ~ "Disjunctive", + str_detect(proliferate.condition, "Dispos_") & str_detect(index, "response_2") ~ "Conjunctive"))%>% + mutate(Order = + case_when( + str_detect(proliferate.condition, "Conpos_") ~ "conjunctive first", + str_detect(proliferate.condition, "Dispos_") ~ "disjunctive first"))%>% + mutate(ResponseType = + case_when( + str_detect(index, "explanation") ~ "explanation", + str_detect(index, "intervention") ~ "intervention"))%>% + select(-index) %>% + spread(ResponseType, response) %>% + mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>% + mutate(explanation = recode (explanation, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(intervention = recode (intervention, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(abnormal_explanation = + case_when( + explanation == "abnormal" ~ "abnormal", + explanation != "abnormal"~ "other")) %>% + mutate(normal_explanation = + case_when( + explanation == "normal" ~ "normal", + explanation != "normal"~ "other")) %>% + mutate(nopreference_explanation = + case_when( + explanation == "no preference" ~ "no preference", + explanation != "no preference"~ "other")) %>% + mutate(abnormal_intervention = + case_when( + intervention== "abnormal" ~ "abnormal", + intervention!= "abnormal"~ "other")) %>% + mutate(normal_intervention = + case_when( + intervention == "normal" ~ "normal", + intervention != "normal"~ "other")) %>% + mutate(nopreference_intervention = + case_when( + intervention== "no preference" ~ "no preference", + intervention!= "no preference"~ "other")) %>% + mutate(intervention = "hard") %>% + mutate(outcome = "positive") + + + + +``` + +## Prepate Dataset + +```{r} +df.exp1_summary <- df.exp1 %>% + group_by(CausalStructure) %>% + summarise( + abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100, + normal_explanation_percentage = mean(normal_explanation == "normal") * 100, + nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100, + + abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100, + normal_intervention_percentage = mean(normal_intervention == "normal") * 100, + nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100 + ) %>% + mutate(Outcome = "positive") %>% + mutate(Experiment = "hardint") + +write_csv(df.exp1_summary, "hardint_pos.csv") + +``` + +# Condition 2: Hard Intervention: Negative Outcome + +## Read in Data + +```{r} + +#### Read in Data +df.responses = read.csv(file = "explanation_selection_negative_outcome-responses.csv", stringsAsFactors = F, sep = ",") %>% + select(-error) + + +df.participants <- read.csv(file = "explanation_selection_negative_outcome-participants.csv", stringsAsFactors = F, sep = ",") %>% + select(-c(proliferate.condition, error)) + + +df.data2 <- merge(df.responses, df.participants, by="workerid") +``` + +## Wrangle Data + +```{r} +df.exp2 <- df.data2 %>% + gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% + mutate(CausalStructure = + case_when( + str_detect(proliferate.condition, "Conneg_") & str_detect(index, "response_1") ~ "Conjunctive", + str_detect(proliferate.condition, "Conneg_") & str_detect(index, "response_2") ~ "Disjunctive", + str_detect(proliferate.condition, "Disneg_") & str_detect(index, "response_1") ~ "Disjunctive", + str_detect(proliferate.condition, "Disneg_") & str_detect(index, "response_2") ~ "Conjunctive"))%>% + mutate(Order = + case_when( + str_detect(proliferate.condition, "Conneg_") ~ "conjunctive first", + str_detect(proliferate.condition, "Disneg_") ~ "disjunctive first"))%>% + mutate(ResponseType = + case_when( + str_detect(index, "explanation") ~ "explanation", + str_detect(index, "intervention") ~ "intervention"))%>% + select(-index) %>% + spread(ResponseType, response) %>% + mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>% + mutate(explanation = recode (explanation, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(intervention = recode (intervention, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(abnormal_explanation = + case_when( + explanation == "abnormal" ~ "abnormal", + explanation != "abnormal"~ "other")) %>% + mutate(normal_explanation = + case_when( + explanation == "normal" ~ "normal", + explanation != "normal"~ "other")) %>% + mutate(nopreference_explanation = + case_when( + explanation == "no preference" ~ "no preference", + explanation != "no preference"~ "other")) %>% + mutate(abnormal_intervention = + case_when( + intervention== "abnormal" ~ "abnormal", + intervention!= "abnormal"~ "other")) %>% + mutate(normal_intervention = + case_when( + intervention == "normal" ~ "normal", + intervention != "normal"~ "other")) %>% + mutate(nopreference_intervention = + case_when( + intervention== "no preference" ~ "no preference", + intervention!= "no preference"~ "other")) %>% + mutate(intervention = "hard") %>% + mutate(outcome = "negative") + +``` + +## Prepare Dataset + +```{r} +df.exp2_summary <- df.exp2 %>% + group_by(CausalStructure) %>% + summarise( + abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100, + normal_explanation_percentage = mean(normal_explanation == "normal") * 100, + nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100, + + abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100, + normal_intervention_percentage = mean(normal_intervention == "normal") * 100, + nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100 + ) %>% + mutate(Outcome = "negative") %>% + mutate(Experiment = "hardint") + +write_csv(df.exp2_summary, "hardint_neg.csv") + +``` + +# Condition 3: Soft Intervention: Positive Outcome + +## Read in Data + +```{r} +###Experiment 4 +#### Read in Data + +df.responses = read.csv(file = "pressbutton_positivecondition-responses.csv", stringsAsFactors = F, sep = ",") %>% + select(-error) + + +df.participants <- read.csv(file = "pressbutton_positivecondition-participants.csv", stringsAsFactors = F, sep = ",") %>% + select(-c(proliferate.condition, error)) + + +df.data3 <- merge(df.responses, df.participants, by="workerid") +``` + +## Wrangle Data + +```{r} +df.exp3 <- df.data3 %>% + gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% + mutate(CausalStructure = + case_when( + str_detect(proliferate.condition, "Condition_1") & str_detect(index, "response_1") ~ "Conjunctive", + str_detect(proliferate.condition, "Condition_1") & str_detect(index, "response_2") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition_2") & str_detect(index, "response_1") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition_2") & str_detect(index, "response_2") ~ "Conjunctive"))%>% + mutate(Order = + case_when( + str_detect(proliferate.condition, "Condition_1") ~ "conjunctive first", + str_detect(proliferate.condition, "Condition_2") ~ "disjunctive first"))%>% + mutate(ResponseType = + case_when( + str_detect(index, "explanation") ~ "explanation", + str_detect(index, "intervention") ~ "intervention"))%>% + select(-index) %>% + spread(ResponseType, response) %>% + mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>% + mutate(explanation = recode (explanation, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(intervention = recode (intervention, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(abnormal_explanation = + case_when( + explanation == "abnormal" ~ "abnormal", + explanation != "abnormal"~ "other")) %>% + mutate(normal_explanation = + case_when( + explanation == "normal" ~ "normal", + explanation != "normal"~ "other")) %>% + mutate(nopreference_explanation = + case_when( + explanation == "no preference" ~ "no preference", + explanation != "no preference"~ "other")) %>% + mutate(abnormal_intervention = + case_when( + intervention== "abnormal" ~ "abnormal", + intervention!= "abnormal"~ "other")) %>% + mutate(normal_intervention = + case_when( + intervention == "normal" ~ "normal", + intervention != "normal"~ "other")) %>% + mutate(nopreference_intervention = + case_when( + intervention== "no preference" ~ "no preference", + intervention!= "no preference"~ "other"))%>% + mutate(intervention = "soft") %>% + mutate(outcome = "positive") + + +``` + +## Prepare Dataset + +```{r} +df.exp3_summary <- df.exp3 %>% + group_by(CausalStructure) %>% + summarise( + abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100, + normal_explanation_percentage = mean(normal_explanation == "normal") * 100, + nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100, + + abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100, + normal_intervention_percentage = mean(normal_intervention == "normal") * 100, + nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100 + ) %>% + mutate(Outcome = "positive") %>% + mutate(Experiment = "softint") + +write_csv(df.exp3_summary, "softint_pos.csv") + +``` + +# Condition 4: Soft Intervention: Negative Outcome +## Read in Data + +```{r} +###Experiment 4 +#### Read in Data + +df.responses = read.csv(file = "pressbutton_negativecondition-responses.csv", stringsAsFactors = F, sep = ",") %>% + select(-error) + + +df.participants <- read.csv(file = "pressbutton_negativecondition-participants.csv", stringsAsFactors = F, sep = ",") %>% + select(-c(proliferate.condition, error)) + + +df.data4 <- merge(df.responses, df.participants, by="workerid") %>% + filter(!row_number() %in% c(71, 72)) +``` + +## Wrangle Data + +```{r} +df.exp4 <- df.data4 %>% + gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% + mutate(CausalStructure = + case_when( + str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_1") ~ "Conjunctive", + str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_2") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_1") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_2") ~ "Conjunctive"))%>% + mutate(Order = + case_when( + str_detect(proliferate.condition, "Condition_3") ~ "conjunctive first", + str_detect(proliferate.condition, "Condition_4") ~ "disjunctive first"))%>% + mutate(ResponseType = + case_when( + str_detect(index, "explanation") ~ "explanation", + str_detect(index, "intervention") ~ "intervention"))%>% + select(-index) %>% + spread(ResponseType, response) %>% + mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>% + mutate(explanation = recode (explanation, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(intervention = recode (intervention, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(abnormal_explanation = + case_when( + explanation == "abnormal" ~ "abnormal", + explanation != "abnormal"~ "other")) %>% + mutate(normal_explanation = + case_when( + explanation == "normal" ~ "normal", + explanation != "normal"~ "other")) %>% + mutate(nopreference_explanation = + case_when( + explanation == "no preference" ~ "no preference", + explanation != "no preference"~ "other")) %>% + mutate(abnormal_intervention = + case_when( + intervention== "abnormal" ~ "abnormal", + intervention!= "abnormal"~ "other")) %>% + mutate(normal_intervention = + case_when( + intervention == "normal" ~ "normal", + intervention != "normal"~ "other")) %>% + mutate(nopreference_intervention = + case_when( + intervention== "no preference" ~ "no preference", + intervention!= "no preference"~ "other")) %>% + mutate(intervention = "soft") %>% + mutate(outcome = "negative") + + +``` + +## Prepare Dataset + +```{r} +df.exp4_summary <- df.exp4 %>% + group_by(CausalStructure) %>% + summarise( + abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100, + normal_explanation_percentage = mean(normal_explanation == "normal") * 100, + nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100, + + abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100, + normal_intervention_percentage = mean(normal_intervention == "normal") * 100, + nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100 + ) %>% + mutate(Outcome = "negative") %>% + mutate(Experiment = "softint") + +write_csv(df.exp4_summary, "softint_neg.csv") + +``` + +# Condition 5: Fixed Intervention: Positive Outcome +## Read in Data + +```{r} +###Experiment 5 +#### Read in Data + +df.responses = read.csv(file = "fixedintervention_positive-responses.csv", stringsAsFactors = F, sep = ",") %>% + select(-error) + + +df.participants <- read.csv(file = "fixedintervention_positive-participants.csv", stringsAsFactors = F, sep = ",") %>% + select(-c(proliferate.condition, error)) + + +df.data5 <- merge(df.responses, df.participants, by="workerid") +``` + +## Wrangle Data + +```{r} +df.exp5 <- df.data5 %>% + gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% + mutate(CausalStructure = + case_when( + str_detect(proliferate.condition, "Condition1") & str_detect(index, "response_1") ~ "Conjunctive", + str_detect(proliferate.condition, "Condition1") & str_detect(index, "response_2") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition2") & str_detect(index, "response_1") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition2") & str_detect(index, "response_2") ~ "Conjunctive"))%>% + mutate(Order = + case_when( + str_detect(proliferate.condition, "Condition1") ~ "conjunctive first", + str_detect(proliferate.condition, "Condition2") ~ "disjunctive first"))%>% + mutate(ResponseType = + case_when( + str_detect(index, "explanation") ~ "explanation", + str_detect(index, "intervention") ~ "intervention"))%>% + select(-index) %>% + spread(ResponseType, response) %>% + mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>% + mutate(explanation = recode (explanation, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(intervention = recode (intervention, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(abnormal_explanation = + case_when( + explanation == "abnormal" ~ "abnormal", + explanation != "abnormal"~ "other")) %>% + mutate(normal_explanation = + case_when( + explanation == "normal" ~ "normal", + explanation != "normal"~ "other")) %>% + mutate(nopreference_explanation = + case_when( + explanation == "no preference" ~ "no preference", + explanation != "no preference"~ "other")) %>% + mutate(abnormal_intervention = + case_when( + intervention== "abnormal" ~ "abnormal", + intervention!= "abnormal"~ "other")) %>% + mutate(normal_intervention = + case_when( + intervention == "normal" ~ "normal", + intervention != "normal"~ "other")) %>% + mutate(nopreference_intervention = + case_when( + intervention== "no preference" ~ "no preference", + intervention!= "no preference"~ "other")) %>% + mutate(intervention = "fixed") %>% + mutate(outcome = "positive") + +``` + +## Prepare Dataset + +```{r} +df.exp5_summary <- df.exp5 %>% + group_by(CausalStructure) %>% + summarise( + abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100, + normal_explanation_percentage = mean(normal_explanation == "normal") * 100, + nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100, + + abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100, + normal_intervention_percentage = mean(normal_intervention == "normal") * 100, + nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100 + ) %>% + mutate(Outcome = "negative") %>% + mutate(Experiment = "fixedint") + +write_csv(df.exp5_summary, "fixedint_pos.csv") + +``` + +# Condition 6: Fixed Intervention: Negative Outcome + + +## Read in Data + +```{r} +###Experiment 6 +#### Read in Data + +df.responses = read.csv(file = "fixedintervention_negative-responses.csv", stringsAsFactors = F, sep = ",") %>% + select(-error) + + +df.participants <- read.csv(file = "fixedintervention_negative-participants.csv", stringsAsFactors = F, sep = ",") %>% + select(-c(proliferate.condition, error)) + + +df.data6 <- merge(df.responses, df.participants, by="workerid") +``` + +## Wrangle Data + +```{r} +df.exp6 <- df.data6 %>% + gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% + mutate(CausalStructure = + case_when( + str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_1") ~ "Conjunctive", + str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_2") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_1") ~ "Disjunctive", + str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_2") ~ "Conjunctive"))%>% + mutate(Order = + case_when( + str_detect(proliferate.condition, "Condition_3") ~ "conjunctive first", + str_detect(proliferate.condition, "Condition_4") ~ "disjunctive first"))%>% + mutate(ResponseType = + case_when( + str_detect(index, "explanation") ~ "explanation", + str_detect(index, "intervention") ~ "intervention"))%>% + select(-index) %>% + spread(ResponseType, response) %>% + mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>% + mutate(explanation = recode (explanation, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(intervention = recode (intervention, + "orange" = "abnormal", + "blue" = "normal" )) %>% + mutate(abnormal_explanation = + case_when( + explanation == "abnormal" ~ "abnormal", + explanation != "abnormal"~ "other")) %>% + mutate(normal_explanation = + case_when( + explanation == "normal" ~ "normal", + explanation != "normal"~ "other")) %>% + mutate(nopreference_explanation = + case_when( + explanation == "no preference" ~ "no preference", + explanation != "no preference"~ "other")) %>% + mutate(abnormal_intervention = + case_when( + intervention== "abnormal" ~ "abnormal", + intervention!= "abnormal"~ "other")) %>% + mutate(normal_intervention = + case_when( + intervention == "normal" ~ "normal", + intervention != "normal"~ "other")) %>% + mutate(nopreference_intervention = + case_when( + intervention== "no preference" ~ "no preference", + intervention!= "no preference"~ "other")) %>% + mutate(intervention = "fixed") %>% + mutate(outcome = "negative") + + +``` + +## Prepare Dataset + +```{r} +df.exp6_summary <- df.exp6 %>% + group_by(CausalStructure) %>% + summarise( + abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100, + normal_explanation_percentage = mean(normal_explanation == "normal") * 100, + nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100, + + abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100, + normal_intervention_percentage = mean(normal_intervention == "normal") * 100, + nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100 + ) %>% + mutate(Outcome = "negative") %>% + mutate(Experiment = "fixedint") + +write_csv(df.exp6_summary, "fixedint_neg.csv") + +``` + +# Create Dataset including raw data from all conditions + +```{r} +df.exp_all <- rbind(df.exp1, df.exp2, df.exp3, df.exp4, df.exp5, df.exp6) + +``` + +# Bootstrapping + +## DATA + +### Data frame + +```{r} +df.boot = df.exp_all %>% + select(workerid, + structure = CausalStructure, + outcome, + action = intervention, + explanation, + abnormal_intervention, + normal_intervention, + nopreference_intervention) %>% + mutate(structure = tolower(structure), + intervention = abnormal_intervention, + intervention = ifelse(normal_intervention != "other", normal_intervention, intervention), + intervention = ifelse(nopreference_intervention != "other", nopreference_intervention, intervention), + intervention = factor(intervention, + levels = c("abnormal", "no preference", "normal")), + explanation = factor(explanation, + levels = c("abnormal", "no preference", "normal"))) %>% + select(-contains("_")) + +``` + +### Percentages + +```{r} +df.percentage = df.boot %>% + count(structure, outcome, action, choice = intervention, + name = "intervention_n") %>% + left_join(df.boot %>% + count(structure, outcome, action, choice = explanation, + name = "explanation_n"), + by = c("structure", "outcome", "action", "choice")) %>% + group_by(structure, outcome, action) %>% + mutate(intervention_p = intervention_n/sum(intervention_n), + explanation_p = explanation_n/sum(explanation_n)) %>% + ungroup() +``` + +### Bootstrapped confidence intervals + +```{r} +set.seed(1) + +# percentages with bootstrapped confidence intervals +df.confidence = df.boot %>% + group_by(structure, outcome, action) %>% + nest() %>% + mutate(bootstraps = map(.x = data, + .f = ~ bootstrap(.x, n = 1000))) %>% + unnest(bootstraps) %>% + mutate(intervention = map(.x = strap, + .f = ~ .x %>% + as_tibble() %>% + count(intervention, + name = "intervention_n", + .drop = F) %>% + mutate(intervention_p = intervention_n/sum(intervention_n))), + explanation = map(.x = strap, + .f = ~ .x %>% + as_tibble() %>% + count(explanation, + name = "explanation_n", + .drop = F) %>% + mutate(explanation_p = explanation_n/sum(explanation_n)))) %>% + select(structure, outcome, action, intervention, explanation) %>% + unnest(c(intervention, explanation)) %>% + select(everything(), choice = intervention, -explanation) %>% + group_by(structure, outcome, action, choice) %>% + summarize(intervention_low = as.numeric(quantile(intervention_p, probs = 0.025)), + intervention_high = as.numeric(quantile(intervention_p, probs = 0.975)), + explanation_low = as.numeric(quantile(explanation_p, probs = 0.025)), + explanation_high = as.numeric(quantile(explanation_p, probs = 0.975))) %>% + ungroup() +``` + +### Combined data frame + +```{r} +df.combined = df.confidence %>% + left_join(df.percentage %>% + select(-(contains("_n"))), + by = c("structure", "outcome", "action", "choice")) %>% + select(structure, outcome, action, choice, contains("intervention"), + contains("explanation")) +``` + +## PLOTS + +### Read in model predictions + +```{r, message=FALSE, warning=FALSE} +df.prediction_intervention = read_csv("intervention_predictions.csv") +df.prediction_explanation = read_csv("explanation_predictions.csv") %>% + select(-truth) + +df.optimal_intervention_model = df.prediction_intervention %>% + mutate(index = "intervention_prediction") %>% + bind_rows(df.prediction_explanation %>% + mutate(index = "explanation_prediction")) %>% + select(structure = causal_structure, + outcome, + action = intervention, + choice, + index, + prediction) %>% + pivot_wider(names_from = index, + values_from = prediction) + +df.intervention_only_model = read_csv("explanation_predictions_intervention_only.csv") +df.truth_only_model = read_csv("explanation_predictions_truth_only.csv") + +df.alternative_models = df.intervention_only_model %>% + mutate(model = "intervention_only") %>% + bind_rows(df.truth_only_model %>% + mutate(model = "truth_only")) %>% + select(structure = causal_structure, + outcome, + action = intervention, + choice, + model, + prediction) %>% + pivot_wider(names_from = model, + values_from = prediction) + +df.models = df.optimal_intervention_model %>% + left_join(df.alternative_models) +``` + +### Bar plot + +```{r, fig.width=20, fig.height=14, warning=FALSE, message=FALSE} + +df.plot = df.combined %>% + mutate(choice = factor(choice, + labels = c("abnormal", "no\npreference", "normal")), + outcome = factor(outcome, + levels = c("positive", "negative"), + labels = c("positive outcome", "negative outcome")), + action = factor(action, + levels = c("hard", "soft", "fixed"))) + +df.model = df.optimal_intervention_model %>% + left_join(df.alternative_models) %>% + mutate(choice = factor(choice, + labels = c("abnormal", "no\npreference", "normal")), + outcome = factor(outcome, + levels = c("positive", "negative"), + labels = c("positive outcome", "negative outcome")), + action = factor(action, + levels = c("hard", "soft", "fixed"))) + +df.model2 = df.model %>% + select(-intervention_prediction) %>% + pivot_longer(cols = c("explanation_prediction", "intervention_only", "truth_only"), + names_to = "model", + values_to = "prediction") %>% + mutate(model = factor(model, + levels = c("truth_only", "explanation_prediction", "intervention_only"))) %>% + arrange(structure, outcome, action, choice, model) + +p_intervention = ggplot(data = df.plot, + mapping = aes(x = action, + y = intervention_p, + group = choice, + fill = action, + alpha = choice)) + + geom_col(color = "black", + position = position_dodge(width = 0.9)) + + geom_linerange(mapping = aes(ymin = intervention_low, + ymax = intervention_high), + position = position_dodge(width = 0.9), + alpha = 1, + linewidth = 1) + + geom_point(data = df.model, + mapping = aes(y = intervention_prediction), + position = position_dodge(width = 0.9), + shape = 21, + size = 4, + show.legend = F) + + facet_grid(structure ~ outcome) + + scale_y_continuous(limits = c(0, 1), + breaks = seq(0, 1, 0.25), + labels = scales::label_percent()) + + scale_alpha_manual(values = c(0.4, 0.65, 0.9)) + + labs(title = "Intervention Task", + x = "type of intervention", + y = "percent selected") + + theme(legend.position = "bottom", + plot.title = element_text(hjust = 0.5)) + + guides(fill = "none", + alpha = "none") + +p_explanation = ggplot(data = df.plot, + mapping = aes(x = action, + y = explanation_p, + group = choice, + fill = action, + alpha = choice)) + + geom_col(color = "black", + position = position_dodge(width = 0.9)) + + geom_linerange(mapping = aes(ymin = explanation_low, + ymax = explanation_high), + position = position_dodge(width = 0.9), + alpha = 1, + linewidth = 1) + + geom_point(data = df.model2, + mapping = aes(y = prediction, + shape = model), + position = position_dodge2(width = 0.9, + padding = 0.2), + size = 4, + show.legend = F) + + facet_grid(structure ~ outcome) + + scale_y_continuous(limits = c(0, 1), + breaks = seq(0, 1, 0.25), + labels = scales::label_percent()) + + scale_alpha_manual(values = c(0.4, 0.65, 0.9)) + + scale_shape_manual(values = c("truth_only" = 22, + "explanation_prediction" = 21, + "intervention_only" = 23)) + + labs(title = "Explanation Task", + x = "type of intervention", + y = "percent selected") + + theme(legend.position = "bottom", + plot.title = element_text(hjust = 0.5)) + + guides(fill = "none") + +p_intervention + p_explanation + + plot_layout(ncol = 1) + plot_annotation(tag_levels = "A") & + theme(plot.tag = element_text(size = 40, face = "bold")) + +ggsave(filename = "../../../figures/plots/bars.pdf", + width = 20, + height = 14) +``` + +### Scatter plots + +#### Scatter plot function + +```{r} +fun.scatter = function(data, xtitle, ytitle, legend = F){ + p = ggplot(data = data, + mapping = aes(x = model, + y = p, + ymin = low, + ymax = high)) + + geom_abline(intercept = 0, slope = 1, linetype = "dashed") + + annotate(geom = "text", + x = 0, + y = 1, + hjust = 0, + label = str_c("r = ", round(cor(data$model, data$p), 2)), + size = 8) + + annotate(geom = "text", + x = 0, + y = 0.92, + hjust = 0, + label = str_c("RMSE = ", round(sqrt(mean((data$model - data$p)^2)), 2)), + size = 8) + + geom_smooth(method = "lm", + color = "black", + alpha = 0.2, + show.legend = F) + + geom_linerange(alpha = 0.2) + + geom_point(mapping = aes(fill = action), + alpha = 0.9, + shape = 21, + size = 4) + + scale_x_continuous(limits = c(0, 1), + breaks = seq(0, 1, 0.25), + labels = scales::label_percent()) + + scale_y_continuous(limits = c(0, 1), + breaks = seq(0, 1, 0.25), + labels = scales::label_percent()) + + labs(x = xtitle, + y = ytitle) + + theme(legend.position = c(1, 0), + legend.justification = c(1.2, -0.1)) + + if(legend == F){ + p = p + theme(legend.position = "none") + } + + return(p) +} +``` + +#### Combined scatter plots + +```{r, warning=FALSE, message=FALSE, fig.width=16, fig.height=12} +df.plot = df.combined %>% + left_join(df.models %>% + mutate(choice = factor(choice, + levels = c("abnormal", "nopreference", "normal"), + labels = c("abnormal", "no preference", "normal")))) %>% + mutate(structure = factor(structure, + levels = c("conjunctive", "disjunctive")), + choice = factor(choice, + labels = c("abnormal", "no\npreference", "normal")), + outcome = factor(outcome, + levels = c("positive", "negative"), + labels = c("positive outcome", "negative outcome")), + action = factor(action, + levels = c("hard", "soft", "fixed"))) + +df.plot.intervention = df.plot %>% + select(structure, outcome, action, choice, + low = intervention_low, high = intervention_high, + p = intervention_p, model = intervention_prediction) + +df.plot.explanation = df.plot %>% + select(structure, outcome, action, choice, + low = explanation_low, high = explanation_high, + p = explanation_p, + explanation_prediction, + intervention_only, + truth_only) + +scatter1 = fun.scatter(data = df.plot.intervention, + xtitle = "model prediction", + ytitle = "intervention choices", + legend = T) + +scatter2 = fun.scatter(data = df.plot.explanation %>% + mutate(model = intervention_only), + xtitle = "relevance only model", + ytitle = "explanation choices") + +scatter3 = fun.scatter(data = df.plot.explanation %>% + mutate(model = truth_only), + xtitle = "accuracy only model", + ytitle = "explanation choices") + +scatter4 = fun.scatter(data = df.plot.explanation %>% + mutate(model = explanation_prediction), + xtitle = "combined model", + ytitle = "explanation choices") + +scatter1 + scatter2 + scatter3 + scatter4 + + plot_layout(ncol = 2) + + plot_annotation(tag_levels = "A") & + theme(text = element_text(size = 30), + plot.tag = element_text(size = 40, face = "bold"), + plot.margin = margin(t = 0, + r = 0.35, + b = 0, + l = 0, + "cm")) + +ggsave(filename = "../../../figures/plots/scatter.pdf", + width = 16, + height = 12) +``` + +#### Overall + +```{r, warning=FALSE, message=FALSE, fig.width=8, fig.height=6} +df.scatter = df.plot %>% + ungroup() %>% + pivot_longer(cols = contains("_"), + names_to = c("task", "index"), + names_sep = "_", + values_to = "value") %>% + pivot_wider(names_from = index, + values_from = value) %>% + left_join(df.model %>% + pivot_longer(cols = contains("_"), + names_to = c("task", "index"), + names_sep = "_", + values_to = "model") %>% + select(-index), + by = c("structure", "outcome", "action", "choice", "task")) + + +ggplot(data = df.scatter, + mapping = aes(x = model, + y = p, + ymin = low, + ymax = high)) + + geom_abline(intercept = 0, slope = 1, linetype = "dashed") + + annotate(geom = "text", + x = 0, + y = 1, + hjust = 0, + label = str_c("r = ", round(cor(df.scatter$model, df.scatter$p), 2)), + size = 8) + + annotate(geom = "text", + x = 0, + y = 0.92, + hjust = 0, + label = str_c("RMSE = ", round(sqrt(mean((df.scatter$model - df.scatter$p)^2)), 2)), + size = 8) + + geom_smooth(method = "lm", + color = "black", + alpha = 0.2, + show.legend = F) + + geom_linerange(alpha = 0.2) + + geom_point(mapping = aes(fill = action), + # shape = task), + shape = 21, + size = 2) + + scale_x_continuous(limits = c(0, 1), + breaks = seq(0, 1, 0.25), + labels = scales::label_percent()) + + scale_y_continuous(limits = c(0, 1), + breaks = seq(0, 1, 0.25), + labels = scales::label_percent()) + + # scale_shape_manual(values = c("intervention" = 21, + # "explanation" = 22)) + + labs(x = "model prediction", + y = "participant choices") + + theme(legend.position = c(1, 0), + legend.justification = c(1.2, -0.1)) + +ggsave(filename = "../../../figures/plots/scatter.pdf", + width = 8, + height = 6) +``` + +## STATS + +### Correlations and RMSE + +```{r, warning=FALSE, message=FALSE} +df.combined %>% + ungroup() %>% + select(-(contains("low") | contains("high"))) %>% + mutate(choice = str_replace(choice, "no preference", "nopreference")) %>% + left_join(df.alternative_models) %>% + left_join(df.optimal_intervention_model %>% + select(-intervention_prediction)) %>% + summarize(across(c(explanation_prediction, intervention_only, truth_only), + list(r = ~ cor(.x, explanation_p), + rmse = ~ sqrt(mean((.x - explanation_p)^2))), + .names = "{.col}.{.fn}")) %>% + pivot_longer(cols = everything(), + names_to = c("model", "stat"), + names_sep = "\\.", + values_to = "value") %>% + pivot_wider(names_from = stat, + values_from = value) %>% + mutate(across(where(is.numeric), + ~ round(., 2))) +``` + + +# Session info + +```{r, echo=F} +sessionInfo() +``` \ No newline at end of file diff --git a/code/R/experiments/Experiment.html b/code/R/experiments/Experiment.html new file mode 100644 index 0000000..9963616 --- /dev/null +++ b/code/R/experiments/Experiment.html @@ -0,0 +1,2706 @@ + + + + + + + + + + + + + + + +Explanation and Intervention Data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + +
library("knitr")
+library("modelr")        # for bootstrapping
+library("patchwork")        # making figure panels
+library("tidyverse") # for data wrangling, visualization, etc. 
+
+

1 Set options

+
theme_set(theme_classic() + 
+    theme(text = element_text(size = 24)))
+
+opts_chunk$set(comment = "",
+               fig.show = "hold")
+
+# suppress grouping warning 
+options(dplyr.summarise.inform = F)
+
+
+

2 Condition 1: Hard Intervention: Positive Outcome

+
+

2.1 Read in Data

+
#### Read in Data
+df.responses = read.csv(file = "explanation_selection_positive_outcome_study_2-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "explanation_selection_positive_outcome_study_2-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data1 <- merge(df.responses, df.participants, by="workerid")
+
+
+

2.2 Wrangle Data

+
df.exp1 <- df.data1 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Conpos_") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Conpos_") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Dispos_") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Dispos_") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Conpos_") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Dispos_") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+   mutate(intervention = "hard") %>% 
+   mutate(outcome = "positive") 
+
+
+

2.3 Prepate Dataset

+
df.exp1_summary <- df.exp1 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "positive") %>% 
+    mutate(Experiment = "hardint")
+
+write_csv(df.exp1_summary, "hardint_pos.csv")
+
+
+
+

3 Condition 2: Hard Intervention: Negative Outcome

+
+

3.1 Read in Data

+
#### Read in Data
+df.responses = read.csv(file = "explanation_selection_negative_outcome-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "explanation_selection_negative_outcome-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data2 <- merge(df.responses, df.participants, by="workerid")
+
+
+

3.2 Wrangle Data

+
df.exp2 <- df.data2 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Conneg_") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Conneg_") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Disneg_") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Disneg_") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Conneg_") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Disneg_") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+     mutate(intervention = "hard") %>% 
+   mutate(outcome = "negative") 
+
+
+

3.3 Prepare Dataset

+
df.exp2_summary <- df.exp2 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "hardint")
+
+write_csv(df.exp2_summary, "hardint_neg.csv")
+
+
+
+

4 Condition 3: Soft Intervention: Positive Outcome

+
+

4.1 Read in Data

+
###Experiment 4
+#### Read in Data
+
+df.responses = read.csv(file = "pressbutton_positivecondition-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "pressbutton_positivecondition-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data3 <- merge(df.responses, df.participants, by="workerid")
+
+
+

4.2 Wrangle Data

+
df.exp3 <- df.data3 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition_1") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition_1") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_2") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_2") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition_1") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition_2") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other"))%>% 
+     mutate(intervention = "soft") %>% 
+   mutate(outcome = "positive")  
+
+
+

4.3 Prepare Dataset

+
df.exp3_summary <- df.exp3 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "positive") %>% 
+    mutate(Experiment = "softint")
+
+write_csv(df.exp3_summary, "softint_pos.csv")
+
+
+
+

5 Condition 4: Soft Intervention: Negative Outcome

+
+

5.1 Read in Data

+
###Experiment 4
+#### Read in Data
+
+df.responses = read.csv(file = "pressbutton_negativecondition-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "pressbutton_negativecondition-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data4 <- merge(df.responses, df.participants, by="workerid") %>% 
+  filter(!row_number() %in% c(71, 72))
+
+
+

5.2 Wrangle Data

+
df.exp4 <- df.data4 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition_4") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+     mutate(intervention = "soft") %>% 
+   mutate(outcome = "negative") 
+
+
+

5.3 Prepare Dataset

+
df.exp4_summary <- df.exp4 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "softint")
+
+write_csv(df.exp4_summary, "softint_neg.csv")
+
+
+
+

6 Condition 5: Fixed Intervention: Positive Outcome

+
+

6.1 Read in Data

+
###Experiment 5
+#### Read in Data
+
+df.responses = read.csv(file = "fixedintervention_positive-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "fixedintervention_positive-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data5 <- merge(df.responses, df.participants, by="workerid") 
+
+
+

6.2 Wrangle Data

+
df.exp5 <- df.data5 %>% 
+   gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition1") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition1") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition2") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition2") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition1") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition2") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+     mutate(intervention = "fixed") %>% 
+   mutate(outcome = "positive") 
+
+
+

6.3 Prepare Dataset

+
df.exp5_summary <- df.exp5 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "fixedint")
+
+write_csv(df.exp5_summary, "fixedint_pos.csv")
+
+
+
+

7 Condition 6: Fixed Intervention: Negative Outcome

+
+

7.1 Read in Data

+
###Experiment 6
+#### Read in Data
+
+df.responses = read.csv(file = "fixedintervention_negative-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "fixedintervention_negative-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data6 <- merge(df.responses, df.participants, by="workerid") 
+
+
+

7.2 Wrangle Data

+
df.exp6 <- df.data6 %>% 
+   gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition_4") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other"))    %>%  
+  mutate(intervention = "fixed") %>% 
+   mutate(outcome = "negative") 
+
+
+

7.3 Prepare Dataset

+
df.exp6_summary <- df.exp6 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "fixedint")
+
+write_csv(df.exp6_summary, "fixedint_neg.csv")
+
+
+
+

8 Create Dataset including raw data from all conditions

+
df.exp_all <- rbind(df.exp1, df.exp2, df.exp3, df.exp4, df.exp5, df.exp6)
+
+
+

9 Bootstrapping

+
+

9.1 DATA

+
+

9.1.1 Data frame

+
df.boot = df.exp_all %>% 
+  select(workerid,
+         structure = CausalStructure,
+         outcome,
+         action = intervention,
+         explanation,
+         abnormal_intervention,
+         normal_intervention,
+         nopreference_intervention) %>% 
+  mutate(structure = tolower(structure),
+         intervention = abnormal_intervention,
+         intervention = ifelse(normal_intervention != "other", normal_intervention, intervention),
+         intervention = ifelse(nopreference_intervention != "other", nopreference_intervention, intervention),
+         intervention = factor(intervention,
+                               levels = c("abnormal", "no preference", "normal")),
+         explanation = factor(explanation,
+                               levels = c("abnormal", "no preference", "normal"))) %>% 
+  select(-contains("_"))
+
+
+

9.1.2 Percentages

+
df.percentage = df.boot %>% 
+  count(structure, outcome, action, choice = intervention,
+        name = "intervention_n") %>% 
+  left_join(df.boot %>% 
+              count(structure, outcome, action, choice = explanation,
+                    name = "explanation_n"),
+            by = c("structure", "outcome", "action", "choice")) %>% 
+  group_by(structure, outcome, action) %>% 
+  mutate(intervention_p = intervention_n/sum(intervention_n),
+         explanation_p = explanation_n/sum(explanation_n)) %>% 
+  ungroup()
+
+
+

9.1.3 Bootstrapped confidence intervals

+
set.seed(1)
+
+# percentages with bootstrapped confidence intervals 
+df.confidence = df.boot %>% 
+  group_by(structure, outcome, action) %>% 
+  nest() %>% 
+  mutate(bootstraps = map(.x = data,
+                          .f = ~ bootstrap(.x, n = 1000))) %>% 
+  unnest(bootstraps) %>% 
+  mutate(intervention = map(.x = strap, 
+                      .f = ~ .x %>% 
+                        as_tibble() %>% 
+                        count(intervention,
+                              name = "intervention_n",
+                              .drop = F) %>% 
+                        mutate(intervention_p = intervention_n/sum(intervention_n))),
+         explanation = map(.x = strap, 
+                      .f = ~ .x %>% 
+                        as_tibble() %>% 
+                        count(explanation,
+                              name = "explanation_n",
+                              .drop = F) %>% 
+                        mutate(explanation_p = explanation_n/sum(explanation_n)))) %>%
+  select(structure, outcome, action, intervention, explanation) %>%
+  unnest(c(intervention, explanation)) %>%
+  select(everything(), choice = intervention, -explanation) %>% 
+  group_by(structure, outcome, action, choice) %>%
+  summarize(intervention_low = as.numeric(quantile(intervention_p, probs = 0.025)),
+            intervention_high = as.numeric(quantile(intervention_p, probs = 0.975)),
+            explanation_low = as.numeric(quantile(explanation_p, probs = 0.025)),
+            explanation_high = as.numeric(quantile(explanation_p, probs = 0.975))) %>% 
+  ungroup()
+
+
+

9.1.4 Combined data frame

+
df.combined = df.confidence %>%
+  left_join(df.percentage %>% 
+              select(-(contains("_n"))), 
+            by = c("structure", "outcome", "action", "choice")) %>% 
+  select(structure, outcome, action, choice, contains("intervention"),
+         contains("explanation"))
+
+
+
+

9.2 PLOTS

+
+

9.2.1 Read in model predictions

+
df.prediction_intervention = read_csv("intervention_predictions.csv")
+df.prediction_explanation = read_csv("explanation_predictions.csv") %>% 
+  select(-truth)
+
+df.optimal_intervention_model = df.prediction_intervention %>% 
+  mutate(index = "intervention_prediction") %>% 
+  bind_rows(df.prediction_explanation %>% 
+              mutate(index = "explanation_prediction")) %>% 
+  select(structure = causal_structure,
+         outcome, 
+         action = intervention,
+         choice,
+         index,
+         prediction) %>% 
+  pivot_wider(names_from = index,
+              values_from = prediction)
+
+df.intervention_only_model = read_csv("explanation_predictions_intervention_only.csv")
+df.truth_only_model = read_csv("explanation_predictions_truth_only.csv")
+
+df.alternative_models = df.intervention_only_model %>% 
+  mutate(model = "intervention_only") %>% 
+  bind_rows(df.truth_only_model %>% 
+              mutate(model = "truth_only")) %>% 
+  select(structure = causal_structure,
+         outcome, 
+         action = intervention,
+         choice,
+         model, 
+         prediction) %>% 
+  pivot_wider(names_from = model,
+              values_from = prediction)
+
+df.models = df.optimal_intervention_model %>% 
+  left_join(df.alternative_models) 
+
+
+

9.2.2 Bar plot

+
df.plot = df.combined %>% 
+  mutate(choice = factor(choice,
+                         labels = c("abnormal", "no\npreference", "normal")),
+         outcome = factor(outcome,
+                          levels = c("positive", "negative"),
+                          labels = c("positive outcome", "negative outcome")),
+         action = factor(action,
+                         levels = c("hard", "soft", "fixed")))
+
+df.model = df.optimal_intervention_model %>%
+  left_join(df.alternative_models) %>% 
+  mutate(choice = factor(choice,
+                         labels = c("abnormal", "no\npreference", "normal")),
+         outcome = factor(outcome,
+                          levels = c("positive", "negative"),
+                          labels = c("positive outcome", "negative outcome")),
+         action = factor(action,
+                         levels = c("hard", "soft", "fixed")))
+
+df.model2 = df.model %>% 
+  select(-intervention_prediction) %>% 
+  pivot_longer(cols = c("explanation_prediction", "intervention_only", "truth_only"),
+               names_to = "model",
+               values_to = "prediction") %>% 
+  mutate(model = factor(model,
+                        levels = c("truth_only", "explanation_prediction", "intervention_only"))) %>% 
+  arrange(structure, outcome, action, choice, model)
+  
+p_intervention = ggplot(data = df.plot,
+                        mapping = aes(x = action,
+                                      y = intervention_p,
+                                      group = choice,
+                                      fill = action,
+                                      alpha = choice)) + 
+  geom_col(color = "black",
+           position = position_dodge(width = 0.9)) +
+  geom_linerange(mapping = aes(ymin = intervention_low,
+                               ymax = intervention_high),
+                 position = position_dodge(width = 0.9),
+                 alpha = 1,
+                 linewidth = 1) +
+  geom_point(data = df.model,
+             mapping = aes(y = intervention_prediction),
+             position = position_dodge(width = 0.9),
+             shape = 21,
+             size = 4,
+             show.legend = F) +
+  facet_grid(structure ~ outcome) +
+  scale_y_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  scale_alpha_manual(values = c(0.4, 0.65, 0.9)) +
+  labs(title = "Intervention Task",
+       x = "type of intervention",
+       y = "percent selected") + 
+  theme(legend.position = "bottom",
+        plot.title = element_text(hjust = 0.5)) + 
+  guides(fill = "none",
+         alpha = "none")
+
+p_explanation = ggplot(data = df.plot,
+               mapping = aes(x = action,
+                             y = explanation_p,
+                             group = choice,
+                             fill = action,
+                             alpha = choice)) + 
+  geom_col(color = "black",
+           position = position_dodge(width = 0.9)) +
+  geom_linerange(mapping = aes(ymin = explanation_low,
+                               ymax = explanation_high),
+                 position = position_dodge(width = 0.9),
+                 alpha = 1,
+                 linewidth = 1) +
+  geom_point(data = df.model2,
+             mapping = aes(y = prediction,
+                           shape = model),
+             position = position_dodge2(width = 0.9,
+                                        padding = 0.2),
+             size = 4,
+             show.legend = F) +
+  facet_grid(structure ~ outcome) +
+  scale_y_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  scale_alpha_manual(values = c(0.4, 0.65, 0.9)) +
+  scale_shape_manual(values = c("truth_only" = 22,
+                                "explanation_prediction" = 21,
+                                "intervention_only" = 23)) +
+  labs(title = "Explanation Task",
+       x = "type of intervention",
+       y = "percent selected") + 
+  theme(legend.position = "bottom",
+        plot.title = element_text(hjust = 0.5)) + 
+  guides(fill = "none")
+
+p_intervention + p_explanation + 
+  plot_layout(ncol = 1) + plot_annotation(tag_levels = "A") & 
+  theme(plot.tag = element_text(size = 40, face = "bold"))
+
+ggsave(filename = "../../../figures/plots/bars.pdf",
+       width = 20,
+       height = 14)
+

+
+
+

9.2.3 Scatter plots

+
+

9.2.3.1 Scatter plot function

+
fun.scatter = function(data, xtitle, ytitle, legend = F){
+  p = ggplot(data = data,
+             mapping = aes(x = model,
+                           y = p,
+                           ymin = low,
+                           ymax = high)) +
+    geom_abline(intercept = 0, slope = 1, linetype = "dashed") +
+    annotate(geom = "text",
+             x = 0,
+             y = 1,
+             hjust = 0,
+             label = str_c("r = ", round(cor(data$model, data$p), 2)),
+             size = 8) +
+    annotate(geom = "text",
+             x = 0,
+             y = 0.92,
+             hjust = 0,
+             label = str_c("RMSE = ", round(sqrt(mean((data$model -  data$p)^2)), 2)),
+             size = 8) +
+    geom_smooth(method = "lm",
+                color = "black",
+                alpha = 0.2,
+                show.legend = F) + 
+    geom_linerange(alpha = 0.2) +
+    geom_point(mapping = aes(fill = action),
+               alpha = 0.9,
+               shape = 21,
+               size = 4) +
+    scale_x_continuous(limits = c(0, 1),
+                       breaks = seq(0, 1, 0.25),
+                       labels = scales::label_percent()) +
+    scale_y_continuous(limits = c(0, 1),
+                       breaks = seq(0, 1, 0.25),
+                       labels = scales::label_percent()) +
+    labs(x = xtitle,
+         y = ytitle) + 
+    theme(legend.position = c(1, 0),
+          legend.justification = c(1.2, -0.1)) 
+  
+  if(legend == F){
+    p = p + theme(legend.position = "none")
+  }
+ 
+ return(p)
+}
+
+
+

9.2.3.2 Combined scatter plots

+
df.plot = df.combined %>% 
+  left_join(df.models %>% 
+              mutate(choice = factor(choice,
+                                     levels = c("abnormal", "nopreference", "normal"),
+                                     labels = c("abnormal", "no preference", "normal")))) %>% 
+  mutate(structure = factor(structure,
+                            levels = c("conjunctive", "disjunctive")),
+         choice = factor(choice,
+                         labels = c("abnormal", "no\npreference", "normal")),
+         outcome = factor(outcome,
+                          levels = c("positive", "negative"),
+                          labels = c("positive outcome", "negative outcome")),
+         action = factor(action,
+                         levels = c("hard", "soft", "fixed")))
+
+df.plot.intervention = df.plot %>% 
+  select(structure, outcome, action, choice,
+         low = intervention_low, high = intervention_high,
+         p = intervention_p, model = intervention_prediction)
+
+df.plot.explanation = df.plot %>%
+  select(structure, outcome, action, choice,
+         low = explanation_low, high = explanation_high,
+         p = explanation_p,
+         explanation_prediction,
+         intervention_only,
+         truth_only)
+  
+scatter1 = fun.scatter(data = df.plot.intervention,
+                       xtitle = "model prediction",
+                       ytitle = "intervention choices",
+                       legend = T)
+
+scatter2 = fun.scatter(data = df.plot.explanation %>% 
+                         mutate(model = intervention_only),
+                       xtitle = "relevance only model",
+                       ytitle = "explanation choices")
+
+scatter3 = fun.scatter(data = df.plot.explanation %>% 
+                         mutate(model = truth_only),
+                       xtitle = "accuracy only model",
+                       ytitle = "explanation choices")
+
+scatter4 = fun.scatter(data = df.plot.explanation %>% 
+                         mutate(model = explanation_prediction),
+                       xtitle = "combined model",
+                       ytitle = "explanation choices")
+
+scatter1 + scatter2 + scatter3 + scatter4 + 
+  plot_layout(ncol = 2) + 
+  plot_annotation(tag_levels = "A") & 
+  theme(text = element_text(size = 30),
+        plot.tag = element_text(size = 40, face = "bold"),
+        plot.margin = margin(t = 0, 
+                             r = 0.35, 
+                             b = 0, 
+                             l = 0,
+                             "cm"))
+
+ggsave(filename = "../../../figures/plots/scatter.pdf",
+       width = 16,
+       height = 12)
+

+
+
+

9.2.3.3 Overall

+
df.scatter = df.plot %>% 
+  ungroup() %>% 
+  pivot_longer(cols = contains("_"),
+               names_to = c("task", "index"),
+               names_sep = "_",
+               values_to = "value") %>% 
+  pivot_wider(names_from = index,
+              values_from = value) %>% 
+  left_join(df.model %>% 
+              pivot_longer(cols = contains("_"),
+                           names_to = c("task", "index"),
+                           names_sep = "_",
+                           values_to = "model") %>% 
+              select(-index),
+            by = c("structure", "outcome", "action", "choice", "task"))
+
+
+ggplot(data = df.scatter,
+       mapping = aes(x = model,
+                     y = p,
+                     ymin = low,
+                     ymax = high)) +
+  geom_abline(intercept = 0, slope = 1, linetype = "dashed") +
+  annotate(geom = "text",
+           x = 0,
+           y = 1,
+           hjust = 0,
+           label = str_c("r = ", round(cor(df.scatter$model, df.scatter$p), 2)),
+           size = 8) +
+  annotate(geom = "text",
+           x = 0,
+           y = 0.92,
+           hjust = 0,
+           label = str_c("RMSE = ", round(sqrt(mean((df.scatter$model -  df.scatter$p)^2)), 2)),
+           size = 8) +
+  geom_smooth(method = "lm",
+              color = "black",
+              alpha = 0.2,
+              show.legend = F) + 
+  geom_linerange(alpha = 0.2) +
+  geom_point(mapping = aes(fill = action),
+                           # shape = task),
+             shape = 21,
+             size = 2) +
+  scale_x_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  scale_y_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  # scale_shape_manual(values = c("intervention" = 21,
+  #                               "explanation" = 22)) +
+  labs(x = "model prediction",
+       y = "participant choices") +
+  theme(legend.position = c(1, 0),
+        legend.justification = c(1.2, -0.1))
+
+ggsave(filename = "../../../figures/plots/scatter.pdf",
+       width = 8,
+       height = 6)
+

+
+
+
+
+

9.3 STATS

+
+

9.3.1 Correlations and RMSE

+
df.combined %>% 
+  ungroup() %>% 
+  select(-(contains("low") | contains("high"))) %>%
+  mutate(choice = str_replace(choice, "no preference", "nopreference")) %>% 
+  left_join(df.alternative_models) %>% 
+  left_join(df.optimal_intervention_model %>% 
+              select(-intervention_prediction)) %>% 
+  summarize(across(c(explanation_prediction, intervention_only, truth_only),
+                   list(r = ~ cor(.x, explanation_p),
+                        rmse = ~ sqrt(mean((.x - explanation_p)^2))),
+                   .names = "{.col}.{.fn}")) %>% 
+  pivot_longer(cols = everything(),
+               names_to = c("model", "stat"),
+               names_sep = "\\.",
+               values_to = "value") %>% 
+  pivot_wider(names_from = stat,
+              values_from = value) %>% 
+  mutate(across(where(is.numeric),
+                ~ round(., 2)))
+
# A tibble: 3 × 3
+  model                      r  rmse
+  <chr>                  <dbl> <dbl>
+1 explanation_prediction  0.81  0.09
+2 intervention_only       0.61  0.13
+3 truth_only              0.47  0.14
+
+
+
+
+

10 Session info

+
R version 4.1.3 (2022-03-10)
+Platform: x86_64-apple-darwin17.0 (64-bit)
+Running under: macOS Big Sur/Monterey 10.16
+
+Matrix products: default
+BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base     
+
+other attached packages:
+ [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.4    
+ [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
+ [9] ggplot2_3.4.4   tidyverse_2.0.0 patchwork_1.1.2 modelr_0.1.11  
+[13] knitr_1.38     
+
+loaded via a namespace (and not attached):
+ [1] tidyselect_1.2.0  xfun_0.30         bslib_0.3.1       lattice_0.20-45  
+ [5] splines_4.1.3     colorspace_2.0-3  vctrs_0.6.5       generics_0.1.2   
+ [9] htmltools_0.5.2   mgcv_1.8-39       yaml_2.3.5        utf8_1.2.2       
+[13] rlang_1.1.1       jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2       
+[17] withr_2.5.0       bit64_4.0.5       lifecycle_1.0.3   munsell_0.5.0    
+[21] gtable_0.3.0      ragg_1.2.5        evaluate_0.15     tzdb_0.4.0       
+[25] fastmap_1.1.0     parallel_4.1.3    fansi_1.0.3       highr_0.9        
+[29] broom_1.0.5       backports_1.4.1   scales_1.2.1      vroom_1.6.3      
+[33] jsonlite_1.8.4    systemfonts_1.0.4 farver_2.1.0      bit_4.0.4        
+[37] textshaping_0.3.6 hms_1.1.3         digest_0.6.29     stringi_1.7.6    
+[41] bookdown_0.25     grid_4.1.3        cli_3.6.1         tools_4.1.3      
+[45] magrittr_2.0.3    sass_0.4.1        crayon_1.5.1      pkgconfig_2.0.3  
+[49] Matrix_1.4-0      timechange_0.2.0  rmarkdown_2.13    rstudioapi_0.15.0
+[53] R6_2.5.1          nlme_3.1-155      compiler_4.1.3   
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/code/R/experiments/experiments.Rproj b/code/R/experiments/experiments.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/code/R/experiments/experiments.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/code/R/experiments/explanation_predictions.csv b/code/R/experiments/explanation_predictions.csv new file mode 100644 index 0000000..f4093ef --- /dev/null +++ b/code/R/experiments/explanation_predictions.csv @@ -0,0 +1,37 @@ +"intervention","causal_structure","outcome","choice","value","truth","probability","prediction" +"fixed","conjunctive","positive","abnormal",0.72,0,0.535353535353535,0.503204613224164 +"fixed","conjunctive","positive","normal",0.18,0,0.0909090909090909,0.102232604560875 +"fixed","conjunctive","positive","nopreference",0.45,1,0.373737373737374,0.39456278221496 +"fixed","conjunctive","negative","abnormal",0.92,0,0.171717171717172,0.244237215269761 +"fixed","conjunctive","negative","normal",0.98,0,0.424242424242424,0.291553306873668 +"fixed","conjunctive","negative","nopreference",0.95,1,0.404040404040404,0.46420947785657 +"fixed","disjunctive","positive","abnormal",0.98,0,0.272727272727273,0.291553306873668 +"fixed","disjunctive","positive","normal",0.92,0,0.181818181818182,0.244237215269762 +"fixed","disjunctive","positive","nopreference",0.95,1,0.545454545454545,0.46420947785657 +"fixed","disjunctive","negative","abnormal",0.18,0,0.0808080808080808,0.102232604560875 +"fixed","disjunctive","negative","normal",0.72,0,0.393939393939394,0.503204613224165 +"fixed","disjunctive","negative","nopreference",0.45,1,0.525252525252525,0.39456278221496 +"hard","conjunctive","positive","abnormal",0.8,0,0.494845360824742,0.529702731783576 +"hard","conjunctive","positive","normal",0.2,0,0.164948453608247,0.0901510713180212 +"hard","conjunctive","positive","nopreference",0.5,1,0.34020618556701,0.380146196898403 +"hard","conjunctive","negative","abnormal",1,0,0.171717171717172,0.267408365272958 +"hard","conjunctive","negative","normal",1,0,0.373737373737374,0.267408365272958 +"hard","conjunctive","negative","nopreference",1,1,0.454545454545455,0.465183269454083 +"hard","disjunctive","positive","abnormal",1,0,0.268041237113402,0.267408365272958 +"hard","disjunctive","positive","normal",1,0,0.195876288659794,0.267408365272958 +"hard","disjunctive","positive","nopreference",1,1,0.536082474226804,0.465183269454083 +"hard","disjunctive","negative","abnormal",0.2,0,0.0808080808080808,0.0901510713180212 +"hard","disjunctive","negative","normal",0.8,0,0.484848484848485,0.529702731783576 +"hard","disjunctive","negative","nopreference",0.5,1,0.434343434343434,0.380146196898403 +"soft","conjunctive","positive","abnormal",0.32,0,0.510204081632653,0.316552018509573 +"soft","conjunctive","positive","normal",0.2,0,0.112244897959184,0.222143097392714 +"soft","conjunctive","positive","nopreference",0.26,1,0.377551020408163,0.461304884097713 +"soft","conjunctive","negative","abnormal",1,0,0.454545454545455,0.316552018509573 +"soft","conjunctive","negative","normal",0.88,0,0.141414141414141,0.222143097392714 +"soft","conjunctive","negative","nopreference",0.94,1,0.404040404040404,0.461304884097713 +"soft","disjunctive","positive","abnormal",0.88,0,0.0612244897959184,0.222143097392714 +"soft","disjunctive","positive","normal",1,0,0.581632653061224,0.316552018509573 +"soft","disjunctive","positive","nopreference",0.94,1,0.357142857142857,0.461304884097713 +"soft","disjunctive","negative","abnormal",0.2,0,0.111111111111111,0.222143097392714 +"soft","disjunctive","negative","normal",0.32,0,0.353535353535354,0.316552018509573 +"soft","disjunctive","negative","nopreference",0.26,1,0.535353535353535,0.461304884097713 diff --git a/code/R/experiments/explanation_predictions_intervention_only.csv b/code/R/experiments/explanation_predictions_intervention_only.csv new file mode 100644 index 0000000..7e70019 --- /dev/null +++ b/code/R/experiments/explanation_predictions_intervention_only.csv @@ -0,0 +1,37 @@ +"intervention","causal_structure","outcome","choice","value","truth","probability","prediction","type" +"fixed","conjunctive","positive","abnormal",0.72,0,0.535353535353535,0.529149726577864,"explanation" +"fixed","conjunctive","positive","normal",0.18,0,0.0909090909090909,0.170491378005861,"explanation" +"fixed","conjunctive","positive","nopreference",0.45,1,0.373737373737374,0.300358895416275,"explanation" +"fixed","conjunctive","negative","abnormal",0.92,0,0.171717171717172,0.312593066503816,"explanation" +"fixed","conjunctive","negative","normal",0.98,0,0.424242424242424,0.354513066613215,"explanation" +"fixed","conjunctive","negative","nopreference",0.95,1,0.404040404040404,0.33289386688297,"explanation" +"fixed","disjunctive","positive","abnormal",0.98,0,0.272727272727273,0.354513066613215,"explanation" +"fixed","disjunctive","positive","normal",0.92,0,0.181818181818182,0.312593066503816,"explanation" +"fixed","disjunctive","positive","nopreference",0.95,1,0.545454545454545,0.33289386688297,"explanation" +"fixed","disjunctive","negative","abnormal",0.18,0,0.0808080808080808,0.170491378005861,"explanation" +"fixed","disjunctive","negative","normal",0.72,0,0.393939393939394,0.529149726577865,"explanation" +"fixed","disjunctive","negative","nopreference",0.45,1,0.525252525252525,0.300358895416275,"explanation" +"hard","conjunctive","positive","abnormal",0.8,0,0.494845360824742,0.550324432912458,"explanation" +"hard","conjunctive","positive","normal",0.2,0,0.164948453608247,0.156347065222755,"explanation" +"hard","conjunctive","positive","nopreference",0.5,1,0.34020618556701,0.293328501864786,"explanation" +"hard","conjunctive","negative","abnormal",1,0,0.171717171717172,0.333333333333333,"explanation" +"hard","conjunctive","negative","normal",1,0,0.373737373737374,0.333333333333333,"explanation" +"hard","conjunctive","negative","nopreference",1,1,0.454545454545455,0.333333333333333,"explanation" +"hard","disjunctive","positive","abnormal",1,0,0.268041237113402,0.333333333333333,"explanation" +"hard","disjunctive","positive","normal",1,0,0.195876288659794,0.333333333333333,"explanation" +"hard","disjunctive","positive","nopreference",1,1,0.536082474226804,0.333333333333333,"explanation" +"hard","disjunctive","negative","abnormal",0.2,0,0.0808080808080808,0.156347065222755,"explanation" +"hard","disjunctive","negative","normal",0.8,0,0.484848484848485,0.550324432912458,"explanation" +"hard","disjunctive","negative","nopreference",0.5,1,0.434343434343434,0.293328501864786,"explanation" +"soft","conjunctive","positive","abnormal",0.32,0,0.510204081632653,0.376046983001778,"explanation" +"soft","conjunctive","positive","normal",0.2,0,0.112244897959184,0.292372348649304,"explanation" +"soft","conjunctive","positive","nopreference",0.26,1,0.377551020408163,0.331580668348918,"explanation" +"soft","conjunctive","negative","abnormal",1,0,0.454545454545455,0.376046983001778,"explanation" +"soft","conjunctive","negative","normal",0.88,0,0.141414141414141,0.292372348649304,"explanation" +"soft","conjunctive","negative","nopreference",0.94,1,0.404040404040404,0.331580668348918,"explanation" +"soft","disjunctive","positive","abnormal",0.88,0,0.0612244897959184,0.292372348649304,"explanation" +"soft","disjunctive","positive","normal",1,0,0.581632653061224,0.376046983001778,"explanation" +"soft","disjunctive","positive","nopreference",0.94,1,0.357142857142857,0.331580668348918,"explanation" +"soft","disjunctive","negative","abnormal",0.2,0,0.111111111111111,0.292372348649304,"explanation" +"soft","disjunctive","negative","normal",0.32,0,0.353535353535354,0.376046983001778,"explanation" +"soft","disjunctive","negative","nopreference",0.26,1,0.535353535353535,0.331580668348918,"explanation" diff --git a/code/R/experiments/explanation_predictions_truth_only.csv b/code/R/experiments/explanation_predictions_truth_only.csv new file mode 100644 index 0000000..76ca78e --- /dev/null +++ b/code/R/experiments/explanation_predictions_truth_only.csv @@ -0,0 +1,37 @@ +"intervention","causal_structure","outcome","choice","value","truth","probability","prediction","type" +"fixed","conjunctive","positive","abnormal",0.72,0,0.535353535353535,0.279677081797963,"explanation" +"fixed","conjunctive","positive","normal",0.18,0,0.0909090909090909,0.279677081797963,"explanation" +"fixed","conjunctive","positive","nopreference",0.45,1,0.373737373737374,0.440645836404074,"explanation" +"fixed","conjunctive","negative","abnormal",0.92,0,0.171717171717172,0.279677081797963,"explanation" +"fixed","conjunctive","negative","normal",0.98,0,0.424242424242424,0.279677081797963,"explanation" +"fixed","conjunctive","negative","nopreference",0.95,1,0.404040404040404,0.440645836404074,"explanation" +"fixed","disjunctive","positive","abnormal",0.98,0,0.272727272727273,0.279677081797963,"explanation" +"fixed","disjunctive","positive","normal",0.92,0,0.181818181818182,0.279677081797963,"explanation" +"fixed","disjunctive","positive","nopreference",0.95,1,0.545454545454545,0.440645836404074,"explanation" +"fixed","disjunctive","negative","abnormal",0.18,0,0.0808080808080808,0.279677081797963,"explanation" +"fixed","disjunctive","negative","normal",0.72,0,0.393939393939394,0.279677081797963,"explanation" +"fixed","disjunctive","negative","nopreference",0.45,1,0.525252525252525,0.440645836404074,"explanation" +"hard","conjunctive","positive","abnormal",0.8,0,0.494845360824742,0.279677081797963,"explanation" +"hard","conjunctive","positive","normal",0.2,0,0.164948453608247,0.279677081797963,"explanation" +"hard","conjunctive","positive","nopreference",0.5,1,0.34020618556701,0.440645836404074,"explanation" +"hard","conjunctive","negative","abnormal",1,0,0.171717171717172,0.279677081797963,"explanation" +"hard","conjunctive","negative","normal",1,0,0.373737373737374,0.279677081797963,"explanation" +"hard","conjunctive","negative","nopreference",1,1,0.454545454545455,0.440645836404074,"explanation" +"hard","disjunctive","positive","abnormal",1,0,0.268041237113402,0.279677081797963,"explanation" +"hard","disjunctive","positive","normal",1,0,0.195876288659794,0.279677081797963,"explanation" +"hard","disjunctive","positive","nopreference",1,1,0.536082474226804,0.440645836404074,"explanation" +"hard","disjunctive","negative","abnormal",0.2,0,0.0808080808080808,0.279677081797963,"explanation" +"hard","disjunctive","negative","normal",0.8,0,0.484848484848485,0.279677081797963,"explanation" +"hard","disjunctive","negative","nopreference",0.5,1,0.434343434343434,0.440645836404074,"explanation" +"soft","conjunctive","positive","abnormal",0.32,0,0.510204081632653,0.279677081797963,"explanation" +"soft","conjunctive","positive","normal",0.2,0,0.112244897959184,0.279677081797963,"explanation" +"soft","conjunctive","positive","nopreference",0.26,1,0.377551020408163,0.440645836404074,"explanation" +"soft","conjunctive","negative","abnormal",1,0,0.454545454545455,0.279677081797963,"explanation" +"soft","conjunctive","negative","normal",0.88,0,0.141414141414141,0.279677081797963,"explanation" +"soft","conjunctive","negative","nopreference",0.94,1,0.404040404040404,0.440645836404074,"explanation" +"soft","disjunctive","positive","abnormal",0.88,0,0.0612244897959184,0.279677081797963,"explanation" +"soft","disjunctive","positive","normal",1,0,0.581632653061224,0.279677081797963,"explanation" +"soft","disjunctive","positive","nopreference",0.94,1,0.357142857142857,0.440645836404074,"explanation" +"soft","disjunctive","negative","abnormal",0.2,0,0.111111111111111,0.279677081797963,"explanation" +"soft","disjunctive","negative","normal",0.32,0,0.353535353535354,0.279677081797963,"explanation" +"soft","disjunctive","negative","nopreference",0.26,1,0.535353535353535,0.440645836404074,"explanation" diff --git a/code/R/experiments/explanation_selection_negative_outcome-participants.csv b/code/R/experiments/explanation_selection_negative_outcome-participants.csv new file mode 100644 index 0000000..e9e9ab5 --- /dev/null +++ b/code/R/experiments/explanation_selection_negative_outcome-participants.csv @@ -0,0 +1,105 @@ +workerid,proliferate.condition,age,ethnicity,feedback,gender,race,error +1510,Disneg_Conneg,30,Non-Hispanic,The factor that determined point loss in each round influenced the way I responded.,Male,Black/African American, +1512,Disneg_Conneg,41,Non-Hispanic,"If both need to be on to lose the point, then our ability to turn either off will make us keep the point - so it's irrelevant why we lost on the first round. ",Male,White, +1494,Disneg_Conneg,25,Non-Hispanic,,Male,White, +1527,Disneg_Conneg,25,Hispanic,statistics knowledge,Female,White, +1549,Disneg_Conneg,27,Non-Hispanic,"I assumed if I told her it was because the blue switch turned on, that's the switch she would manually turn to off in the next round. That way, the blue switch is off and no matter which way the orange switch goes, we won't lose a point. ",Non-binary,White, +1565,Disneg_Conneg,22,Non-Hispanic,,Female,White, +1539,Disneg_Conneg,25,Non-Hispanic,"I was mostly focused on the orange switch due to its lower chance of switching on. Looking back, I would've explained to the other player that the point was lost due to the blue switch.",Female,Asian, +1559,Disneg_Conneg,42,Non-Hispanic,I look at the probability of the switches.,Male,Asian, +1525,Disneg_Conneg,39,Non-Hispanic,I just tried to answer honestly. No one switch was the problem if both switches were on.,Female,White, +1468,Disneg_Conneg,33,Non-Hispanic,"I think it is more like a ""blaming"" game, like we are trying to figure out what we find is blamable. I think we tend to blame something if it has a lower chance to happen and it happened, so we tend to focus on that part like a person usually don't sleep during the day and one day he sleeps, his car got stolen, we blame him for sleeping that day.",Male,White, +1569,Disneg_Conneg,26,Non-Hispanic,,Female,White, +1535,Disneg_Conneg,38,Non-Hispanic,,Male,Multiracial, +1496,Disneg_Conneg,20,Non-Hispanic,It wouldn't make sense if I said one color is turned on so we should turn if off if we had one choice on which one we should turn off when both of them our off and she doesn't know the probability.,Female,Multiracial, +1555,Disneg_Conneg,35,Hispanic,I just read and thought about the chances.,Male,White, +1537,Disneg_Conneg,25,Non-Hispanic,I would turn them off everytime because of the likelihood of losing a point,Male,White, +1490,Disneg_Conneg,25,Non-Hispanic,,Non-binary,White, +1502,Disneg_Conneg,26,Non-Hispanic,i made my decisions based on the probability of each switch turning on. I assumed that 80% probability meant it would indeed go in that direction,Male,White, +1561,Disneg_Conneg,49,Non-Hispanic,,Male,White, +1486,Disneg_Conneg,41,Non-Hispanic,There is a higher probability of the blue switch to turn on. ,Female,White, +1563,Disneg_Conneg,31,Non-Hispanic,Knowing about the probabilities for each switch influenced how I decided to respond. The conditions for losing a point also affected relevant probabilities.,Male,Asian, +1545,Disneg_Conneg,48,Non-Hispanic,no,Male,White, +1474,Disneg_Conneg,21,Non-Hispanic,I was influenced by which switch had a higher likelihood of turning on. ,Female,Black/African American, +1529,Disneg_Conneg,20,Non-Hispanic,"I tried to place myself in the other participant's position, and say what I thought would make them more likely turn the blue switch off",Female,White, +1570,Disneg_Conneg,26,Non-Hispanic,,Male,White, +1488,Disneg_Conneg,26,Non-Hispanic,I responded the way I did because it was the most likely to allow my team to win in future plays,Non-binary,White, +1498,Disneg_Conneg,37,Non-Hispanic,calculated the odds,Male,White, +1519,Disneg_Conneg,53,Non-Hispanic,I answered purely based on the probability given.,Female,White, +1541,Disneg_Conneg,22,Non-Hispanic, just based on the facts given,Female,Black/African American, +1523,Disneg_Conneg,32,Non-Hispanic,"For the first scenario I wanted to get Alice to turn off the blue switch. So I made it seem like that was the reason we lost. + +For the second one, it really doesn't matter what switch is turned off because no matter what we won't lose a point",Male,White, +1553,Disneg_Conneg,30,Non-Hispanic,I tried to use statistics to explain how favorable an outcome would be and how my choices would lead to the best outcome,Female,White, +1492,Disneg_Conneg,35,Non-Hispanic,The probability of the blue switch turning on was higher and therefore turning it off was more likely to positively impact the outcome of the game.,Female,White, +1557,Disneg_Conneg,36,Hispanic,giving us the best chance to win,Male,Multiracial, +1482,Disneg_Conneg,24,Non-Hispanic,Just reading the explanations,Female,White, +1571,Disneg_Conneg,19,Non-Hispanic,,Female,Asian, +1514,Disneg_Conneg,21,Non-Hispanic,"I told them about the blue switch because if the blue switch is guaranteed to be off, we have a really high probability of not losing a point.",Male,White, +1567,Disneg_Conneg,55,Non-Hispanic,my understanding of probability ,Male,White, +1472,Disneg_Conneg,46,Non-Hispanic,I thought there would be more rounds to the game and telling the first player it was the blue one would maker her less likely to turn blue on which I don't know even has any effect on whether or not the machine actually turns to on. This was confusing.,Female,White, +1484,Disneg_Conneg,35,Non-Hispanic,Just what I thought was the best way to look at it,Female,White, +1533,Disneg_Conneg,36,Non-Hispanic,no,Female,White, +1521,Disneg_Conneg,43,Hispanic,The blue switch had the more likely chance of influencing a negative outcome in both games so I tried to maximize the chances of winning by instructing the player in such a way as to minimize blue being the cause of a loss.,Male,Multiracial, +1500,Disneg_Conneg,26,Non-Hispanic,,Male,White, +1476,Disneg_Conneg,26,Non-Hispanic,The likelihood of the switches turning on and off and the rules of the game.,Female,White, +1480,Disneg_Conneg,42,Non-Hispanic,The instructions influenced how I responded. ,Female,White, +1506,Disneg_Conneg,51,Non-Hispanic,answered based on info i was given as i remembered it,Female,White, +1516,Disneg_Conneg,24,Hispanic,,Male,White, +1531,Disneg_Conneg,29,Non-Hispanic,very straight forward,Male,White, +1508,Disneg_Conneg,28,Non-Hispanic,,Male,Black/African American, +1470,Disneg_Conneg,23,Non-Hispanic,if i would lose a point or not,Female,White, +1547,Disneg_Conneg,31,Non-Hispanic,I decided to focus on the blue switch because it was the most likely to turn on. I wanted my teammates to focus on turning that switch off because it was the one most likely to lose us a point. ,Female,White, +1478,Disneg_Conneg,23,Non-Hispanic,Great survey!,Male,White, +1504,Disneg_Conneg,22,Non-Hispanic,"What my teammate knew influenced my decisions to respond. + +By the way, this experiment is quite hard to understand. There's a lot going on - lots of angles you expect the survey participants to remember.",Male,White, +1520,Conneg_Disneg,36,Non-Hispanic,Just the math. I tried my best to prevent my team form losing points.,Male,Multiracial, +1554,Conneg_Disneg,51,Non-Hispanic,if you want to play an honest game you tell the truth as far as picking the one with the most likely to turn on. i was confused because you said at first we worked together then you said I scored a point. which would mean i stole her point so we were actually competitors.,Female,White, +1556,Conneg_Disneg,26,Non-Hispanic,NA,Male,White, +1505,Conneg_Disneg,30,Non-Hispanic,,Female,White, +1483,Conneg_Disneg,27,Non-Hispanic,n/a,Female,White, +1564,Conneg_Disneg,30,Non-Hispanic,My decisions were based on how many switches needed to be turned on in order to lose points.,Female,Asian, +1558,Conneg_Disneg,25,Hispanic,No,Female,White, +1562,Conneg_Disneg,38,Non-Hispanic,the percentage chance of blue turning on,Female,White, +1471,Conneg_Disneg,29,,I decided based on the chances of the switches being turned on or off by themselves. ,Male,Asian, +1526,Conneg_Disneg,47,Non-Hispanic,the likelihood of each switch turning on,Female,Multiracial, +1493,Conneg_Disneg,20,Non-Hispanic,,Male,White, +1499,Conneg_Disneg,25,Non-Hispanic,,Female,Asian, +1566,Conneg_Disneg,39,Non-Hispanic,The probability of each switch turning on.,Male,Black/African American, +1530,Conneg_Disneg,27,Non-Hispanic,"Trying to maximize points and winning, based on probability of outcomes",Male,Asian, +1518,Conneg_Disneg,52,Non-Hispanic,n/a,Male,White, +1487,Conneg_Disneg,31,Hispanic,I decided my answers based on how I would feel playing such a game.,Male,Asian, +1507,Conneg_Disneg,37,Non-Hispanic,no,Male,White, +1538,Conneg_Disneg,29,Hispanic,what my partner knew,Male,White, +1534,Conneg_Disneg,19,Non-Hispanic,"The whole game seemed predicated on whether a switch would be turned on, so I thought that the blue switch was the most important.",Male,Asian, +1473,Conneg_Disneg,41,Non-Hispanic,,Male,White, +1513,Conneg_Disneg,56,Non-Hispanic,I told my partner based on which had a higher likelihood of being switched on,Female,White, +1546,Conneg_Disneg,37,Non-Hispanic,I know the chances of the blue one turning on is higher so I want Alice to think the blue one needs to be turned off. That will give us the highest chance of both of the switches staying off and us not losing a point,Male,Asian, +1491,Conneg_Disneg,24,Non-Hispanic,,Male,White, +1511,Conneg_Disneg,51,Non-Hispanic,,Female,White, +1536,Conneg_Disneg,35,Non-Hispanic,the conditions that lead to losing a point and the information the other player has and what decision they are making and how that will effect the outcome.,Male,Asian, +1548,Conneg_Disneg,24,Hispanic,logic,Male,White, +1522,Conneg_Disneg,21,Non-Hispanic,The choice with the higher percentage of turning on should be prioritized at all points,Male,Asian, +1540,Conneg_Disneg,30,Non-Hispanic,I was influenced both by the fact that neither of my partners knew the likelihood that a switch would turn on as well as the fact that only 1 switch had to be turned on in the second round to lose a point,Non-binary,White, +1497,Conneg_Disneg,22,Non-Hispanic,Primarily the likelihoods of which switch turning on and off.,Male,White, +1517,Conneg_Disneg,53,Non-Hispanic,BLue was more like to switch on so pick blue all the time Alice. Also this seemed very repetitive and tedious with all the comprehension checks. You should make a real interactive game for future iterations of this study. ,other_gender,White, +1479,Conneg_Disneg,34,Non-Hispanic,I responded with the blue switch being the problem because it has the higher probability of being turned on and therefore the easiest to control. ,Female,White, +1509,Conneg_Disneg,54,Non-Hispanic,I assessed the chances of the blue and orange switches being turned on to make a decision. I looked at the probability of the switches turning on.,Male,White, +1515,Conneg_Disneg,60,Non-Hispanic,common sense,Female,White, +1552,Conneg_Disneg,34,Non-Hispanic,Logic. You always remove the variable with the higher chance of having the outcome you do not want. ,Non-binary,Multiracial, +1568,Conneg_Disneg,21,Non-Hispanic,i just tried to avoid the blue (80% chance) of turning on,Female,Asian, +1542,Conneg_Disneg,24,Non-Hispanic,The percentage that the switch would turn on or off,Female,White, +1489,Conneg_Disneg,47,,"The probability of the switch turning on. +",Male,White, +1524,Conneg_Disneg,21,Non-Hispanic,the percentage of the switches being turned on and off,Female,White, +1532,Conneg_Disneg,19,Non-Hispanic,The percentages influenced my choices.,Male,White, +1475,Conneg_Disneg,36,Non-Hispanic,Just tried to give the best response based on the probability. ,Female,White, +1560,Conneg_Disneg,44,Non-Hispanic,The probability of each switch being on,Female,Multiracial, +1550,Conneg_Disneg,35,Non-Hispanic,I told them of the higher probability of them turning on so they'd likely turn off that switch.,Male,White, +1481,Conneg_Disneg,27,Non-Hispanic,,Female,White, +1495,Conneg_Disneg,40,Non-Hispanic,The blue switch had the biggest influence on what would happen in each round because of it's 80% probability of being in the on position. ,Female,White, +1503,Conneg_Disneg,33,Non-Hispanic,"If I say we lost because the blue switch was on, they will manually turn off the blue switch the next round, which is most advantageous.",Male,White, +1501,Conneg_Disneg,51,Non-Hispanic,The chances influenced me a lot.,Female,White, +1485,Conneg_Disneg,44,,I would always suggest the blue switch because of the higher probability of it turning on since the other player is unaware of the probability of both switches.,Female,White, +1469,Conneg_Disneg,18,Hispanic,,Male,White, \ No newline at end of file diff --git a/code/R/experiments/explanation_selection_negative_outcome-responses.csv b/code/R/experiments/explanation_selection_negative_outcome-responses.csv new file mode 100644 index 0000000..5856201 --- /dev/null +++ b/code/R/experiments/explanation_selection_negative_outcome-responses.csv @@ -0,0 +1,100 @@ +workerid,proliferate.condition,explanation_response_1,explanation_response_2,intervention_response_1,intervention_response_2,error +1510,Disneg_Conneg,blue,no preference,blue,blue, +1512,Disneg_Conneg,blue,no preference,blue,no preference, +1494,Disneg_Conneg,blue,orange,orange,blue, +1527,Disneg_Conneg,no preference,no preference,orange,orange, +1549,Disneg_Conneg,blue,blue,blue,orange, +1565,Disneg_Conneg,blue,no preference,blue,no preference, +1539,Disneg_Conneg,orange,orange,blue,blue, +1559,Disneg_Conneg,no preference,no preference,blue,blue, +1525,Disneg_Conneg,no preference,no preference,blue,blue, +1468,Disneg_Conneg,no preference,orange,blue,orange, +1569,Disneg_Conneg,no preference,no preference,orange,blue, +1535,Disneg_Conneg,blue,no preference,blue,no preference, +1496,Disneg_Conneg,no preference,no preference,blue,blue, +1555,Disneg_Conneg,orange,no preference,blue,orange, +1537,Disneg_Conneg,no preference,no preference,no preference,no preference, +1490,Disneg_Conneg,no preference,no preference,blue,no preference, +1502,Disneg_Conneg,blue,orange,blue,orange, +1561,Disneg_Conneg,blue,no preference,blue,no preference, +1486,Disneg_Conneg,blue,blue,blue,blue, +1563,Disneg_Conneg,no preference,orange,blue,no preference, +1545,Disneg_Conneg,blue,blue,orange,blue, +1474,Disneg_Conneg,no preference,no preference,blue,blue, +1529,Disneg_Conneg,blue,blue,blue,blue, +1570,Disneg_Conneg,no preference,no preference,blue,blue, +1488,Disneg_Conneg,orange,blue,blue,blue, +1498,Disneg_Conneg,no preference,blue,blue,blue, +1519,Disneg_Conneg,blue,no preference,blue,blue, +1541,Disneg_Conneg,no preference,no preference,no preference,blue, +1523,Disneg_Conneg,blue,no preference,blue,no preference, +1553,Disneg_Conneg,blue,orange,blue,blue, +1492,Disneg_Conneg,blue,blue,blue,blue, +1557,Disneg_Conneg,blue,blue,blue,blue, +1482,Disneg_Conneg,no preference,no preference,blue,blue, +1571,Disneg_Conneg,no preference,no preference,blue,orange, +1514,Disneg_Conneg,blue,blue,blue,blue, +1567,Disneg_Conneg,blue,blue,blue,blue, +1472,Disneg_Conneg,blue,no preference,blue,no preference, +1484,Disneg_Conneg,orange,no preference,blue,no preference, +1533,Disneg_Conneg,no preference,orange,blue,orange, +1521,Disneg_Conneg,blue,blue,blue,blue, +1500,Disneg_Conneg,blue,no preference,blue,no preference, +1476,Disneg_Conneg,no preference,no preference,blue,blue, +1480,Disneg_Conneg,blue,no preference,orange,blue, +1506,Disneg_Conneg,no preference,no preference,blue,blue, +1516,Disneg_Conneg,blue,orange,blue,blue, +1531,Disneg_Conneg,blue,orange,blue,orange, +1508,Disneg_Conneg,no preference,no preference,orange,orange, +1470,Disneg_Conneg,blue,no preference,blue,blue, +1547,Disneg_Conneg,blue,blue,blue,blue, +1478,Disneg_Conneg,orange,no preference,blue,orange, +1504,Disneg_Conneg,no preference,no preference,blue,no preference, +1520,Conneg_Disneg,blue,blue,blue,blue, +1554,Conneg_Disneg,orange,blue,blue,blue, +1556,Conneg_Disneg,blue,blue,no preference,no preference, +1505,Conneg_Disneg,blue,no preference,orange,orange, +1483,Conneg_Disneg,no preference,no preference,blue,blue, +1564,Conneg_Disneg,blue,no preference,blue,no preference, +1558,Conneg_Disneg,blue,no preference,blue,blue, +1562,Conneg_Disneg,no preference,no preference,blue,blue, +1471,Conneg_Disneg,no preference,no preference,blue,blue, +1526,Conneg_Disneg,no preference,orange,orange,orange, +1493,Conneg_Disneg,no preference,no preference,blue,blue, +1499,Conneg_Disneg,blue,blue,orange,blue, +1566,Conneg_Disneg,no preference,no preference,blue,blue, +1530,Conneg_Disneg,orange,no preference,blue,blue, +1518,Conneg_Disneg,orange,no preference,blue,no preference, +1487,Conneg_Disneg,no preference,no preference,blue,blue, +1507,Conneg_Disneg,no preference,no preference,no preference,blue, +1538,Conneg_Disneg,no preference,no preference,orange,blue, +1534,Conneg_Disneg,blue,blue,blue,blue, +1473,Conneg_Disneg,no preference,no preference,orange,blue, +1513,Conneg_Disneg,blue,blue,blue,blue, +1546,Conneg_Disneg,no preference,blue,no preference,blue, +1491,Conneg_Disneg,orange,no preference,blue,blue, +1511,Conneg_Disneg,blue,orange,orange,blue, +1536,Conneg_Disneg,orange,no preference,no preference,blue, +1548,Conneg_Disneg,blue,no preference,blue,orange, +1522,Conneg_Disneg,blue,blue,blue,blue, +1540,Conneg_Disneg,blue,no preference,blue,blue, +1497,Conneg_Disneg,orange,blue,blue,blue, +1517,Conneg_Disneg,blue,blue,blue,blue, +1479,Conneg_Disneg,blue,blue,blue,blue, +1509,Conneg_Disneg,orange,orange,blue,orange, +1515,Conneg_Disneg,no preference,no preference,blue,orange, +1552,Conneg_Disneg,blue,blue,blue,blue, +1568,Conneg_Disneg,no preference,blue,orange,blue, +1542,Conneg_Disneg,blue,blue,blue,blue, +1489,Conneg_Disneg,orange,blue,blue,blue, +1524,Conneg_Disneg,blue,blue,blue,blue, +1532,Conneg_Disneg,no preference,no preference,orange,orange, +1475,Conneg_Disneg,blue,no preference,blue,blue, +1560,Conneg_Disneg,no preference,no preference,blue,blue, +1550,Conneg_Disneg,blue,blue,blue,blue, +1481,Conneg_Disneg,blue,no preference,orange,orange, +1495,Conneg_Disneg,blue,blue,blue,blue, +1503,Conneg_Disneg,blue,blue,blue,blue, +1501,Conneg_Disneg,blue,blue,blue,blue, +1485,Conneg_Disneg,blue,blue,blue,blue, +1469,Conneg_Disneg,blue,blue,blue,blue, diff --git a/code/R/experiments/explanation_selection_positive_outcome_study_2-participants.csv b/code/R/experiments/explanation_selection_positive_outcome_study_2-participants.csv new file mode 100644 index 0000000..befb62e --- /dev/null +++ b/code/R/experiments/explanation_selection_positive_outcome_study_2-participants.csv @@ -0,0 +1,100 @@ +workerid,proliferate.condition,age,ethnicity,feedback,gender,race,error +1415,Conpos_Dispos,20,Non-Hispanic,The probability of the switch turning on and whether or not both switches needed to be on ,Non-binary,White, +1429,Conpos_Dispos,23,Non-Hispanic,"logic, i guess",Female,White, +1439,Conpos_Dispos,25,Hispanic,Only thing that factored was weather the other person knew the chance of the switches on.,Male,other_race, +1407,Conpos_Dispos,27,Non-Hispanic,"The factors like, the chances that I knew.",Female,Black/African American, +1393,Conpos_Dispos,45,,,Female,White, +1377,Conpos_Dispos,47,,none,Male,Black/African American, +1400,Conpos_Dispos,51,Non-Hispanic,because either way there would be a win,Female,White, +1435,Conpos_Dispos,44,Non-Hispanic,"In the first choice, given only those two options, I chose to tell Zarah the orange switch was what enabled us to score (even though this wasn't technically true). That way, if she was asked to throw a switch herself, she would choose that one and we'd be more likely to win.",Male,White, +1397,Conpos_Dispos,31,Non-Hispanic,,Male,White, +1417,Conpos_Dispos,37,Non-Hispanic,None,Male,White, +1403,Conpos_Dispos,68,Non-Hispanic,The factor that influenced my response was the probability of the switch turning on or off.,Female,White, +1425,Conpos_Dispos,24,Non-Hispanic,,Male,Asian, +1389,Conpos_Dispos,19,Non-Hispanic,i just went by the facts that were presented to me,Female,White, +1447,Conpos_Dispos,27,Non-Hispanic,first round needed both sitches so it would be better to flip the switch with the lower probability of being on; in the second round it didn't matter since only one switch was needed,Female,White, +1437,Conpos_Dispos,33,Non-Hispanic,,Male,White, +1365,Conpos_Dispos,24,Non-Hispanic,Just a basic understanding of the game - it wasn't super hard to follow.,Female,White, +1375,Conpos_Dispos,26,Hispanic,I don't actually think any of the explanations provided as options are legitimate enough to be a satisfactory answer.,Male,White, +1455,Conpos_Dispos,43,Non-Hispanic,My answers were based on the information that was provided to me.,Male,Asian, +1449,Conpos_Dispos,50,Non-Hispanic,The 80% chance of the switch being on,Female,White, +1359,Conpos_Dispos,66,Non-Hispanic,"I made my best guesses about probability in each scenario. I have never formally studied probability, though. I'm curious about what this study is actually investigating. ",Female,White, +1459,Conpos_Dispos,24,Non-Hispanic,"In the first round I thought about what I was most likely to do with each piece of advice and chose the one that I thought would cause me to turn on the orange switch. In the second one, it did not matter which switch was on, so the explanation itself did not matter as long as it involved one switch being turned on.",Female,White, +1443,Conpos_Dispos,38,Non-Hispanic,I thought about which statement would be more likely to help my game partner.,Female,White, +1369,Conpos_Dispos,71,Non-Hispanic,Just tried to go wth the one with greater probability,Female,White, +1379,Conpos_Dispos,51,,,Female,Asian, +1409,Conpos_Dispos,54,Hispanic,I chose to state that we scored a point because orange switch turned on hoping to influence my teammate to turn on the orange switch if we were ever given an opportunity to do so. The blue switch would switch on 80% of the time.,Male,White, +1427,Conpos_Dispos,39,Non-Hispanic,I thought about the odds of each choice happening and the importance of final result. ,Male,White, +1383,Conpos_Dispos,24,Non-Hispanic,"the factors that influenced me was I always went for the higher percentage of turning the switch on, regardless of only one switch needed to be turned on or not",Female,Black/African American, +1453,Conpos_Dispos,32,Non-Hispanic,the percentage of being turned on.,Female,Asian, +1433,Conpos_Dispos,22,Non-Hispanic,,Female,Asian, +1457,Conpos_Dispos,49,Non-Hispanic,"I tried to respond in a way that nudged the other player to manually turn on the switch with the lower chance of randomly ending up in the ""on"" position.",Female,White, +1423,Conpos_Dispos,26,Hispanic,Just trying to play with the oddds,Male,Multiracial, +1431,Conpos_Dispos,32,Non-Hispanic,,Non-binary,Asian, +1451,Conpos_Dispos,36,Hispanic,I responded based on the information given in the prompts.,Female,Black/African American, +1381,Conpos_Dispos,30,Non-Hispanic,I didn't feel like the information I could provide would really help,Non-binary,White, +1361,Conpos_Dispos,24,Non-Hispanic,THe chance that the would help me win,Female,Black/African American, +1441,Conpos_Dispos,21,Hispanic,probability,Female,Multiracial, +1421,Conpos_Dispos,21,Non-Hispanic,"The probability of each switch turning ""on"" or ""off"" influenced my decisions. ",Female,White, +1385,Conpos_Dispos,61,Non-Hispanic,I looked at the logic given.,Male,White, +1401,Conpos_Dispos,28,Non-Hispanic,The chance of orange being on and as long was it was it was the highest chance of winning.,Male,White, +1405,Conpos_Dispos,37,Non-Hispanic,My only concern was the math or probability behind the chance of winning.,Male,White, +1387,Conpos_Dispos,42,Non-Hispanic,How it would make sense to me. ,Female,White, +1371,Conpos_Dispos,48,Non-Hispanic,,Female,Multiracial, +1419,Conpos_Dispos,46,Non-Hispanic,"The way you win in each scenario was how I decided how to respond (either both switches on or only one on). In the first scenario, I also took the percentage of turning on into account when responding.",Female,White, +1445,Conpos_Dispos,31,Non-Hispanic,Based on the chances of scoring a point is switching on,Male,Black/African American, +1363,Conpos_Dispos,57,Non-Hispanic,I just based it on the information given to me.,Male,White, +1391,Conpos_Dispos,26,Non-Hispanic,probability of each switch turning on,Male,White, +1411,Conpos_Dispos,34,Hispanic,I decided to always turn on the orange switch because it was likely that the blue would turn on anyway.,Male,American Indian/Alaska Native, +1373,Conpos_Dispos,44,Non-Hispanic,"If you can choose to turn either one on, and only one needs to be on, then as long as you turn one on, you will win. Any explination about which one is on is unnecessary.",Male,White, +1367,Conpos_Dispos,34,Non-Hispanic,Tried to consider the odds.,Female,White, +1398,Dispos_Conpos,44,Non-Hispanic,"I'm trying to win. and I know inside information and extra information. So, I'm not going to give extra information that I do not need to increase my chance of winning. ",Male,White, +1396,Dispos_Conpos,31,Non-Hispanic,,Male,White, +1406,Dispos_Conpos,52,Non-Hispanic,Knowing the orange switch only had a 20 percent chance to turn on made it of the upmost performance to make sure my partner turned it on when they had the chance.,Male,White, +1394,Dispos_Conpos,28,Hispanic,,Female,White, +1432,Dispos_Conpos,34,Hispanic,"My interpretation of the instructions influenced how I responded. For example, if both switches came on, then it made sense to me that explaining either one was on to the other player made the most sense, especially since they knew that either one or both had to be on to get a point. + +This was an interesting survey, but a bit strange to me. I thought an actual game would be played in between, didn't realize the entire thing would be about the explanations I chose, so this page came up abruptly, in my opinion.",other_gender,Multiracial, +1442,Dispos_Conpos,55,Non-Hispanic,no,Female,White, +1434,Dispos_Conpos,55,Non-Hispanic,Since the blue switch turns on 80% of the time then telling her the orange switch since it has the least probability of turning on.,Female,White, +1366,Dispos_Conpos,21,Non-Hispanic,I wanted to get the most points and from a probability standpoint switching the orange one on yielding the best result.,Male,White, +1374,Dispos_Conpos,23,Non-Hispanic,,Male,White, +1384,Dispos_Conpos,20,Non-Hispanic,no,Female,Black/African American, +1410,Dispos_Conpos,38,Non-Hispanic,"The blue seems like the greatest asset toward winning any of the rounds, so I would try to aim toward using the blue whenever possible ",Male,White, +1388,Dispos_Conpos,24,Non-Hispanic,The rules of the game (which changed per scenario) influenced my responses. ,Female,Multiracial, +1380,Dispos_Conpos,45,Non-Hispanic,No matter which way you put it the answer still comes out the same vice versa.,Female,White, +1368,Dispos_Conpos,30,Non-Hispanic,reading comprehension and short term memory,Male,White, +1399,Dispos_Conpos,21,Non-Hispanic,how logical each answer seemed,Male,White, +1370,Dispos_Conpos,40,Non-Hispanic,I just used to information given to me.,Male,White, +1372,Dispos_Conpos,31,Non-Hispanic,,Female,Asian, +1358,Dispos_Conpos,40,Non-Hispanic,"If we both got to pick a switch to manually turn on, I'd want to influence Zarah to pick the blue knowing I'd pick the orange to turn on.",Female,White, +1450,Dispos_Conpos,38,Non-Hispanic,"The blue had a much higher chance of being turned ""ON""",Female,White, +1418,Dispos_Conpos,25,Non-Hispanic,"I didn't give them truthful information. I gave them information that would give me the desired result, which is the highest percent chance both switches turn on. ",Male,Multiracial, +1414,Dispos_Conpos,27,Non-Hispanic,I chose based on the facts I was given. I tried picking an answer that would get the other participant to turn on the switch that would benefit us. ,Female,White, +1454,Dispos_Conpos,21,Hispanic,The likely hood of me winning a point was the deciding factor of all my responses. I wanted as many lights on as possible even if that meant that my teammate had slightly less information ,Male,Multiracial, +1408,Dispos_Conpos,23,Hispanic,the probability of the switch turning on influenced my response,Male,White, +1360,Dispos_Conpos,30,Non-Hispanic,,Female,White, +1428,Dispos_Conpos,25,Non-Hispanic,"I have a feeling she would've turned on the blue switch, so bc the orange switch also turned on, we won the point",Female,Asian, +1386,Dispos_Conpos,29,Non-Hispanic,,Female,White, +1402,Dispos_Conpos,32,Non-Hispanic,probability ,Male,White, +1424,Dispos_Conpos,36,Non-Hispanic,I was influenced by the fact that I wanted Zarah to choose the orange switch to turn on in future rounds. By saying orange I put Zarah's focus on the orange switch so Zarah would have a high likelihood of choose the Orange Switch in the future.,Male,White, +1416,Dispos_Conpos,23,Non-Hispanic,"I mean, especially in the second instance, it wouldn't matter which of the three options I chose because it didn't matter which of the switches was on (they both needed to be on) so it would not have benefitted Zarah to hear either explanation, therefore both were equally helpful. Now, if there was an option to explain that the orange was less likely to turn on, that would be helpful, but that wasn't an option.",Female,White, +1362,Dispos_Conpos,54,Hispanic,"The orange switch turning on is far more unlikely and event, so I felt that was the one to include in my explanation.",Male,White, +1461,Dispos_Conpos,24,Non-Hispanic,I tried to turn the switch that was least likely to be turned on otherwise.,Non-binary,American Indian/Alaska Native, +1376,Dispos_Conpos,48,Non-Hispanic,Depending on how many switches needed to be on to win a point was what influenced my response.,Female,White, +1462,Dispos_Conpos,40,Non-Hispanic,What needed done to win the point.,Male,White, +1390,Dispos_Conpos,20,Non-Hispanic,I chose answers logically based on the given situation to maximize the chances of getting a point.,Male,White, +1436,Dispos_Conpos,25,Non-Hispanic,"If shes focused on turning orange on, blue's high probability will handle the rest",Male,White, +1452,Dispos_Conpos,34,Non-Hispanic,I chose an explanation that would likely make my teammate choose the switch that was less likely to turn on.,Male,White, +1440,Dispos_Conpos,29,Non-Hispanic,,Female,White, +1446,Dispos_Conpos,36,Non-Hispanic,I tried to respond in a way that would give the player with less information a hint.,Female,White, +1392,Dispos_Conpos,23,Non-Hispanic,Depending if one or both switches needed to be on,Female,Asian, +1422,Dispos_Conpos,34,Non-Hispanic,"I always play fair so I would inform everyone involved how likely we are to get a point. It was unlikely the orange would turn on the second game, so that is why I would say we won.",Female,White, +1364,Dispos_Conpos,27,,I feel it's more indicative of the blue switch having a higher chance of turning on if I tell my partner that we scored a point because of it.,other_gender,other_race, +1456,Dispos_Conpos,47,Non-Hispanic,The orange switch has a lower probability of being on and needs to be on in order to win,Female,White, +1438,Dispos_Conpos,57,Non-Hispanic,None,Female,White, +1378,Dispos_Conpos,40,Non-Hispanic,The percentage of each switch turning on influenced my decision. By stating that the reason we got a point was because of the orange switch turning on I was trying to hint to the importance of it being on since it had relatively lower odds of turning on.,Female,White, +1430,Dispos_Conpos,50,Non-Hispanic,I tried to give them a better chance of choosing the hardest switch to turn on.,Female,White, +1382,Dispos_Conpos,21,Hispanic,The lower probability led me to attempt to convince my teammate to turn on the orange switch when it was their turn to turn a switch on manually.,Male,Multiracial, +1404,Dispos_Conpos,27,Non-Hispanic,the likelihood of a payout,Male,White, +1426,Dispos_Conpos,36,Non-Hispanic,I went with the higher probability because it mattered more i think.,Male,White, \ No newline at end of file diff --git a/code/R/experiments/explanation_selection_positive_outcome_study_2-responses.csv b/code/R/experiments/explanation_selection_positive_outcome_study_2-responses.csv new file mode 100644 index 0000000..2a97c2e --- /dev/null +++ b/code/R/experiments/explanation_selection_positive_outcome_study_2-responses.csv @@ -0,0 +1,98 @@ +workerid,proliferate.condition,explanation_response_1,explanation_response_2,intervention_response_1,intervention_response_2,error +1415,Conpos_Dispos,orange,no preference,orange,no preference, +1429,Conpos_Dispos,orange,no preference,orange,no preference, +1439,Conpos_Dispos,no preference,no preference,orange,orange, +1407,Conpos_Dispos,blue,orange,blue,orange, +1393,Conpos_Dispos,no preference,no preference,blue,blue, +1377,Conpos_Dispos,no preference,no preference,blue,no preference, +1400,Conpos_Dispos,orange,no preference,orange,orange, +1435,Conpos_Dispos,orange,no preference,orange,no preference, +1397,Conpos_Dispos,orange,no preference,orange,no preference, +1417,Conpos_Dispos,orange,no preference,orange,orange, +1403,Conpos_Dispos,no preference,no preference,orange,no preference, +1425,Conpos_Dispos,blue,no preference,blue,blue, +1389,Conpos_Dispos,no preference,no preference,blue,no preference, +1447,Conpos_Dispos,orange,no preference,orange,no preference, +1437,Conpos_Dispos,orange,orange,blue,blue, +1365,Conpos_Dispos,orange,no preference,orange,no preference, +1375,Conpos_Dispos,orange,no preference,orange,orange, +1455,Conpos_Dispos,blue,no preference,blue,blue, +1449,Conpos_Dispos,no preference,no preference,blue,blue, +1359,Conpos_Dispos,orange,no preference,orange,orange, +1459,Conpos_Dispos,orange,no preference,orange,no preference, +1443,Conpos_Dispos,orange,no preference,orange,no preference, +1369,Conpos_Dispos,blue,blue,blue,blue, +1379,Conpos_Dispos,no preference,no preference,blue,blue, +1409,Conpos_Dispos,orange,orange,orange,no preference, +1427,Conpos_Dispos,orange,no preference,orange,no preference, +1383,Conpos_Dispos,no preference,no preference,blue,blue, +1453,Conpos_Dispos,no preference,blue,blue,no preference, +1433,Conpos_Dispos,orange,no preference,orange,no preference, +1457,Conpos_Dispos,orange,orange,orange,orange, +1423,Conpos_Dispos,orange,no preference,orange,blue, +1431,Conpos_Dispos,blue,orange,blue,orange, +1451,Conpos_Dispos,no preference,no preference,orange,blue, +1381,Conpos_Dispos,no preference,no preference,orange,no preference, +1361,Conpos_Dispos,orange,blue,orange,orange, +1441,Conpos_Dispos,no preference,no preference,orange,orange, +1421,Conpos_Dispos,orange,orange,orange,orange, +1385,Conpos_Dispos,no preference,orange,blue,orange, +1401,Conpos_Dispos,orange,orange,orange,orange, +1405,Conpos_Dispos,orange,no preference,orange,no preference, +1387,Conpos_Dispos,no preference,no preference,no preference,no preference, +1371,Conpos_Dispos,blue,blue,blue,blue, +1419,Conpos_Dispos,orange,no preference,orange,no preference, +1445,Conpos_Dispos,blue,blue,blue,orange, +1363,Conpos_Dispos,no preference,no preference,orange,orange, +1391,Conpos_Dispos,orange,orange,orange,orange, +1411,Conpos_Dispos,orange,orange,orange,orange, +1373,Conpos_Dispos,orange,no preference,orange,no preference, +1367,Conpos_Dispos,no preference,no preference,orange,orange, +1398,Dispos_Conpos,no preference,no preference,blue,blue, +1396,Dispos_Conpos,no preference,orange,no preference,orange, +1406,Dispos_Conpos,orange,orange,orange,orange, +1394,Dispos_Conpos,blue,no preference,blue,blue, +1432,Dispos_Conpos,no preference,no preference,no preference,no preference, +1442,Dispos_Conpos,blue,orange,no preference,blue, +1434,Dispos_Conpos,no preference,orange,orange,orange, +1366,Dispos_Conpos,orange,orange,orange,orange, +1374,Dispos_Conpos,no preference,no preference,orange,orange, +1384,Dispos_Conpos,no preference,no preference,blue,blue, +1410,Dispos_Conpos,blue,blue,blue,blue, +1388,Dispos_Conpos,no preference,blue,orange,orange, +1380,Dispos_Conpos,no preference,no preference,blue,blue, +1368,Dispos_Conpos,blue,blue,blue,blue, +1399,Dispos_Conpos,no preference,no preference,orange,orange, +1370,Dispos_Conpos,blue,no preference,orange,orange, +1372,Dispos_Conpos,no preference,no preference,blue,blue, +1358,Dispos_Conpos,blue,blue,blue,orange, +1450,Dispos_Conpos,blue,blue,blue,blue, +1418,Dispos_Conpos,orange,orange,orange,orange, +1414,Dispos_Conpos,orange,orange,orange,orange, +1454,Dispos_Conpos,orange,orange,no preference,orange, +1408,Dispos_Conpos,orange,orange,orange,orange, +1360,Dispos_Conpos,orange,blue,orange,orange, +1428,Dispos_Conpos,no preference,orange,blue,no preference, +1386,Dispos_Conpos,orange,orange,orange,orange, +1402,Dispos_Conpos,blue,no preference,blue,orange, +1424,Dispos_Conpos,no preference,orange,no preference,orange, +1416,Dispos_Conpos,no preference,no preference,orange,orange, +1362,Dispos_Conpos,no preference,orange,no preference,orange, +1461,Dispos_Conpos,orange,orange,orange,orange, +1376,Dispos_Conpos,blue,no preference,blue,no preference, +1462,Dispos_Conpos,no preference,no preference,orange,orange, +1390,Dispos_Conpos,orange,orange,orange,orange, +1436,Dispos_Conpos,orange,orange,orange,orange, +1452,Dispos_Conpos,orange,orange,orange,orange, +1440,Dispos_Conpos,no preference,no preference,blue,orange, +1446,Dispos_Conpos,orange,no preference,orange,orange, +1392,Dispos_Conpos,blue,no preference,blue,no preference, +1422,Dispos_Conpos,blue,orange,blue,blue, +1364,Dispos_Conpos,blue,blue,orange,orange, +1456,Dispos_Conpos,no preference,orange,no preference,orange, +1438,Dispos_Conpos,no preference,no preference,no preference,blue, +1378,Dispos_Conpos,orange,orange,orange,orange, +1430,Dispos_Conpos,orange,blue,orange,orange, +1382,Dispos_Conpos,orange,orange,orange,orange, +1404,Dispos_Conpos,blue,orange,blue,blue, +1426,Dispos_Conpos,blue,blue,orange,orange, diff --git a/code/R/experiments/fixedint_neg.csv b/code/R/experiments/fixedint_neg.csv new file mode 100644 index 0000000..b8e32e6 --- /dev/null +++ b/code/R/experiments/fixedint_neg.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,17.17171717171717,42.42424242424242,40.4040404040404,20.2020202020202,66.66666666666666,13.131313131313133,negative,fixedint +Disjunctive,8.080808080808081,39.39393939393939,52.52525252525253,10.1010101010101,79.7979797979798,10.1010101010101,negative,fixedint diff --git a/code/R/experiments/fixedint_pos.csv b/code/R/experiments/fixedint_pos.csv new file mode 100644 index 0000000..827a1dc --- /dev/null +++ b/code/R/experiments/fixedint_pos.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,53.535353535353536,9.090909090909092,37.37373737373738,80.8080808080808,11.11111111111111,8.080808080808081,negative,fixedint +Disjunctive,27.27272727272727,18.181818181818183,54.54545454545454,65.65656565656566,12.121212121212121,22.22222222222222,negative,fixedint diff --git a/code/R/experiments/fixedintervention_negative-participants.csv b/code/R/experiments/fixedintervention_negative-participants.csv new file mode 100644 index 0000000..7819441 --- /dev/null +++ b/code/R/experiments/fixedintervention_negative-participants.csv @@ -0,0 +1,101 @@ +workerid,proliferate.condition,age,ethnicity,feedback,gender,race,error +2728,Condition_3,32,Non-Hispanic,The information I had influenced my response. i wanted to make sure that the statistics were lessened if they were high and lower the probability of losing,Female,White, +2658,Condition_3,21,Non-Hispanic,"The instructions were overly complicated, which I only realized at the end confused my answer. ",Female,White, +2717,Condition_3,49,Non-Hispanic,Pushing the blue button has the greater influence on the stated odds,Male,White, +2769,Condition_3,62,Non-Hispanic,I chose because I knew which switch was more likely to be turned on,Female,White, +2719,Condition_3,39,Non-Hispanic,"The percentages on the blue button were much more impactful at 80% of being on vs 20%, so i tried to limit that as much as possible by recommending the blue button be pressed to reduce to 10%. ",Male,White, +2713,Condition_3,51,Non-Hispanic,Blue seemed most likely to be on with 80% ,Female,White, +2748,Condition_3,34,Non-Hispanic,"The wording on the explanation of lowering the on chance % was a bit ambiguous. During the first explanation, I was under the impression pressing the button decreased the on chance BY 10% rather than TO 10%. Had I recognized that, I would have chosen blue so that blue would have 10% on chance and the orange a 20% on chance to optimally minimize the chances of losing a point. ",Male,Multiracial, +2690,Condition_3,31,Non-Hispanic,I decided based on what made the most logical sense to inform the other partner.,Female,White, +2672,Condition_3,62,Non-Hispanic,"The blue switch always has a higher probability of turning on, so if turning on causes point loss it would be best to choose to press blue button to lower probability of on. The orange always has a lower probability of turning on so no need to press the orange button",Female,Black/African American, +2740,Condition_3,33,Hispanic,The percentage of the probability each color would turn on. ,Male,Black/African American, +2675,Condition_3,27,Hispanic,I feel like the obvious answer would be to say because both turned on but I hovered towards blue since it was more likely to turn on,Male,other_race, +2772,Condition_3,29,Hispanic,Doing some math to find the odds,Male,Multiracial, +2757,Condition_3,40,Non-Hispanic,The blue switch needed to be manipulated the most in order to achieve the highest chance of both being off.,Male,White, +2738,Condition_3,51,Non-Hispanic,"I'm not sure I understand what it was about, and I feel quite stupid and pretty sure I messed it up.",Female,White, +2705,Condition_3,30,Non-Hispanic,,Male,White, +2685,Condition_3,67,Non-Hispanic,Just tried to use reasoning,Female,White, +2715,Condition_3,29,Non-Hispanic,What information I and my partner had access to. ,Female,White, +2720,Condition_3,26,Hispanic,Blue's probability of turning on being higher,Female,other_race, +2677,Condition_3,24,Non-Hispanic,I tried my best to be honest with the other participants without being as vague with the answers I was given.,Male,Black/African American, +2727,Condition_3,30,Non-Hispanic,My responses simply depended on if it was true or not. ,Male,White, +2694,Condition_3,59,Non-Hispanic,I mainly looked at the percentage it would take to turn each switch on.,Female,White, +2668,Condition_3,29,Non-Hispanic,factors - just reading and picking the best result and answering the questions. there is still a 50/50 chance of us having the switch on at the same time.,Female,Black/African American, +2733,Condition_3,54,Non-Hispanic,I think the higher percentage of the blue carried more weight in whether it would turn on or not. ,Female,White, +2711,Condition_3,23,Non-Hispanic,The number of switches that had to be on to lose a point changed the way I communicated with my team mate. ,Female,White, +2763,Condition_3,34,Non-Hispanic,"I tried to gt them to understand that having one of the on's could make us loose points,",Female,White, +2752,Condition_3,32,Non-Hispanic,I'd rather them know the full information.,Female,Black/African American, +2759,Condition_3,28,Non-Hispanic,Trying to decrease the probability of losing a point.,Female,White, +2673,Condition_3,21,Non-Hispanic,What I knew vs what the other participant knew,Female,White, +2725,Condition_3,29,Non-Hispanic,,Male,Asian, +2761,Condition_3,33,Non-Hispanic,,Male,White, +2767,Condition_3,34,Non-Hispanic,pressing blue button turned 70% of ON to OFF. pressing orange button turned 10% of ON to OFF.,Non-binary,White, +2735,Condition_3,52,Non-Hispanic,If either switch was on we lose a point point regardless. ,Male,Black/African American, +2660,Condition_3,73,Non-Hispanic,,Female,White, +2771,Condition_3,62,Non-Hispanic,that was fun,Female,White, +2688,Condition_3,35,Hispanic,,Male,other_race, +2755,Condition_3,26,Non-Hispanic,,Male,White, +2753,Condition_3,25,Non-Hispanic,,Female,Asian, +2666,Condition_3,29,Hispanic,"Even though all the explanations were technically correct, I chose the one that would most likely influence the teammate to press the button for the blue switch.",Female,White, +2696,Condition_3,38,Non-Hispanic,Odds of turning on,Male,White, +2681,Condition_3,55,Non-Hispanic,"Instruction +No",Female,Asian, +2662,Condition_3,54,Non-Hispanic,,Female,White, +2692,Condition_3,51,Non-Hispanic,I would guess based on the probablities and discover if my chance was correct.,Male,White, +2683,Condition_3,37,Hispanic,"Very confusing, but I told her it was bc of the blue button, but I don't recall if it was the blue or orange.",Female,White, +2765,Condition_3,66,Non-Hispanic,"If we can't press both buttons, pressing the blue button would more decrease the chances of turning on.",Female,White, +2723,Condition_3,30,Non-Hispanic,,Female,White, +2770,Condition_3,41,Non-Hispanic,"Mostly I'm trying to get my teammate to make the optimal decision by, in either case, depressing the blue button and creating the best odds of not losing points in future rounds.",Male,White, +2707,Condition_3,41,Non-Hispanic,I gave the answer that had the most probability to turn on so they would choose the other color.,Male,White, +2679,Condition_3,29,Non-Hispanic,Only one switch had to be on so it didnt matter.,Female,White, +2698,Condition_3,55,Non-Hispanic,I just tried to explain the results according to my instructions.,Female,White, +2746,Condition_3,24,Hispanic,n/a,Male,other_race, +2750,Condition_3,23,Non-Hispanic,,Female,White, +2742,Condition_3,34,Hispanic,Based on the rules of the game and the info the other player had. I tried to select answers that might give them a hint as to which buttons are more likely to turn on / which button to push next time.,Female,White, +2756,Condition_4,57,Non-Hispanic,I had to evaluate what color button I expected Alice and Zarah to press in the next round. ,Female,White, +2697,Condition_4,55,Non-Hispanic,The 80% chance of the blue switch being on,Female,White, +2731,Condition_4,22,Hispanic,The chances of each switch turning on was the most important factor. Interesting experiment.,Male,White, +2689,Condition_4,26,Non-Hispanic,I focused on the percentages of probability that the switches would turn on to determine my answer.s,Female,White, +2743,Condition_4,71,,the 80 vs 20 odds,Male,White, +2745,Condition_4,28,Non-Hispanic,The language was slightly confusing ,Female,White, +2661,Condition_4,65,Non-Hispanic,,Female,White, +2749,Condition_4,21,Non-Hispanic,I noticed the change in percentages between the two variables and tried to make my best judgment accordingly ,Male,White, +2710,Condition_4,29,Non-Hispanic,"placing emphasis on which switch has a higher likelyhood of turning on, so that pushing its button would have the most effect",Male,White, +2676,Condition_4,33,Non-Hispanic,no,Male,White, +2768,Condition_4,43,Non-Hispanic,what I learned from the game,Female,White, +2684,Condition_4,32,Hispanic,Had to pay close attention to the game instructions. ,Female,White, +2724,Condition_4,68,Non-Hispanic,not sure and no68,Female,White, +2712,Condition_4,21,Non-Hispanic,,Female,White, +2706,Condition_4,56,Non-Hispanic,no,Female,White, +2739,Condition_4,62,Non-Hispanic,"Pressing the blue switch greatly increased the probability that the blue switch would not turn on if pressed, but only increased the odds re the orange switch not turning on a comparatively small amount. ",Female,White, +2691,Condition_4,37,Non-Hispanic,"The factors that made the biggest difference was percentages of the switches turning on, and how helpful any given explanation to the other player would be.",Male,White, +2695,Condition_4,51,Non-Hispanic,I just tried to remember it was better if the blue button was on.,Female,White, +2703,Condition_4,32,Non-Hispanic,The math of it,Male,White, +2686,Condition_4,30,Non-Hispanic,My understanding on probability determined how I decided to respond.,Female,White, +2665,Condition_4,20,Hispanic,The percentages of turning off and on,Female,White, +2667,Condition_4,25,Non-Hispanic,"I want to influence the decision they make in order to avoid losing points. So I would want them to focus on the blue switch for the next round, so I would tell them it was the blue one no matter what",Female,White, +2659,Condition_4,22,Non-Hispanic,I'm not. sure if anything really influenced how I responded,Female,Black/African American, +2762,Condition_4,51,Non-Hispanic,I read the instructions carefully and took my time to answer the questions carefully. ,Male,White, +2751,Condition_4,38,Non-Hispanic,Because either answer would have worked. i have no questions.,Female,White, +2722,Condition_4,36,Non-Hispanic,"In both instances it still makes the most sense to lower the chance of the blue switch turning on, so that's what I chose. ",Female,White, +2716,Condition_4,22,Non-Hispanic,,Female,Asian, +2734,Condition_4,22,Non-Hispanic,,Female,Asian, +2760,Condition_4,39,Non-Hispanic,My undiagnosed ADHD brain was drowning in the word soup so I was just gambling.,Male,Multiracial, +2736,Condition_4,26,Non-Hispanic,i responded in a way that i thought would influence my partner to make the choice that would produce the best odds for us to win,Female,White, +2678,Condition_4,32,Hispanic,I am not really sure. I guess it just depended on my mood.,Female,other_race, +2680,Condition_4,66,Non-Hispanic,The percentages allowed me to somewhat predict the outcome of the game. My partners knew some of the information but not as much as I did. ,Female,White, +2708,Condition_4,27,Non-Hispanic,,Non-binary,White, +2754,Condition_4,31,Hispanic,I responded based on probability and which choices would give us the best chance to win in my eyes.,Male,other_race, +2766,Condition_4,28,Non-Hispanic,,Female,White, +2669,Condition_4,45,,The orange switch was the one I was counting on to stay off. I knew it was more likely that the blue switch would turn on and I was trying to prevent the orange switch from turning on,Female,White, +2729,Condition_4,27,Non-Hispanic,Just using inferences and the explanation of the game from the previous pages. ,Male,White, +2699,Condition_4,29,Non-Hispanic,,Male,White, +2657,Condition_4,25,Hispanic,repeatability,Male,White, +2663,Condition_4,23,Non-Hispanic,"nothing really influenced my answers except for what made sense in that moment. In the first section with ""Alice"" one of the questions mentions ""Zara"" before we knew of her ",Female,White, +2704,Condition_4,56,Non-Hispanic,I responded based on the instructions and the probability of each switch turning on.,Female,White, +2764,Condition_4,23,,"I chose blue even though it was either or both switches in hopes that she would associate losing a point with blue and hit the blue button, since in both rounds blue was the better option to reduce to 10%",Male,White, +2671,Condition_4,34,Non-Hispanic,,Male,White, +2732,Condition_4,59,Non-Hispanic,The fact that in the first round only one button needed to turn on to lose a point but in the next round two buttons must be turned on to lose a point.,Female,White, +2747,Condition_4,52,Non-Hispanic,,Female,White, +2693,Condition_4,29,Non-Hispanic,,Female,White, +2726,Condition_4,42,Non-Hispanic,I tried to factor in the percentages of turning on/off when making my decisions,Female,Multiracial, \ No newline at end of file diff --git a/code/R/experiments/fixedintervention_negative-responses.csv b/code/R/experiments/fixedintervention_negative-responses.csv new file mode 100644 index 0000000..b87bb9f --- /dev/null +++ b/code/R/experiments/fixedintervention_negative-responses.csv @@ -0,0 +1,100 @@ +workerid,proliferate.condition,explanation_response_1,explanation_response_2,intervention_response_1,intervention_response_2,error +2728,Condition_3,no preference,no preference,blue,no preference, +2658,Condition_3,no preference,blue,no preference,blue, +2717,Condition_3,blue,blue,blue,blue, +2769,Condition_3,blue,blue,blue,blue, +2719,Condition_3,blue,blue,blue,blue, +2713,Condition_3,no preference,no preference,blue,blue, +2748,Condition_3,no preference,no preference,orange,blue, +2690,Condition_3,orange,blue,orange,blue, +2672,Condition_3,blue,blue,no preference,blue, +2740,Condition_3,blue,blue,blue,blue, +2675,Condition_3,blue,blue,blue,blue, +2772,Condition_3,orange,orange,blue,blue, +2757,Condition_3,blue,blue,blue,blue, +2738,Condition_3,blue,blue,blue,blue, +2705,Condition_3,orange,no preference,no preference,blue, +2685,Condition_3,blue,blue,blue,blue, +2715,Condition_3,no preference,no preference,blue,blue, +2720,Condition_3,blue,blue,blue,blue, +2677,Condition_3,no preference,no preference,orange,blue, +2727,Condition_3,no preference,no preference,blue,blue, +2694,Condition_3,orange,orange,blue,blue, +2668,Condition_3,orange,orange,no preference,blue, +2733,Condition_3,orange,no preference,orange,blue, +2711,Condition_3,orange,blue,blue,blue, +2763,Condition_3,no preference,no preference,blue,blue, +2752,Condition_3,no preference,no preference,orange,blue, +2759,Condition_3,no preference,no preference,blue,blue, +2673,Condition_3,no preference,blue,orange,blue, +2725,Condition_3,no preference,no preference,blue,blue, +2761,Condition_3,blue,blue,blue,blue, +2767,Condition_3,blue,blue,blue,blue, +2735,Condition_3,blue,no preference,blue,blue, +2660,Condition_3,no preference,no preference,orange,no preference, +2771,Condition_3,no preference,no preference,orange,blue, +2688,Condition_3,orange,no preference,no preference,no preference, +2755,Condition_3,blue,blue,blue,blue, +2753,Condition_3,orange,no preference,blue,blue, +2666,Condition_3,blue,blue,blue,blue, +2696,Condition_3,no preference,blue,blue,blue, +2681,Condition_3,no preference,no preference,blue,no preference, +2662,Condition_3,blue,no preference,blue,blue, +2692,Condition_3,no preference,no preference,orange,blue, +2683,Condition_3,blue,blue,orange,orange, +2765,Condition_3,blue,blue,blue,blue, +2723,Condition_3,blue,no preference,blue,blue, +2770,Condition_3,blue,blue,blue,blue, +2707,Condition_3,blue,orange,orange,orange, +2679,Condition_3,blue,no preference,blue,blue, +2698,Condition_3,blue,no preference,blue,blue, +2746,Condition_3,blue,orange,orange,orange, +2750,Condition_3,no preference,no preference,orange,blue, +2742,Condition_3,blue,blue,blue,blue, +2756,Condition_4,no preference,orange,blue,blue, +2697,Condition_4,blue,blue,blue,blue, +2731,Condition_4,blue,blue,blue,blue, +2689,Condition_4,no preference,no preference,blue,blue, +2743,Condition_4,blue,blue,blue,blue, +2745,Condition_4,no preference,no preference,no preference,no preference, +2661,Condition_4,no preference,no preference,blue,blue, +2749,Condition_4,orange,blue,orange,blue, +2710,Condition_4,blue,blue,blue,blue, +2676,Condition_4,blue,no preference,blue,blue, +2768,Condition_4,no preference,no preference,blue,blue, +2684,Condition_4,no preference,no preference,no preference,blue, +2724,Condition_4,no preference,no preference,blue,blue, +2712,Condition_4,no preference,no preference,blue,blue, +2706,Condition_4,no preference,orange,blue,blue, +2739,Condition_4,blue,blue,blue,blue, +2691,Condition_4,no preference,no preference,blue,blue, +2695,Condition_4,orange,orange,blue,blue, +2703,Condition_4,no preference,orange,blue,no preference, +2686,Condition_4,no preference,no preference,orange,orange, +2665,Condition_4,blue,orange,blue,orange, +2667,Condition_4,blue,blue,blue,blue, +2659,Condition_4,blue,blue,blue,blue, +2762,Condition_4,no preference,no preference,blue,blue, +2751,Condition_4,no preference,no preference,blue,blue, +2722,Condition_4,blue,blue,blue,blue, +2716,Condition_4,no preference,no preference,blue,orange, +2734,Condition_4,blue,blue,blue,blue, +2760,Condition_4,no preference,no preference,no preference,no preference, +2736,Condition_4,blue,blue,blue,blue, +2678,Condition_4,blue,no preference,orange,blue, +2680,Condition_4,no preference,no preference,blue,orange, +2708,Condition_4,no preference,no preference,blue,blue, +2754,Condition_4,orange,no preference,blue,blue, +2766,Condition_4,blue,blue,blue,blue, +2669,Condition_4,no preference,orange,blue,orange, +2729,Condition_4,blue,blue,orange,orange, +2699,Condition_4,no preference,orange,orange,no preference, +2657,Condition_4,no preference,no preference,no preference,no preference, +2663,Condition_4,no preference,orange,orange,no preference, +2704,Condition_4,no preference,no preference,blue,blue, +2764,Condition_4,blue,blue,blue,blue, +2671,Condition_4,no preference,no preference,blue,blue, +2732,Condition_4,no preference,no preference,no preference,no preference, +2747,Condition_4,no preference,blue,no preference,no preference, +2693,Condition_4,no preference,blue,orange,blue, +2726,Condition_4,no preference,blue,blue,orange, \ No newline at end of file diff --git a/code/R/experiments/fixedintervention_positive-participants.csv b/code/R/experiments/fixedintervention_positive-participants.csv new file mode 100644 index 0000000..dc0fdd2 --- /dev/null +++ b/code/R/experiments/fixedintervention_positive-participants.csv @@ -0,0 +1,100 @@ +workerid,proliferate.condition,age,ethnicity,feedback,gender,race,error +2609,Condition1,30,Non-Hispanic,I just used the knowledge I was given from the instructions,Female,White, +2575,Condition1,45,Non-Hispanic,,Female,White, +2569,Condition1,61,Non-Hispanic,I picked the answers that logically made sense to me.,Female,White, +2595,Condition1,27,Non-Hispanic,I thought about what made most sense given the scenraio.,Male,Multiracial, +2537,Condition1,49,Non-Hispanic,I just made my responses based on how it would get the other partcipant to press the button that would increase our chances most.,Female,White, +2607,Condition1,26,Non-Hispanic,The aggregate probabilities of desirable outcomes.,Male,White, +2635,Condition1,26,Non-Hispanic,,Male,Black/African American, +2555,Condition1,33,Non-Hispanic,"The probability of both colors to be switched ""On"" was the driving factor behind the way I chose to respond. Because the blue already had a high probability to switch on, it was important to give the orange color the boost by pushing the button so that it too had a high probability to switch on. ",Female,White, +2626,Condition1,20,Non-Hispanic,None,Male,Multiracial, +2611,Condition1,27,Non-Hispanic,"I was trying to have the highest possibility of one or both of the lights turning on, depending on what was needed. ",Non-binary,White, +2543,Condition1,35,Non-Hispanic,I was trying to simplify my responses as easily as possible.,Non-binary,White, +2622,Condition1,48,Non-Hispanic,This was interesting!,Female,White, +2630,Condition1,30,Non-Hispanic,"Since both switches were on both times, it didn't matter what the probability was. Both switches being on gave us points, regardless of the color. ",Female,White, +2579,Condition1,25,Non-Hispanic,n/a,Female,White, +2620,Condition1,53,Non-Hispanic,I chose the third option because it was accurate.,Male,White, +2563,Condition1,28,Non-Hispanic,"I often pressed the orange button to increase the probability to 90% because I knew the blue switch would still have a high probability of 80%. No, I have no more questions nor comments. ",Female,White, +2571,Condition1,38,Non-Hispanic,"In the first one I said orange one turned on because it was more vital for the orange to turn on due to the low chance without the button pressed. The second one, just needed one to turn on so it was okay to say because either one turned on. ",Male,White, +2593,Condition1,22,Non-Hispanic,I tried to think logically about the probability and how it would or would not change with each scenario.,Female,White, +2632,Condition1,57,Non-Hispanic,I was influenced by the higher percentages. No additional comments.,Male,Black/African American, +2615,Condition1,42,Non-Hispanic,The best one to score points,Male,White, +2616,Condition1,25,Non-Hispanic,Depending on the rules to earn points is how i responded,Female,Black/African American, +2605,Condition1,50,Non-Hispanic,Tried to follow the instructions and make percentages better,Male,White, +2640,Condition1,65,Non-Hispanic,"In both cases, it improves chances of winning significantly if the orange switch probability goes from 20% to 90%. I'd answer the question ""because the orange switch..."" to signal my partner that she should press the orange button in the next round.",Male,White, +2591,Condition1,26,Non-Hispanic,I choose the most straight forward answer,Female,Asian, +2561,Condition1,32,Hispanic,,Male,White, +2547,Condition1,27,Hispanic,,Female,Asian, +2624,Condition1,31,Non-Hispanic,what was written in the descriptions influenced how I answered. ,Female,White, +2585,Condition1,24,Non-Hispanic,,Female,White, +2618,Condition1,34,Non-Hispanic,,Male,White, +2549,Condition1,24,Hispanic,,Female,White, +2603,Condition1,20,Non-Hispanic,,Female,Black/African American, +2581,Condition1,19,Non-Hispanic,,Male,White, +2599,Condition1,19,Non-Hispanic,My judgement,Male,White, +2634,Condition1,23,Non-Hispanic,"I was trying to choose the option that would lead to her being more likely to press the orange button. I considered that she might think that the orange switch doesn't need any extra manipulation if I told her that we won because of it. I decided to still go with that option though because I thought it would be more likely that she would want the orange switch to keep turning on, leading to her pressing the orange button instead of the blue one.",Female,White, +2573,Condition1,28,Non-Hispanic,I wanted to encourage them to click the orange button so they probability of both switches would be high,Female,White, +2557,Condition1,21,Non-Hispanic,I was influenced by the percentages for each lever and how much the other person knew,Female,White, +2559,Condition1,20,Non-Hispanic,I chose to explain that the orange one caused the win; I thought this would influence my teammate to increase winning chances further by pressing the orange button. Pressing it is always the best choice (both switches needed and one switch needed).,Male,Asian, +2551,Condition1,55,Non-Hispanic,The probability was always higher with the blue button.,Female,Black/African American, +2553,Condition1,34,Non-Hispanic,I responded based on my own judgements and on the information provided. I felt that the experiment was asymmetrically favored to make the player push the button that had the higher probability. ,Male,Asian, +2589,Condition1,41,Non-Hispanic,"I just went for the best odds, and if something had fewer odds of being turned ""on"", I tried to respond in a way which would encourage my teammate to push that button to increase it's odds.",Female,White, +2639,Condition1,39,Non-Hispanic,Depending on what the other person knew ,Female,White, +2583,Condition1,26,Non-Hispanic,"I responded based on what seemed like it was most likely to get the best outcome, which was usually pressing the orange button.",Male,White, +2587,Condition1,23,Non-Hispanic,,Female,White, +2613,Condition1,28,Non-Hispanic,The button was basically an easy chance to have both switches on no matter the round.,Male,White, +2539,Condition1,41,Non-Hispanic,,Male,White, +2545,Condition1,27,Non-Hispanic,I felt like the options were both technically right. No questions.,Female,White, +2577,Condition1,40,Hispanic,so i guess this was to see if the rules of the game were easy to understand.,Male,White, +2597,Condition1,22,Non-Hispanic,"I thought mentioning orange as the one that helped us gain a point would be better regardless of the situation, as it was the one with the lower percentage. If they thought we won because of the orange switch turning on, they'd be more likely to press the orange button next time.",Male,White, +2565,Condition1,30,Non-Hispanic,the overall probability of winning the game and how to tell my teammate how to win,Female,White, +2637,Condition1,22,Non-Hispanic,the probability of winning,Female,White, +2572,Condition2,24,Non-Hispanic,,Female,Black/African American, +2606,Condition2,27,Hispanic,,Male,Multiracial, +2552,Condition2,24,Non-Hispanic,,Male,White, +2592,Condition2,45,Non-Hispanic,We are better off pressing the orange switch.,Male,White, +2602,Condition2,30,Non-Hispanic,I chose the easiest explanation. It didn't matter if my teammate knew all the rules because the outcome wouldn't change.,Female,Black/African American, +2594,Condition2,33,Non-Hispanic,I answered based on probabilities. ,Female,White, +2612,Condition2,31,Non-Hispanic,It was less likely for the orange switch to turn on. ,Male,Black/African American, +2590,Condition2,25,Hispanic,I want to see which is optimal and which is the best explanation.,Male,Multiracial, +2548,Condition2,29,Non-Hispanic,The experiment is really educative,Male,White, +2614,Condition2,39,Non-Hispanic,Neither answer was true in the last round. Both had to be on,Male,White, +2584,Condition2,29,Non-Hispanic,I gave the switch color that had more impact on the outcome.,Female,Asian, +2617,Condition2,58,Non-Hispanic,the instructions provided. no questions,Male,White, +2542,Condition2,27,Non-Hispanic,Explaining that the we won because a certain switch was on was a true explanation to give. ,Female,White, +2568,Condition2,28,Non-Hispanic,,Male,White, +2574,Condition2,42,Non-Hispanic,I tried to go by the information that I read.,Female,Black/African American, +2604,Condition2,31,Non-Hispanic,The main factor for me was that the orange button definitely needs the button pressed to have a higher chance of winning.,Female,Multiracial, +2562,Condition2,37,Non-Hispanic,,Female,White, +2560,Condition2,51,Non-Hispanic,I would respond that the orange switch was more important to try to influence the other player to click the orange button even if they didn't understand why. ,Male,White, +2558,Condition2,21,Non-Hispanic,the likelihood of a switch turning on helped me decide my response.,Female,Asian, +2638,Condition2,32,Non-Hispanic,"Obviously in both instances you wanted to have the best probability that both switches could in fact turn on, so I wanted to maximize my chances of that. I do believe I misclicked on the last occasion that I was asked about that, I meant to say that I would click the button to turn the orange switch to on, that way there would be the highest probability of both switches turning on. Regardless, it was mainly important to achieve the highest percentage probability that both switches would turn on.",Male,White, +2619,Condition2,67,Non-Hispanic,that one was always 90% chance of winning and it didn't really matter which one,Female,White, +2610,Condition2,28,Hispanic,To quickly persuade the person to make the smarter choice. ,Male,White, +2629,Condition2,25,Non-Hispanic,I wanted to subtly imply to my teammate that the orange switch turning on is more critically important because it has a lower probability.,Female,Black/African American, +2546,Condition2,54,Non-Hispanic,I selected what I thought was the most significant fact,Female,Multiracial, +2582,Condition2,31,Non-Hispanic,,Female,White, +2550,Condition2,42,Non-Hispanic,,Male,White, +2564,Condition2,36,Non-Hispanic,chose the lower chance because I thought there would be more opportunity ,Male,Asian, +2598,Condition2,41,Non-Hispanic,Trying to maximize chance of positive outcome,Male,White, +2576,Condition2,38,Non-Hispanic,"Because the blue switch is more likely to be on, saying the orange switch needs to be on makes them more likely to press the orange button, thus improving the overall odds.",Male,White, +2608,Condition2,22,Non-Hispanic,The factors was mostly if both sides managed to go to the on side which would have gotten the win.,Male,White, +2631,Condition2,24,Non-Hispanic,,Female,Black/African American, +2556,Condition2,43,Non-Hispanic,"The only factors that influenced how I chose to respond were my own reasoning skills, whether that was good or bad. I do not have any questions or comments regarding this study.",Female,White, +2627,Condition2,21,Non-Hispanic,,Male,White, +2580,Condition2,,,the button pressing moving it up to 90%. if either of the people press the orange button it dramatically increases the chances,other_gender,other_race, +2621,Condition2,41,Non-Hispanic,I tried to think about what information my partner would need to press the correct button on a second turn.,Female,White, +2578,Condition2,33,Non-Hispanic,,Male,White, +2554,Condition2,39,Non-Hispanic,I was just thinking about emphasizing the orange switch so the teammate would be more likely to press the orange button,Non-binary,White, +2633,Condition2,35,Non-Hispanic,,Male,White, +2623,Condition2,24,Non-Hispanic,If the information I can provide would be helpful to my partner.,Male,Asian, +2570,Condition2,30,Non-Hispanic,I answered any way I could to try and get my teammate to press the button that turned the orange switch to a likelihood of 90% turning on since the blue switch was already highly likely to turn on. ,Female,White, +2566,Condition2,29,Hispanic,Both answers sounded fair since both options needed to be on it wouldn't make sense to say one color had the most impact,Male,other_race, +2536,Condition2,24,Non-Hispanic,"The blue switch already had an 80% chance of turning on which is pretty high, whereas the orange only had 20%. If I could increase the orange to 90%, then I would have an 80% and a 90% which significantly increases the likelihood of winning points. Each decision I made was to try and influence my partner to choose the button for the orange switch",Female,White, +2588,Condition2,27,Hispanic,"In other rounds it would be better for my teammate to select the blue switch, so it would be best to say that even if it's not the whole story.",Male,White, +2586,Condition2,26,Non-Hispanic,,Female,Multiracial, +2544,Condition2,32,Non-Hispanic,N/A - Just what I thought was acceptable.,Female,White, +2540,Condition2,57,Non-Hispanic,,Male,White, +2600,Condition2,49,Non-Hispanic,"Trying to play the odds. In the latter example, Zarah could be directed to choose the orange button if she thinks that was the reason we got a point even though both need to be ""on"" to get a point.",Male,White, +2596,Condition2,58,Non-Hispanic,read the directions,Female,White, +2636,Condition2,36,Non-Hispanic,I just determine what will give us the highest probability of winning and go by that. ,Female,White, diff --git a/code/R/experiments/fixedintervention_positive-responses.csv b/code/R/experiments/fixedintervention_positive-responses.csv new file mode 100644 index 0000000..2761e99 --- /dev/null +++ b/code/R/experiments/fixedintervention_positive-responses.csv @@ -0,0 +1,100 @@ +workerid,proliferate.condition,explanation_response_1,explanation_response_2,intervention_response_1,intervention_response_2,error +2609,Condition1,blue,no preference,blue,blue, +2575,Condition1,orange,no preference,orange,orange, +2569,Condition1,no preference,no preference,orange,no preference, +2595,Condition1,orange,no preference,orange,orange, +2537,Condition1,orange,no preference,orange,orange, +2607,Condition1,orange,orange,orange,no preference, +2635,Condition1,no preference,no preference,orange,no preference, +2555,Condition1,orange,orange,orange,blue, +2626,Condition1,orange,no preference,orange,orange, +2611,Condition1,orange,orange,orange,orange, +2543,Condition1,orange,orange,orange,orange, +2622,Condition1,no preference,no preference,orange,orange, +2630,Condition1,no preference,no preference,orange,no preference, +2579,Condition1,no preference,no preference,orange,orange, +2620,Condition1,no preference,no preference,orange,orange, +2563,Condition1,no preference,no preference,orange,orange, +2571,Condition1,orange,no preference,orange,orange, +2593,Condition1,orange,orange,orange,orange, +2632,Condition1,no preference,blue,blue,blue, +2615,Condition1,blue,blue,blue,blue, +2616,Condition1,orange,no preference,orange,orange, +2605,Condition1,orange,no preference,orange,orange, +2640,Condition1,orange,orange,orange,orange, +2591,Condition1,blue,blue,blue,blue, +2561,Condition1,no preference,no preference,no preference,orange, +2547,Condition1,blue,no preference,orange,no preference, +2624,Condition1,no preference,no preference,no preference,no preference, +2585,Condition1,orange,no preference,orange,no preference, +2618,Condition1,no preference,no preference,orange,no preference, +2549,Condition1,orange,no preference,orange,orange, +2603,Condition1,orange,no preference,orange,no preference, +2581,Condition1,no preference,no preference,no preference,no preference, +2599,Condition1,orange,no preference,orange,orange, +2634,Condition1,orange,orange,orange,orange, +2573,Condition1,orange,orange,orange,orange, +2557,Condition1,orange,orange,orange,no preference, +2559,Condition1,orange,orange,orange,orange, +2551,Condition1,no preference,blue,blue,blue, +2553,Condition1,orange,no preference,orange,orange, +2589,Condition1,orange,no preference,orange,no preference, +2639,Condition1,no preference,no preference,orange,orange, +2583,Condition1,orange,orange,orange,orange, +2587,Condition1,no preference,no preference,blue,blue, +2613,Condition1,orange,no preference,orange,orange, +2539,Condition1,orange,no preference,orange,no preference, +2545,Condition1,no preference,no preference,orange,orange, +2577,Condition1,no preference,no preference,no preference,no preference, +2597,Condition1,orange,orange,orange,orange, +2565,Condition1,orange,no preference,orange,orange, +2637,Condition1,orange,blue,orange,blue, +2572,Condition2,no preference,no preference,orange,orange, +2606,Condition2,blue,orange,orange,orange, +2552,Condition2,blue,no preference,orange,orange, +2592,Condition2,orange,orange,orange,orange, +2602,Condition2,no preference,no preference,no preference,no preference, +2594,Condition2,no preference,no preference,orange,orange, +2612,Condition2,no preference,orange,orange,orange, +2590,Condition2,no preference,no preference,orange,orange, +2548,Condition2,blue,no preference,no preference,blue, +2614,Condition2,no preference,no preference,orange,orange, +2584,Condition2,blue,orange,orange,orange, +2617,Condition2,no preference,no preference,orange,orange, +2542,Condition2,no preference,no preference,orange,orange, +2568,Condition2,blue,orange,no preference,orange, +2574,Condition2,no preference,blue,no preference,blue, +2604,Condition2,orange,orange,orange,orange, +2562,Condition2,orange,orange,orange,orange, +2560,Condition2,orange,orange,orange,orange, +2558,Condition2,no preference,no preference,blue,blue, +2638,Condition2,blue,no preference,orange,blue, +2619,Condition2,no preference,no preference,no preference,orange, +2610,Condition2,blue,orange,blue,orange, +2629,Condition2,orange,orange,orange,orange, +2546,Condition2,blue,orange,no preference,orange, +2582,Condition2,orange,orange,orange,orange, +2550,Condition2,no preference,no preference,orange,orange, +2564,Condition2,orange,orange,orange,orange, +2598,Condition2,orange,orange,orange,orange, +2576,Condition2,orange,orange,orange,orange, +2608,Condition2,no preference,no preference,orange,orange, +2631,Condition2,blue,blue,blue,blue, +2556,Condition2,orange,orange,orange,orange, +2627,Condition2,no preference,no preference,orange,orange, +2580,Condition2,orange,orange,orange,orange, +2621,Condition2,orange,orange,orange,orange, +2578,Condition2,blue,orange,orange,orange, +2554,Condition2,orange,orange,orange,orange, +2633,Condition2,blue,orange,orange,orange, +2623,Condition2,no preference,no preference,orange,orange, +2570,Condition2,blue,blue,orange,orange, +2566,Condition2,no preference,no preference,orange,orange, +2536,Condition2,orange,orange,orange,orange, +2588,Condition2,blue,blue,no preference,no preference, +2586,Condition2,no preference,blue,orange,orange, +2544,Condition2,no preference,no preference,blue,no preference, +2540,Condition2,orange,orange,orange,orange, +2600,Condition2,no preference,orange,orange,orange, +2596,Condition2,no preference,no preference,no preference,no preference, +2636,Condition2,no preference,no preference,orange,orange, diff --git a/code/R/experiments/grateful-refs.bib b/code/R/experiments/grateful-refs.bib new file mode 100644 index 0000000..f1f3e55 --- /dev/null +++ b/code/R/experiments/grateful-refs.bib @@ -0,0 +1,118 @@ +@Manual{base, +title = {{R}: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2023}, + url = {https://www.R-project.org/}, +} +@Manual{bookdown2023, +title = {{bookdown}: Authoring Books and Technical Documents with R Markdown}, + author = {Yihui Xie}, + year = {2023}, + note = {R package version 0.37}, + url = {https://github.com/rstudio/bookdown}, +} + +@Book{bookdown2016, +title = {{bookdown}: Authoring Books and Technical Documents with {R} Markdown}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2016}, + isbn = {978-1138700109}, + url = {https://bookdown.org/yihui/bookdown}, +} +@Manual{janitor, +title = {{janitor}: Simple Tools for Examining and Cleaning Dirty Data}, + author = {Sam Firke}, + year = {2023}, + note = {R package version 2.2.0}, + url = {https://CRAN.R-project.org/package=janitor}, +} +@Manual{knitr2023, +title = {{knitr}: A General-Purpose Package for Dynamic Report Generation in R}, + author = {Yihui Xie}, + year = {2023}, + note = {R package version 1.45}, + url = {https://yihui.org/knitr/}, +} + +@Book{knitr2015, + title = {Dynamic Documents with {R} and knitr}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2015}, + edition = {2nd}, + note = {ISBN 978-1498716963}, + url = {https://yihui.org/knitr/}, +} + +@InCollection{knitr2014, + booktitle = {Implementing Reproducible Computational Research}, + editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, +title = {{knitr}: A Comprehensive Tool for Reproducible Research in {R}}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + year = {2014}, + note = {ISBN 978-1466561595}, +} +@Manual{Metrics, +title = {{Metrics}: Evaluation Metrics for Machine Learning}, + author = {Ben Hamner and Michael Frasco}, + year = {2018}, + note = {R package version 0.1.4}, + url = {https://CRAN.R-project.org/package=Metrics}, +} +@Manual{patchwork, +title = {{patchwork}: The Composer of Plots}, + author = {Thomas Lin Pedersen}, + year = {2024}, + note = {R package version 1.2.0}, + url = {https://CRAN.R-project.org/package=patchwork}, +} +@Manual{rmarkdown2023, +title = {{rmarkdown}: Dynamic Documents for R}, + author = {JJ Allaire and Yihui Xie and Christophe Dervieux and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, + year = {2023}, + note = {R package version 2.25}, + url = {https://github.com/rstudio/rmarkdown}, +} + +@Book{rmarkdown2018, + title = {R Markdown: The Definitive Guide}, + author = {Yihui Xie and J.J. Allaire and Garrett Grolemund}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2018}, + isbn = {9781138359338}, + url = {https://bookdown.org/yihui/rmarkdown}, +} + +@Book{rmarkdown2020, + title = {R Markdown Cookbook}, + author = {Yihui Xie and Christophe Dervieux and Emily Riederer}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2020}, + isbn = {9780367563837}, + url = {https://bookdown.org/yihui/rmarkdown-cookbook}, +} +@Manual{scales, +title = {{scales}: Scale Functions for Visualization}, + author = {Hadley Wickham and Thomas Lin Pedersen and Dana Seidel}, + year = {2023}, + note = {R package version 1.3.0}, + url = {https://CRAN.R-project.org/package=scales}, +} +@Article{tidyverse, + title = {Welcome to the {tidyverse}}, + author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani}, + year = {2019}, + journal = {Journal of Open Source Software}, + volume = {4}, + number = {43}, + pages = {1686}, + doi = {10.21105/joss.01686}, +} diff --git a/code/R/experiments/hardint_neg.csv b/code/R/experiments/hardint_neg.csv new file mode 100644 index 0000000..0ba2d44 --- /dev/null +++ b/code/R/experiments/hardint_neg.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,17.17171717171717,37.37373737373738,45.45454545454545,19.19191919191919,64.64646464646465,16.161616161616163,negative,hardint +Disjunctive,8.080808080808081,48.484848484848484,43.43434343434344,13.131313131313133,81.81818181818183,5.05050505050505,negative,hardint diff --git a/code/R/experiments/hardint_pos.csv b/code/R/experiments/hardint_pos.csv new file mode 100644 index 0000000..d0896b6 --- /dev/null +++ b/code/R/experiments/hardint_pos.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,49.48453608247423,16.49484536082474,34.02061855670103,65.97938144329896,28.865979381443296,5.154639175257731,positive,hardint +Disjunctive,26.804123711340207,19.587628865979383,53.608247422680414,43.29896907216495,27.835051546391753,28.865979381443296,positive,hardint diff --git a/code/R/experiments/intervention_predictions.csv b/code/R/experiments/intervention_predictions.csv new file mode 100644 index 0000000..09f60ab --- /dev/null +++ b/code/R/experiments/intervention_predictions.csv @@ -0,0 +1,37 @@ +"intervention","causal_structure","outcome","choice","value","prediction","probability" +"fixed","conjunctive","positive","abnormal",0.72,0.994041787821888,0.808080808080808 +"fixed","conjunctive","positive","normal",0.18,3.52912649879559e-05,0.111111111111111 +"fixed","conjunctive","positive","nopreference",0.45,0.00592292091312418,0.0808080808080808 +"fixed","conjunctive","negative","abnormal",0.92,0.16981516371875,0.202020202020202 +"fixed","conjunctive","negative","normal",0.98,0.530141419509461,0.666666666666667 +"fixed","conjunctive","negative","nopreference",0.95,0.300043416771789,0.131313131313131 +"fixed","disjunctive","positive","abnormal",0.98,0.530141419509461,0.656565656565657 +"fixed","disjunctive","positive","normal",0.92,0.16981516371875,0.121212121212121 +"fixed","disjunctive","positive","nopreference",0.95,0.300043416771789,0.222222222222222 +"fixed","disjunctive","negative","abnormal",0.18,3.52912649879559e-05,0.101010101010101 +"fixed","disjunctive","negative","normal",0.72,0.994041787821888,0.797979797979798 +"fixed","disjunctive","negative","nopreference",0.45,0.00592292091312416,0.101010101010101 +"hard","conjunctive","positive","abnormal",0.8,0.99662775798199,0.65979381443299 +"hard","conjunctive","positive","normal",0.2,1.1333923951288e-05,0.288659793814433 +"hard","conjunctive","positive","nopreference",0.5,0.00336090809405889,0.0515463917525773 +"hard","conjunctive","negative","abnormal",1,0.333333333333333,0.191919191919192 +"hard","conjunctive","negative","normal",1,0.333333333333333,0.646464646464647 +"hard","conjunctive","negative","nopreference",1,0.333333333333333,0.161616161616162 +"hard","disjunctive","positive","abnormal",1,0.333333333333333,0.43298969072165 +"hard","disjunctive","positive","normal",1,0.333333333333333,0.278350515463918 +"hard","disjunctive","positive","nopreference",1,0.333333333333333,0.288659793814433 +"hard","disjunctive","negative","abnormal",0.2,1.1333923951288e-05,0.131313131313131 +"hard","disjunctive","negative","normal",0.8,0.99662775798199,0.818181818181818 +"hard","disjunctive","negative","nopreference",0.5,0.00336090809405889,0.0505050505050505 +"soft","conjunctive","positive","abnormal",0.32,0.702777373322948,0.653061224489796 +"soft","conjunctive","positive","normal",0.2,0.0721086281913351,0.306122448979592 +"soft","conjunctive","positive","nopreference",0.26,0.225113998485717,0.0408163265306122 +"soft","conjunctive","negative","abnormal",1,0.702777373322948,0.676767676767677 +"soft","conjunctive","negative","normal",0.88,0.0721086281913351,0.222222222222222 +"soft","conjunctive","negative","nopreference",0.94,0.225113998485717,0.101010101010101 +"soft","disjunctive","positive","abnormal",0.88,0.0721086281913351,0.122448979591837 +"soft","disjunctive","positive","normal",1,0.702777373322948,0.795918367346939 +"soft","disjunctive","positive","nopreference",0.94,0.225113998485717,0.0816326530612245 +"soft","disjunctive","negative","abnormal",0.2,0.0721086281913352,0.353535353535354 +"soft","disjunctive","negative","normal",0.32,0.702777373322947,0.555555555555556 +"soft","disjunctive","negative","nopreference",0.26,0.225113998485717,0.0909090909090909 diff --git a/code/R/experiments/pressbutton_negativecondition-participants.csv b/code/R/experiments/pressbutton_negativecondition-participants.csv new file mode 100644 index 0000000..c023a0c --- /dev/null +++ b/code/R/experiments/pressbutton_negativecondition-participants.csv @@ -0,0 +1,102 @@ +workerid,proliferate.condition,age,ethnicity,feedback,gender,race,error +2427,Condition_4,31,Non-Hispanic,,Female,Multiracial, +2531,Condition_4,43,Non-Hispanic,"Even though all the choices were technically correct, I just felt picking one as an explanation to my teammate might be unintentionally misleading, which is why I kept deferring to the ""either explanation"" being ok option.",Female,White, +2504,Condition_4,23,Non-Hispanic,,Female,Asian, +2453,Condition_4,64,Non-Hispanic,I tried to respond based on the information given to me. I do not have any additional comments or questions.,Male,White, +2514,Condition_4,42,Non-Hispanic,none,Female,Black/African American, +2529,Condition_4,40,Non-Hispanic,"I was honestly a little confused about what was different, so I went with instinct.",Female,White, +2471,Condition_4,37,Hispanic,I took the facts that I was presented to make my choice. Which ever decision that would give me the lowest percentage chance possible of losing a point.,Female,other_race, +2457,Condition_4,66,Non-Hispanic,I want to influence her to push the button for the orange switch so it has zero chance of turning on.,Female,White, +2458,Condition_4,56,Non-Hispanic,Going over the information on how the game works.,Female,White, +2467,Condition_4,31,Non-Hispanic,I think zarah should press the orange button,Male,White, +2508,Condition_4,21,Non-Hispanic,"In the second scenario with Zarah, we can easily reduce the orange's probability to zero and therefore guarantee that we do not lose a point. In the first, I thought pressing the blue button would be better so that it is less certain that the blue button turns on",Female,White, +2455,Condition_4,53,Non-Hispanic,,Female,White, +2475,Condition_4,54,Non-Hispanic,"I told Zarah that we lost a point because Orange turned on in the hopes that she would turn it off in future rounds to reduce the chance of happening again to 0%, so we wouldn't lose any more points. Interesting, different study.",Male,White, +2425,Condition_4,36,Non-Hispanic,,Female,White, +2492,Condition_4,60,Non-Hispanic,"I just decided how to respond based on a which felt right. Pressing the button would lower the chance 20% on either color so during the round with Alice it didn't make much of a difference. During the 2nd round with Zhara, I might have been confused, on what knowledge she had of the probability/teammate's knowledge.",Female,White, +2510,Condition_4,38,Non-Hispanic,"No, I did make a mistake though. In the slightly different version of the game I should have selected the switch for the orange one to make it impossible to turn on. that was a lapse in judgement on my part but I couldn't go back to fix it. When answering about which explanation to use, I chose the one that had the color name in it that needed to have the switch pressed in the hope that they would take that as a hint to just press that colors switch.",Male,White, +2447,Condition_4,50,Non-Hispanic,,Female,White, +2437,Condition_4,46,Non-Hispanic,Trying to remember these rules precisely was extremely frustrating. It seemed as if the study or the game demanded a level of precision of thinking I am not used to.,Male,White, +2464,Condition_4,31,,I responded with my best understanding of the prompt.,Female,White, +2461,Condition_4,73,Non-Hispanic,I went with the odds...blue is a better risk,Female,White, +2485,Condition_4,49,Non-Hispanic,n/a,Male,White, +2445,Condition_4,43,Non-Hispanic,first one I took a chance in the secondone I took away the orange abilit to turn on.,Male,White, +2443,Condition_4,31,Non-Hispanic,I responded based solely on the information given to me,Female,White, +2499,Condition_4,24,Non-Hispanic,How much the percentage would actually affect our chance to win the game.,Male,White, +2473,Condition_4,38,Non-Hispanic,I replied based on what the rules and instructions told me to.,Male,White, +2423,Condition_4,25,Non-Hispanic,Just what I thought would be best to maximize points.,Male,Multiracial, +2494,Condition_4,27,,The odds of the buttons being switched and trying to lower the odds of the less favorable outcome. ,Male,Multiracial, +2469,Condition_4,25,Non-Hispanic,The slightly different rules of each round influenced my responses,Female,White, +2483,Condition_4,48,Non-Hispanic,none,Male,White, +2520,Condition_4,68,Non-Hispanic,I was trying to figure out the odds. I'm not sure if I made the right choice in the first round. ,Female,White, +2516,Condition_4,35,Non-Hispanic,My reasoning is that my partner would be more inclined to press the blue button if I blamed that button for losing points. This would lead to better odds of the blue switch staying off.,Male,White, +2479,Condition_4,40,Non-Hispanic,Because either of the responses being on contributed to losing a point.,Female,White, +2435,Condition_4,30,Non-Hispanic,"the first game it didn't really matter which button was pressed, just that the overall chance of a switch being on was lower. However the second game, getting the partner to lower the orange chance of being on was the main priority because if that button was pressed it would be an automatic win",Male,White, +2500,Condition_4,57,Non-Hispanic,I was trying to make the odds better and easier for the person who did not have the full information,Male,White, +2421,Condition_4,24,Non-Hispanic,,Female,Multiracial, +2502,Condition_4,54,Hispanic,,Male,White, +2481,Condition_4,35,Hispanic,sicne the orange one I can eliminate to 0% chance of turning on I always chose that one.,Male,other_race, +2433,Condition_4,62,Non-Hispanic,I picked the blue one because it reduced it to 60%,Male,White, +2497,Condition_4,29,Non-Hispanic,"I didn't want to take a chance at all on the blue switch at first because if a switch was on then no matter what we would lose. But then when the rules switched, I was fine with chancing the blue. ",Female,White, +2431,Condition_4,24,Non-Hispanic,,Female,White, +2451,Condition_4,24,Non-Hispanic,The percentages of each button and what result was needed to win,Male,White, +2487,Condition_4,67,Non-Hispanic,I was always trying to make the orange switch tun off with the orange button.,Female,White, +2521,Condition_4,38,Non-Hispanic,Reducing the chances of losing points.,Female,White, +2490,Condition_4,22,Non-Hispanic,Knowing that at least one of the switches would turn completely off was a major factor in deciding when to press the button that would turn down the probability.,Female,White, +2429,Condition_4,28,Non-Hispanic,,Male,White, +2528,Condition_4,26,Non-Hispanic,The probability of the blue switch turning on mainly influenced my decision.,Male,White, +2448,Condition_4,39,Non-Hispanic,IT would be hard to do in any scenario I think and very hard to predict.,Male,White, +2530,Condition_4,29,Non-Hispanic,depending on the new rules given to me I would decide whether which percentage was more likely to be the cause of the point loss or not,Female,White, +2462,Condition_4,30,Non-Hispanic,I realized I got the 2nd part wrong after I answered it,Male,White, +2501,Condition_3,30,Non-Hispanic,I tried to maximize our changes of not losing points. ,Female,White, +2476,Condition_3,30,Non-Hispanic,Thinking about how to influence the team partner in their own decision based on the parameters I was given on how to explain what happened.,Female,Asian, +2532,Condition_3,40,Non-Hispanic,"I responded that the blue switch being turned on caused the team to lose a point the first time because the blue switch had an 80% chance of being turned on and both switches needed to be on to lose the point. It was less likely the orange switch would be turned on. + +For the second answer, I said both blue or orange turned on equally caused the point to be lost as either switch turned on would lose a point.",Male,White, +2524,Condition_3,32,Non-Hispanic,"The explanations seemed a bit awkward. Unless it was part of the study, why not just state that we needed both switches to be ""off"" in order to not lose a point?",Male,other_race, +2511,Condition_3,52,Non-Hispanic,I just responded accordingly based on the instructions presented.,Male,Multiracial, +2486,Condition_3,24,Non-Hispanic,,Female,White, +2426,Condition_3,53,Non-Hispanic,I was trying to be clear while using few words.,Female,Black/African American, +2440,Condition_3,21,Non-Hispanic,,Female,Asian, +2466,Condition_3,28,Non-Hispanic,The correct approach was to carefully consider the instructions and proceed accordingly.,Female,White, +2482,Condition_3,28,Non-Hispanic,The probability of each switch mostly influenced my decisions.,Male,Asian, +2463,Condition_3,24,Non-Hispanic,no i just answered normally how i would,Male,White, +2460,Condition_3,33,Non-Hispanic,I would respond in a way to influence my partner to press the related button,Male,White, +2454,Condition_3,39,Non-Hispanic,I said what made the most sense. ,Female,Black/African American, +2428,Condition_3,32,Non-Hispanic,The chance of it turning on,Female,White, +2493,Condition_3,33,Non-Hispanic,I wanted to push them towards hitting the button that would help us the most ,Male,White, +2503,Condition_3,32,Non-Hispanic,,Male,White, +2525,Condition_3,46,Non-Hispanic,I felt that either choice would be fine,Male,White, +2420,Condition_3,22,Non-Hispanic,"Each scenario influenced how I responded, I did so based on what made sense to me.",Female,Multiracial, +2468,Condition_3,65,,Ut was the best answer.,Male,White, +2434,Condition_3,45,Hispanic,"If I could make the orange switch have a probability of 0% turning on, then the only reason why there was a loss was that the blue switch turned on.",Male,Multiracial, +2522,Condition_3,52,Non-Hispanic,I would want to let my teammate know that it would be more important to press the button on the switch that I mentioned in my response. This is a hint at which button to press if we have a next round.,Male,Black/African American, +2526,Condition_3,26,Non-Hispanic,"I considered the rules of the game before responding. Since in both cases both switches turned on, all instances of switches turning to on contributed to why we lost a point.",Male,White, +2472,Condition_3,26,Non-Hispanic,"I considered the rules and the probability of the switches turning on. I'm not sure if I did it correctly because it was a lot to remember for me, but I tried my best.",Female,other_race, +2446,Condition_3,27,Hispanic,,Female,White, +2498,Condition_3,21,Non-Hispanic,"The biggest influence on the outcome of the round, IE which switch was more important to influence.",Non-binary,White, +2456,Condition_3,56,Non-Hispanic,"I want a do-over. I think I got my answers backwards. In the first section I should have said to reduce orange to zero since that prevented 2 from being on. I think I made a similar mistake in part 2 when only one being on could lose a point. I don't remember my answer but, in that case, it should have been orange also. I was trying to make up for my previous misunderstanding of the game. Ugh. I probably got them all wrong. I want another chance!",Female,White, +2474,Condition_3,24,Non-Hispanic,i have no idea,Non-binary,White, +2498,Condition_3,38,Non-Hispanic,The probability of each switch turning on and off affected my decisions.,Female,other_race, +2507,Condition_3,21,Non-Hispanic,The rules of the game were the only factors that influenced how I responded.,Female,White, +2424,Condition_3,41,Non-Hispanic,The instructions. ,Female,White, +2509,Condition_3,63,Non-Hispanic,"Oops - I made a mistake. I meant to tell Alice that we lost because the blue switch turned on, hoping this would cause her to press the button attached to the blue switch, making it less likely we would lose another point.",Male,White, +2505,Condition_3,32,Non-Hispanic,I tried to think about the instructions and how best to translate them to another person.,Female,White, +2444,Condition_3,49,Non-Hispanic,,Male,White, +2459,Condition_3,21,Hispanic,I mainly made my decisions based on the explanation of the game.,Male,White, +2523,Condition_3,40,Non-Hispanic,I responded based on whatever the instructions told me. Perhaps I don't understand this question?,Male,White, +2513,Condition_3,36,Non-Hispanic,I just answered based on what I read,Female,White, +2495,Condition_3,39,Non-Hispanic,"I blamed the color switch that she should push the button. In the first round, there would be a 0% chance of losing. In the second round, there would still only be 20% chance of the orange one, but the blue one would drop from 80% to 60%",Male,other_race, +2449,Condition_3,39,Non-Hispanic,,Male,White, +2430,Condition_3,49,Non-Hispanic,"Its a game of chance. Additionally, the instructions gave a clue. ",Female,White, +2452,Condition_3,29,Non-Hispanic,"Confusing the participant so I could win, but I felt like I barely understood it myself.",Female,White, +2470,Condition_3,40,Non-Hispanic,Mainly just which one has the higher percentage,Male,White, +2527,Condition_3,23,Non-Hispanic,I have no comments.,Non-binary,White, +2439,Condition_3,33,Non-Hispanic,"I kind of guessed at how the statistics work on the second game. I thought I would try to lower the chance on the switch that already had the greatest chance of turning on, but very unsure if that's the right move or not. Then in terms of how to communicate that, I tried to place the ""blame"" on the switch that I wanted the other player to press the button for. ",Male,White, +2436,Condition_3,58,Non-Hispanic,probability of switches being on influenced me. I have no other comments,Female,White, +2484,Condition_3,31,Hispanic,"The rules of the game factored how I decided, on the first round both switches needed to be on so I chose to give out the information that the orange switch with a lower percentage was turned on. I have no questions or comments, thank you for the study.",Male,White, +2491,Condition_3,27,Non-Hispanic,"I used intuition to decide how to respond, not any statistical analysis.",Male,Asian, +2422,Condition_3,32,Non-Hispanic,I did my best to explain to someone in the simplest way in order to get points.,Female,White, +2480,Condition_3,24,Non-Hispanic,My responses were influenced by the likelihood of the switches being on,Female,White, +2432,Condition_3,35,Non-Hispanic,The probabillity being lowered was my first and foremost reason for my answers.,Female,White, +2517,Condition_3,49,,Common sense,Female,Asian, diff --git a/code/R/experiments/pressbutton_negativecondition-responses.csv b/code/R/experiments/pressbutton_negativecondition-responses.csv new file mode 100644 index 0000000..2b185b2 --- /dev/null +++ b/code/R/experiments/pressbutton_negativecondition-responses.csv @@ -0,0 +1,100 @@ +workerid,proliferate.condition,explanation_response_1,explanation_response_2,intervention_response_1,intervention_response_2,error +2427,Condition_4,no preference,no preference,blue,blue, +2531,Condition_4,no preference,no preference,orange,orange, +2504,Condition_4,no preference,no preference,blue,orange, +2453,Condition_4,no preference,no preference,orange,orange, +2514,Condition_4,blue,no preference,blue,no preference, +2529,Condition_4,no preference,no preference,blue,blue, +2471,Condition_4,no preference,no preference,blue,orange, +2457,Condition_4,blue,orange,blue,orange, +2458,Condition_4,blue,blue,no preference,blue, +2467,Condition_4,no preference,orange,no preference,orange, +2508,Condition_4,no preference,orange,blue,orange, +2455,Condition_4,no preference,no preference,no preference,no preference, +2475,Condition_4,no preference,orange,blue,orange, +2425,Condition_4,blue,orange,blue,orange, +2492,Condition_4,blue,orange,blue,blue, +2510,Condition_4,blue,orange,blue,blue, +2447,Condition_4,blue,orange,orange,orange, +2437,Condition_4,no preference,no preference,orange,blue, +2464,Condition_4,no preference,no preference,orange,orange, +2461,Condition_4,orange,orange,blue,blue, +2485,Condition_4,no preference,orange,blue,no preference, +2445,Condition_4,no preference,no preference,orange,orange, +2443,Condition_4,no preference,no preference,blue,blue, +2499,Condition_4,no preference,orange,orange,orange, +2473,Condition_4,no preference,no preference,orange,blue, +2423,Condition_4,no preference,orange,orange,orange, +2494,Condition_4,blue,no preference,blue,orange, +2469,Condition_4,no preference,no preference,blue,no preference, +2483,Condition_4,no preference,no preference,no preference,no preference, +2520,Condition_4,blue,blue,orange,orange, +2516,Condition_4,blue,blue,blue,blue, +2479,Condition_4,no preference,no preference,blue,blue, +2435,Condition_4,no preference,orange,no preference,orange, +2500,Condition_4,no preference,blue,orange,orange, +2421,Condition_4,no preference,orange,blue,orange, +2502,Condition_4,no preference,no preference,orange,orange, +2481,Condition_4,orange,orange,orange,orange, +2433,Condition_4,blue,blue,blue,blue, +2497,Condition_4,blue,orange,orange,blue, +2431,Condition_4,blue,blue,blue,blue, +2451,Condition_4,no preference,no preference,orange,orange, +2487,Condition_4,orange,orange,orange,orange, +2521,Condition_4,blue,orange,blue,orange, +2490,Condition_4,orange,orange,no preference,orange, +2429,Condition_4,orange,orange,orange,orange, +2528,Condition_4,no preference,no preference,orange,no preference, +2448,Condition_4,no preference,no preference,orange,orange, +2530,Condition_4,no preference,blue,orange,blue, +2462,Condition_4,blue,orange,blue,blue, +2501,Condition_3,orange,orange,orange,orange, +2476,Condition_3,orange,blue,orange,blue, +2532,Condition_3,blue,no preference,orange,blue, +2524,Condition_3,orange,blue,orange,blue, +2511,Condition_3,no preference,no preference,no preference,no preference, +2486,Condition_3,no preference,no preference,blue,blue, +2426,Condition_3,orange,no preference,orange,blue, +2440,Condition_3,orange,blue,orange,blue, +2466,Condition_3,blue,orange,orange,orange, +2482,Condition_3,orange,no preference,orange,orange, +2463,Condition_3,orange,no preference,orange,blue, +2460,Condition_3,orange,blue,orange,blue, +2454,Condition_3,no preference,no preference,blue,blue, +2428,Condition_3,blue,blue,blue,blue, +2493,Condition_3,orange,blue,orange,blue, +2503,Condition_3,blue,no preference,orange,blue, +2525,Condition_3,no preference,no preference,orange,orange, +2420,Condition_3,blue,no preference,orange,blue, +2468,Condition_3,no preference,no preference,orange,orange, +2434,Condition_3,no preference,blue,orange,orange, +2522,Condition_3,orange,blue,orange,blue, +2526,Condition_3,no preference,no preference,orange,blue, +2472,Condition_3,blue,blue,orange,blue, +2446,Condition_3,no preference,no preference,orange,orange, +2498,Condition_3,orange,blue,orange,blue, +2456,Condition_3,orange,blue,blue,orange, +2474,Condition_3,no preference,no preference,blue,blue, +2498,Condition_3,no preference,no preference,orange,orange, +2507,Condition_3,orange,orange,orange,orange, +2424,Condition_3,orange,orange,blue,orange, +2509,Condition_3,orange,orange,orange,blue, +2505,Condition_3,no preference,no preference,orange,blue, +2444,Condition_3,no preference,no preference,orange,orange, +2459,Condition_3,no preference,blue,orange,orange, +2523,Condition_3,no preference,no preference,orange,blue, +2513,Condition_3,no preference,no preference,no preference,no preference, +2495,Condition_3,orange,blue,orange,blue, +2449,Condition_3,orange,blue,orange,blue, +2430,Condition_3,no preference,no preference,no preference,orange, +2452,Condition_3,no preference,no preference,no preference,orange, +2470,Condition_3,orange,blue,orange,blue, +2527,Condition_3,blue,orange,orange,orange, +2439,Condition_3,orange,blue,orange,blue, +2436,Condition_3,no preference,no preference,orange,blue, +2484,Condition_3,orange,no preference,orange,blue, +2491,Condition_3,orange,no preference,orange,no preference, +2422,Condition_3,orange,blue,orange,blue, +2480,Condition_3,orange,blue,orange,blue, +2432,Condition_3,no preference,blue,orange,blue, +2517,Condition_3,no preference,no preference,orange,blue, diff --git a/code/R/experiments/pressbutton_positivecondition-participants.csv b/code/R/experiments/pressbutton_positivecondition-participants.csv new file mode 100644 index 0000000..252dac7 --- /dev/null +++ b/code/R/experiments/pressbutton_positivecondition-participants.csv @@ -0,0 +1,107 @@ +workerid,proliferate.condition,age,ethnicity,feedback,gender,race,error +2317,Condition_2,51,Non-Hispanic,You want to increase chances of orange turned on because blue probability is already 80%. ,Female,Asian, +2353,Condition_2,20,Hispanic,"The blue switch had a higher chance of switching on, therefore i always answered that the blue switch was the reason we won the point. The association between the blue switch and winning is created to my teammate through that response. I don't have any questions or comments.",Female,White, +2321,Condition_2,23,Non-Hispanic,i went with the option i thought would increase the likely hood of winning.,Female,White, +2307,Condition_2,21,Non-Hispanic,,Female,White, +2389,Condition_2,37,Non-Hispanic,"By telling the teammate that we got a point because the Blue switch was on, they are more likely to choose the Blue button to up the percentage to 100%, thus guaranteeing that the Blue switch is always on.",Female,Multiracial, +2357,Condition_2,50,Non-Hispanic,just following the guidelines,Male,Asian, +2314,Condition_2,55,Non-Hispanic,just used logic,Female,White, +2337,Condition_2,65,Non-Hispanic,no,Female,White, +2381,Condition_2,36,Non-Hispanic,I gave the explanation that I thought would make them keep pressing the button that they needed to to maximise the points. Blue for the first contestant and orange for the second.,Male,White, +2345,Condition_2,55,Non-Hispanic,no,Female,White, +2339,Condition_2,31,Non-Hispanic,whether one or both needed to be on.,Male,White, +2325,Condition_2,25,Hispanic,probability theory,Male,other_race, +2319,Condition_2,22,Non-Hispanic,,Female,White, +2409,Condition_2,24,Non-Hispanic,The chance of each switch turning on and what the other contestant knew is what influenced my decision.,Male,White, +2393,Condition_2,64,Non-Hispanic,none,Female,White, +2400,Condition_2,26,Non-Hispanic,"If I could, I would choose to explain the reasons for us winning differently to my partner. The selections one could choose from could either make the partner choose the right one, the wrong one, or have no effect. ",Male,White, +2398,Condition_2,68,,I would want my team mate to feel that the blue button will create a better outcome,Female,White, +2343,Condition_2,47,Non-Hispanic,"I just thought that with Zarah the fact that I told her we got the point was because the orange switch turned on, that would help her realize that the orange switch probably has a lower probability and she should press the orange button. + +With Alice since only one switch needed to be on, and orange had the lesser odds, I wanted her to think that the orange switch was good, and have her press the blue button so it would be 100%",Female,White, +2331,Condition_2,22,Non-Hispanic,,Female,White, +2341,Condition_2,29,Non-Hispanic,The lack of my partner's knowledge mainly gave me reasons to respond the way I did. ,Male,White, +2311,Condition_2,35,Non-Hispanic,I was choosing the answer with the highest probability.,Male,White, +2333,Condition_2,37,Non-Hispanic,"I just chose what made the most sense, though with the last scenario, I would have said that we scored a point because both were on.",Female,White, +2379,Condition_2,59,Non-Hispanic,I chose the switch with the most likely percentage to turn on,Female,White, +2355,Condition_2,38,Non-Hispanic,"I thought about choosing the explanation that would make sense to my partner so if we played another round they would be more informed on which choice would be better. Now that I think about it, it would have been better if I press the 20% button on the blue switch.",Male,Asian, +2361,Condition_2,26,Non-Hispanic,Percentage of switch being turned on,Female,White, +2367,Condition_2,44,Non-Hispanic,,Male,White, +2373,Condition_2,24,Non-Hispanic,I was trying to influence them to pick to increase the button I wanted.,Female,White, +2335,Condition_2,71,,"I believe there is an error in instructions with Alice. ever, Alice does not know whether the blue switch is more likely to turn ON or whether the BLUE switch is more likely to turn ON. + +BLUE indicated twice insead of BLUE or ORANGE",Female,White, +2391,Condition_2,35,Non-Hispanic,"The probability at which they would turn on, and whether both or one had to be on.",Female,Black/African American, +2385,Condition_2,29,Non-Hispanic,The percentage chances dictated how I made decisions .,Male,Black/African American, +2412,Condition_2,40,Non-Hispanic,I would want her to push the orange button to increase our odds of getting a point.,Male,White, +2363,Condition_2,35,Non-Hispanic,I thought about what would be the most precise answer even if it was concise and not the full explanation,Male,Asian, +2359,Condition_2,21,Non-Hispanic,"My response varied based on which switch having an increased chance of turning on would be better for the scenario. For example, changing the blue switch to have an 100% chance of turning on is the best thing to do in the first scenario in order to get the largest number of points, so I told my partner that we won because the blue switch turned on. This way, they are more likely to press the blue button and we are more likely to win more points.",Female,White, +2323,Condition_2,27,Non-Hispanic,It was confusing to make the last call because neither explanation really sufficed.,Female,White, +2395,Condition_2,28,Non-Hispanic,I decided to tell my partner which every switch went on that would be the most helpful for winning,Female,Multiracial, +2375,Condition_2,37,Non-Hispanic,I decided to respond with the best answer that'll help score a point.,Male,White, +2411,Condition_2,23,Non-Hispanic,,Male,Asian, +2404,Condition_2,19,Non-Hispanic,None,Female,White, +2347,Condition_2,24,Non-Hispanic,N/A,Male,Asian, +2397,Condition_2,63,Non-Hispanic,no,Male,White, +2406,Condition_2,29,Non-Hispanic,,Female,Multiracial, +2371,Condition_2,31,Non-Hispanic,I said we won because the orange one was on because in future rounds I wanted them to boost the orange light because it had lower chances,Male,White, +2327,Condition_2,38,Non-Hispanic,Just trying to maximize the chances of winning,Male,White, +2402,Condition_2,53,Non-Hispanic,I tried to make a decision which would cause the blue light to turn on at 100% probability so that only the chance of the other one being on would be a factor.,Female,White, +2387,Condition_2,57,Non-Hispanic,My responses were based on the probabilities of winning and communicating the correct button choice to my partner.,Male,White, +2351,Condition_2,24,Non-Hispanic,"I thought it would be best in each situation to emphasize the switch that, when its button is pressed, would increase the odds of gaining a point more.",Female,White, +2417,Condition_2,35,Non-Hispanic,the percentages of the buttons ,Male,White, +2377,Condition_2,58,Non-Hispanic,"Guiding factor was always which option could result in the best chance of receiving points. For the second game that meant increasing the chance that the Orange stick would switch to on. + +Very interesting study. Thank you for the opportunity to participate in your research.",Female,other_race, +2410,Condition_2,30,Non-Hispanic,Reading all the directions and images.,Female,Asian, +2386,Condition_1,69,Non-Hispanic,The rules of the game.,Male,White, +2374,Condition_1,23,Non-Hispanic,,Male,White, +2396,Condition_1,23,Non-Hispanic,I tried to respond in terms of my intuition and instructions for how the switches would behave,Male,White, +2342,Condition_1,41,Non-Hispanic,I tried to give my teammate an explanation that would make them more likely to choose the optimal button to press. ,Male,White, +2306,Condition_1,58,Non-Hispanic,,Male,White, +2354,Condition_1,27,Non-Hispanic,I wanted Alice to pick the blue switch to make it a 100% chance of turning on so I emphasized the importantance of the blue switch ,Female,White, +2378,Condition_1,30,Hispanic,just my first impression and the information provided,Male,White, +2312,Condition_1,20,Non-Hispanic,,Female,White, +2358,Condition_1,21,Non-Hispanic,I tried to figure out what option would make getting a point statistically the most likely. ,Female,White, +2364,Condition_1,39,Non-Hispanic,I chose to answer in a way that I thought was most likely to get the other team member to boost the chances of the orange switch turning on.,Female,White, +2338,Condition_1,38,Non-Hispanic,"A point is a point, I don't have to choose one switch color or the other. But in the one choice, I would rather a 100% chance",Male,White, +2403,Condition_1,44,Non-Hispanic,"I think that my answers hopefully conveyed that it was important to adjust the orange one in the first set, and the blue one in the second.",Female,White, +2413,Condition_1,32,Non-Hispanic,Great study.,Male,White, +2313,Condition_1,20,Non-Hispanic,"Saying that we only won because the orange switch turned on, in my opinion, implies that the orange switch is the more troublesome one to fix.",Female,Multiracial, +2401,Condition_1,44,Non-Hispanic,,Female,White, +2416,Condition_1,29,Non-Hispanic,Percentages the orange button needed the switch more ,Male,Black/African American, +2376,Condition_1,33,Non-Hispanic,,other_gender,White, +2334,Condition_1,29,Non-Hispanic,Understanding and comprehending the rules.,Male,Black/African American, +2382,Condition_1,27,Non-Hispanic,Which explanation would have the best chance of the other person having the desirable outcome,Male,White, +2370,Condition_1,31,Non-Hispanic,What the person knew who I was guiding.,Male,White, +2326,Condition_1,33,Non-Hispanic,In the first round the orange witch was the deciding factor. In the second round either one could decide and the blue would guarantee a win if the button was pressed.,Male,White, +2384,Condition_1,19,Non-Hispanic,,Male,White, +2388,Condition_1,41,Non-Hispanic,Whatever explanation I could give that was truthful and helped best explain the rules was what I chose. Thank you for all your hard work!,Male,White, +2380,Condition_1,26,Non-Hispanic,no,Female,White, +2372,Condition_1,35,Non-Hispanic,I decided based on the odds of scoring a point and the information that the other participant had to be able to make a choice.,Female,White, +2366,Condition_1,35,Non-Hispanic,"The probability of the lower % switch turning on. Orange only had a 20% success rate of being turned on, so increasing it would increase the probability of winning a point more than the blue.",Male,White, +2352,Condition_1,53,Non-Hispanic,The factors that influenced my response were just the facts of the situation. Whatever was most likely to get the team to score a point. ,Female,White, +2392,Condition_1,25,Non-Hispanic,how i would need to understand it,Female,Asian, +2348,Condition_1,31,Non-Hispanic,none,Male,White, +2346,Condition_1,30,Non-Hispanic,not really,Male,Black/African American, +2407,Condition_1,30,Non-Hispanic,I wanted to try to explain the game to the other player,Female,White, +2320,Condition_1,24,Non-Hispanic,I didn't overthink about how to respond to the other players because I don't think hinting at one colored button or the other would have really helped them know that much more about the percentages on the buttons.,Female,White, +2322,Condition_1,24,Hispanic,,Female,White, +2330,Condition_1,40,Non-Hispanic,"When we got points for either switch being on, the explanation didn't seem to matter as much.",Female,White, +2340,Condition_1,48,Non-Hispanic,The chances of getting the point and ON played the factor. No comments on experiment.,Female,White, +2390,Condition_1,25,Hispanic,honestly just reading the text. i may be simple minded or just bad at math but i had to read it a few times to decide my response,Non-binary,Multiracial, +2399,Condition_1,18,Non-Hispanic,,Female,Black/African American, +2336,Condition_1,30,Hispanic,I tried to make both explanations straightforward with the limited options I had,Male,American Indian/Alaska Native, +2308,Condition_1,23,Hispanic,,Female,White, +2414,Condition_1,42,Non-Hispanic,I chose the answer that was most likely to direct the other player to the correct choice.,Female,White, +2362,Condition_1,39,Non-Hispanic,,Female,Multiracial, +2360,Condition_1,48,Non-Hispanic,"Knowing that only one switch needed to be on, I made sure my partner knew that was the reason so they would continue to select it.",Male,White, +2318,Condition_1,52,Hispanic,i tried following directions,Male,White, +2356,Condition_1,29,Non-Hispanic,Just the rules of the game.,Male,White, +2332,Condition_1,74,Non-Hispanic,because only one switch needs to be turned on,Male,White, +2316,Condition_1,75,,probability,Female,White, +2324,Condition_1,66,Non-Hispanic,The greatest chance of turning on is what I pick because I know the chances.,Female,White, +2328,Condition_1,29,Hispanic,everything blue was better,Female,Multiracial, +2344,Condition_1,35,Non-Hispanic,"If both switches need to be own, it is better to increase the likelihood of the 20% switch to 40%. If only one switch needs to be on, then you should just bring the 80% switch to 100%. I gave answer that I thought would cause my partner to choose the switch I wanted. + +Other than that, I think having a new page and illustration for every sentence was unnecessary.",Male,Black/African American, \ No newline at end of file diff --git a/code/R/experiments/pressbutton_positivecondition-responses.csv b/code/R/experiments/pressbutton_positivecondition-responses.csv new file mode 100644 index 0000000..9870fb2 --- /dev/null +++ b/code/R/experiments/pressbutton_positivecondition-responses.csv @@ -0,0 +1,99 @@ +workerid,proliferate.condition,explanation_response_1,explanation_response_2,intervention_response_1,intervention_response_2,error +2317,Condition_2,no preference,orange,blue,orange, +2353,Condition_2,blue,blue,no preference,no preference, +2321,Condition_2,blue,orange,blue,orange, +2307,Condition_2,no preference,orange,blue,orange, +2389,Condition_2,blue,blue,blue,blue, +2357,Condition_2,no preference,orange,blue,blue, +2314,Condition_2,no preference,no 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+Conjunctive,45.45454545454545,14.14141414141414,40.4040404040404,67.67676767676768,22.22222222222222,10.1010101010101,negative,softint +Disjunctive,11.11111111111111,35.35353535353536,53.535353535353536,35.35353535353536,55.55555555555556,9.090909090909092,negative,softint diff --git a/code/R/experiments/softint_pos.csv b/code/R/experiments/softint_pos.csv new file mode 100644 index 0000000..67992ef --- /dev/null +++ b/code/R/experiments/softint_pos.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,51.02040816326531,11.224489795918368,37.755102040816325,65.3061224489796,30.612244897959183,4.081632653061225,positive,softint +Disjunctive,6.122448979591836,58.16326530612245,35.714285714285715,12.244897959183673,79.59183673469387,8.16326530612245,positive,softint diff --git a/code/R/optimal_intervention_model/.Rapp.history b/code/R/optimal_intervention_model/.Rapp.history new file mode 100644 index 0000000..e69de29 diff --git a/code/R/optimal_intervention_model/data.csv b/code/R/optimal_intervention_model/data.csv new file mode 100644 index 0000000..c3835d7 --- /dev/null +++ b/code/R/optimal_intervention_model/data.csv @@ -0,0 +1,73 @@ +causal_structure,outcome,experiment,choice,type,probability +conjunctive,positive,hard,abnormal,explanation,0.4948453608247423 +conjunctive,positive,hard,normal,explanation,0.1649484536082474 +conjunctive,positive,hard,nopreference,explanation,0.3402061855670103 +conjunctive,positive,hard,abnormal,intervention,0.6597938144329896 +conjunctive,positive,hard,normal,intervention,0.28865979381443296 +conjunctive,positive,hard,nopreference,intervention,0.05154639175257732 +disjunctive,positive,hard,abnormal,explanation,0.26804123711340205 +disjunctive,positive,hard,normal,explanation,0.19587628865979384 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Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2023}, + url = {https://www.R-project.org/}, +} +@Manual{bookdown2023, +title = {{bookdown}: Authoring Books and Technical Documents with R Markdown}, + author = {Yihui Xie}, + year = {2023}, + note = {R package version 0.37}, + url = {https://github.com/rstudio/bookdown}, +} + +@Book{bookdown2016, +title = {{bookdown}: Authoring Books and Technical Documents with {R} Markdown}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2016}, + isbn = {978-1138700109}, + url = {https://bookdown.org/yihui/bookdown}, +} +@Manual{janitor, +title = {{janitor}: Simple Tools for Examining and Cleaning Dirty Data}, + author = {Sam Firke}, + year = {2023}, + note = {R package version 2.2.0}, + url = {https://CRAN.R-project.org/package=janitor}, +} +@Manual{knitr2023, +title = {{knitr}: A General-Purpose Package for Dynamic Report Generation in R}, + author = {Yihui Xie}, + year = {2023}, + note = {R package version 1.45}, + url = {https://yihui.org/knitr/}, +} + +@Book{knitr2015, + title = {Dynamic Documents with {R} and knitr}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2015}, + edition = {2nd}, + note = {ISBN 978-1498716963}, + url = {https://yihui.org/knitr/}, +} + +@InCollection{knitr2014, + booktitle = {Implementing Reproducible Computational Research}, + editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, +title = {{knitr}: A Comprehensive Tool for Reproducible Research in {R}}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + year = {2014}, + note = {ISBN 978-1466561595}, +} +@Manual{Metrics, +title = {{Metrics}: Evaluation Metrics for Machine Learning}, + author = {Ben Hamner and Michael Frasco}, + year = {2018}, + note = {R package version 0.1.4}, + url = {https://CRAN.R-project.org/package=Metrics}, +} +@Manual{patchwork, +title = {{patchwork}: The Composer of Plots}, + author = {Thomas Lin Pedersen}, + year = {2024}, + note = {R package version 1.2.0}, + url = {https://CRAN.R-project.org/package=patchwork}, +} +@Manual{rmarkdown2023, +title = {{rmarkdown}: Dynamic Documents for R}, + author = {JJ Allaire and Yihui Xie and Christophe Dervieux and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, + year = {2023}, + note = {R package version 2.25}, + url = {https://github.com/rstudio/rmarkdown}, +} + +@Book{rmarkdown2018, + title = {R Markdown: The Definitive Guide}, + author = {Yihui Xie and J.J. Allaire and Garrett Grolemund}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2018}, + isbn = {9781138359338}, + url = {https://bookdown.org/yihui/rmarkdown}, +} + +@Book{rmarkdown2020, + title = {R Markdown Cookbook}, + author = {Yihui Xie and Christophe Dervieux and Emily Riederer}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2020}, + isbn = {9780367563837}, + url = {https://bookdown.org/yihui/rmarkdown-cookbook}, +} +@Manual{scales, +title = {{scales}: Scale Functions for Visualization}, + author = {Hadley Wickham and Thomas Lin Pedersen and Dana Seidel}, + year = {2023}, + note = {R package version 1.3.0}, + url = {https://CRAN.R-project.org/package=scales}, +} +@Article{tidyverse, + title = {Welcome to the {tidyverse}}, + author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani}, + year = {2019}, + journal = {Journal of Open Source Software}, + volume = {4}, + number = {43}, + pages = {1686}, + doi = {10.21105/joss.01686}, +} diff --git a/code/R/optimal_intervention_model/intervention_predictions.csv b/code/R/optimal_intervention_model/intervention_predictions.csv new file mode 100644 index 0000000..4fc19fb --- /dev/null +++ b/code/R/optimal_intervention_model/intervention_predictions.csv @@ -0,0 +1,37 @@ +"intervention","causal_structure","outcome","choice","value","prediction","probability" +"fixed","conjunctive","positive","abnormal",0.72,0.994041787821903,0.808080808080808 +"fixed","conjunctive","positive","normal",0.18,3.52912649877719e-05,0.111111111111111 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+"soft","disjunctive","negative","nopreference",0.26,0.225113998485635,0.0909090909090909 diff --git a/code/R/optimal_intervention_model/optimal_intervention_model.Rmd b/code/R/optimal_intervention_model/optimal_intervention_model.Rmd new file mode 100644 index 0000000..cfcded5 --- /dev/null +++ b/code/R/optimal_intervention_model/optimal_intervention_model.Rmd @@ -0,0 +1,385 @@ +--- +title: "Optimal intervention model" +author: "Jacqueline Harding, Tobias Gerstenberg & Lara Kirfel" +date: "`r format(Sys.Date(), '%B %d, %Y')`" +output: + bookdown::html_document2: + toc: true + toc_depth: 4 + toc_float: true + theme: cosmo + highlight: tango +--- + +# Load packages + +```{r, message=F} +library("knitr") +library("janitor") +library("patchwork") +library("Metrics") +library("tidyverse") +``` + +# Set options + +```{r} +theme_set(theme_classic() + + theme(text = element_text(size = 24))) + +opts_chunk$set(comment = "", + fig.show = "hold") + +# suppress grouping warning +options(dplyr.summarise.inform = F) +``` + +# Functions + +```{r} +fun.softmax = function(x, temp = 3) { + out = exp(x*temp) / sum(exp(x*temp)) + return(out) +} +``` + +# DATA + +```{r, warning=FALSE, message=FALSE} +df.data = bind_rows( + read_csv(file = "../../../data/hardint_pos.csv"), + read_csv(file = "../../../data/hardint_neg.csv"), + read_csv(file = "../../../data/softint_pos.csv"), + read_csv(file = "../../../data/softint_neg.csv"), + read_csv(file = "../../../data/fixedint_pos.csv"), + read_csv(file = "../../../data/fixedint_neg.csv")) %>% + clean_names() %>% + mutate(causal_structure = str_to_lower(causal_structure), + experiment = str_remove(experiment, "int")) %>% + rename_with(.fn = ~str_remove_all(., "_percentage")) %>% + pivot_longer(cols = -c(causal_structure, outcome, experiment), + values_to = "probability") %>% + separate(col = name, + into = c("choice", "type")) %>% + mutate(across(.cols = -probability, + .fns = ~ as.factor(.)), + choice = factor(choice, levels = c("abnormal", "nopreference", "normal"))) %>% + mutate(probability = probability / 100) + +df.intervention = df.data %>% + filter(type == "intervention") %>% + rename(intervention = experiment) + +df.explanation = df.data %>% + filter(type == "explanation") + +colnames(df.data) + +# write_csv(df.data, "data.csv") + +``` + + + +# MODEL + +## Interventions + +### Model structure + +```{r} + +fun.success = function(p_abnormal, p_normal, causal_structure, outcome){ + if (causal_structure == "conjunctive"){ + p = p_abnormal * p_normal + } else{ + p = 1 - (1 - p_abnormal) * (1 - p_normal) + } + if (outcome == "negative"){ + p = 1 - p + } + return(p) +} + +causal_structure = c("conjunctive", "disjunctive") +outcome = c("positive", "negative") + +df.model = expand_grid(causal_structure, outcome) %>% + mutate(p_abnormal = 0.2, + p_normal = 0.8, + int_hard_abnormal = ifelse(outcome == "positive", 1, 0), + int_hard_normal = ifelse(outcome == "positive", 1, 0), + int_soft_abnormal = ifelse(outcome == "positive", + p_abnormal + 0.2, + p_abnormal - 0.2), + int_soft_normal = ifelse(outcome == "positive", + p_normal + 0.2, + p_normal - 0.2), + int_fixed_abnormal = ifelse(outcome == "positive", + 0.9, + 0.1), + int_fixed_normal = ifelse(outcome == "positive", + 0.9, + 0.1), + p_success = pmap_dbl(.l = list(p_abnormal, + p_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4)), + p_success_int_hard_abnormal = pmap_dbl(.l = list(int_hard_abnormal, + p_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4)), + p_success_int_hard_normal = pmap_dbl(.l = list(p_abnormal, + int_hard_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4)), + p_success_int_soft_abnormal = pmap_dbl(.l = list(int_soft_abnormal, + p_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4)), + p_success_int_soft_normal = pmap_dbl(.l = list(p_abnormal, + int_soft_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4)), + p_success_int_fixed_abnormal = pmap_dbl(.l = list(int_fixed_abnormal, + p_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4)), + p_success_int_fixed_normal = pmap_dbl(.l = list(p_abnormal, + int_fixed_normal, + causal_structure, + outcome), + .f = ~ fun.success(..1, ..2, ..3, ..4))) +``` + +### Choice probabilities + +```{r} +# gives expected value for each intervention +df.choice = df.model %>% + select(causal_structure, outcome, + contains("p_success_int")) %>% + pivot_longer(-c(causal_structure, outcome)) %>% + mutate(name = str_remove(name, "p_success_int_")) %>% + separate(name, into = c("intervention", "target")) %>% + pivot_wider(names_from = target, + values_from = value) %>% + arrange(intervention, causal_structure) %>% + relocate(intervention) %>% + mutate(nopreference = 0.5 * abnormal + 0.5 * normal) %>% + pivot_longer(c(abnormal, normal, nopreference), + names_to = "choice") %>% + mutate(choice = factor(choice, levels = c("abnormal", "nopreference", "normal")), + across(.cols = c(intervention, causal_structure, outcome), + .fns = ~ as.factor(.))) +``` + +### Fit softmax parameter + +```{r} +fun.fit_temperature = function(df_data, df_prediction, temperature){ + df_prediction %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(value, temp = temperature)) %>% + ungroup() %>% + left_join(df_data, + by = c("intervention", "causal_structure", "outcome", "choice")) %>% + summarize(loss = sum((prediction - probability) ^ 2)) %>% + pull(loss) +} + +fit.temperature = optim(par = 10, + fn = fun.fit_temperature, + method = "L-BFGS-B", + lower = 0, + upper = 100, + df_data = df.intervention, + df_prediction = df.choice) + +print(fit.temperature$par) +``` +### Predictions + +```{r} +df.prediction_intervention = df.choice %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(value, temp = fit.temperature$par)) %>% + left_join(df.data %>% + filter(type == "intervention") %>% + select(-type) %>% + rename(intervention = experiment), + by = c("causal_structure", "outcome", "intervention", "choice")) +``` + +## Explanations + +### Model structure + +```{r} +df.prediction_explanation = df.choice %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(truth = ifelse(choice == "nopreference", 1, 0)) %>% + left_join(df.data %>% + filter(type == "explanation") %>% + select(-type) %>% + rename(intervention = experiment), + by = c("causal_structure", "outcome", "intervention", "choice")) %>% + ungroup() +``` + +### Model fitting functions + +#### Combined model + +```{r} +fun.fit_params = function(params, df_prediction){ + + weight <- params[1] + temperature <- params[2] + + df_prediction %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(weight * value + (1 - weight) * truth, temp = temperature)) %>% + ungroup() %>% + summarize(loss = sum((prediction - probability) ^ 2)) %>% + pull(loss) +} +``` + +#### Intervention only model + +```{r} +fun.fit_params_intervention_only = function(params, df_prediction){ + + temperature <- params[1] + + df_prediction %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(value, temp = temperature)) %>% + ungroup() %>% + summarize(loss = sum((prediction - probability) ^ 2)) %>% + pull(loss) +} +``` + +#### Truth only model + +```{r} +fun.fit_params_truth_only = function(params, df_prediction){ + + temperature <- params[1] + + df_prediction %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(truth, temp = temperature)) %>% + ungroup() %>% + summarize(loss = sum((prediction - probability) ^ 2)) %>% + pull(loss) +} +``` + +### Fit paramters + +#### Combined model + +```{r} + +initial_params <- c(weight = 0.5, temperature = 10) +lower_bounds <- c(weight = 0, temperature = 0) +upper_bounds <- c(weight = 1, temperature = 100) + +fit.params <- optim(par = initial_params, + fn = fun.fit_params, + method = "L-BFGS-B", + lower = lower_bounds, + upper = upper_bounds, + df_prediction = df.prediction_explanation) + +print(fit.params$par) +``` + +#### Intervention only model + +```{r} + +initial_params <- c(temperature = 10) +lower_bounds <- c(temperature = 0) +upper_bounds <- c(temperature = 100) + +fit.params_intervention_only <- optim(par = initial_params, + fn = fun.fit_params_intervention_only, + method = "L-BFGS-B", + lower = lower_bounds, + upper = upper_bounds, + df_prediction = df.prediction_explanation) + +print(fit.params_intervention_only$par) +``` + +#### Truth only model + +```{r} + +initial_params <- c(temperature = 10) +lower_bounds <- c(temperature = 0) +upper_bounds <- c(temperature = 100) + +fit.params_truth_only <- optim(par = initial_params, + fn = fun.fit_params_truth_only, + method = "L-BFGS-B", + lower = lower_bounds, + upper = upper_bounds, + df_prediction = df.prediction_explanation) + +print(fit.params_truth_only$par) +``` + +### Predictions + +#### Combined model + +```{r} +df.prediction_explanation = df.prediction_explanation %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(fit.params$par[1] * value + (1 - fit.params$par[1]) * truth, temp = fit.params$par[2])) %>% + ungroup() + +write.csv(df.prediction_explanation, file = "explanation_predictions.csv", row.names = FALSE) +write.csv(df.prediction_intervention, file = "intervention_predictions.csv", row.names = FALSE) +``` + +#### Intervention only model + +```{r} +df.prediction_explanation_intervention_only = df.prediction_explanation %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(value, temp = fit.params_intervention_only$par[1])) %>% + ungroup() + +write.csv(df.prediction_explanation_intervention_only, file = "explanation_predictions_intervention_only.csv", row.names = FALSE) +``` + +#### Truth only model + +```{r} +df.prediction_explanation_truth_only = df.prediction_explanation %>% + group_by(intervention, causal_structure, outcome) %>% + mutate(prediction = fun.softmax(truth, temp = fit.params_truth_only$par[1])) %>% + ungroup() + +write.csv(df.prediction_explanation_truth_only, file = "explanation_predictions_truth_only.csv", row.names = FALSE) +``` + + + +# Session info + +```{r, echo=F} +sessionInfo() +``` \ No newline at end of file diff --git a/code/R/optimal_intervention_model/optimal_intervention_model.html b/code/R/optimal_intervention_model/optimal_intervention_model.html new file mode 100644 index 0000000..1fcb41d --- /dev/null +++ b/code/R/optimal_intervention_model/optimal_intervention_model.html @@ -0,0 +1,2040 @@ + + + + + + + + + + + + + + + +Optimal intervention model + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + +
+

1 Load packages

+
library("knitr")
+library("janitor")
+library("patchwork")
+library("Metrics")
+library("tidyverse")
+
+
+

2 Set options

+
theme_set(theme_classic() + 
+    theme(text = element_text(size = 24)))
+
+opts_chunk$set(comment = "",
+               fig.show = "hold")
+
+# suppress grouping warning 
+options(dplyr.summarise.inform = F)
+
+
+

3 Functions

+
fun.softmax = function(x, temp = 3) {
+    out = exp(x*temp) / sum(exp(x*temp))
+    return(out)
+}
+
+
+

4 DATA

+
df.data = bind_rows(
+  read_csv(file = "../../../data/hardint_pos.csv"),
+  read_csv(file = "../../../data/hardint_neg.csv"),
+  read_csv(file = "../../../data/softint_pos.csv"),
+  read_csv(file = "../../../data/softint_neg.csv"),
+  read_csv(file = "../../../data/fixedint_pos.csv"),
+  read_csv(file = "../../../data/fixedint_neg.csv")) %>% 
+  clean_names() %>% 
+  mutate(causal_structure = str_to_lower(causal_structure),
+         experiment = str_remove(experiment, "int")) %>% 
+  rename_with(.fn = ~str_remove_all(., "_percentage")) %>% 
+  pivot_longer(cols = -c(causal_structure, outcome, experiment),
+               values_to = "probability") %>% 
+  separate(col = name,
+           into = c("choice", "type")) %>% 
+  mutate(across(.cols = -probability,
+                .fns = ~ as.factor(.)),
+         choice = factor(choice, levels = c("abnormal", "nopreference", "normal"))) %>%
+  mutate(probability = probability / 100)
+
+df.intervention = df.data %>% 
+  filter(type == "intervention") %>% 
+  rename(intervention = experiment)
+
+df.explanation = df.data %>% 
+  filter(type == "explanation")
+
+colnames(df.data)
+
[1] "causal_structure" "outcome"          "experiment"       "choice"          
+[5] "type"             "probability"     
+
# write_csv(df.data, "data.csv")
+
+
+

5 MODEL

+
+

5.1 Interventions

+
+

5.1.1 Model structure

+
fun.success = function(p_abnormal, p_normal, causal_structure, outcome){ 
+  if (causal_structure == "conjunctive"){
+    p = p_abnormal *  p_normal
+  } else{
+    p = 1 - (1 - p_abnormal) * (1 - p_normal)
+  }
+  if (outcome == "negative"){
+    p = 1 - p
+  }
+  return(p)
+}
+
+causal_structure = c("conjunctive", "disjunctive")
+outcome = c("positive", "negative") 
+
+df.model = expand_grid(causal_structure, outcome) %>% 
+  mutate(p_abnormal = 0.2,
+         p_normal = 0.8,
+         int_hard_abnormal = ifelse(outcome == "positive", 1, 0),
+         int_hard_normal = ifelse(outcome == "positive", 1, 0),
+         int_soft_abnormal = ifelse(outcome == "positive",
+                                  p_abnormal + 0.2,
+                                  p_abnormal - 0.2),
+         int_soft_normal = ifelse(outcome == "positive",
+                                  p_normal + 0.2,
+                                  p_normal - 0.2),
+         int_fixed_abnormal = ifelse(outcome == "positive",
+                                  0.9,
+                                  0.1),
+         int_fixed_normal = ifelse(outcome == "positive",
+                                  0.9,
+                                  0.1),
+         p_success = pmap_dbl(.l = list(p_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_hard_abnormal = pmap_dbl(.l = list(int_hard_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_hard_normal = pmap_dbl(.l = list(p_abnormal, 
+                                        int_hard_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_soft_abnormal = pmap_dbl(.l = list(int_soft_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_soft_normal = pmap_dbl(.l = list(p_abnormal, 
+                                        int_soft_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_fixed_abnormal = pmap_dbl(.l = list(int_fixed_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_fixed_normal = pmap_dbl(.l = list(p_abnormal, 
+                                        int_fixed_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)))
+
+
+

5.1.2 Choice probabilities

+
# gives expected value for each intervention
+df.choice = df.model %>% 
+  select(causal_structure, outcome,
+         contains("p_success_int")) %>% 
+  pivot_longer(-c(causal_structure, outcome)) %>% 
+  mutate(name = str_remove(name, "p_success_int_")) %>% 
+  separate(name, into = c("intervention", "target")) %>% 
+  pivot_wider(names_from = target,
+              values_from = value) %>% 
+  arrange(intervention, causal_structure) %>% 
+  relocate(intervention) %>% 
+  mutate(nopreference = 0.5 * abnormal + 0.5 * normal) %>% 
+  pivot_longer(c(abnormal, normal, nopreference),
+               names_to = "choice") %>% 
+  mutate(choice = factor(choice, levels = c("abnormal", "nopreference", "normal")),
+         across(.cols = c(intervention, causal_structure, outcome),
+                .fns = ~ as.factor(.)))
+
+
+

5.1.3 Fit softmax parameter

+
fun.fit_temperature = function(df_data, df_prediction, temperature){
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(value, temp = temperature)) %>% 
+    ungroup() %>% 
+    left_join(df_data,
+              by = c("intervention", "causal_structure", "outcome", "choice")) %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+fit.temperature = optim(par = 10, 
+                        fn = fun.fit_temperature,
+                        method = "L-BFGS-B",
+                        lower = 0, 
+                        upper = 100,
+                        df_data = df.intervention,
+                        df_prediction = df.choice)
+
+print(fit.temperature$par)
+
[1] 18.97389
+
+
+

5.1.4 Predictions

+
df.prediction_intervention = df.choice %>% 
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(value, temp = fit.temperature$par)) %>%
+    left_join(df.data %>% 
+                filter(type == "intervention") %>% 
+                select(-type) %>% 
+                rename(intervention = experiment),
+              by = c("causal_structure", "outcome", "intervention", "choice"))
+
+
+
+

5.2 Explanations

+
+

5.2.1 Model structure

+
df.prediction_explanation =  df.choice %>% 
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(truth = ifelse(choice == "nopreference", 1, 0)) %>%
+    left_join(df.data %>% 
+                filter(type == "explanation") %>% 
+                select(-type) %>% 
+                rename(intervention = experiment),
+              by = c("causal_structure", "outcome", "intervention", "choice")) %>% 
+  ungroup()
+
+
+

5.2.2 Model fitting functions

+
+

5.2.2.1 Combined model

+
fun.fit_params = function(params, df_prediction){
+  
+  weight <- params[1]
+  temperature <- params[2]
+  
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(weight * value + (1 - weight) * truth, temp = temperature)) %>% 
+    ungroup() %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+
+

5.2.2.2 Intervention only model

+
fun.fit_params_intervention_only = function(params, df_prediction){
+  
+  temperature <- params[1]
+  
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(value, temp = temperature)) %>% 
+    ungroup() %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+
+

5.2.2.3 Truth only model

+
fun.fit_params_truth_only = function(params, df_prediction){
+  
+  temperature <- params[1]
+  
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(truth, temp = temperature)) %>% 
+    ungroup() %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+
+
+

5.2.3 Fit paramters

+
+

5.2.3.1 Combined model

+
initial_params <- c(weight = 0.5, temperature = 10)
+lower_bounds <- c(weight = 0, temperature = 0)
+upper_bounds <- c(weight = 1, temperature = 100)
+
+fit.params <- optim(par = initial_params, 
+                         fn = fun.fit_params,
+                         method = "L-BFGS-B",
+                         lower = lower_bounds, 
+                         upper = upper_bounds,
+                         df_prediction = df.prediction_explanation)
+
+print(fit.params$par)
+
     weight temperature 
+  0.8420403   3.5050364 
+
+
+

5.2.3.2 Intervention only model

+
initial_params <- c(temperature = 10)
+lower_bounds <- c(temperature = 0)
+upper_bounds <- c(temperature = 100)
+
+fit.params_intervention_only <- optim(par = initial_params, 
+                         fn = fun.fit_params_intervention_only,
+                         method = "L-BFGS-B",
+                         lower = lower_bounds, 
+                         upper = upper_bounds,
+                         df_prediction = df.prediction_explanation)
+
+print(fit.params_intervention_only$par)
+
temperature 
+   2.097383 
+
+
+

5.2.3.3 Truth only model

+
initial_params <- c(temperature = 10)
+lower_bounds <- c(temperature = 0)
+upper_bounds <- c(temperature = 100)
+
+fit.params_truth_only <- optim(par = initial_params, 
+                         fn = fun.fit_params_truth_only,
+                         method = "L-BFGS-B",
+                         lower = lower_bounds, 
+                         upper = upper_bounds,
+                         df_prediction = df.prediction_explanation)
+
+print(fit.params_truth_only$par)
+
temperature 
+  0.4546058 
+
+
+
+

5.2.4 Predictions

+
+

5.2.4.1 Combined model

+
df.prediction_explanation = df.prediction_explanation %>%
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(fit.params$par[1] * value + (1 - fit.params$par[1]) * truth, temp = fit.params$par[2])) %>%
+  ungroup()
+
+write.csv(df.prediction_explanation, file = "explanation_predictions.csv", row.names = FALSE)
+write.csv(df.prediction_intervention, file = "intervention_predictions.csv", row.names = FALSE)
+
+
+

5.2.4.2 Intervention only model

+
df.prediction_explanation_intervention_only = df.prediction_explanation %>%
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(value, temp = fit.params_intervention_only$par[1])) %>%
+  ungroup()
+
+write.csv(df.prediction_explanation_intervention_only, file = "explanation_predictions_intervention_only.csv", row.names = FALSE)
+
+
+

5.2.4.3 Truth only model

+
df.prediction_explanation_truth_only = df.prediction_explanation %>%
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(truth, temp = fit.params_truth_only$par[1])) %>%
+  ungroup()
+
+write.csv(df.prediction_explanation_truth_only, file = "explanation_predictions_truth_only.csv", row.names = FALSE)
+
+
+
+
+
+

6 Session info

+
R version 4.1.3 (2022-03-10)
+Platform: x86_64-apple-darwin17.0 (64-bit)
+Running under: macOS Big Sur/Monterey 10.16
+
+Matrix products: default
+BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base     
+
+other attached packages:
+ [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.4    
+ [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
+ [9] ggplot2_3.4.4   tidyverse_2.0.0 Metrics_0.1.4   patchwork_1.1.2
+[13] janitor_2.2.0   knitr_1.38     
+
+loaded via a namespace (and not attached):
+ [1] bslib_0.3.1       compiler_4.1.3    pillar_1.9.0      jquerylib_0.1.4  
+ [5] tools_4.1.3       bit_4.0.4         digest_0.6.29     jsonlite_1.8.4   
+ [9] evaluate_0.15     lifecycle_1.0.3   gtable_0.3.0      timechange_0.2.0 
+[13] pkgconfig_2.0.3   rlang_1.1.1       cli_3.6.1         rstudioapi_0.15.0
+[17] parallel_4.1.3    yaml_2.3.5        xfun_0.30         fastmap_1.1.0    
+[21] withr_2.5.0       hms_1.1.3         generics_0.1.2    vctrs_0.6.5      
+[25] sass_0.4.1        bit64_4.0.5       grid_4.1.3        tidyselect_1.2.0 
+[29] glue_1.6.2        snakecase_0.11.0  R6_2.5.1          fansi_1.0.3      
+[33] vroom_1.6.3       rmarkdown_2.13    bookdown_0.25     tzdb_0.4.0       
+[37] magrittr_2.0.3    scales_1.2.1      htmltools_0.5.2   colorspace_2.0-3 
+[41] utf8_1.2.2        stringi_1.7.6     munsell_0.5.0     crayon_1.5.1     
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/data/data-readme.md b/data/data-readme.md new file mode 100644 index 0000000..bb1d483 --- /dev/null +++ b/data/data-readme.md @@ -0,0 +1,23 @@ +# Data readme + +Information about the different data files. + +## data_int_combined.csv + +- causal_structure: conjunctive/disjunctive +- outcome: positive/negative +- experiment: + - hard: intervention sets it to 1 (positive) or 0 (negative) + - soft: intervention increases by 0.2 (positive) or decreases by 0.2 (negative) + - fixed: intervention sets to 0.9 (positive) or 0.1 (negative) +- choice: abnormal/normal/nopreference +- type: + - explanation: selections in the explanation phase of the experiment + - intervention: selections in the intervention phase of the experiment + +- note: + - the abnormal switch has a 20% of turning on + - the normal switch has an 80% of turning on + +- "experiment" and "outcome" were varied between participants +- "causal_structure" was varied within participants \ No newline at end of file diff --git a/data/data_int_combined.csv b/data/data_int_combined.csv new file mode 100644 index 0000000..b3eabec --- /dev/null +++ b/data/data_int_combined.csv @@ -0,0 +1,73 @@ +causal_structure,outcome,experiment,choice,type,probability +conjunctive,positive,hard,abnormal,explanation,49.48453608247423 +conjunctive,positive,hard,normal,explanation,16.49484536082474 +conjunctive,positive,hard,nopreference,explanation,34.02061855670103 +conjunctive,positive,hard,abnormal,intervention,65.97938144329896 +conjunctive,positive,hard,normal,intervention,28.865979381443296 +conjunctive,positive,hard,nopreference,intervention,5.154639175257731 +disjunctive,positive,hard,abnormal,explanation,26.804123711340207 +disjunctive,positive,hard,normal,explanation,19.587628865979383 +disjunctive,positive,hard,nopreference,explanation,53.608247422680414 +disjunctive,positive,hard,abnormal,intervention,43.29896907216495 +disjunctive,positive,hard,normal,intervention,27.835051546391753 +disjunctive,positive,hard,nopreference,intervention,28.865979381443296 +conjunctive,negative,hard,abnormal,explanation,17.17171717171717 +conjunctive,negative,hard,normal,explanation,37.37373737373738 +conjunctive,negative,hard,nopreference,explanation,45.45454545454545 +conjunctive,negative,hard,abnormal,intervention,19.19191919191919 +conjunctive,negative,hard,normal,intervention,64.64646464646465 +conjunctive,negative,hard,nopreference,intervention,16.161616161616163 +disjunctive,negative,hard,abnormal,explanation,8.080808080808081 +disjunctive,negative,hard,normal,explanation,48.48484848484848 +disjunctive,negative,hard,nopreference,explanation,43.43434343434344 +disjunctive,negative,hard,abnormal,intervention,13.131313131313131 +disjunctive,negative,hard,normal,intervention,81.81818181818183 +disjunctive,negative,hard,nopreference,intervention,5.05050505050505 +conjunctive,positive,soft,abnormal,explanation,51.02040816326531 +conjunctive,positive,soft,normal,explanation,11.224489795918368 +conjunctive,positive,soft,nopreference,explanation,37.755102040816325 +conjunctive,positive,soft,abnormal,intervention,65.3061224489796 +conjunctive,positive,soft,normal,intervention,30.612244897959183 +conjunctive,positive,soft,nopreference,intervention,4.081632653061225 +disjunctive,positive,soft,abnormal,explanation,6.122448979591836 +disjunctive,positive,soft,normal,explanation,58.16326530612245 +disjunctive,positive,soft,nopreference,explanation,35.714285714285715 +disjunctive,positive,soft,abnormal,intervention,12.244897959183673 +disjunctive,positive,soft,normal,intervention,79.59183673469387 +disjunctive,positive,soft,nopreference,intervention,8.16326530612245 +conjunctive,negative,soft,abnormal,explanation,45.45454545454545 +conjunctive,negative,soft,normal,explanation,14.14141414141414 +conjunctive,negative,soft,nopreference,explanation,40.4040404040404 +conjunctive,negative,soft,abnormal,intervention,67.67676767676768 +conjunctive,negative,soft,normal,intervention,22.22222222222222 +conjunctive,negative,soft,nopreference,intervention,10.1010101010101 +disjunctive,negative,soft,abnormal,explanation,11.11111111111111 +disjunctive,negative,soft,normal,explanation,35.35353535353536 +disjunctive,negative,soft,nopreference,explanation,53.535353535353536 +disjunctive,negative,soft,abnormal,intervention,35.35353535353536 +disjunctive,negative,soft,normal,intervention,55.55555555555556 +disjunctive,negative,soft,nopreference,intervention,9.090909090909092 +conjunctive,negative,fixed,abnormal,explanation,17.17171717171717 +conjunctive,negative,fixed,normal,explanation,42.42424242424242 +conjunctive,negative,fixed,nopreference,explanation,40.4040404040404 +conjunctive,negative,fixed,abnormal,intervention,20.2020202020202 +conjunctive,negative,fixed,normal,intervention,66.66666666666666 +conjunctive,negative,fixed,nopreference,intervention,13.131313131313131 +disjunctive,negative,fixed,abnormal,explanation,8.080808080808081 +disjunctive,negative,fixed,normal,explanation,39.39393939393939 +disjunctive,negative,fixed,nopreference,explanation,52.52525252525253 +disjunctive,negative,fixed,abnormal,intervention,10.1010101010101 +disjunctive,negative,fixed,normal,intervention,79.7979797979798 +disjunctive,negative,fixed,nopreference,intervention,10.1010101010101 +conjunctive,negative,fixed,abnormal,explanation,17.17171717171717 +conjunctive,negative,fixed,normal,explanation,42.42424242424242 +conjunctive,negative,fixed,nopreference,explanation,40.4040404040404 +conjunctive,negative,fixed,abnormal,intervention,20.2020202020202 +conjunctive,negative,fixed,normal,intervention,66.66666666666666 +conjunctive,negative,fixed,nopreference,intervention,13.131313131313131 +disjunctive,negative,fixed,abnormal,explanation,8.080808080808081 +disjunctive,negative,fixed,normal,explanation,39.39393939393939 +disjunctive,negative,fixed,nopreference,explanation,52.52525252525253 +disjunctive,negative,fixed,abnormal,intervention,10.1010101010101 +disjunctive,negative,fixed,normal,intervention,79.7979797979798 +disjunctive,negative,fixed,nopreference,intervention,10.1010101010101 diff --git a/data/fixedint_neg.csv b/data/fixedint_neg.csv new file mode 100644 index 0000000..b8e32e6 --- /dev/null +++ b/data/fixedint_neg.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,17.17171717171717,42.42424242424242,40.4040404040404,20.2020202020202,66.66666666666666,13.131313131313133,negative,fixedint +Disjunctive,8.080808080808081,39.39393939393939,52.52525252525253,10.1010101010101,79.7979797979798,10.1010101010101,negative,fixedint diff --git a/data/fixedint_pos.csv b/data/fixedint_pos.csv new file mode 100644 index 0000000..c294d3d --- /dev/null +++ b/data/fixedint_pos.csv @@ -0,0 +1,3 @@ +"CausalStructure","abnormal_explanation_percentage","normal_explanation_percentage","nopreference_explanation_percentage","abnormal_intervention_percentage","normal_intervention_percentage","nopreference_intervention_percentage","Outcome","Experiment" +"Conjunctive","53.535353535353536","9.090909090909092","37.37373737373738","80.8080808080808","11.11111111111111","8.080808080808081","positive","fixedint" +"Disjunctive","27.27272727272727","18.181818181818183","54.54545454545454","65.65656565656566","12.121212121212121","22.22222222222222","positive","fixedint" \ No newline at end of file diff --git a/data/hardint_neg.csv b/data/hardint_neg.csv new file mode 100644 index 0000000..0ba2d44 --- /dev/null +++ b/data/hardint_neg.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,17.17171717171717,37.37373737373738,45.45454545454545,19.19191919191919,64.64646464646465,16.161616161616163,negative,hardint +Disjunctive,8.080808080808081,48.484848484848484,43.43434343434344,13.131313131313133,81.81818181818183,5.05050505050505,negative,hardint diff --git a/data/hardint_pos.csv b/data/hardint_pos.csv new file mode 100644 index 0000000..d0896b6 --- /dev/null +++ b/data/hardint_pos.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,49.48453608247423,16.49484536082474,34.02061855670103,65.97938144329896,28.865979381443296,5.154639175257731,positive,hardint +Disjunctive,26.804123711340207,19.587628865979383,53.608247422680414,43.29896907216495,27.835051546391753,28.865979381443296,positive,hardint diff --git a/data/softint_neg.csv b/data/softint_neg.csv new file mode 100644 index 0000000..a13a879 --- /dev/null +++ b/data/softint_neg.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,45.45454545454545,14.14141414141414,40.4040404040404,67.67676767676768,22.22222222222222,10.1010101010101,negative,softint +Disjunctive,11.11111111111111,35.35353535353536,53.535353535353536,35.35353535353536,55.55555555555556,9.090909090909092,negative,softint diff --git a/data/softint_pos.csv b/data/softint_pos.csv new file mode 100644 index 0000000..67992ef --- /dev/null +++ b/data/softint_pos.csv @@ -0,0 +1,3 @@ +CausalStructure,abnormal_explanation_percentage,normal_explanation_percentage,nopreference_explanation_percentage,abnormal_intervention_percentage,normal_intervention_percentage,nopreference_intervention_percentage,Outcome,Experiment +Conjunctive,51.02040816326531,11.224489795918368,37.755102040816325,65.3061224489796,30.612244897959183,4.081632653061225,positive,softint +Disjunctive,6.122448979591836,58.16326530612245,35.714285714285715,12.244897959183673,79.59183673469387,8.16326530612245,positive,softint diff --git a/docs/analyses/experiments/index.html b/docs/analyses/experiments/index.html new file mode 100644 index 0000000..9963616 --- /dev/null +++ b/docs/analyses/experiments/index.html @@ -0,0 +1,2706 @@ + + + + + + + + + + + + + + + +Explanation and Intervention Data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + +
library("knitr")
+library("modelr")        # for bootstrapping
+library("patchwork")        # making figure panels
+library("tidyverse") # for data wrangling, visualization, etc. 
+
+

1 Set options

+
theme_set(theme_classic() + 
+    theme(text = element_text(size = 24)))
+
+opts_chunk$set(comment = "",
+               fig.show = "hold")
+
+# suppress grouping warning 
+options(dplyr.summarise.inform = F)
+
+
+

2 Condition 1: Hard Intervention: Positive Outcome

+
+

2.1 Read in Data

+
#### Read in Data
+df.responses = read.csv(file = "explanation_selection_positive_outcome_study_2-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "explanation_selection_positive_outcome_study_2-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data1 <- merge(df.responses, df.participants, by="workerid")
+
+
+

2.2 Wrangle Data

+
df.exp1 <- df.data1 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Conpos_") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Conpos_") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Dispos_") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Dispos_") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Conpos_") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Dispos_") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+   mutate(intervention = "hard") %>% 
+   mutate(outcome = "positive") 
+
+
+

2.3 Prepate Dataset

+
df.exp1_summary <- df.exp1 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "positive") %>% 
+    mutate(Experiment = "hardint")
+
+write_csv(df.exp1_summary, "hardint_pos.csv")
+
+
+
+

3 Condition 2: Hard Intervention: Negative Outcome

+
+

3.1 Read in Data

+
#### Read in Data
+df.responses = read.csv(file = "explanation_selection_negative_outcome-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "explanation_selection_negative_outcome-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data2 <- merge(df.responses, df.participants, by="workerid")
+
+
+

3.2 Wrangle Data

+
df.exp2 <- df.data2 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Conneg_") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Conneg_") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Disneg_") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Disneg_") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Conneg_") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Disneg_") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+     mutate(intervention = "hard") %>% 
+   mutate(outcome = "negative") 
+
+
+

3.3 Prepare Dataset

+
df.exp2_summary <- df.exp2 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "hardint")
+
+write_csv(df.exp2_summary, "hardint_neg.csv")
+
+
+
+

4 Condition 3: Soft Intervention: Positive Outcome

+
+

4.1 Read in Data

+
###Experiment 4
+#### Read in Data
+
+df.responses = read.csv(file = "pressbutton_positivecondition-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "pressbutton_positivecondition-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data3 <- merge(df.responses, df.participants, by="workerid")
+
+
+

4.2 Wrangle Data

+
df.exp3 <- df.data3 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition_1") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition_1") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_2") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_2") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition_1") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition_2") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other"))%>% 
+     mutate(intervention = "soft") %>% 
+   mutate(outcome = "positive")  
+
+
+

4.3 Prepare Dataset

+
df.exp3_summary <- df.exp3 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "positive") %>% 
+    mutate(Experiment = "softint")
+
+write_csv(df.exp3_summary, "softint_pos.csv")
+
+
+
+

5 Condition 4: Soft Intervention: Negative Outcome

+
+

5.1 Read in Data

+
###Experiment 4
+#### Read in Data
+
+df.responses = read.csv(file = "pressbutton_negativecondition-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "pressbutton_negativecondition-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data4 <- merge(df.responses, df.participants, by="workerid") %>% 
+  filter(!row_number() %in% c(71, 72))
+
+
+

5.2 Wrangle Data

+
df.exp4 <- df.data4 %>% 
+    gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition_4") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+     mutate(intervention = "soft") %>% 
+   mutate(outcome = "negative") 
+
+
+

5.3 Prepare Dataset

+
df.exp4_summary <- df.exp4 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "softint")
+
+write_csv(df.exp4_summary, "softint_neg.csv")
+
+
+
+

6 Condition 5: Fixed Intervention: Positive Outcome

+
+

6.1 Read in Data

+
###Experiment 5
+#### Read in Data
+
+df.responses = read.csv(file = "fixedintervention_positive-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "fixedintervention_positive-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data5 <- merge(df.responses, df.participants, by="workerid") 
+
+
+

6.2 Wrangle Data

+
df.exp5 <- df.data5 %>% 
+   gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition1") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition1") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition2") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition2") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition1") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition2") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other")) %>% 
+     mutate(intervention = "fixed") %>% 
+   mutate(outcome = "positive") 
+
+
+

6.3 Prepare Dataset

+
df.exp5_summary <- df.exp5 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "fixedint")
+
+write_csv(df.exp5_summary, "fixedint_pos.csv")
+
+
+
+

7 Condition 6: Fixed Intervention: Negative Outcome

+
+

7.1 Read in Data

+
###Experiment 6
+#### Read in Data
+
+df.responses = read.csv(file = "fixedintervention_negative-responses.csv", stringsAsFactors = F, sep = ",") %>% 
+      select(-error)
+
+
+df.participants <- read.csv(file = "fixedintervention_negative-participants.csv", stringsAsFactors = F, sep = ",") %>% 
+    select(-c(proliferate.condition, error))
+
+
+df.data6 <- merge(df.responses, df.participants, by="workerid") 
+
+
+

7.2 Wrangle Data

+
df.exp6 <- df.data6 %>% 
+   gather("index", "response", -c(workerid, proliferate.condition, age, ethnicity, gender, feedback, race))%>% 
+  mutate(CausalStructure  =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_1") ~ "Conjunctive",
+       str_detect(proliferate.condition, "Condition_3") & str_detect(index, "response_2") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_1") ~ "Disjunctive",
+       str_detect(proliferate.condition, "Condition_4") & str_detect(index, "response_2") ~ "Conjunctive"))%>% 
+  mutate(Order =
+           case_when(
+       str_detect(proliferate.condition, "Condition_3") ~ "conjunctive first",
+       str_detect(proliferate.condition, "Condition_4") ~ "disjunctive first"))%>% 
+  mutate(ResponseType =
+           case_when(
+       str_detect(index, "explanation") ~ "explanation",
+       str_detect(index, "intervention")  ~ "intervention"))%>% 
+  select(-index) %>% 
+  spread(ResponseType, response) %>% 
+  mutate(CausalStructure= factor(CausalStructure, levels=c("Conjunctive", "Disjunctive"))) %>%
+  mutate(explanation = recode (explanation, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(intervention = recode (intervention, 
+                               "orange" = "abnormal",
+                               "blue" = "normal" )) %>% 
+  mutate(abnormal_explanation =
+           case_when(
+        explanation == "abnormal" ~ "abnormal",
+        explanation != "abnormal"~ "other")) %>% 
+  mutate(normal_explanation =
+           case_when(
+        explanation == "normal" ~ "normal",
+        explanation != "normal"~ "other")) %>% 
+  mutate(nopreference_explanation =
+           case_when(
+        explanation == "no preference" ~ "no preference",
+        explanation != "no preference"~ "other")) %>% 
+  mutate(abnormal_intervention =
+           case_when(
+        intervention== "abnormal" ~ "abnormal",
+        intervention!= "abnormal"~ "other")) %>% 
+  mutate(normal_intervention  =
+           case_when(
+        intervention  == "normal" ~ "normal",
+        intervention  != "normal"~ "other")) %>% 
+  mutate(nopreference_intervention  =
+           case_when(
+        intervention== "no preference" ~ "no preference",
+        intervention!= "no preference"~ "other"))    %>%  
+  mutate(intervention = "fixed") %>% 
+   mutate(outcome = "negative") 
+
+
+

7.3 Prepare Dataset

+
df.exp6_summary <- df.exp6 %>%
+  group_by(CausalStructure) %>%
+  summarise(
+    abnormal_explanation_percentage = mean(abnormal_explanation == "abnormal") * 100,
+    normal_explanation_percentage = mean(normal_explanation == "normal") * 100,
+    nopreference_explanation_percentage = mean(nopreference_explanation == "no preference") * 100,
+    
+    abnormal_intervention_percentage = mean(abnormal_intervention == "abnormal") * 100,
+    normal_intervention_percentage = mean(normal_intervention == "normal") * 100,
+    nopreference_intervention_percentage = mean(nopreference_intervention == "no preference") * 100
+  ) %>% 
+    mutate(Outcome = "negative") %>% 
+    mutate(Experiment = "fixedint")
+
+write_csv(df.exp6_summary, "fixedint_neg.csv")
+
+
+
+

8 Create Dataset including raw data from all conditions

+
df.exp_all <- rbind(df.exp1, df.exp2, df.exp3, df.exp4, df.exp5, df.exp6)
+
+
+

9 Bootstrapping

+
+

9.1 DATA

+
+

9.1.1 Data frame

+
df.boot = df.exp_all %>% 
+  select(workerid,
+         structure = CausalStructure,
+         outcome,
+         action = intervention,
+         explanation,
+         abnormal_intervention,
+         normal_intervention,
+         nopreference_intervention) %>% 
+  mutate(structure = tolower(structure),
+         intervention = abnormal_intervention,
+         intervention = ifelse(normal_intervention != "other", normal_intervention, intervention),
+         intervention = ifelse(nopreference_intervention != "other", nopreference_intervention, intervention),
+         intervention = factor(intervention,
+                               levels = c("abnormal", "no preference", "normal")),
+         explanation = factor(explanation,
+                               levels = c("abnormal", "no preference", "normal"))) %>% 
+  select(-contains("_"))
+
+
+

9.1.2 Percentages

+
df.percentage = df.boot %>% 
+  count(structure, outcome, action, choice = intervention,
+        name = "intervention_n") %>% 
+  left_join(df.boot %>% 
+              count(structure, outcome, action, choice = explanation,
+                    name = "explanation_n"),
+            by = c("structure", "outcome", "action", "choice")) %>% 
+  group_by(structure, outcome, action) %>% 
+  mutate(intervention_p = intervention_n/sum(intervention_n),
+         explanation_p = explanation_n/sum(explanation_n)) %>% 
+  ungroup()
+
+
+

9.1.3 Bootstrapped confidence intervals

+
set.seed(1)
+
+# percentages with bootstrapped confidence intervals 
+df.confidence = df.boot %>% 
+  group_by(structure, outcome, action) %>% 
+  nest() %>% 
+  mutate(bootstraps = map(.x = data,
+                          .f = ~ bootstrap(.x, n = 1000))) %>% 
+  unnest(bootstraps) %>% 
+  mutate(intervention = map(.x = strap, 
+                      .f = ~ .x %>% 
+                        as_tibble() %>% 
+                        count(intervention,
+                              name = "intervention_n",
+                              .drop = F) %>% 
+                        mutate(intervention_p = intervention_n/sum(intervention_n))),
+         explanation = map(.x = strap, 
+                      .f = ~ .x %>% 
+                        as_tibble() %>% 
+                        count(explanation,
+                              name = "explanation_n",
+                              .drop = F) %>% 
+                        mutate(explanation_p = explanation_n/sum(explanation_n)))) %>%
+  select(structure, outcome, action, intervention, explanation) %>%
+  unnest(c(intervention, explanation)) %>%
+  select(everything(), choice = intervention, -explanation) %>% 
+  group_by(structure, outcome, action, choice) %>%
+  summarize(intervention_low = as.numeric(quantile(intervention_p, probs = 0.025)),
+            intervention_high = as.numeric(quantile(intervention_p, probs = 0.975)),
+            explanation_low = as.numeric(quantile(explanation_p, probs = 0.025)),
+            explanation_high = as.numeric(quantile(explanation_p, probs = 0.975))) %>% 
+  ungroup()
+
+
+

9.1.4 Combined data frame

+
df.combined = df.confidence %>%
+  left_join(df.percentage %>% 
+              select(-(contains("_n"))), 
+            by = c("structure", "outcome", "action", "choice")) %>% 
+  select(structure, outcome, action, choice, contains("intervention"),
+         contains("explanation"))
+
+
+
+

9.2 PLOTS

+
+

9.2.1 Read in model predictions

+
df.prediction_intervention = read_csv("intervention_predictions.csv")
+df.prediction_explanation = read_csv("explanation_predictions.csv") %>% 
+  select(-truth)
+
+df.optimal_intervention_model = df.prediction_intervention %>% 
+  mutate(index = "intervention_prediction") %>% 
+  bind_rows(df.prediction_explanation %>% 
+              mutate(index = "explanation_prediction")) %>% 
+  select(structure = causal_structure,
+         outcome, 
+         action = intervention,
+         choice,
+         index,
+         prediction) %>% 
+  pivot_wider(names_from = index,
+              values_from = prediction)
+
+df.intervention_only_model = read_csv("explanation_predictions_intervention_only.csv")
+df.truth_only_model = read_csv("explanation_predictions_truth_only.csv")
+
+df.alternative_models = df.intervention_only_model %>% 
+  mutate(model = "intervention_only") %>% 
+  bind_rows(df.truth_only_model %>% 
+              mutate(model = "truth_only")) %>% 
+  select(structure = causal_structure,
+         outcome, 
+         action = intervention,
+         choice,
+         model, 
+         prediction) %>% 
+  pivot_wider(names_from = model,
+              values_from = prediction)
+
+df.models = df.optimal_intervention_model %>% 
+  left_join(df.alternative_models) 
+
+
+

9.2.2 Bar plot

+
df.plot = df.combined %>% 
+  mutate(choice = factor(choice,
+                         labels = c("abnormal", "no\npreference", "normal")),
+         outcome = factor(outcome,
+                          levels = c("positive", "negative"),
+                          labels = c("positive outcome", "negative outcome")),
+         action = factor(action,
+                         levels = c("hard", "soft", "fixed")))
+
+df.model = df.optimal_intervention_model %>%
+  left_join(df.alternative_models) %>% 
+  mutate(choice = factor(choice,
+                         labels = c("abnormal", "no\npreference", "normal")),
+         outcome = factor(outcome,
+                          levels = c("positive", "negative"),
+                          labels = c("positive outcome", "negative outcome")),
+         action = factor(action,
+                         levels = c("hard", "soft", "fixed")))
+
+df.model2 = df.model %>% 
+  select(-intervention_prediction) %>% 
+  pivot_longer(cols = c("explanation_prediction", "intervention_only", "truth_only"),
+               names_to = "model",
+               values_to = "prediction") %>% 
+  mutate(model = factor(model,
+                        levels = c("truth_only", "explanation_prediction", "intervention_only"))) %>% 
+  arrange(structure, outcome, action, choice, model)
+  
+p_intervention = ggplot(data = df.plot,
+                        mapping = aes(x = action,
+                                      y = intervention_p,
+                                      group = choice,
+                                      fill = action,
+                                      alpha = choice)) + 
+  geom_col(color = "black",
+           position = position_dodge(width = 0.9)) +
+  geom_linerange(mapping = aes(ymin = intervention_low,
+                               ymax = intervention_high),
+                 position = position_dodge(width = 0.9),
+                 alpha = 1,
+                 linewidth = 1) +
+  geom_point(data = df.model,
+             mapping = aes(y = intervention_prediction),
+             position = position_dodge(width = 0.9),
+             shape = 21,
+             size = 4,
+             show.legend = F) +
+  facet_grid(structure ~ outcome) +
+  scale_y_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  scale_alpha_manual(values = c(0.4, 0.65, 0.9)) +
+  labs(title = "Intervention Task",
+       x = "type of intervention",
+       y = "percent selected") + 
+  theme(legend.position = "bottom",
+        plot.title = element_text(hjust = 0.5)) + 
+  guides(fill = "none",
+         alpha = "none")
+
+p_explanation = ggplot(data = df.plot,
+               mapping = aes(x = action,
+                             y = explanation_p,
+                             group = choice,
+                             fill = action,
+                             alpha = choice)) + 
+  geom_col(color = "black",
+           position = position_dodge(width = 0.9)) +
+  geom_linerange(mapping = aes(ymin = explanation_low,
+                               ymax = explanation_high),
+                 position = position_dodge(width = 0.9),
+                 alpha = 1,
+                 linewidth = 1) +
+  geom_point(data = df.model2,
+             mapping = aes(y = prediction,
+                           shape = model),
+             position = position_dodge2(width = 0.9,
+                                        padding = 0.2),
+             size = 4,
+             show.legend = F) +
+  facet_grid(structure ~ outcome) +
+  scale_y_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  scale_alpha_manual(values = c(0.4, 0.65, 0.9)) +
+  scale_shape_manual(values = c("truth_only" = 22,
+                                "explanation_prediction" = 21,
+                                "intervention_only" = 23)) +
+  labs(title = "Explanation Task",
+       x = "type of intervention",
+       y = "percent selected") + 
+  theme(legend.position = "bottom",
+        plot.title = element_text(hjust = 0.5)) + 
+  guides(fill = "none")
+
+p_intervention + p_explanation + 
+  plot_layout(ncol = 1) + plot_annotation(tag_levels = "A") & 
+  theme(plot.tag = element_text(size = 40, face = "bold"))
+
+ggsave(filename = "../../../figures/plots/bars.pdf",
+       width = 20,
+       height = 14)
+

+
+
+

9.2.3 Scatter plots

+
+

9.2.3.1 Scatter plot function

+
fun.scatter = function(data, xtitle, ytitle, legend = F){
+  p = ggplot(data = data,
+             mapping = aes(x = model,
+                           y = p,
+                           ymin = low,
+                           ymax = high)) +
+    geom_abline(intercept = 0, slope = 1, linetype = "dashed") +
+    annotate(geom = "text",
+             x = 0,
+             y = 1,
+             hjust = 0,
+             label = str_c("r = ", round(cor(data$model, data$p), 2)),
+             size = 8) +
+    annotate(geom = "text",
+             x = 0,
+             y = 0.92,
+             hjust = 0,
+             label = str_c("RMSE = ", round(sqrt(mean((data$model -  data$p)^2)), 2)),
+             size = 8) +
+    geom_smooth(method = "lm",
+                color = "black",
+                alpha = 0.2,
+                show.legend = F) + 
+    geom_linerange(alpha = 0.2) +
+    geom_point(mapping = aes(fill = action),
+               alpha = 0.9,
+               shape = 21,
+               size = 4) +
+    scale_x_continuous(limits = c(0, 1),
+                       breaks = seq(0, 1, 0.25),
+                       labels = scales::label_percent()) +
+    scale_y_continuous(limits = c(0, 1),
+                       breaks = seq(0, 1, 0.25),
+                       labels = scales::label_percent()) +
+    labs(x = xtitle,
+         y = ytitle) + 
+    theme(legend.position = c(1, 0),
+          legend.justification = c(1.2, -0.1)) 
+  
+  if(legend == F){
+    p = p + theme(legend.position = "none")
+  }
+ 
+ return(p)
+}
+
+
+

9.2.3.2 Combined scatter plots

+
df.plot = df.combined %>% 
+  left_join(df.models %>% 
+              mutate(choice = factor(choice,
+                                     levels = c("abnormal", "nopreference", "normal"),
+                                     labels = c("abnormal", "no preference", "normal")))) %>% 
+  mutate(structure = factor(structure,
+                            levels = c("conjunctive", "disjunctive")),
+         choice = factor(choice,
+                         labels = c("abnormal", "no\npreference", "normal")),
+         outcome = factor(outcome,
+                          levels = c("positive", "negative"),
+                          labels = c("positive outcome", "negative outcome")),
+         action = factor(action,
+                         levels = c("hard", "soft", "fixed")))
+
+df.plot.intervention = df.plot %>% 
+  select(structure, outcome, action, choice,
+         low = intervention_low, high = intervention_high,
+         p = intervention_p, model = intervention_prediction)
+
+df.plot.explanation = df.plot %>%
+  select(structure, outcome, action, choice,
+         low = explanation_low, high = explanation_high,
+         p = explanation_p,
+         explanation_prediction,
+         intervention_only,
+         truth_only)
+  
+scatter1 = fun.scatter(data = df.plot.intervention,
+                       xtitle = "model prediction",
+                       ytitle = "intervention choices",
+                       legend = T)
+
+scatter2 = fun.scatter(data = df.plot.explanation %>% 
+                         mutate(model = intervention_only),
+                       xtitle = "relevance only model",
+                       ytitle = "explanation choices")
+
+scatter3 = fun.scatter(data = df.plot.explanation %>% 
+                         mutate(model = truth_only),
+                       xtitle = "accuracy only model",
+                       ytitle = "explanation choices")
+
+scatter4 = fun.scatter(data = df.plot.explanation %>% 
+                         mutate(model = explanation_prediction),
+                       xtitle = "combined model",
+                       ytitle = "explanation choices")
+
+scatter1 + scatter2 + scatter3 + scatter4 + 
+  plot_layout(ncol = 2) + 
+  plot_annotation(tag_levels = "A") & 
+  theme(text = element_text(size = 30),
+        plot.tag = element_text(size = 40, face = "bold"),
+        plot.margin = margin(t = 0, 
+                             r = 0.35, 
+                             b = 0, 
+                             l = 0,
+                             "cm"))
+
+ggsave(filename = "../../../figures/plots/scatter.pdf",
+       width = 16,
+       height = 12)
+

+
+
+

9.2.3.3 Overall

+
df.scatter = df.plot %>% 
+  ungroup() %>% 
+  pivot_longer(cols = contains("_"),
+               names_to = c("task", "index"),
+               names_sep = "_",
+               values_to = "value") %>% 
+  pivot_wider(names_from = index,
+              values_from = value) %>% 
+  left_join(df.model %>% 
+              pivot_longer(cols = contains("_"),
+                           names_to = c("task", "index"),
+                           names_sep = "_",
+                           values_to = "model") %>% 
+              select(-index),
+            by = c("structure", "outcome", "action", "choice", "task"))
+
+
+ggplot(data = df.scatter,
+       mapping = aes(x = model,
+                     y = p,
+                     ymin = low,
+                     ymax = high)) +
+  geom_abline(intercept = 0, slope = 1, linetype = "dashed") +
+  annotate(geom = "text",
+           x = 0,
+           y = 1,
+           hjust = 0,
+           label = str_c("r = ", round(cor(df.scatter$model, df.scatter$p), 2)),
+           size = 8) +
+  annotate(geom = "text",
+           x = 0,
+           y = 0.92,
+           hjust = 0,
+           label = str_c("RMSE = ", round(sqrt(mean((df.scatter$model -  df.scatter$p)^2)), 2)),
+           size = 8) +
+  geom_smooth(method = "lm",
+              color = "black",
+              alpha = 0.2,
+              show.legend = F) + 
+  geom_linerange(alpha = 0.2) +
+  geom_point(mapping = aes(fill = action),
+                           # shape = task),
+             shape = 21,
+             size = 2) +
+  scale_x_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  scale_y_continuous(limits = c(0, 1),
+                     breaks = seq(0, 1, 0.25),
+                     labels = scales::label_percent()) +
+  # scale_shape_manual(values = c("intervention" = 21,
+  #                               "explanation" = 22)) +
+  labs(x = "model prediction",
+       y = "participant choices") +
+  theme(legend.position = c(1, 0),
+        legend.justification = c(1.2, -0.1))
+
+ggsave(filename = "../../../figures/plots/scatter.pdf",
+       width = 8,
+       height = 6)
+

+
+
+
+
+

9.3 STATS

+
+

9.3.1 Correlations and RMSE

+
df.combined %>% 
+  ungroup() %>% 
+  select(-(contains("low") | contains("high"))) %>%
+  mutate(choice = str_replace(choice, "no preference", "nopreference")) %>% 
+  left_join(df.alternative_models) %>% 
+  left_join(df.optimal_intervention_model %>% 
+              select(-intervention_prediction)) %>% 
+  summarize(across(c(explanation_prediction, intervention_only, truth_only),
+                   list(r = ~ cor(.x, explanation_p),
+                        rmse = ~ sqrt(mean((.x - explanation_p)^2))),
+                   .names = "{.col}.{.fn}")) %>% 
+  pivot_longer(cols = everything(),
+               names_to = c("model", "stat"),
+               names_sep = "\\.",
+               values_to = "value") %>% 
+  pivot_wider(names_from = stat,
+              values_from = value) %>% 
+  mutate(across(where(is.numeric),
+                ~ round(., 2)))
+
# A tibble: 3 × 3
+  model                      r  rmse
+  <chr>                  <dbl> <dbl>
+1 explanation_prediction  0.81  0.09
+2 intervention_only       0.61  0.13
+3 truth_only              0.47  0.14
+
+
+
+
+

10 Session info

+
R version 4.1.3 (2022-03-10)
+Platform: x86_64-apple-darwin17.0 (64-bit)
+Running under: macOS Big Sur/Monterey 10.16
+
+Matrix products: default
+BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base     
+
+other attached packages:
+ [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.4    
+ [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
+ [9] ggplot2_3.4.4   tidyverse_2.0.0 patchwork_1.1.2 modelr_0.1.11  
+[13] knitr_1.38     
+
+loaded via a namespace (and not attached):
+ [1] tidyselect_1.2.0  xfun_0.30         bslib_0.3.1       lattice_0.20-45  
+ [5] splines_4.1.3     colorspace_2.0-3  vctrs_0.6.5       generics_0.1.2   
+ [9] htmltools_0.5.2   mgcv_1.8-39       yaml_2.3.5        utf8_1.2.2       
+[13] rlang_1.1.1       jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2       
+[17] withr_2.5.0       bit64_4.0.5       lifecycle_1.0.3   munsell_0.5.0    
+[21] gtable_0.3.0      ragg_1.2.5        evaluate_0.15     tzdb_0.4.0       
+[25] fastmap_1.1.0     parallel_4.1.3    fansi_1.0.3       highr_0.9        
+[29] broom_1.0.5       backports_1.4.1   scales_1.2.1      vroom_1.6.3      
+[33] jsonlite_1.8.4    systemfonts_1.0.4 farver_2.1.0      bit_4.0.4        
+[37] textshaping_0.3.6 hms_1.1.3         digest_0.6.29     stringi_1.7.6    
+[41] bookdown_0.25     grid_4.1.3        cli_3.6.1         tools_4.1.3      
+[45] magrittr_2.0.3    sass_0.4.1        crayon_1.5.1      pkgconfig_2.0.3  
+[49] Matrix_1.4-0      timechange_0.2.0  rmarkdown_2.13    rstudioapi_0.15.0
+[53] R6_2.5.1          nlme_3.1-155      compiler_4.1.3   
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/docs/analyses/model/index.html b/docs/analyses/model/index.html new file mode 100644 index 0000000..1fcb41d --- /dev/null +++ b/docs/analyses/model/index.html @@ -0,0 +1,2040 @@ + + + + + + + + + + + + + + + +Optimal intervention model + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + +
+

1 Load packages

+
library("knitr")
+library("janitor")
+library("patchwork")
+library("Metrics")
+library("tidyverse")
+
+
+

2 Set options

+
theme_set(theme_classic() + 
+    theme(text = element_text(size = 24)))
+
+opts_chunk$set(comment = "",
+               fig.show = "hold")
+
+# suppress grouping warning 
+options(dplyr.summarise.inform = F)
+
+
+

3 Functions

+
fun.softmax = function(x, temp = 3) {
+    out = exp(x*temp) / sum(exp(x*temp))
+    return(out)
+}
+
+
+

4 DATA

+
df.data = bind_rows(
+  read_csv(file = "../../../data/hardint_pos.csv"),
+  read_csv(file = "../../../data/hardint_neg.csv"),
+  read_csv(file = "../../../data/softint_pos.csv"),
+  read_csv(file = "../../../data/softint_neg.csv"),
+  read_csv(file = "../../../data/fixedint_pos.csv"),
+  read_csv(file = "../../../data/fixedint_neg.csv")) %>% 
+  clean_names() %>% 
+  mutate(causal_structure = str_to_lower(causal_structure),
+         experiment = str_remove(experiment, "int")) %>% 
+  rename_with(.fn = ~str_remove_all(., "_percentage")) %>% 
+  pivot_longer(cols = -c(causal_structure, outcome, experiment),
+               values_to = "probability") %>% 
+  separate(col = name,
+           into = c("choice", "type")) %>% 
+  mutate(across(.cols = -probability,
+                .fns = ~ as.factor(.)),
+         choice = factor(choice, levels = c("abnormal", "nopreference", "normal"))) %>%
+  mutate(probability = probability / 100)
+
+df.intervention = df.data %>% 
+  filter(type == "intervention") %>% 
+  rename(intervention = experiment)
+
+df.explanation = df.data %>% 
+  filter(type == "explanation")
+
+colnames(df.data)
+
[1] "causal_structure" "outcome"          "experiment"       "choice"          
+[5] "type"             "probability"     
+
# write_csv(df.data, "data.csv")
+
+
+

5 MODEL

+
+

5.1 Interventions

+
+

5.1.1 Model structure

+
fun.success = function(p_abnormal, p_normal, causal_structure, outcome){ 
+  if (causal_structure == "conjunctive"){
+    p = p_abnormal *  p_normal
+  } else{
+    p = 1 - (1 - p_abnormal) * (1 - p_normal)
+  }
+  if (outcome == "negative"){
+    p = 1 - p
+  }
+  return(p)
+}
+
+causal_structure = c("conjunctive", "disjunctive")
+outcome = c("positive", "negative") 
+
+df.model = expand_grid(causal_structure, outcome) %>% 
+  mutate(p_abnormal = 0.2,
+         p_normal = 0.8,
+         int_hard_abnormal = ifelse(outcome == "positive", 1, 0),
+         int_hard_normal = ifelse(outcome == "positive", 1, 0),
+         int_soft_abnormal = ifelse(outcome == "positive",
+                                  p_abnormal + 0.2,
+                                  p_abnormal - 0.2),
+         int_soft_normal = ifelse(outcome == "positive",
+                                  p_normal + 0.2,
+                                  p_normal - 0.2),
+         int_fixed_abnormal = ifelse(outcome == "positive",
+                                  0.9,
+                                  0.1),
+         int_fixed_normal = ifelse(outcome == "positive",
+                                  0.9,
+                                  0.1),
+         p_success = pmap_dbl(.l = list(p_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_hard_abnormal = pmap_dbl(.l = list(int_hard_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_hard_normal = pmap_dbl(.l = list(p_abnormal, 
+                                        int_hard_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_soft_abnormal = pmap_dbl(.l = list(int_soft_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_soft_normal = pmap_dbl(.l = list(p_abnormal, 
+                                        int_soft_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_fixed_abnormal = pmap_dbl(.l = list(int_fixed_abnormal, 
+                                        p_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)),
+         p_success_int_fixed_normal = pmap_dbl(.l = list(p_abnormal, 
+                                        int_fixed_normal, 
+                                        causal_structure, 
+                                        outcome),
+                              .f = ~ fun.success(..1, ..2, ..3, ..4)))
+
+
+

5.1.2 Choice probabilities

+
# gives expected value for each intervention
+df.choice = df.model %>% 
+  select(causal_structure, outcome,
+         contains("p_success_int")) %>% 
+  pivot_longer(-c(causal_structure, outcome)) %>% 
+  mutate(name = str_remove(name, "p_success_int_")) %>% 
+  separate(name, into = c("intervention", "target")) %>% 
+  pivot_wider(names_from = target,
+              values_from = value) %>% 
+  arrange(intervention, causal_structure) %>% 
+  relocate(intervention) %>% 
+  mutate(nopreference = 0.5 * abnormal + 0.5 * normal) %>% 
+  pivot_longer(c(abnormal, normal, nopreference),
+               names_to = "choice") %>% 
+  mutate(choice = factor(choice, levels = c("abnormal", "nopreference", "normal")),
+         across(.cols = c(intervention, causal_structure, outcome),
+                .fns = ~ as.factor(.)))
+
+
+

5.1.3 Fit softmax parameter

+
fun.fit_temperature = function(df_data, df_prediction, temperature){
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(value, temp = temperature)) %>% 
+    ungroup() %>% 
+    left_join(df_data,
+              by = c("intervention", "causal_structure", "outcome", "choice")) %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+fit.temperature = optim(par = 10, 
+                        fn = fun.fit_temperature,
+                        method = "L-BFGS-B",
+                        lower = 0, 
+                        upper = 100,
+                        df_data = df.intervention,
+                        df_prediction = df.choice)
+
+print(fit.temperature$par)
+
[1] 18.97389
+
+
+

5.1.4 Predictions

+
df.prediction_intervention = df.choice %>% 
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(value, temp = fit.temperature$par)) %>%
+    left_join(df.data %>% 
+                filter(type == "intervention") %>% 
+                select(-type) %>% 
+                rename(intervention = experiment),
+              by = c("causal_structure", "outcome", "intervention", "choice"))
+
+
+
+

5.2 Explanations

+
+

5.2.1 Model structure

+
df.prediction_explanation =  df.choice %>% 
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(truth = ifelse(choice == "nopreference", 1, 0)) %>%
+    left_join(df.data %>% 
+                filter(type == "explanation") %>% 
+                select(-type) %>% 
+                rename(intervention = experiment),
+              by = c("causal_structure", "outcome", "intervention", "choice")) %>% 
+  ungroup()
+
+
+

5.2.2 Model fitting functions

+
+

5.2.2.1 Combined model

+
fun.fit_params = function(params, df_prediction){
+  
+  weight <- params[1]
+  temperature <- params[2]
+  
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(weight * value + (1 - weight) * truth, temp = temperature)) %>% 
+    ungroup() %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+
+

5.2.2.2 Intervention only model

+
fun.fit_params_intervention_only = function(params, df_prediction){
+  
+  temperature <- params[1]
+  
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(value, temp = temperature)) %>% 
+    ungroup() %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+
+

5.2.2.3 Truth only model

+
fun.fit_params_truth_only = function(params, df_prediction){
+  
+  temperature <- params[1]
+  
+  df_prediction %>% 
+    group_by(intervention, causal_structure, outcome) %>% 
+    mutate(prediction = fun.softmax(truth, temp = temperature)) %>% 
+    ungroup() %>% 
+    summarize(loss = sum((prediction - probability) ^ 2)) %>% 
+    pull(loss)
+}
+
+
+
+

5.2.3 Fit paramters

+
+

5.2.3.1 Combined model

+
initial_params <- c(weight = 0.5, temperature = 10)
+lower_bounds <- c(weight = 0, temperature = 0)
+upper_bounds <- c(weight = 1, temperature = 100)
+
+fit.params <- optim(par = initial_params, 
+                         fn = fun.fit_params,
+                         method = "L-BFGS-B",
+                         lower = lower_bounds, 
+                         upper = upper_bounds,
+                         df_prediction = df.prediction_explanation)
+
+print(fit.params$par)
+
     weight temperature 
+  0.8420403   3.5050364 
+
+
+

5.2.3.2 Intervention only model

+
initial_params <- c(temperature = 10)
+lower_bounds <- c(temperature = 0)
+upper_bounds <- c(temperature = 100)
+
+fit.params_intervention_only <- optim(par = initial_params, 
+                         fn = fun.fit_params_intervention_only,
+                         method = "L-BFGS-B",
+                         lower = lower_bounds, 
+                         upper = upper_bounds,
+                         df_prediction = df.prediction_explanation)
+
+print(fit.params_intervention_only$par)
+
temperature 
+   2.097383 
+
+
+

5.2.3.3 Truth only model

+
initial_params <- c(temperature = 10)
+lower_bounds <- c(temperature = 0)
+upper_bounds <- c(temperature = 100)
+
+fit.params_truth_only <- optim(par = initial_params, 
+                         fn = fun.fit_params_truth_only,
+                         method = "L-BFGS-B",
+                         lower = lower_bounds, 
+                         upper = upper_bounds,
+                         df_prediction = df.prediction_explanation)
+
+print(fit.params_truth_only$par)
+
temperature 
+  0.4546058 
+
+
+
+

5.2.4 Predictions

+
+

5.2.4.1 Combined model

+
df.prediction_explanation = df.prediction_explanation %>%
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(fit.params$par[1] * value + (1 - fit.params$par[1]) * truth, temp = fit.params$par[2])) %>%
+  ungroup()
+
+write.csv(df.prediction_explanation, file = "explanation_predictions.csv", row.names = FALSE)
+write.csv(df.prediction_intervention, file = "intervention_predictions.csv", row.names = FALSE)
+
+
+

5.2.4.2 Intervention only model

+
df.prediction_explanation_intervention_only = df.prediction_explanation %>%
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(value, temp = fit.params_intervention_only$par[1])) %>%
+  ungroup()
+
+write.csv(df.prediction_explanation_intervention_only, file = "explanation_predictions_intervention_only.csv", row.names = FALSE)
+
+
+

5.2.4.3 Truth only model

+
df.prediction_explanation_truth_only = df.prediction_explanation %>%
+  group_by(intervention, causal_structure, outcome) %>%
+  mutate(prediction = fun.softmax(truth, temp = fit.params_truth_only$par[1])) %>%
+  ungroup()
+
+write.csv(df.prediction_explanation_truth_only, file = "explanation_predictions_truth_only.csv", row.names = FALSE)
+
+
+
+
+
+

6 Session info

+
R version 4.1.3 (2022-03-10)
+Platform: x86_64-apple-darwin17.0 (64-bit)
+Running under: macOS Big Sur/Monterey 10.16
+
+Matrix products: default
+BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base     
+
+other attached packages:
+ [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.4    
+ [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
+ [9] ggplot2_3.4.4   tidyverse_2.0.0 Metrics_0.1.4   patchwork_1.1.2
+[13] janitor_2.2.0   knitr_1.38     
+
+loaded via a namespace (and not attached):
+ [1] bslib_0.3.1       compiler_4.1.3    pillar_1.9.0      jquerylib_0.1.4  
+ [5] tools_4.1.3       bit_4.0.4         digest_0.6.29     jsonlite_1.8.4   
+ [9] evaluate_0.15     lifecycle_1.0.3   gtable_0.3.0      timechange_0.2.0 
+[13] pkgconfig_2.0.3   rlang_1.1.1       cli_3.6.1         rstudioapi_0.15.0
+[17] parallel_4.1.3    yaml_2.3.5        xfun_0.30         fastmap_1.1.0    
+[21] withr_2.5.0       hms_1.1.3         generics_0.1.2    vctrs_0.6.5      
+[25] sass_0.4.1        bit64_4.0.5       grid_4.1.3        tidyselect_1.2.0 
+[29] glue_1.6.2        snakecase_0.11.0  R6_2.5.1          fansi_1.0.3      
+[33] vroom_1.6.3       rmarkdown_2.13    bookdown_0.25     tzdb_0.4.0       
+[37] magrittr_2.0.3    scales_1.2.1      htmltools_0.5.2   colorspace_2.0-3 
+[41] utf8_1.2.2        stringi_1.7.6     munsell_0.5.0     crayon_1.5.1     
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/docs/experiment_1/css/jquery-ui.css b/docs/experiment_1/css/jquery-ui.css new file mode 100644 index 0000000..96bf06d --- /dev/null +++ b/docs/experiment_1/css/jquery-ui.css @@ -0,0 +1,1933 @@ +/** + * jspsych.js + * Josh de Leeuw + * de Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47(1), 1-12. doi:10.3758/s13428-014-0458-y. + * + * documentation: docs.jspsych.org + * + **/ + +/*! jQuery UI - v1.11.3 - 2015-02-12 +* http://jqueryui.com +* Includes: core.css, accordion.css, autocomplete.css, button.css, datepicker.css, dialog.css, draggable.css, menu.css, progressbar.css, resizable.css, selectable.css, selectmenu.css, slider.css, sortable.css, spinner.css, tabs.css, tooltip.css, theme.css +* To view and modify this theme, visit http://jqueryui.com/themeroller/?ffDefault=Verdana%2CArial%2Csans-serif&fwDefault=normal&fsDefault=1.1em&cornerRadius=4px&bgColorHeader=cccccc&bgTextureHeader=highlight_soft&bgImgOpacityHeader=75&borderColorHeader=aaaaaa&fcHeader=222222&iconColorHeader=222222&bgColorContent=ffffff&bgTextureContent=flat&bgImgOpacityContent=75&borderColorContent=aaaaaa&fcContent=222222&iconColorContent=222222&bgColorDefault=e6e6e6&bgTextureDefault=glass&bgImgOpacityDefault=75&borderColorDefault=d3d3d3&fcDefault=555555&iconColorDefault=888888&bgColorHover=dadada&bgTextureHover=glass&bgImgOpacityHover=75&borderColorHover=999999&fcHover=212121&iconColorHover=454545&bgColorActive=ffffff&bgTextureActive=glass&bgImgOpacityActive=65&borderColorActive=aaaaaa&fcActive=212121&iconColorActive=454545&bgColorHighlight=fbf9ee&bgTextureHighlight=glass&bgImgOpacityHighlight=55&borderColorHighlight=fcefa1&fcHighlight=363636&iconColorHighlight=2e83ff&bgColorError=fef1ec&bgTextureError=glass&bgImgOpacityError=95&borderColorError=cd0a0a&fcError=cd0a0a&iconColorError=cd0a0a&bgColorOverlay=aaaaaa&bgTextureOverlay=flat&bgImgOpacityOverlay=0&opacityOverlay=30&bgColorShadow=aaaaaa&bgTextureShadow=flat&bgImgOpacityShadow=0&opacityShadow=30&thicknessShadow=8px&offsetTopShadow=-8px&offsetLeftShadow=-8px&cornerRadiusShadow=8px +* Copyright 2015 jQuery Foundation and other contributors; Licensed MIT */ + +/* Layout helpers +----------------------------------*/ +.ui-helper-hidden { + display: none; +} + +.ui-helper-hidden-accessible { + border: 0; + clip: rect(0 0 0 0); + height: 1px; + margin: -1px; + overflow: hidden; + padding: 0; + position: absolute; + width: 1px; +} + +.ui-helper-reset { + margin: 0; + padding: 0; + border: 0; + outline: 0; + line-height: 1.3; + text-decoration: none; + font-size: 100%; + list-style: none; +} + +.ui-helper-clearfix:before, +.ui-helper-clearfix:after { + content: ""; + display: table; + border-collapse: collapse; +} + +.ui-helper-clearfix:after { + clear: both; +} + +.ui-helper-clearfix { + min-height: 0; + /* support: IE7 */ +} + +.ui-helper-zfix { + width: 100%; + height: 100%; + top: 0; + left: 0; + position: absolute; + opacity: 0; + filter: Alpha(Opacity=0); + /* support: IE8 */ +} + +.ui-front { + z-index: 100; +} + + +/* Interaction Cues +----------------------------------*/ +.ui-state-disabled { + cursor: default !important; +} + + +/* Icons +----------------------------------*/ + +/* states and images */ +.ui-icon { + display: block; + text-indent: -99999px; + overflow: hidden; + background-repeat: no-repeat; +} + + +/* Misc visuals +----------------------------------*/ + +/* Overlays */ +.ui-widget-overlay { + position: fixed; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.ui-accordion .ui-accordion-header { + display: block; + cursor: pointer; + position: relative; + margin: 2px 0 0 0; + padding: .5em .5em .5em .7em; + min-height: 0; + /* support: IE7 */ + font-size: 100%; +} + +.ui-accordion .ui-accordion-icons { + padding-left: 2.2em; +} + +.ui-accordion .ui-accordion-icons .ui-accordion-icons { + padding-left: 2.2em; +} + +.ui-accordion .ui-accordion-header .ui-accordion-header-icon { + position: absolute; + left: .5em; + top: 50%; + margin-top: -8px; +} + +.ui-accordion .ui-accordion-content { + padding: 1em 2.2em; + border-top: 0; + overflow: auto; +} + +.ui-autocomplete { + position: absolute; + top: 0; + left: 0; + cursor: default; +} + +.ui-button { + display: inline-block; + position: relative; + padding: 0; + line-height: normal; + margin-right: .1em; + cursor: pointer; + vertical-align: middle; + text-align: center; + overflow: visible; + /* removes extra width in IE */ +} + +.ui-button, +.ui-button:link, +.ui-button:visited, +.ui-button:hover, +.ui-button:active { + text-decoration: none; +} + +/* to make room for the icon, a width needs to be set here */ +.ui-button-icon-only { + width: 2.2em; +} + +/* button elements seem to need a little more width */ +button.ui-button-icon-only { + width: 2.4em; +} + +.ui-button-icons-only { + width: 3.4em; +} + +button.ui-button-icons-only { + width: 3.7em; +} + +/* button text element */ +.ui-button .ui-button-text { + display: block; + line-height: normal; +} + +.ui-button-text-only .ui-button-text { + padding: .4em 1em; +} + +.ui-button-icon-only .ui-button-text, +.ui-button-icons-only .ui-button-text { + padding: .4em; + text-indent: -9999999px; +} + +.ui-button-text-icon-primary .ui-button-text, +.ui-button-text-icons .ui-button-text { + padding: .4em 1em .4em 2.1em; +} + +.ui-button-text-icon-secondary .ui-button-text, +.ui-button-text-icons .ui-button-text { + padding: .4em 2.1em .4em 1em; +} + +.ui-button-text-icons .ui-button-text { + padding-left: 2.1em; + padding-right: 2.1em; +} + +/* no icon support for input elements, provide padding by default */ +input.ui-button { + padding: .4em 1em; +} + +/* button icon element(s) */ +.ui-button-icon-only .ui-icon, +.ui-button-text-icon-primary .ui-icon, +.ui-button-text-icon-secondary .ui-icon, +.ui-button-text-icons .ui-icon, +.ui-button-icons-only .ui-icon { + position: absolute; + top: 50%; + margin-top: -8px; +} + +.ui-button-icon-only .ui-icon { + left: 50%; + margin-left: -8px; +} + +.ui-button-text-icon-primary .ui-button-icon-primary, +.ui-button-text-icons .ui-button-icon-primary, +.ui-button-icons-only .ui-button-icon-primary { + left: .5em; +} + +.ui-button-text-icon-secondary .ui-button-icon-secondary, +.ui-button-text-icons .ui-button-icon-secondary, +.ui-button-icons-only .ui-button-icon-secondary { + right: .5em; +} + +/* button sets */ +.ui-buttonset { + margin-right: 7px; +} + +.ui-buttonset .ui-button { + margin-left: 0; + margin-right: -.3em; +} + +/* workarounds */ +/* reset extra padding in Firefox, see h5bp.com/l */ +input.ui-button::-moz-focus-inner, +button.ui-button::-moz-focus-inner { + border: 0; + padding: 0; +} + +.ui-datepicker { + width: 17em; + padding: .2em .2em 0; + display: none; +} + +.ui-datepicker .ui-datepicker-header { + position: relative; + padding: .2em 0; +} + +.ui-datepicker .ui-datepicker-prev, +.ui-datepicker .ui-datepicker-next { + position: absolute; + top: 2px; + width: 1.8em; + height: 1.8em; +} + +.ui-datepicker .ui-datepicker-prev-hover, +.ui-datepicker .ui-datepicker-next-hover { + top: 1px; +} + +.ui-datepicker .ui-datepicker-prev { + left: 2px; +} + +.ui-datepicker .ui-datepicker-next { + right: 2px; +} + +.ui-datepicker .ui-datepicker-prev-hover { + left: 1px; +} + +.ui-datepicker .ui-datepicker-next-hover { + right: 1px; +} + +.ui-datepicker .ui-datepicker-prev span, +.ui-datepicker .ui-datepicker-next span { + display: block; + position: absolute; + left: 50%; + margin-left: -8px; + top: 50%; + margin-top: -8px; +} + +.ui-datepicker .ui-datepicker-title { + margin: 0 2.3em; + line-height: 1.8em; + text-align: center; +} + +.ui-datepicker .ui-datepicker-title select { + font-size: 1em; + margin: 1px 0; +} + +.ui-datepicker select.ui-datepicker-month, +.ui-datepicker select.ui-datepicker-year { + width: 45%; +} + +.ui-datepicker table { + width: 100%; + font-size: .9em; + border-collapse: collapse; + margin: 0 0 .4em; +} + +.ui-datepicker th { + padding: .7em .3em; + text-align: center; + font-weight: bold; + border: 0; +} + +.ui-datepicker td { + border: 0; + padding: 1px; +} + +.ui-datepicker td span, +.ui-datepicker td a { + display: block; + padding: .2em; + text-align: right; + text-decoration: none; +} + +.ui-datepicker .ui-datepicker-buttonpane { + background-image: none; + margin: .7em 0 0 0; + padding: 0 .2em; + border-left: 0; + border-right: 0; + border-bottom: 0; +} + +.ui-datepicker .ui-datepicker-buttonpane button { + float: right; + margin: .5em .2em .4em; + cursor: pointer; + padding: .2em .6em .3em .6em; + width: auto; + overflow: visible; +} + +.ui-datepicker .ui-datepicker-buttonpane button.ui-datepicker-current { + float: left; +} + +/* with multiple calendars */ +.ui-datepicker.ui-datepicker-multi { + width: auto; +} + +.ui-datepicker-multi .ui-datepicker-group { + float: left; +} + +.ui-datepicker-multi .ui-datepicker-group table { + width: 95%; + margin: 0 auto .4em; +} + +.ui-datepicker-multi-2 .ui-datepicker-group { + width: 50%; +} + +.ui-datepicker-multi-3 .ui-datepicker-group { + width: 33.3%; +} + +.ui-datepicker-multi-4 .ui-datepicker-group { + width: 25%; +} + +.ui-datepicker-multi .ui-datepicker-group-last .ui-datepicker-header, +.ui-datepicker-multi .ui-datepicker-group-middle .ui-datepicker-header { + border-left-width: 0; +} + +.ui-datepicker-multi .ui-datepicker-buttonpane { + clear: left; +} + +.ui-datepicker-row-break { + clear: both; + width: 100%; + font-size: 0; +} + +/* RTL support */ +.ui-datepicker-rtl { + direction: rtl; +} + +.ui-datepicker-rtl .ui-datepicker-prev { + right: 2px; + left: auto; +} + +.ui-datepicker-rtl .ui-datepicker-next { + left: 2px; + right: auto; +} + +.ui-datepicker-rtl .ui-datepicker-prev:hover { + right: 1px; + left: auto; +} + +.ui-datepicker-rtl .ui-datepicker-next:hover { + left: 1px; + right: auto; +} + +.ui-datepicker-rtl .ui-datepicker-buttonpane { + clear: right; +} + +.ui-datepicker-rtl .ui-datepicker-buttonpane button { + float: left; +} + +.ui-datepicker-rtl .ui-datepicker-buttonpane button.ui-datepicker-current, +.ui-datepicker-rtl .ui-datepicker-group { + float: right; +} + +.ui-datepicker-rtl .ui-datepicker-group-last .ui-datepicker-header, +.ui-datepicker-rtl .ui-datepicker-group-middle .ui-datepicker-header { + border-right-width: 0; + border-left-width: 1px; +} + +.ui-dialog { + overflow: hidden; + position: absolute; + top: 0; + left: 0; + padding: .2em; + outline: 0; +} + +.ui-dialog .ui-dialog-titlebar { + padding: .4em 1em; + position: relative; +} + +.ui-dialog .ui-dialog-title { + float: left; + margin: .1em 0; + white-space: nowrap; + width: 90%; + overflow: hidden; + text-overflow: ellipsis; +} + +.ui-dialog .ui-dialog-titlebar-close { + position: absolute; + right: .3em; + top: 50%; + width: 20px; + margin: -10px 0 0 0; + padding: 1px; + height: 20px; +} + +.ui-dialog .ui-dialog-content { + position: relative; + border: 0; + padding: .5em 1em; + background: none; + overflow: auto; +} + +.ui-dialog .ui-dialog-buttonpane { + text-align: left; + border-width: 1px 0 0 0; + background-image: none; + margin-top: .5em; + padding: .3em 1em .5em .4em; +} + +.ui-dialog .ui-dialog-buttonpane .ui-dialog-buttonset { + float: right; +} + +.ui-dialog .ui-dialog-buttonpane button { + margin: .5em .4em .5em 0; + cursor: pointer; +} + +.ui-dialog .ui-resizable-se { + width: 12px; + height: 12px; + right: -5px; + bottom: -5px; + background-position: 16px 16px; +} + +.ui-draggable .ui-dialog-titlebar { + cursor: move; +} + +.ui-draggable-handle { + -ms-touch-action: none; + touch-action: none; +} + +.ui-menu { + list-style: none; + padding: 0; + margin: 0; + display: block; + outline: none; +} + +.ui-menu .ui-menu { + position: absolute; +} + +.ui-menu .ui-menu-item { + position: relative; + margin: 0; + padding: 3px 1em 3px .4em; + cursor: pointer; + min-height: 0; + /* support: IE7 */ + /* support: IE10, see #8844 */ + list-style-image: url("data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"); +} + +.ui-menu .ui-menu-divider { + margin: 5px 0; + height: 0; + font-size: 0; + line-height: 0; + border-width: 1px 0 0 0; +} + +.ui-menu .ui-state-focus, +.ui-menu .ui-state-active { + margin: -1px; +} + +/* icon support */ +.ui-menu-icons { + position: relative; +} + +.ui-menu-icons .ui-menu-item { + padding-left: 2em; +} + +/* left-aligned */ +.ui-menu .ui-icon { + position: absolute; + top: 0; + bottom: 0; + left: .2em; + margin: auto 0; +} + +/* right-aligned */ +.ui-menu .ui-menu-icon { + left: auto; + right: 0; +} + +.ui-progressbar { + height: 2em; + text-align: left; + overflow: hidden; +} + +.ui-progressbar .ui-progressbar-value { + margin: -1px; + height: 100%; +} + +.ui-progressbar .ui-progressbar-overlay { + background: url("data:image/gif;base64,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"); + height: 100%; + filter: alpha(opacity=25); + /* support: IE8 */ + opacity: 0.25; +} + +.ui-progressbar-indeterminate .ui-progressbar-value { + background-image: none; +} + +.ui-resizable { + position: relative; +} + +.ui-resizable-handle { + position: absolute; + font-size: 0.1px; + display: block; + -ms-touch-action: none; + touch-action: none; +} + +.ui-resizable-disabled .ui-resizable-handle, +.ui-resizable-autohide .ui-resizable-handle { + display: none; +} + +.ui-resizable-n { + cursor: n-resize; + height: 7px; + width: 100%; + top: -5px; + left: 0; +} + +.ui-resizable-s { + cursor: s-resize; + height: 7px; + width: 100%; + bottom: -5px; + left: 0; +} + +.ui-resizable-e { + cursor: e-resize; + width: 7px; + right: -5px; + top: 0; + height: 100%; +} + +.ui-resizable-w { + cursor: w-resize; + width: 7px; + left: -5px; + top: 0; + height: 100%; +} + +.ui-resizable-se { + cursor: se-resize; + width: 12px; + height: 12px; + right: 1px; + bottom: 1px; +} + +.ui-resizable-sw { + cursor: sw-resize; + width: 9px; + height: 9px; + left: -5px; + bottom: -5px; +} + +.ui-resizable-nw { + cursor: nw-resize; + width: 9px; + height: 9px; + left: -5px; + top: -5px; +} + +.ui-resizable-ne { + cursor: ne-resize; + width: 9px; + height: 9px; + right: -5px; + top: -5px; +} + +.ui-selectable { + -ms-touch-action: none; + touch-action: none; +} + +.ui-selectable-helper { + position: absolute; + z-index: 100; + border: 1px dotted black; +} + +.ui-selectmenu-menu { + padding: 0; + margin: 0; + position: absolute; + top: 0; + left: 0; + display: none; +} + +.ui-selectmenu-menu .ui-menu { + overflow: auto; + /* Support: IE7 */ + overflow-x: hidden; + padding-bottom: 1px; +} + +.ui-selectmenu-menu .ui-menu .ui-selectmenu-optgroup { + font-size: 1em; + font-weight: bold; + line-height: 1.5; + padding: 2px 0.4em; + margin: 0.5em 0 0 0; + height: auto; + border: 0; +} + +.ui-selectmenu-open { + display: block; +} + +.ui-selectmenu-button { + display: inline-block; + overflow: hidden; + position: relative; + text-decoration: none; + cursor: pointer; +} + +.ui-selectmenu-button span.ui-icon { + right: 0.5em; + left: auto; + margin-top: -8px; + position: absolute; + top: 50%; +} + +.ui-selectmenu-button span.ui-selectmenu-text { + text-align: left; + padding: 0.4em 2.1em 0.4em 1em; + display: block; + line-height: 1.4; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.ui-slider { + position: relative; + text-align: left; +} + +.ui-slider .ui-slider-handle { + position: absolute; + z-index: 2; + width: 1.2em; + height: 1.2em; + cursor: default; + -ms-touch-action: none; + touch-action: none; +} + +.ui-slider .ui-slider-range { + position: absolute; + z-index: 1; + font-size: .7em; + display: block; + border: 0; + background-position: 0 0; +} + +/* support: IE8 - See #6727 */ +.ui-slider.ui-state-disabled .ui-slider-handle, +.ui-slider.ui-state-disabled .ui-slider-range { + filter: inherit; +} + +.ui-slider-horizontal { + height: .8em; +} + +.ui-slider-horizontal .ui-slider-handle { + top: -.3em; + margin-left: -.6em; +} + +.ui-slider-horizontal .ui-slider-range { + top: 0; + height: 100%; +} + +.ui-slider-horizontal .ui-slider-range-min { + left: 0; +} + +.ui-slider-horizontal .ui-slider-range-max { + right: 0; +} + +.ui-slider-vertical { + width: .8em; + height: 100px; +} + +.ui-slider-vertical .ui-slider-handle { + left: -.3em; + margin-left: 0; + margin-bottom: -.6em; +} + +.ui-slider-vertical .ui-slider-range { + left: 0; + width: 100%; +} + +.ui-slider-vertical .ui-slider-range-min { + bottom: 0; +} + +.ui-slider-vertical .ui-slider-range-max { + top: 0; +} + +.ui-sortable-handle { + -ms-touch-action: none; + touch-action: none; +} + +.ui-spinner { + position: relative; + display: inline-block; + overflow: hidden; + padding: 0; + vertical-align: middle; +} + +.ui-spinner-input { + border: none; + background: none; + color: inherit; + padding: 0; + margin: .2em 0; + vertical-align: middle; + margin-left: .4em; + margin-right: 22px; +} + +.ui-spinner-button { + width: 16px; + height: 50%; + font-size: .5em; + padding: 0; + margin: 0; + text-align: center; + position: absolute; + cursor: default; + display: block; + overflow: hidden; + right: 0; +} + +/* more specificity required here to override default borders */ +.ui-spinner a.ui-spinner-button { + border-top: none; + border-bottom: none; + border-right: none; +} + +/* vertically center icon */ +.ui-spinner .ui-icon { + position: absolute; + margin-top: -8px; + top: 50%; + left: 0; +} + +.ui-spinner-up { + top: 0; +} + +.ui-spinner-down { + bottom: 0; +} + +/* TR overrides */ +.ui-spinner .ui-icon-triangle-1-s { + /* need to fix icons sprite */ + background-position: -65px -16px; +} + +.ui-tabs { + position: relative; + /* position: relative prevents IE scroll bug (element with position: relative inside container with overflow: auto appear as "fixed") */ + padding: .2em; +} + +.ui-tabs .ui-tabs-nav { + margin: 0; + padding: .2em .2em 0; +} + +.ui-tabs .ui-tabs-nav li { + list-style: none; + float: left; + position: relative; + top: 0; + margin: 1px .2em 0 0; + border-bottom-width: 0; + padding: 0; + white-space: nowrap; +} + +.ui-tabs .ui-tabs-nav .ui-tabs-anchor { + float: left; + padding: .5em 1em; + text-decoration: none; +} + +.ui-tabs .ui-tabs-nav li.ui-tabs-active { + margin-bottom: -1px; + padding-bottom: 1px; +} + +.ui-tabs .ui-tabs-nav li.ui-tabs-active .ui-tabs-anchor, +.ui-tabs .ui-tabs-nav li.ui-state-disabled .ui-tabs-anchor, +.ui-tabs .ui-tabs-nav li.ui-tabs-loading .ui-tabs-anchor { + cursor: text; +} + +.ui-tabs-collapsible .ui-tabs-nav li.ui-tabs-active .ui-tabs-anchor { + cursor: pointer; +} + +.ui-tabs .ui-tabs-panel { + display: block; + border-width: 0; + padding: 1em 1.4em; + background: none; +} + +.ui-tooltip { + padding: 8px; + position: absolute; + z-index: 9999; + max-width: 300px; + -webkit-box-shadow: 0 0 5px #aaa; + box-shadow: 0 0 5px #aaa; +} + +body .ui-tooltip { + border-width: 2px; +} + +/* Component containers +----------------------------------*/ +.ui-widget { + font-family: Verdana, Arial, sans-serif; + font-size: 1.1em; +} + +.ui-widget .ui-widget { + font-size: 1em; +} + +.ui-widget input, +.ui-widget select, +.ui-widget textarea, +.ui-widget button { + font-family: Verdana, Arial, sans-serif; + font-size: 1em; +} + +.ui-widget-content { + border: 1px solid #aaaaaa; + background: #ffffff url("images/ui-bg_flat_75_ffffff_40x100.png") 50% 50% repeat-x; + color: #222222; +} + +.ui-widget-content a { + color: #222222; +} + +.ui-widget-header { + border: 1px solid #aaaaaa; + background: #cccccc url("images/ui-bg_highlight-soft_75_cccccc_1x100.png") 50% 50% repeat-x; + color: #222222; + font-weight: bold; +} + +.ui-widget-header a { + color: #222222; +} + +/* Interaction states +----------------------------------*/ +.ui-state-default, +.ui-widget-content .ui-state-default, +.ui-widget-header .ui-state-default { + border: 1px solid #d3d3d3; + background: #e6e6e6 url("images/ui-bg_glass_75_e6e6e6_1x400.png") 50% 50% repeat-x; + font-weight: normal; + color: #555555; +} + +.ui-state-default a, +.ui-state-default a:link, +.ui-state-default a:visited { + color: #555555; + text-decoration: none; +} + +.ui-state-hover, +.ui-widget-content .ui-state-hover, +.ui-widget-header .ui-state-hover, +.ui-state-focus, +.ui-widget-content .ui-state-focus, +.ui-widget-header .ui-state-focus { + border: 1px solid #999999; + background: #dadada url("images/ui-bg_glass_75_dadada_1x400.png") 50% 50% repeat-x; + font-weight: normal; + color: #212121; +} + +.ui-state-hover a, +.ui-state-hover a:hover, +.ui-state-hover a:link, +.ui-state-hover a:visited, +.ui-state-focus a, +.ui-state-focus a:hover, +.ui-state-focus a:link, +.ui-state-focus a:visited { + color: #212121; + text-decoration: none; +} + +.ui-state-active, +.ui-widget-content .ui-state-active, +.ui-widget-header .ui-state-active { + border: 1px solid #aaaaaa; + background: #ffffff url("images/ui-bg_glass_65_ffffff_1x400.png") 50% 50% repeat-x; + font-weight: normal; + color: #212121; +} + +.ui-state-active a, +.ui-state-active a:link, +.ui-state-active a:visited { + color: #212121; + text-decoration: none; +} + +/* Interaction Cues +----------------------------------*/ +.ui-state-highlight, +.ui-widget-content .ui-state-highlight, +.ui-widget-header .ui-state-highlight { + border: 1px solid #fcefa1; + background: #fbf9ee url("images/ui-bg_glass_55_fbf9ee_1x400.png") 50% 50% repeat-x; + color: #363636; +} + +.ui-state-highlight a, +.ui-widget-content .ui-state-highlight a, +.ui-widget-header .ui-state-highlight a { + color: #363636; +} + +.ui-state-error, +.ui-widget-content .ui-state-error, +.ui-widget-header .ui-state-error { + border: 1px solid #cd0a0a; + background: #fef1ec url("images/ui-bg_glass_95_fef1ec_1x400.png") 50% 50% repeat-x; + color: #cd0a0a; +} + +.ui-state-error a, +.ui-widget-content .ui-state-error a, +.ui-widget-header .ui-state-error a { + color: #cd0a0a; +} + +.ui-state-error-text, +.ui-widget-content .ui-state-error-text, +.ui-widget-header .ui-state-error-text { + color: #cd0a0a; +} + +.ui-priority-primary, +.ui-widget-content .ui-priority-primary, +.ui-widget-header .ui-priority-primary { + font-weight: bold; +} + +.ui-priority-secondary, +.ui-widget-content .ui-priority-secondary, +.ui-widget-header .ui-priority-secondary { + opacity: .7; + filter: Alpha(Opacity=70); + /* support: IE8 */ + font-weight: normal; +} + +.ui-state-disabled, +.ui-widget-content .ui-state-disabled, +.ui-widget-header .ui-state-disabled { + opacity: .35; + filter: Alpha(Opacity=35); + /* support: IE8 */ + background-image: none; +} + +.ui-state-disabled .ui-icon { + filter: Alpha(Opacity=35); + /* support: IE8 - See #6059 */ +} + +/* Icons +----------------------------------*/ + +/* states and images */ +.ui-icon { + width: 16px; + height: 16px; +} + +.ui-icon, +.ui-widget-content .ui-icon { + background-image: url("images/ui-icons_222222_256x240.png"); +} + +.ui-widget-header .ui-icon { + background-image: url("images/ui-icons_222222_256x240.png"); +} + +.ui-state-default .ui-icon { + background-image: url("images/ui-icons_888888_256x240.png"); +} + +.ui-state-hover .ui-icon, +.ui-state-focus .ui-icon { + background-image: url("images/ui-icons_454545_256x240.png"); +} + +.ui-state-active .ui-icon { + background-image: url("images/ui-icons_454545_256x240.png"); +} + +.ui-state-highlight .ui-icon { + background-image: url("images/ui-icons_2e83ff_256x240.png"); +} + +.ui-state-error .ui-icon, +.ui-state-error-text .ui-icon { + background-image: url("images/ui-icons_cd0a0a_256x240.png"); +} + +/* positioning */ +.ui-icon-blank { + background-position: 16px 16px; +} + +.ui-icon-carat-1-n { + background-position: 0 0; +} + +.ui-icon-carat-1-ne { + background-position: -16px 0; +} + +.ui-icon-carat-1-e { + background-position: -32px 0; +} + +.ui-icon-carat-1-se { + background-position: -48px 0; +} + +.ui-icon-carat-1-s { + background-position: -64px 0; +} + +.ui-icon-carat-1-sw { + background-position: -80px 0; +} + +.ui-icon-carat-1-w { + background-position: -96px 0; +} + +.ui-icon-carat-1-nw { + background-position: -112px 0; +} + +.ui-icon-carat-2-n-s { + background-position: -128px 0; +} + +.ui-icon-carat-2-e-w { + background-position: -144px 0; +} + +.ui-icon-triangle-1-n { + background-position: 0 -16px; +} + +.ui-icon-triangle-1-ne { + background-position: -16px -16px; +} + +.ui-icon-triangle-1-e { + background-position: -32px -16px; +} + +.ui-icon-triangle-1-se { + background-position: -48px -16px; +} + +.ui-icon-triangle-1-s { + background-position: -64px -16px; +} + +.ui-icon-triangle-1-sw { + background-position: -80px -16px; +} + +.ui-icon-triangle-1-w { + background-position: -96px -16px; +} + +.ui-icon-triangle-1-nw { + background-position: -112px -16px; +} + +.ui-icon-triangle-2-n-s { + background-position: -128px -16px; +} + +.ui-icon-triangle-2-e-w { + background-position: -144px -16px; +} + +.ui-icon-arrow-1-n { + background-position: 0 -32px; +} + +.ui-icon-arrow-1-ne { + background-position: -16px -32px; +} + +.ui-icon-arrow-1-e { + background-position: -32px -32px; +} + +.ui-icon-arrow-1-se { + background-position: -48px -32px; +} + +.ui-icon-arrow-1-s { + background-position: -64px -32px; +} + +.ui-icon-arrow-1-sw { + background-position: -80px -32px; +} + +.ui-icon-arrow-1-w { + background-position: -96px -32px; +} + +.ui-icon-arrow-1-nw { + background-position: -112px -32px; +} + +.ui-icon-arrow-2-n-s { + background-position: -128px -32px; +} + +.ui-icon-arrow-2-ne-sw { + background-position: -144px -32px; +} + +.ui-icon-arrow-2-e-w { + background-position: -160px -32px; +} + +.ui-icon-arrow-2-se-nw { + background-position: -176px -32px; +} + +.ui-icon-arrowstop-1-n { + background-position: -192px -32px; +} + +.ui-icon-arrowstop-1-e { + background-position: -208px -32px; +} + +.ui-icon-arrowstop-1-s { + background-position: -224px -32px; +} + +.ui-icon-arrowstop-1-w { + background-position: -240px -32px; +} + +.ui-icon-arrowthick-1-n { + background-position: 0 -48px; +} + +.ui-icon-arrowthick-1-ne { + background-position: -16px -48px; +} + +.ui-icon-arrowthick-1-e { + background-position: -32px -48px; +} + +.ui-icon-arrowthick-1-se { + background-position: -48px -48px; +} + +.ui-icon-arrowthick-1-s { + background-position: -64px -48px; +} + +.ui-icon-arrowthick-1-sw { + background-position: -80px -48px; +} + +.ui-icon-arrowthick-1-w { + background-position: -96px -48px; +} + +.ui-icon-arrowthick-1-nw { + background-position: -112px -48px; +} + +.ui-icon-arrowthick-2-n-s { + background-position: -128px -48px; +} + +.ui-icon-arrowthick-2-ne-sw { + background-position: -144px -48px; +} + +.ui-icon-arrowthick-2-e-w { + background-position: -160px -48px; +} + +.ui-icon-arrowthick-2-se-nw { + background-position: -176px -48px; +} + +.ui-icon-arrowthickstop-1-n { + background-position: -192px -48px; +} + +.ui-icon-arrowthickstop-1-e { + background-position: -208px -48px; +} + +.ui-icon-arrowthickstop-1-s { + background-position: -224px -48px; +} + +.ui-icon-arrowthickstop-1-w { + background-position: -240px -48px; +} + +.ui-icon-arrowreturnthick-1-w { + background-position: 0 -64px; +} + +.ui-icon-arrowreturnthick-1-n { + background-position: -16px -64px; +} + +.ui-icon-arrowreturnthick-1-e { + background-position: -32px -64px; +} + +.ui-icon-arrowreturnthick-1-s { + background-position: -48px -64px; +} + +.ui-icon-arrowreturn-1-w { + background-position: -64px -64px; +} + +.ui-icon-arrowreturn-1-n { + background-position: -80px -64px; +} + +.ui-icon-arrowreturn-1-e { + background-position: -96px -64px; +} + +.ui-icon-arrowreturn-1-s { + background-position: -112px -64px; +} + +.ui-icon-arrowrefresh-1-w { + background-position: -128px -64px; +} + +.ui-icon-arrowrefresh-1-n { + background-position: -144px -64px; +} + +.ui-icon-arrowrefresh-1-e { + background-position: -160px -64px; +} + +.ui-icon-arrowrefresh-1-s { + background-position: -176px -64px; +} + +.ui-icon-arrow-4 { + background-position: 0 -80px; +} + +.ui-icon-arrow-4-diag { + background-position: -16px -80px; +} + +.ui-icon-extlink { + background-position: -32px -80px; +} + +.ui-icon-newwin { + background-position: -48px -80px; +} + +.ui-icon-refresh { + background-position: -64px -80px; +} + +.ui-icon-shuffle { + background-position: -80px -80px; +} + +.ui-icon-transfer-e-w { + background-position: -96px -80px; +} + +.ui-icon-transferthick-e-w { + background-position: -112px -80px; +} + +.ui-icon-folder-collapsed { + background-position: 0 -96px; +} + +.ui-icon-folder-open { + background-position: -16px -96px; +} + +.ui-icon-document { + background-position: -32px -96px; +} + +.ui-icon-document-b { + background-position: -48px -96px; +} + +.ui-icon-note { + background-position: -64px -96px; +} + +.ui-icon-mail-closed { + background-position: -80px -96px; +} + +.ui-icon-mail-open { + background-position: -96px -96px; +} + +.ui-icon-suitcase { + background-position: -112px -96px; +} + +.ui-icon-comment { + background-position: -128px -96px; +} + +.ui-icon-person { + background-position: -144px -96px; +} + +.ui-icon-print { + background-position: -160px -96px; +} + +.ui-icon-trash { + background-position: -176px -96px; +} + +.ui-icon-locked { + background-position: -192px -96px; +} + +.ui-icon-unlocked { + background-position: -208px -96px; +} + +.ui-icon-bookmark { + background-position: -224px -96px; +} + +.ui-icon-tag { + background-position: -240px -96px; +} + +.ui-icon-home { + background-position: 0 -112px; +} + +.ui-icon-flag { + background-position: -16px -112px; +} + +.ui-icon-calendar { + background-position: -32px -112px; +} + +.ui-icon-cart { + background-position: -48px -112px; +} + +.ui-icon-pencil { + background-position: -64px -112px; +} + +.ui-icon-clock { + background-position: -80px -112px; +} + +.ui-icon-disk { + background-position: -96px -112px; +} + +.ui-icon-calculator { + background-position: -112px -112px; +} + +.ui-icon-zoomin { + background-position: -128px -112px; +} + +.ui-icon-zoomout { + background-position: -144px -112px; +} + +.ui-icon-search { + background-position: -160px -112px; +} + +.ui-icon-wrench { + background-position: -176px -112px; +} + +.ui-icon-gear { + background-position: -192px -112px; +} + +.ui-icon-heart { + background-position: -208px -112px; +} + +.ui-icon-star { + background-position: -224px -112px; +} + +.ui-icon-link { + background-position: -240px -112px; +} + +.ui-icon-cancel { + background-position: 0 -128px; +} + +.ui-icon-plus { + background-position: -16px -128px; +} + +.ui-icon-plusthick { + background-position: -32px -128px; +} + +.ui-icon-minus { + background-position: -48px -128px; +} + +.ui-icon-minusthick { + background-position: -64px -128px; +} + +.ui-icon-close { + background-position: -80px -128px; +} + +.ui-icon-closethick { + background-position: -96px -128px; +} + +.ui-icon-key { + background-position: -112px -128px; +} + +.ui-icon-lightbulb { + background-position: -128px -128px; +} + +.ui-icon-scissors { + background-position: -144px -128px; +} + +.ui-icon-clipboard { + background-position: -160px -128px; +} + +.ui-icon-copy { + background-position: -176px -128px; +} + +.ui-icon-contact { + background-position: -192px -128px; +} + +.ui-icon-image { + background-position: -208px -128px; +} + +.ui-icon-video { + background-position: -224px -128px; +} + +.ui-icon-script { + background-position: -240px -128px; +} + +.ui-icon-alert { + background-position: 0 -144px; +} + +.ui-icon-info { + background-position: -16px -144px; +} + +.ui-icon-notice { + background-position: -32px -144px; +} + +.ui-icon-help { + background-position: -48px -144px; +} + +.ui-icon-check { + background-position: -64px -144px; +} + +.ui-icon-bullet { + background-position: -80px -144px; +} + +.ui-icon-radio-on { + background-position: -96px -144px; +} + +.ui-icon-radio-off { + background-position: -112px -144px; +} + +.ui-icon-pin-w { + background-position: -128px -144px; +} + +.ui-icon-pin-s { + background-position: -144px -144px; +} + +.ui-icon-play { + background-position: 0 -160px; +} + +.ui-icon-pause { + background-position: -16px -160px; +} + +.ui-icon-seek-next { + background-position: -32px -160px; +} + +.ui-icon-seek-prev { + background-position: -48px -160px; +} + +.ui-icon-seek-end { + background-position: -64px -160px; +} + +.ui-icon-seek-start { + background-position: -80px -160px; +} + +/* ui-icon-seek-first is deprecated, use ui-icon-seek-start instead */ +.ui-icon-seek-first { + background-position: -80px -160px; +} + +.ui-icon-stop { + background-position: -96px -160px; +} + +.ui-icon-eject { + background-position: -112px -160px; +} + +.ui-icon-volume-off { + background-position: -128px -160px; +} + +.ui-icon-volume-on { + background-position: -144px -160px; +} + +.ui-icon-power { + background-position: 0 -176px; +} + +.ui-icon-signal-diag { + background-position: -16px -176px; +} + +.ui-icon-signal { + background-position: -32px -176px; +} + +.ui-icon-battery-0 { + background-position: -48px -176px; +} + +.ui-icon-battery-1 { + background-position: -64px -176px; +} + +.ui-icon-battery-2 { + background-position: -80px -176px; +} + +.ui-icon-battery-3 { + background-position: -96px -176px; +} + +.ui-icon-circle-plus { + background-position: 0 -192px; +} + +.ui-icon-circle-minus { + background-position: -16px -192px; +} + +.ui-icon-circle-close { + background-position: -32px -192px; +} + +.ui-icon-circle-triangle-e { + background-position: -48px -192px; +} + +.ui-icon-circle-triangle-s { + background-position: -64px -192px; +} + +.ui-icon-circle-triangle-w { + background-position: -80px -192px; +} + +.ui-icon-circle-triangle-n { + background-position: -96px -192px; +} + +.ui-icon-circle-arrow-e { + background-position: -112px -192px; +} + +.ui-icon-circle-arrow-s { + background-position: -128px -192px; +} + +.ui-icon-circle-arrow-w { + background-position: -144px -192px; +} + +.ui-icon-circle-arrow-n { + background-position: -160px -192px; +} + +.ui-icon-circle-zoomin { + background-position: -176px -192px; +} + +.ui-icon-circle-zoomout { + background-position: -192px -192px; +} + +.ui-icon-circle-check { + background-position: -208px -192px; +} + +.ui-icon-circlesmall-plus { + background-position: 0 -208px; +} + +.ui-icon-circlesmall-minus { + background-position: -16px -208px; +} + +.ui-icon-circlesmall-close { + background-position: -32px -208px; +} + +.ui-icon-squaresmall-plus { + background-position: -48px -208px; +} + +.ui-icon-squaresmall-minus { + background-position: -64px -208px; +} + +.ui-icon-squaresmall-close { + background-position: -80px -208px; +} + +.ui-icon-grip-dotted-vertical { + background-position: 0 -224px; +} + +.ui-icon-grip-dotted-horizontal { + background-position: -16px -224px; +} + +.ui-icon-grip-solid-vertical { + background-position: -32px -224px; +} + +.ui-icon-grip-solid-horizontal { + background-position: -48px -224px; +} + +.ui-icon-gripsmall-diagonal-se { + background-position: -64px -224px; +} + +.ui-icon-grip-diagonal-se { + background-position: -80px -224px; +} + + +/* Misc visuals +----------------------------------*/ + +/* Corner radius */ +.ui-corner-all, +.ui-corner-top, +.ui-corner-left, +.ui-corner-tl { + border-top-left-radius: 4px; +} + +.ui-corner-all, +.ui-corner-top, +.ui-corner-right, +.ui-corner-tr { + border-top-right-radius: 4px; +} + +.ui-corner-all, +.ui-corner-bottom, +.ui-corner-left, +.ui-corner-bl { + border-bottom-left-radius: 4px; +} + +.ui-corner-all, +.ui-corner-bottom, +.ui-corner-right, +.ui-corner-br { + border-bottom-right-radius: 4px; +} + +/* Overlays */ +.ui-widget-overlay { + background: #aaaaaa url("images/ui-bg_flat_0_aaaaaa_40x100.png") 50% 50% repeat-x; + opacity: .3; + filter: Alpha(Opacity=30); + /* support: IE8 */ +} + +.ui-widget-shadow { + margin: -8px 0 0 -8px; + padding: 8px; + background: #aaaaaa url("images/ui-bg_flat_0_aaaaaa_40x100.png") 50% 50% repeat-x; + opacity: .3; + filter: Alpha(Opacity=30); + /* support: IE8 */ + border-radius: 8px; +} \ No newline at end of file diff --git a/docs/experiment_1/css/jspsych.css b/docs/experiment_1/css/jspsych.css new file mode 100644 index 0000000..3990b06 --- /dev/null +++ b/docs/experiment_1/css/jspsych.css @@ -0,0 +1,582 @@ +/** + * jspsych.js + * Josh de Leeuw + * de Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47(1), 1-12. doi:10.3758/s13428-014-0458-y. + * + * documentation: docs.jspsych.org + * + **/ + +/*!*************************************************************************************************************!*\ + !*** css ../../node_modules/css-loader/dist/cjs.js!../../node_modules/@fontsource/open-sans/400-italic.css ***! + \*************************************************************************************************************/ +/* open-sans-cyrillic-ext-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; +} + +/* open-sans-cyrillic-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; +} + +/* open-sans-greek-ext-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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XxIXeOP0fZ4JDIO+pt+ofvE3/KyZXOl63dby6buV46/TK6UN6uzJBRbla9W+v47j+g2OuV+ghfATiQxPgV/BR4Ff4QQb1FXCcTXxC/CXf/vwJ91d7+HPU/xyBGlSVRFQOfBLRL6l+pJYr59xHH6nlD7xEbord63QdCwLCmEomQcObVrO8yEhisyecJDFzYjq7ZK/6bg==) format('woff2'); + unicode-range: U+1F00-1FFF; +} + +/* open-sans-greek-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0370-03FF; +} + +/* open-sans-hebrew-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0590-05FF, U+20AA, U+25CC, U+FB1D-FB4F; +} + +/* open-sans-vietnamese-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB; +} + +/* open-sans-latin-ext-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; +} + +/* open-sans-latin-400-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +/*!*************************************************************************************************************!*\ + !*** css ../../node_modules/css-loader/dist/cjs.js!../../node_modules/@fontsource/open-sans/700-italic.css ***! + \*************************************************************************************************************/ +/* open-sans-cyrillic-ext-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; +} + +/* open-sans-cyrillic-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; +} + +/* open-sans-greek-ext-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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CqR8iBeKEjiuaxzmc08Xq5x+YrYf5ELjorngBawVxv4ikxt2UJG+5iSbgBtDdCim5N5kJICM3L/tmQnNu9MkTWIfONz1fk32Lgsg69Hozs8JWZd8s7DkXKgMBGIFApvJmwQSKE0CXolAyMJaxEpI8uhlY1EUFipo/0ayYuZNA+TV/NWYA) format('woff2'); + unicode-range: U+1F00-1FFF; +} + +/* open-sans-greek-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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wblkYxKCNzUQxyZauXRiM/adIjyZ/dHwv6Sg1toPA7FYLnuYWLMOXNR4wgFU9S88GJrEjPMGkiG7ktAPAtxartkP+GZXj4GSI2eJeHkWN765RksyLCqrvQ9ogVQwn+BmPQtEZ8kT8j2I9oFhK6CoMyvFY+IeJT3EDtWgOYYZnHhWVFnkyUv2uOf4UglgMJ94AFPKxPz/wSiweNe6EHlosru4LCKph08k1WMexxdmm+H+6eFSEDd0qmezNQcawWCLZkgETWMXIXh8qYRlheEL2W/nJk1aCGipdXTuH9xvUgtQz3gfz3DwbYAWlxwj8zyL6s7rK7wRd+m2HWPmnM6MBC2zpfkvHZEORecsmR1c3g7WtDMkaL5NdndxaTcqNKak64BvfipNyG8VPpnfYkVMjZfGn0v3xM0f46nEDxqeDk+KZKauVqlgbA4V8TTnUC5ickj1nVjC6YT+DaLlbXa2KtVBqExqRcln4Boqygy95xu/WSMukNh/vIGjrI1f/+0irPbJX9/ZEGPW/r5XbowVKbnr6H3zpKClBGf6qaXnErl7A2jUvpGME2A6ElWXfsXjd9g2q8K3cGXwSP95Hd50FAlapwZNBtqG260Fb+bSVl4sxu9YgQ0cxa2hA2soYPrg6fsKz4FoOaRi8jqyuQ0id4eGjqHjNVRSiORJvtHjkdSVNKbHWi4jQWmXYdoRQaDR4oP10c9K08FU+ZLpmJ+V5aGk7h5978eb1IXLp3wW4ff0bkPelBwLJp4w1nuzpF1Hg6FF4oUbsAVvpH5Wt9IiasRYwRAFsWXUhPSGGIchVQ6sJf82VGpLm3C1la1MyXHIcuiJkayVG39VH/QRNQgjmJrvuRmi2l6axd3anq1CQNHRZV/7ojgfgtMOHdLMxBT7hKdFxUr78qO+bIVaeawiPSk+kC3di6h3Wl/QFmGb9GvDlV04FQHJ0Nq4IXXHS5xZry1bkLNoPUVpUEdo5ozfey64re+6NPM9O+NbxA+8Bp4aRZ2hTlB8EIxmGjnzG2Pa5rHpn4HSXvW67Phk6DarReotqSIzlkP1xs8TtYxWXUKHoJEgOqs26/5v+GfOX31WhBZ0HhRnJzBtQzYd7T1juQpx+3l79cVyDe8R7WHdmqnUtRgs18nhf7vofd6lmdqJllV2gF6r2TObFV5B32p/Nbq05WE3R+ChlcbXkXbbHFCkhzUVUUzHf4M7TyznKS/WNyVemG/4JbxvJ2wqlGdTK2kJii7hypSil1xJqsjGrDAC9dCJl+WYQpZRPT76J2Kmfoy/GLsL+iJmy77S3JpTh4QutgQ2cUo7Q9XDr4a/FMszlB+LAE4y/bhz3I/HRY76/Dq2PqLucmXdZW45fVcxtb7nALm+8kJV5vUvj9UA229lyka187ZvgS5F6lFm0M4OsxX+SyMJQisq9FtkcJmMlmQZtvCdHE8Pi4rlMPxY+jBUtvDVd4dhk7/7vmpELfFHhHfBm1qleH/LDH+IX72xV3LnD5dEsmbUTfw2DwElxKU5Jy7ZW5YEiipyRXADHCdTWZM8cnPpnMHKerRLmvKaW+DGIYXFxXBqRiQ9jRQl/TVZsYG0Atxy63ww86QtwsnPwGcTsl84W0KkKj78Fdxumj9WvMpqMbQKNa9z/5d2umxivWqIW68VU+6RIo2sIOywmpO3aqTpOAzktldmE1x0PcmTh7ZVxMeX+X+bdN4iuxl+fF0LOzLvpzdDjwZcAXlM8c0xtvVCE8PTVtfrrOioCvoyQv6jD4wQZIMU8EaGa87GV2dv+mzX1JL6w/xD5B/HfDtIXQtgfr5b5/jqMF4L42xnjB72SxeLdOeuXB/Xjdus7z9ia395p48W2lLvVPcnrZ/cZCiZBxD/O3W3M4oKb/RVJvloRnZCnouRuEepryXIlXsunEXNUlHgQNkhxymTsbm7yqWwsJNU4MumsW/pv8mrbSCInzZFtBqVBbOWnM+M3EmR1VqWqP93z9a1KVPMHmzeidqBkpxyMjKDZ/76ZdiCapxfubyM+kn/b0HKQlZRXkuLGgoy+Y5h9zQ3rCJ5ASKkUvLuRI9zWRDTzVCJEBIanYqnMj5AYPlvj7fcyMJEaVoJg/WWxLYBLSY4kcAMjlN7VVm3U1Ei5U+QmajjNrqvu24eKPe0X+Wl71KW8oyNpBeYVqP7x9mjPY+p91zJ2qdt9zxY5ym6ESz1CYu3SglILBHKC74sQkgAbFolVRtBZ7HAixyMsG6mAPsLeUmPdgjdNE33HGONefMg1rpvrK7ctXBAn3tkQHR0/6RL77inNsfh1RJn0RvBZhMzyCWLkcoFkRtVX/uvSUJdTZAC/R1BRtu+HuJqGKyEC/7JlJqGLxAoWB1Jc/mVTX0UHZrYG7pa0ctaGd6/xMl+TBH7hMoeqaHZAyca1bHacyrOSzo9NQv+JaP6eWtCoVCbUDDBTTCA0dEoet6qpb5FSte26VwxO9mu0U7e3RKgJovkrCemtXg8G40qwlAgCWRomKB8NoDq+ZTlfccfQqvzFygCJO0ZP5KbwaCyk7srsVjyZXnp8pzt3qwps99TnNyPXM/dGkcB3H2knxcFeCZwQZhS2iEXN5lI9NbzsUdPgTRPnyqJqNEUWQFeXcBfQZSl3vOGLig3kHYgHNUvVEByvbIin9KS5wGSH4BvZdpuInunSmBv1QbAe9EEEz21fpq5n9jultn4iU/DjQIihFjVonojdmbal9+KNLM0v3nGB1KLgEzalani8SVukgJxqOZNtHGvcGMUhJQd6x4XQNaHj6EZE876CfisI/fedD7x3b8jFRi4PcO4s89/X/261g5N7kZzM0JAKI4ntD+lbO73PZo5c+PL6BtOV7mIoM0yiRIZYR/Od3kwqYf7Fg/zEQ/AS1RdovK3oNAb+ivH/Z7m5zUvINYVbWKZYr95bnN36OgOPu+Tn8rC38XnqmcMIWpW/UOkvccf0Sd3T8Q2FoR3CMtmBw6ocG4jCIlfDoRFTRYxSIs2UAS7ZGkgi/vbC675VaJ1JqZM8NEBK4ZTyNRle96OaJyK4agbJjRHvP81wKGLRsuD9ayRQTZGNXfWV1h3Jantz2EKlL+xV5XPb/oWEffiN57m2+HiYa5Gz03Pd2sNSjF3kJpwe7DRe/mZFO9N2Li/pamOl16potq75MDNd2EhvgcRZ7zLlhVhF3wjg08PycY2IWh9RZColmE8P7bbIC9haFedl0555FLjyvPT8X4U6ak9+Mn4h6XRP1+WEKtVu3FfZBrkQGxIoxzMTfLPQxcGRXnwO2S1+4RYwxLUnfy1Xkh7kTh2sXo7URuJ/s43x35tJst/FHm3svS4tf+2bQKQIPTQWLdFUiwwbO0lDfGKz21FROisLZWvO/3uIFRprG9v+CwmPiRvxGmu8lyHuBc6dbf77+tsWO0sEYvm7+5POEFZmQ7JnQ2ZqrjUnOpaB5+JowoBO1+sI8Kj3LgtKlGvnv/+0mXZtZq2Xm35Tkv1dx5Dg/4PfFyYtTMTGFe4JGU1huqpoITfVep7Xrv9wbv7Hf6/Xh62WnBivX2bW6yevDdxhQHNp8em+1VatdA4ulYSjxqOUDnqbbq9q/eOMYt0jPBAG0X5PTmajSLV13RYH0HOUe7FOjwxYd6BUH0/kG9zT4zKzUGFheGQTPBNn1UBzvLyZcuc31K37e9X+s42ViQVgsyUK1ux6CAtWHidtD71jb3POe9qeyrc8uKbXdtE09Q7mDOLE9G+nW6m6gv/UXAIzt/Qd/Us5x18eGhe/DV0e7mpHcWXWsxEBn/19E6MoVZ57LY6LeCYd8Ei7U2Z99GEPCH9128gxfn5Cng9Vahsg26P2xf+FuPvr5TZlN1Ev1cq9bmZcmRh5L5+4DOpqfDo0EvdP7ZE+NQ2rDtArDUYo/TqaxfvcZ9NzInvr2Iqktojk/ODt6L3xzYyLbGYwAkNynMGVQpSQJbjrsU8g/Zg3MISHHSrLN24AxUG0T/DimlgepuzVrj8trPdjkBirFe8Se1ZudjbfD5X0Y7/OmjBM9eY5x8TT6kJOW5WdNfynLPd0WkPhzPdJTTvmmhMf9Zm5YIaIG1pUOX0j6/rg0C2+WdMrHM6yJLqG4rXDNppA9OFp/D28v5R8T8a0KnppVJZHeGx7rNiHaVRL/nsw0BI79m5/VIq8u/Q6u6nEGY3TaZMEN8sax9PNBlac0Quk7+m1AqqiqtxvJJ6p7R5LyK6sU6NlcHVkPPanBXkVmHLs2+ycq+3/ZJK4IcE5zp1m+/kkP8x79wTKhPMYl+Mnd9P7l7BhLTFtbkdZId8I/f8DFViEStTeIQojFqN5N13oSdtfVmYcM+9n1TrxeF2FoMuKSZFFZqKoM6/OOPpuolcOfY59hmuZm3s348+ApW2iMtjeLc2pkAh0aHgEkZlbGnvVojBlcYz+Ra6bnYDG8URyb5JfrGsINNwwBhaXQqVgAA9QCrDFFWBLgLYCyKKlTsxWAATMKRm4Dgmsg5cSpE6YrqqAv1b1gOerRsD08W0QuTD8qwOGTMJdBhZmSFDFz0lqAGwJVI+CqQnVpwZ0K4VQQwqlMGpE4cq4O/VqZk4VMfW0B7nHeDgzAJDzNKGLZnXPdNsKcxnS0QCr9WKEGkkUQUx9xaXNHIjMxlKY9mnLNgXk9HC7JxEQDSMJ3zKM9m9zVp6DI0walACAT74iwZfACt0IANgh/Tngrtpu8QEXAiyAWJsyOSK1F7Qr548TXf82YF7qKaskLQU8DNMmg1mddsA6BYl7H5GRAd1CeBID8QjxI8FnYKwECZlk82p/HHfq324ljweAceO+cZ5QKrpOhJU9rtbSrfISo5Wl/tjWDBSAm0E4xaWIBKsDJQYUMMqPGTeYPRnYczC0KR9cXDvOzwAsBpgzmKdcoxMJXXQXvyLBdw0wXz9Lsfiei4fyi0d4EsYdHx49Ou2lBAwkQO+mG3bSq4Orkzwfh3Yw6PfBQm8ujm12kl1d0ZwSaA3iABNgdgq3FuR5aPH2ynG4w7jKlQhI+kyU1UzXRjz1PLeUUPsrESrDjjhi42qXj1HMqYQwjSobIueg5uVLlTNKk2SMUyyUJzK8/MgqFAJkwMKhKWea2NGJk9csOReMUKmqYIFDsbE+htdSAbH4oRdJlh2cnBwLGIq8tFdtRJIDu9irTt8EmHTc/Va5NmwaFwQuHYois5j1P/mdtSKAbIBxp+9Yf+8rH3plk8OrB/1/5Fo1jHIpMGg/yKPhDKcK8XtcbG/1LrSFVj5mZ7sFrfoAHqAdJczcQIhjUCbPA2/9IYnAMBdzpTQDIdcYo71iI64alL2Xnbb1NuQ0bZoKwfDwx3eKE+avmdR5jgvGYFhZNbkYdQJQ9ipq0VCVv3e2E00Ty14eQ4WZOWo/PWo4cnzlS66Pc2xyj1kqyV1cPNxls7c3mM8hlhex23vqiuOua0oq8Bf52QxuCZCgmuENktKO3Ds64TyHLJCFqAGvhx3K7+sBhypGWjbr5VqxaR6MxzCWKFpNW1+8uvd6uCgBsZjkgDI7iCnlaVXCQiTJ2wD5XL5/wC3HY7jFI3VMWBYoXilOo4fWJRqKHspxS62EjObVrkZCKJKgXG6EH6xuxiPe6/X7gzjZ3VWncMMGLsvDNDPYwHFH01q61/Y65c5FkoylhrVTUhct6jqLw+lxoIDzIFskFCs9E9Bmiq0738O60chIKmdOtcBLrS1xjgL9L6w76KMd02wywVFINlmzrURNHx+cxDvkmaRZjglYQWVaD4fWYjTfyJ36Jyd5juONKbzapq1Z3lmyi38UrJLUPzg4ieP+ckx2dZC8Tkel66LR5vLDQUetIuQ4qHx+e4vHeOo4lU3T7uwgvjhuk4Bja88YoZSFTk6+8MXZ/f3lsuj2gpmJSGOIvWZ88PmAA+sAL+pzx6mHggMPembGqf/W2yokf3pqJxOIErX9fmeCnYJlrpDO2aLETPjxctmO8yKVni/hwUqpOy8T2tFoOMSyyPUa2b/Kw5+MLwDoKXlmTL3CUoS91Y4Nf4FAAMrz4zxnJJvc/oOkAYBfn178BPw7Zz/9d/wv4VJQD4qEAYAAsn+o12UnKxbkk8S/ic2Swzsp7TZXhgk6Gui09haSCZdW3F0XevCzf0SXQGGkhrhCta3Xvg0K47vmndbE+WX7gh1HagVywNb9gnXiYgWBlL4LYbTzkcDk7pFhpXpYSlZqes14HvN0DlZ4Dm9caDASvTA2iOVy9Whtt+ve+Mw8fcmCFXu4jUCcwgooe61UW2IiT7chL6bvA6nZ4GZLuITnKO0p5TjYW79oaEJsFnKtorNg9cyOk67hD6FxwI9YBhllKX94dfEcClto4brgpc/0K4peA8mullOUyiPp8UZAD0H+DuN/vmIrfjN2Umq7Ofe3oYgu1GRv/bat+7wVtPMX+P2X9LKgDH2YFAK7AaKjpgm9Sj22+P4ymKGzfwQQMcg0MaNcJMHDLJLkRjElxaQEk4cVftDmbqkKgQAkcpxAQCTnAS5ZR/UV2mF9+y8DQn/JKaCQSjRkk0kWJWDxhegFCz/TVMKhUJAPSSQFKHbCRdOGnBKnNUgkpYKsnDU1hesUOxQmEgzhOGWSXY00cyp40pxSF2JBwTTnoSJjK/mUqEFANq4pnn65GxAqBS1Iz8DaICWus0nDDzzEoxNCNJw4tCGN5dR6Y4PDB3lmwCREBc62+8YOvD6kfGLSKVZw2tPPhQ+c8T2RJ8kTiczakkMUsYi4eIkgYk53m44cr1bqFPMH1vSAv/ZPNELE/A3N8t+0oftQPjifgBlYNVn/OpX1rTp619tZz6Jj+8LIumce1jWZYjqnP4yOMQjTPmqytlFH6ziVtYyZmXbQ0dzCXG6RmMZex7Z2ioZWR317m7pOR203RU3Hprq5TVWjo7LWUVEzKK8UlFVTlFY6Sgo7THExgKbQUSRWYakLynKH/DICda4jL2uzcjOdk5KVneqstF6XmbLJSPA6qkRHeqIhLXHFpEb5sJRIhzLWybHOUoQ6KSRLHmiZC0vqaonPw8R+AyJvB+HgXiYCK7H4lk60qsN4loIEW3MdrzlOU1287YOJM6OONWDF6JqtoI6lNMDUysOiNR1R6uswlvkZJYxDeJiQoEB0CyApIBGdHOSJ3vl3K/YV4otwdJCHjj4YooMNhuDNAe+NxXg76PDynMLTIwzjgcfay02sOQaH0eFmz8bVOQDj4uyCcbbX4eTjgJEJwfaM76iLSdzlQCn+MX+uPuaCs4+p4CI5SfaFyrFgK3PLa+y8DZnWOHxFoXxJIZVtCKrz8jzlX9NQCuTHqOeEYcSznUwBhYXSWghL85DXhPNhKGgtem5WlEF60KzzjPmXOG/rK1vV2yc3mrGJYX5WQk9fWrxr7j29m/pF7h1q797lNmfre7BWPKDc1r37Q5i7+oplmlFP4flLTxQLGA6O37hoBx1xB2Z47yovYRkCBOcf3Mxf+FQGbc4L8Kl1llY=) format('woff2'); + unicode-range: U+0370-03FF; +} + +/* open-sans-hebrew-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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format('woff2'); + unicode-range: U+0590-05FF, U+20AA, U+25CC, U+FB1D-FB4F; +} + +/* open-sans-vietnamese-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: url(data:font/woff2;charset=utf-8;base64,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) format('woff2'); + unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB; +} + +/* open-sans-latin-ext-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; +} + +/* open-sans-latin-700-italic*/ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +/*!******************************************************************************************************!*\ + !*** css ../../node_modules/css-loader/dist/cjs.js!../../node_modules/@fontsource/open-sans/400.css ***! + \******************************************************************************************************/ +/* open-sans-cyrillic-ext-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; +} + +/* open-sans-cyrillic-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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QLLqTaZZb7cJMkKHojJk2r/u1E/IZxumS4nGlLDoZxwazizqGtWMXlnOJWTIV2G1LXA6yCrzHvt8venbSg7hqDt6VqCSqyHA9efLeOjYWpPaW6xwtTgFB0JhC/tch9/XA81L9IJAyrL/bpPVWyZe+VrD+L/n3MvkIa5N7bU7T9V7V/V8PvuGTGgNrhh6Gc1ylaHQBPW4haXqAnzjbulxvtR8QUm3Lh28+KwmXIOfE94yAZHhlFWtGC46rJUtmnefjx/ZKOyDbACHxfZNMlUBZL1MGW6+agT3kMpB6UE6mjdT9d8uSDuXxEq52Uwce7UWAPVvB45jIrIKmDLpQyA02LbQ2Lpk94GSb1XKMUI7H6wfU2pBH+YFu5ZSElBJitUGYzGDTCH2UZZt4mgKHHNtFTHEgRXogMfC+4MAxjCAgqBdRtdl6zl0/S/HccdLImvz43Dww4Ki5laHPfix9VmkppzD18fuH7qtKHMuH57n4DNotVeBeu4Z/dvMh6R43nA5BROVKn0rYbhW+9Nm94mo1ItSK941kq/QgqaBXmVWLc3wzcwoRu60JOiPwrNf7QoauSIWl3EKJy0tb6soglvISMErJ4Z8lZuiPkyI6Uq3XP/RW5adUtWdmqnivVuj3vZg01ST1w7lOe+TEg3+eElRD2Q/PJ4BZuPCwqb4JJjzu//mGTN9wn7oxorWz3KxVuNzIGLpKUiap+JU1oYiKzumjV1VkCuc65BV6d3zsizL8AhHwMLk6AqU6sThz8U1o2+/wq05Ka7VZnKyx+LV/krXMtrqb8ihgao9tibp5gyVnnVzU5vasfIEl97ZuJGIES1KqJ2DGVTkvDBbqSe8EwfJTe9C4wcRrhmQ9p2oXdYEJ4jcTIGqTxoJHYfVhmAI0aR14H2AF6KFE4NgQer9F049UBs7De8gO8/88Bze0DjG0YoYAvBoGpKWB0wJ/GQ+1WTmYtUSqeBnY1mHv/kf1l3UpDH39L3l2Dgw4iOdJpBjA1BVlVIM2nw36IAd2/4ztH/onVrBHVhx8WUcyEr15pLfYRATXD5aYRx/XDQ49rIs6uqTyxfbvoSf8GBmEduScCvTp9ZCzguLsp2DXlJ1O/yuJr9mmdlYsnQkpSqpdrDpuf6wwjzf3PqydUnc0A3PQ6672wCIt4eJp7wRvDwd6EznfJBhb6R1GM8v3SU0T3PYvTd01JGtb8L2xE4ahobyTTjQXL/vIKu9UA57JdHRIo2rpMICuNjo0XlZo2ijLr8mffffntmglzjXBdWZrQflYkUaWa5J3jIRS8lkqgeLvRDRCymvXB26vjG985Kh3w57QiHl6YfQWF//K+D7x8SNSy1JOVZzkRN/WEy4a+KxzuDVLNxuB5FUpYWpcaHJAaO1id49WpEL9K07Wn6t7Bf985DSQm7CwE2UKeeqnWI3h7ivlhaCoJiLer1y7pD62CIPAUYMr9jKYzxuifhV7sXhpMLoT56NdS9bu0Ujs+MTL/XN2Mk3sYXlNcWH7qFyrM5Cfrk8noxWpICgAqc36fdZW/fqGhVbWBQiDz+YecovFH//Sc6Z40P8U/Eq3q8r3AP6YtS4w5PuXVMFVTKfY5U4+f8kzLWHOs4AP3ime4fzzuqC98WVh5j8W2OkClw0+k82qZbpsFOS7rPKjUoY7XiEMtYOv/YxWESX+ezm3zC/fOMTrq4wOLF5/UwgUlSp2ww63kQvMbMpZZX52Y5B8bPnZtQpaOqM43nGbt9n4FmflYMSMrtp0K2YLtV42O2IXLcOrcoIIxnjndHumTrRukzc5Oaj8Zf08bUTHHWtV4KTarEtkcz0cza0dsk4xdBi4BiKRpTxtxkWtYvvV+5e5qGqzVfyqxeqyKvAETh1muGkxVEIypG6Z92Cob7nTmLsfcXR6QzWRYeoGbyykvte0DTRxCMg606ysEMnC2+joeuFP2OvqmOWYLS7G6YScdUQdnJmcWpoG8qwzkeJPJZxNVPrVI8YtKUte0RcpWsxeXNET93aQRkd7F+l75o9T/ScqBH2tpZnFudyaYNupPGXBk9bsVpGW3b25pnKAH9xKLvYch4UBAH1PvxsIa8srM+/Fj1WnHzZH221Vt/0InRB7NfBqRuwwr2w/u0FPdNisrf/uOyI249wvvhvhN34h+/zcPr0FCs83MemE7kWFzGdy/MZ2z2vI2GCRpiQZTv1H5yMm+BBdG0ZoMZeTxud9mO3HS23cabzUU3Xl0fIs/9K1HQ24haJl0pxdUTZpQZpzt/4RtMDlel7VmnsOYxpLPWB+v9ZVUz4bi2rIrweCkTO7R2JHePumFD1n/jntIWO0TB5Ky0l2sJpZibUFww3aED6yrho3cVA9Q2SnMKH85i9M5iJn+/93CxnglCjtr7ShsqmypNwv66hc5rGZzbC2H6zbmCurovmzp0+UpKkfSBsk5CWMJJTe/JkZI3qC4TOVN4V/WVu3NpJM/ahStexn55r+7bm4f2dxXnrw9ww2u6Ls3RDAJzdh4HBiDfDs8eztRwNNfk92QHHq/0qCGicfvrnzNer8YsblHy+P/8P/vNtXVkUjsIdPVFxHn4ucA/gzqN7sP7J7UUnuOXm2jGD7MFj3YVMwLMPG7LxdGs4heUXZ5/uZnRXzre2+bfOtlTPbxB/bDm8P+g4c325rOtwNa5gfaUZLTaNF07W5vE1I7Hr6A+0Ag/Zn1Yb1KOfdq03l1V/y7t+uvlKz4hjKpbIFGUXls64+ViR2c53ztF50653g+F51mf65Ti8zSJJ4LNTvOAz/9rIIoDudLMXToIkS/lJH8L/cXFaGv7zeefBuP+/artraKtpBP/Sbpceyieo/axxqPEBQ0GWem9mMaC1n2eY1+ntiB9INWo+SZJw0buBgesYKnfKKBMU5zLa2TXmOaDyk+HQEIm6QFYy08Io3oTBwVC1n+yVl8RPZKCWaVOj730NQbKvjQbxGZG+2l2t/NgA6vjUYmqJ/y5x34+nqtPOQEYfHrc3YNErXvzSISU8CDv4AbnD80Dj+rZHjvPXJZmMQTMfdGYk8uBJSod9/lz6N/yV83/sYw1hK3/79nZQU/9e54bIqGoU9cIL3GB0w5tNu34l8xt/ewYCamx0q7yWzBGY5D2xeT+U0RSqcBdTut5iYZ4S8kzLiq8zNlv3GBrBHTuRAjmP6LSOHjJ3VXaqoevcMWeNiMYNblCOdX1uDrFc+50yoUjD1VNSEJr3o8rnSavCGWOGdxlswttdcfs6UIzob/2yQiOze+xHTeVN5rN5W6h5jwjO/1iSm7FajpkLnpTtqS3Ukl5qXogjEtfquvht14UeXls7LXuP39lyvocloJEVkROw15RKUyoKpvbx9xC05vvj/F6BtvuIIrHzSw8vn61deY1L5nwLcl/frtXM0pfCZ8qr8D6WoUiWO8lcqipvsuid5UeZJlE+1dWDVCjfsWcM09+dn3MaUuYDsUce//4xhJQT3kfKxgu3aoDrPSCaTgVk715wXYORAs4xKZ4UqwBiy9Wnc0IDqNVLLQ/Bu2oxFVHpxYHitTwU6CVNWQ8W6eCd75mmdkSwu9VK3KAArfxIJQTkbbVDUDuvOgGV0wnxDcjMs7aiQ0Yv5UBjf0TgeAUZKqQRZgHvIb/jVF4ZGrUbPDVe/4feR82sKyFbkhgJWtT44Y9oxPCeYFpsaioY5HotztI8jBdNqr1DavEoRieoNF100RObx1EIdb183BxdqmV5MXQmnUkgII+cSOYGl2gSN0wU+OBFOHXkXnESqibuK2SNxtO6rYGsXFkITPw/HOXVdA4/D8W/B+YeI/05uHRoaCUEhEn0wuOKlIHY2KfADLy5A5VfJw1tbCGN397MXTT9on5XdL1KkOINg42UjaKCNJGorpxJlniNpLBaI374IvkBp8VBsnooiHbxKuyoABd9p34Ug2pkKgsJFcFEIIk71ipiiPhG9MlkBv4tc7pE9vFF4KOzj/fzjYED2ac/THukfNwp/Fgp5Px0eG5ZZds2G9zK9Z3NyCHO9zCzpI3eo1CyZnljv2azV2fs4r+6MnoxSUChyEQlWmtFIKFgWlRQeFz1o53vyqQAUfA46PemUObgw3tiIYdwFwaCXwF4gOhAJRe9Exxnb4Eo7VMgJMuv90WI06eC2Td2aS0gpgxHQsE5qfS4KrDSPIJ5Mtubx/Rn0Ei/0OCjcNqqYcfGzfKu+DQukemzRscw6v6jIUu9clFX3X6AAVPHJJ1YsugT7/+gDG5ZJDV9BofaWWGSVN8+GbXuhwf94uEJICWaY/7BSyz+pixQokMw0KjWQGREK+s5UYFOMi5u7NMmtH9l5f1xaK/33l300TUxnmvU/fjaUX09PO4D1aX0XgIKrONmL6feuJJfV3C1K+cfs5VSZ7XoRPS62iGa7Xl5qt1IYERtbFGG3ovF288w1Z7lUEMwJ67jdN3oAChZQUh4R5aF5CK4xL4mBkcG/+mLsIFdaOhiDPe3uEeFdmlHI49QE203lKQ88a81sFQlEDZkNa7ri3ppH0EzvIn2PggmK/0Q5JdKmmuuhD7+g0PIxPe+Pjf2Sf7+lN1Ha1JvNSoqKVc/EN3iQG+FJBqVGElwvQQMlKbBaa5/fWoZBy5zWBrfhlXMu/qUMhn/DKqmlXSQrL3Y2MfB9Tpyf2oFkZs920BVsmkw/4jaNf038nPwtq8GfQa/0UrengruSNfPO/iU+ECs3j+/7CfjpDjh0GXq5kfBz8s/Z9djXWualBYeb8kChyFMkANM5PYr2u292Wr7WGNdvjLCYLd36dVcI/uWA2FHWXzZ4uyWlIFkqXoJ3vzh9SwuG1D0YTrIcWmblZJYvmS9+zL+Y4NmSJOydrBG/Iq+mpIH+qbLEIEo6O4WcHBzkk5zCpqbrnNWRhtHJxZ3OWbltju7Fkow2eHwYIe58PAXOlIef/vF9YOAb8eZq+cVLeU8Lns6rsXPOmDWaMXB2v4DKMyCapxIniMPwVJT7wNHPcRoB+Isy9rCj9pZtGkEHkRo+AlAg45impsC/pWRq0ONkb7Sc48A4UWAvqo9YzW3OfPia1cJ/FZdzqzkjcGWsMEvbVZvl4RThRgpO2vK2sLg9GKDYFULvy28enW7qHpnrjfLgl8ag69nogALYAjWNlFi4qpc0aeBiSUDRK6G040peVg8/akaLrbj1S1HX/rJr1i7UqHY7jUViSy73vstd4grdY1e6iavdwTWu00Ge40Oq2kXR2aObu96tdMnpHrP1r3ZM1Fxbs3VR68gVF9qpuzSdZrplxH4iOq4TKaklDzmE1wE2KcaW2N2cOFb1X1vOsjbKreO+JXduMqjm+BLhvHwAAD8ArF/p5WZUn+5gTv4DInGl28ZLZc4XYmk/M9kfblIMg15hbzEHdfNhugho85kSy0g9YUMOb5xf5LLh2zFksS1e4XNIZJEoeO7a+Xyqyo7RO+JKuUK+dPYS5mT51ZF0bpDQjnwlArGwgkIEG68fqdIod745uIPtgCZzbbfYsIUYFKTr5FRmYjvK7Q5rU9UQxU7KM3YB8ER5PXmG5vRbkz04u/Bwi62+mqM7v8clkFeVYt8FqSb6LZdyoM9P9H4xjVaEFNEZqYNxGsPj8zk8vrhcXNYt7TpwHbm3AAcrUwx+ottD/gAtcTFFP7WfUEvOISK6DHp2AgaKlCgWeXEYzS9IChAxSu+nKTAAb6ZHWlypB2Tthl56LgLFqlTq9f1FCuKpcXHpVSkSkrW8B/vJYU35qccv0MymyCwiEfE2D3HqsRiLcrBFulxSN1oxvJ0tQeD8dXNr91aWyJ731E2aglIcm5cg8dsxwkbP9KbEadIHimsOv6QCYiqvRjsTRt4VG4kj2zHe9Yq9UeIM3Jn1faY/Q09eEFmbyCL1YF3Vh21WPM/qWibsiOZXNdpz+r4eFJjvauaKyx1b1RMLJBxUizkJchcF1jlaue4MQxWca6BCh/50R55kOJ/N+4BQKyTrbg0DZG4PwLf+LjbC7IHhpd8oortWjVY9PQ3oGPhT89swnJdV4jvS7k3NDsbbaUKN21s/tw4UgJW7O/OjBl/X4CzLcuSRO4MnWB94v/7DRF4Af7qouoPrxM0uQg/yrjLn8LT+oOV+ESO49oiwkUy3hcCIrrvQOc28L6Aq4Ijn8CleoyWa6fliYtBnaDMRqbhge7GLTIcZjkW0nqlDx9MS8OodWUggu7i4GnVS4CQKq9tpXmlnevgUZBpVJPReFu48PrN4Ka3qOTqmy7Zp1OOYeKps09Fzj2GTrikGvXOXgJi4ceePBhb7eRpMRpxjdDQXRVekUm/hpZSQrz315DpQWSdpMFOiLG6qKU1h17MDof7wxXaMEVKxG/S59TSqJETnzAFyiFjTZa12OQ3MhS/DKYmAKGuOhKCEVC0045g8pyhxUh6dvnjT9+x0h5tlZAjLstGZxaesKs2I7+POzmN2n9TS6SfWZEq0DCrgIJvLz46PixBjUbDHQblR5RR4mHn+Jro6RFDAd+Zu0/h56FsjXmpDiCkirkHfR5tWLBw548/RYNUEiV49ZaBNk3h75OKKOKVsJ05Om/Rhe2sLnHnlR3tUiJpqyIyCHs6fktYpXVmuNBsNKQUtPwBOhooO6k7Ls8hd7EaN5jFWFSgx5N9FB1VjHQfNWuyUMvlYaMCfJcQo4okd4ZvKNooC7sw3Rb7RxeVwAJgc2GBURmcWl4PT8DYfaMKwOzHXMSxv/eGGXp6SuIgR7fR21CRuQs70WaYrPWUmDKV190GDMkTkJRmQf+UCwIp4lHb317ZaIVTMGaip1o42Z35MmWtxFT5giwvGsdTs4RD9D6CDYfvz/lrzPfCJ86rr8r4/2LH9tQa+y09CegxH7grsrduktRUF+ExHmn50U91QMlswrOa2fKCOyreB4AUyAVd/fWHa2D31UpVhKDg8L1S5EOSQhOIyc4BPrdHcHwSfYarruUvENeasUScb+1WWGwKRk52GJqym80jOaUj/TWspa7v8uQ4ODOev50bBAi5AnO2gdcU+pKk6CsZHpTFWooEok7sUc/gZkF0BxwiYMy+eqxxBXh4brfr4cWF+oZWGOiNQ/uKHWhAcBeVZiwTAvXlTAgKA5cmE1/44mn32p/BhQFn46fWr37bw23W9+593/xbTNooAgA0NNxD4T7SK+2BDxkDpL5/KW3HNRjkKAL7IfoEDV8HK4l2abM5260xeWrz9ekonN8cGDTOiyUcnycYaGR0fmu9hRTZWIwF8aiT+v4NlWHFgPOeNo8KMbogBKHrTSf5H+gAZ+vbLHfv1PKaq+nPytBQjSz7U0qQlVT+QAdIO8ZBXrTgEWRT6euwTdGY45ZCRNbKKQGMIoSJdriDSKl0gNsAm2AFx2AJbgSATusAm8BSVCsKRw+TIYyHL0XopS/m0JA2sRhGq1Znda8SmtGgyntncima6EIwsr0Dzc04FZRxNhmaznmzRLh4rCAYN+Q3wALZW3qaPWmVRElSk+L3xT7S0asZbE+Mlh07FaXIBX/gZdrVHpgk0VQFKqDb5JweoOfzqdx8U1XmyLQY80IFh+We+2cp+vtAz0rETHR5BBpggj+nbzQFYE0BF1q9JCpatv4UPJYwExDPVAqBns/OJM9NlPrcqccRd8THgdigakQjJ/7098cDV27HtJHY70STt26nNZHubdS+F7gUBQJBq0e0QQKaOAaj8CkSZ8wNSlfMzEoNvfZ2TREk4WJiiREuBhWdWJVhUxYkUBIRmmcAJRThJdkyIWKMyRtc0ocRBapbRmmaxlZ40GmVSkZxmek6jMDkothDBBC2qeCrNMnFaiBOJIWQCItl6nKgyZp+Op6+FJJeFKKnidmcxZ8LsQ+EMNwTpJrhQeBpijFa0bTkO6TvBJsl04AvVvF6yQwDYifY1DWadZKE2kSjjMgCTUf0sgStPfi70hfRzml5ob64kXviAOKegoa5OQwYRyqfFlS+tfYP3TXweI8rnOcefnc8pAqnL/5xehjk1XeN1kt6Jqej4/D/HxqKjfSjxvTk8aN6hwTvYFR1ooohu1tjXsGA0LkS20uxuRzNnLYGIGvfCa5NVbMKqEW9nldlRneSlZLOt6HmBRSYtovDPXcYvr2MhL818xt5caigp7s2kRdMJj6lETmZvIjbjUfLGQjMalkUjoT3nAm/IN4Oe8wY80++NFjm7dSRuUS/XWMYYet5LTuZF54GNTeZ5i4qeM185bR7wjHnAavs1DRmLXmRONDlpks8CPcnQXmKVkijcgls12kug3OJr4GmCHh5g2aJuqvKLrWRiKYJNsiDqNO6qtTzfdZRTfscl9FoWN+y0tcvQWmW8+qtRZcUchuCvmc95eWuRTCohoxg0AxK+91kaSJ8cgYe+Cw9BPKq0UIYiuGvqYw6BBjR3DuO3eKeZc6PstORRuFN7wLcbNVu/1kwa2OEpw3yzJwnxVvrt5k3Y3DvCnVn51YJ5xieiZNL0mNW697q472tC+TojQ4GCveFosPygCsSqEmvK9ZKCl+iwfOVLeOp/4s+qBQAA) format('woff2'); + unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; +} + +/* open-sans-greek-ext-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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format('woff2'); + unicode-range: U+1F00-1FFF; +} + +/* open-sans-greek-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0370-03FF; +} + +/* open-sans-hebrew-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0590-05FF, U+20AA, U+25CC, U+FB1D-FB4F; +} + +/* open-sans-vietnamese-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB; +} + +/* open-sans-latin-ext-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; +} + +/* open-sans-latin-400-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 400; + src: 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) format('woff2'); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +/*!******************************************************************************************************!*\ + !*** css ../../node_modules/css-loader/dist/cjs.js!../../node_modules/@fontsource/open-sans/700.css ***! + \******************************************************************************************************/ +/* open-sans-cyrillic-ext-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; +} + +/* open-sans-cyrillic-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; +} + +/* open-sans-greek-ext-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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format('woff2'); + unicode-range: U+1F00-1FFF; +} + +/* open-sans-greek-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0370-03FF; +} + +/* open-sans-hebrew-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0590-05FF, U+20AA, U+25CC, U+FB1D-FB4F; +} + +/* open-sans-vietnamese-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB; +} + +/* open-sans-latin-ext-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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) format('woff2'); + unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; +} + +/* open-sans-latin-700-normal*/ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-display: swap; + font-weight: 700; + src: 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100%; +} + +.jspsych-content { + max-width: 95%; + /* this is mainly an IE 10-11 fix */ + text-align: center; + margin: auto; + /* this is for overflowing content */ +} + +.jspsych-top { + align-items: flex-start; +} + +.jspsych-middle { + align-items: center; +} + +.cp_image { + height: 450px; + display: block; + margin-left: auto; + margin-right: auto; +} + +.dp_image { + height: 450px; + display: block; + margin-left: auto; + margin-right: auto; +} + +.cn_image { + height: 450px; + display: block; + margin-left: auto; + margin-right: auto; +} + +.dn_image { + height: 450px; + display: block; + margin-left: auto; + margin-right: auto; +} + +/* fonts and type */ +.jspsych-display-element { + font-family: "Open Sans", "Arial", sans-serif; + font-size: 18px; + line-height: 1.6em; +} + +/* Form elements like input fields and buttons */ +.jspsych-display-element input[type=text] { + font-family: "Open Sans", "Arial", sans-serif; + font-size: 14px; +} + +/* borrowing Bootstrap style for btn elements, but combining styles a bit */ +.jspsych-btn { + display: inline-block; + padding: 6px 12px; + margin: 0px; + font-size: 14px; + font-weight: 400; + font-family: "Open Sans", "Arial", sans-serif; + cursor: pointer; + line-height: 1.4; + text-align: center; + white-space: nowrap; + vertical-align: middle; + background-image: none; + border: 1px solid transparent; + border-radius: 4px; + color: #333; + background-color: #fff; + border-color: #ccc; +} + +/* only apply the hover style on devices with a mouse/pointer that can hover - issue #977 */ +@media (hover: hover) { + .jspsych-btn:hover { + background-color: #ddd; + border-color: #aaa; + } +} + +.jspsych-btn:active { + background-color: #ddd; + border-color: #000000; +} + +.jspsych-btn:disabled { + background-color: #eee; + color: #aaa; + border-color: #ccc; + cursor: not-allowed; +} + +/* custom style for input[type="range] (slider) to improve alignment between positions and labels */ +.jspsych-slider { + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; + width: 100%; + background: transparent; +} + +.jspsych-slider:focus { + outline: none; +} + +/* track */ +.jspsych-slider::-webkit-slider-runnable-track { + appearance: none; + -webkit-appearance: none; + width: 100%; + height: 8px; + cursor: pointer; + background: #eee; + box-shadow: 0px 0px 0px #000000, 0px 0px 0px #0d0d0d; + border-radius: 2px; + border: 1px solid #aaa; +} + +.jspsych-slider::-moz-range-track { + appearance: none; + width: 100%; + height: 8px; + cursor: pointer; + background: #eee; + box-shadow: 0px 0px 0px #000000, 0px 0px 0px #0d0d0d; + border-radius: 2px; + border: 1px solid #aaa; +} + +.jspsych-slider::-ms-track { + appearance: none; + width: 99%; + height: 14px; + cursor: pointer; + background: #eee; + box-shadow: 0px 0px 0px #000000, 0px 0px 0px #0d0d0d; + border-radius: 2px; + border: 1px solid #aaa; +} + +/* thumb */ +.jspsych-slider::-webkit-slider-thumb { + border: 1px solid #666; + height: 24px; + width: 15px; + border-radius: 5px; + background: #ffffff; + cursor: pointer; + -webkit-appearance: none; + margin-top: -9px; +} + +.jspsych-slider::-moz-range-thumb { + border: 1px solid #666; + height: 24px; + width: 15px; + border-radius: 5px; + background: #ffffff; + cursor: pointer; +} + +.jspsych-slider::-ms-thumb { + border: 1px solid #666; + height: 20px; + width: 15px; + border-radius: 5px; + background: #ffffff; + cursor: pointer; + margin-top: -2px; +} + +/* jsPsych progress bar */ +#jspsych-progressbar-container { + color: #555; + border-bottom: 1px solid #dedede; + background-color: #f9f9f9; + margin-bottom: 1em; + text-align: center; + padding: 8px 0px; + width: 100%; + line-height: 1em; +} + +#jspsych-progressbar-container span { + font-size: 14px; + padding-right: 14px; +} + +#jspsych-progressbar-outer { + background-color: #eee; + width: 50%; + margin: auto; + height: 14px; + display: inline-block; + vertical-align: middle; + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} + +#jspsych-progressbar-inner { + background-color: #aaa; + width: 0%; + height: 100%; +} + +/* Control appearance of jsPsych.data.displayData() */ +#jspsych-data-display { + text-align: left; +} \ No newline at end of file diff --git a/docs/experiment_1/index.html b/docs/experiment_1/index.html new file mode 100644 index 0000000..d20dd3d --- /dev/null +++ b/docs/experiment_1/index.html @@ -0,0 +1,195 @@ + + + + Explanation Valence + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/experiment_1/js/conj_neg_long.js b/docs/experiment_1/js/conj_neg_long.js new file mode 100644 index 0000000..c2c726f --- /dev/null +++ b/docs/experiment_1/js/conj_neg_long.js @@ -0,0 +1,352 @@ +/* +* File: conj_neg_long.js +* ----- +* Information for putting the pages for the CN trial together. +* This is the variation in which the participant runs through the CN trial first, then the DN trial. +*/ + +// CN long instructions +let cn_arr_long = []; +let cn_len_long = trialData["conjunctive_negative"]["instructions_long"].length; +for (let i = 0; i < cn_len_long; i++) { + cn_arr_long.push( + trialData["conjunctive_negative"]["instructions_long"][i] + ); +} + +var instruction_pages_long = []; +for (var i = 0; i < cn_len_long; i++) { + let slide = '
' + + '

' + cn_arr_long[i] + '

'; + instruction_pages_long.push(slide); +} + +const cn_instructions_long = { + type: jsPsychInstructions, + pages: instruction_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.0625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +cn_arr_long = []; +cn_len_long = trialData["conjunctive_negative"]["instructions_comp"].length; + +for (let i = 0; i < cn_len_long; i++) { + cn_arr_long.push({ + name: 'cn_instructions_comp_' + [i], + prompt: trialData["conjunctive_negative"]["instructions_comp"][i]["question"], + options: trialData["conjunctive_negative"]["instructions_comp"][i]["options"], + required: true + }); +} +const cn_instructions_comp_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: cn_arr_long, + on_start: function() { + jsPsych.setProgressBar(0.125); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +cn_arr_long = []; +cn_len_long = trialData["conjunctive_negative"]["round_one_long"].length; +for (let i = 0; i < cn_len_long; i++) { + cn_arr_long.push( + trialData["conjunctive_negative"]["round_one_long"][i] + ); +} + +var round_one_pages_long = []; +for (var i = 0; i < cn_len_long; i++) { + let slide = '
' + + '

' + cn_arr_long[i] + '

'; + round_one_pages_long.push(slide); +} +const cn_round_one_long = { + type: jsPsychInstructions, + pages: round_one_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.1875); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const cn_round_one_comp_q1_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cn_round_one_comp_q1_0', + prompt: trialData["conjunctive_negative"]["round_one_comp_q1"][0]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'cn_round_one_comp_q1_1', + prompt: trialData["conjunctive_negative"]["round_one_comp_q1"][1]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.25); + } +}; +const cn_round_one_comp_q2_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cn_round_two_comp_q2_0', + prompt: trialData["conjunctive_negative"]["round_one_comp_q2"][0]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'cn_round_two_comp_q2_1', + prompt: trialData["conjunctive_negative"]["round_one_comp_q2"][1]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +cn_arr_long = []; +cn_len_long = trialData["conjunctive_negative"]["round_two_long"].length; +for (let i = 0; i < cn_len_long; i++) { + cn_arr_long.push( + trialData["conjunctive_negative"]["round_two_long"][i] + ); +} + +var round_two_pages_long = []; +for (var i = 0; i < cn_len_long; i++) { + let slide = '
' + + '

' + cn_arr_long[i] + '

'; + round_two_pages_long.push(slide); +} +const cn_round_two_long = { + type: jsPsychInstructions, + pages: round_two_pages_long, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.3125); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +const cn_selection_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Zarah gets to turn one of the switches OFF. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn OFF, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'cn_selection_task', + prompt: trialData["conjunctive_negative"]["selection_task"]["question"], + options: trialData["conjunctive_negative"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + +const cn_loop_node_instructions_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cn_instructions_long, + cn_instructions_comp_long + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_instructions_key = trialData["conjunctive_negative"]["cn_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(cn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const cn_conditional_node_instructions_long = { + timeline: [ + cn_loop_node_instructions_long + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + + const cn_instructions_key = trialData["conjunctive_negative"]["cn_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(cn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const cn_loop_node_round_one_q1_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cn_round_one_long, + cn_round_one_comp_q1_long, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_participant_key = trialData["conjunctive_negative"]["cn_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(cn_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const cn_conditional_node_round_one_q1_long = { + timeline: [ + cn_loop_node_round_one_q1_long + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_participant_key = trialData["conjunctive_negative"]["cn_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(cn_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const cn_loop_node_round_one_q2_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cn_round_one_long, + cn_round_one_comp_q2_long, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_teammate_key = trialData["conjunctive_negative"]["cn_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(cn_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const cn_conditional_node_round_one_q2_long = { + timeline: [ + cn_loop_node_round_one_q2_long + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_teammate_key = trialData["conjunctive_negative"]["cn_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(cn_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const cn_intervention_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches OFF manually. \ + The other switch will turn ON or OFF based on its probability.

" + + "", + questions: [ + { + name: 'cn_intervention_task', + prompt: trialData["conjunctive_negative"]["intervention_task"]["question"], + options: trialData["conjunctive_negative"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const cn_long = [ + cn_instructions_long, + cn_instructions_comp_long, + cn_conditional_node_instructions_long, + cn_intervention_task_long, + cn_round_one_long, + cn_round_one_comp_q1_long, + cn_conditional_node_round_one_q1_long, + cn_round_one_comp_q2_long, + cn_conditional_node_round_one_q2_long, + cn_round_two_long, + cn_selection_task_long +] \ No newline at end of file diff --git a/docs/experiment_1/js/conj_neg_short.js b/docs/experiment_1/js/conj_neg_short.js new file mode 100644 index 0000000..03a2a38 --- /dev/null +++ b/docs/experiment_1/js/conj_neg_short.js @@ -0,0 +1,349 @@ +/* +* File: conj_neg_short.js +* ----- +* Information for putting the pages for the CN trial together. +* This is the variation in which the participant runs through the DN trial first, then the CN trial. +*/ + +// CN short instructions +let cn_arr_short = []; +let cn_len_short = trialData["conjunctive_negative"]["instructions_short"].length; +for (let i = 0; i < cn_len_short; i++) { + cn_arr_short.push( + trialData["conjunctive_negative"]["instructions_short"][i] + ); +} + +var instruction_pages_short = []; +for (var i = 0; i < cn_len_short; i++) { + let slide = '
' + + '

' + cn_arr_short[i] + '

'; + instruction_pages_short.push(slide); +} + +const cn_instructions_short = { + type: jsPsychInstructions, + pages: instruction_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.4375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +cn_arr_short = []; +cn_len_short = trialData["conjunctive_negative"]["instructions_comp"].length; +for (let i = 0; i < cn_len_short; i++) { + cn_arr_short.push({ + name: 'cn_instructions_comp_' + [i], + prompt: trialData["conjunctive_negative"]["instructions_comp"][i]["question"], + options: trialData["conjunctive_negative"]["instructions_comp"][i]["options"], + required: true + }); +} +const cn_instructions_comp_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: cn_arr_short, + on_start: function() { + jsPsych.setProgressBar(0.5); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +cn_arr_short = []; +cn_len_short = trialData["conjunctive_negative"]["round_one_short"].length; +for (let i = 0; i < cn_len_short; i++) { + cn_arr_short.push( + trialData["conjunctive_negative"]["round_one_short"][i] + ); +} + +var round_one_pages_short = []; +for (var i = 0; i < cn_len_short; i++) { + let slide = '
' + + '

' + cn_arr_short[i] + '

'; + round_one_pages_short.push(slide); +} +const cn_round_one_short = { + type: jsPsychInstructions, + pages: round_one_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.5625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const cn_round_one_comp_q1_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cn_round_one_comp_q1_0', + prompt: trialData["conjunctive_negative"]["round_one_comp_q1"][0]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'cn_round_one_comp_q1_1', + prompt: trialData["conjunctive_negative"]["round_one_comp_q1"][1]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.625); + } +}; +const cn_round_one_comp_q2_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cn_round_two_comp_q2_0', + prompt: trialData["conjunctive_negative"]["round_one_comp_q2"][0]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'cn_round_two_comp_q2_1', + prompt: trialData["conjunctive_negative"]["round_one_comp_q2"][1]["question"], + options: trialData["conjunctive_negative"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +cn_arr_short = []; +cn_len_short = trialData["conjunctive_negative"]["round_two_short"].length; +for (let i = 0; i < cn_len_short; i++) { + cn_arr_short.push( + trialData["conjunctive_negative"]["round_two_short"][i] + ); +} + +var round_two_pages_short = []; +for (var i = 0; i < cn_len_short; i++) { + let slide = '
' + + '

' + cn_arr_short[i] + '

'; + round_two_pages_short.push(slide); +} +const cn_round_two_short = { + type: jsPsychInstructions, + pages: round_two_pages_short, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.6875); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +const cn_selection_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Zarah gets to turn one of the switches OFF. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn OFF, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'cn_selection_task', + prompt: trialData["conjunctive_negative"]["selection_task"]["question"], + options: trialData["conjunctive_negative"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.75); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +} + +const cn_loop_node_instructions_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cn_instructions_short, + cn_instructions_comp_short + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_instructions_key = trialData["conjunctive_negative"]["cn_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(cn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const cn_conditional_node_instructions_short = { + timeline: [ + cn_loop_node_instructions_short + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_instructions_key = trialData["conjunctive_negative"]["cn_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(cn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const cn_loop_node_round_one_q1_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cn_round_one_short, + cn_round_one_comp_q1_short, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_participant_key = trialData["conjunctive_negative"]["cn_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(cn_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const cn_conditional_node_round_one_q1_short = { + timeline: [ + cn_loop_node_round_one_q1_short + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_participant_key = trialData["conjunctive_negative"]["cn_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(cn_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const cn_loop_node_round_one_q2_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cn_round_one_short, + cn_round_one_comp_q2_short, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_teammate_key = trialData["conjunctive_negative"]["cn_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(cn_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const cn_conditional_node_round_one_q2_short = { + timeline: [ + cn_loop_node_round_one_q2_short + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cn_teammate_key = trialData["conjunctive_negative"]["cn_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(cn_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const cn_intervention_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches OFF manually. \ + The other switch will turn ON or OFF based on its probability.

" + + "", + questions: [ + { + name: 'cn_intervention_task', + prompt: trialData["conjunctive_negative"]["intervention_task"]["question"], + options: trialData["conjunctive_negative"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const cn_short = [ + cn_instructions_short, + cn_instructions_comp_short, + cn_conditional_node_instructions_short, + cn_intervention_task_short, + cn_round_one_short, + cn_round_one_comp_q1_short, + cn_conditional_node_round_one_q1_short, + cn_round_one_comp_q2_short, + cn_conditional_node_round_one_q2_short, + cn_round_two_short, + cn_selection_task_short +] \ No newline at end of file diff --git a/docs/experiment_1/js/conj_pos_long.js b/docs/experiment_1/js/conj_pos_long.js new file mode 100644 index 0000000..ec0a55f --- /dev/null +++ b/docs/experiment_1/js/conj_pos_long.js @@ -0,0 +1,357 @@ +/* +* File: conj_pos_long.js +* ----- +* Information for putting the pages for the CP trial together. +* This is the variation in which the participant runs through the CP trial first, then the DP trial. +*/ + +// CP long instructions +let cp_arr_long = []; +let cp_len_long = trialData["conjunctive_positive"]["instructions_long"].length; +for (let i = 0; i < cp_len_long; i++) { + cp_arr_long.push( + trialData["conjunctive_positive"]["instructions_long"][i] + ); +} + +var instruction_pages_long = []; +for (var i = 0; i < cp_len_long; i++) { + let slide = '
' + + '

' + cp_arr_long[i] + '

'; + instruction_pages_long.push(slide); +} + +const cp_instructions_long = { + type: jsPsychInstructions, + pages: instruction_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.0625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +cp_arr_long = []; +cp_len_long = trialData["conjunctive_positive"]["instructions_comp"].length; + +for (let i = 0; i < cp_len_long; i++) { + cp_arr_long.push({ + name: 'cp_instructions_comp_' + [i], + prompt: trialData["conjunctive_positive"]["instructions_comp"][i]["question"], + options: trialData["conjunctive_positive"]["instructions_comp"][i]["options"], + required: true + }); +} +const cp_instructions_comp_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: cp_arr_long, + on_start: function() { + jsPsych.setProgressBar(0.125); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +cp_arr_long = []; +cp_len_long = trialData["conjunctive_positive"]["round_one_long"].length; +for (let i = 0; i < cp_len_long; i++) { + cp_arr_long.push( + trialData["conjunctive_positive"]["round_one_long"][i] + ); +} + +var round_one_pages_long = []; +for (var i = 0; i < cp_len_long; i++) { + let slide = '
' + + '

' + cp_arr_long[i] + '

'; + round_one_pages_long.push(slide); +} +const cp_round_one_long = { + type: jsPsychInstructions, + pages: round_one_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.1875); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const cp_round_one_comp_q1_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cp_round_one_comp_q1_0', + prompt: trialData["conjunctive_positive"]["round_one_comp_q1"][0]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'cp_round_one_comp_q1_1', + prompt: trialData["conjunctive_positive"]["round_one_comp_q1"][1]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.25); + } +}; +const cp_round_one_comp_q2_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cp_round_two_comp_q2_0', + prompt: trialData["conjunctive_positive"]["round_one_comp_q2"][0]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'cp_round_two_comp_q2_1', + prompt: trialData["conjunctive_positive"]["round_one_comp_q2"][1]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +cp_arr_long = []; +cp_len_long = trialData["conjunctive_positive"]["round_two_long"].length; +for (let i = 0; i < cp_len_long; i++) { + cp_arr_long.push( + trialData["conjunctive_positive"]["round_two_long"][i] + ); +} + +var round_two_pages_long = []; +for (var i = 0; i < cp_len_long; i++) { + let slide = '
' + + '

' + cp_arr_long[i] + '

'; + round_two_pages_long.push(slide); +} +const cp_round_two_long = { + type: jsPsychInstructions, + pages: round_two_pages_long, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.3125); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +const cp_selection_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Zarah gets to turn one of the switches ON. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn ON, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'cp_selection_task', + prompt: trialData["conjunctive_positive"]["selection_task"]["question"], + options: trialData["conjunctive_positive"]["selection_task"]["options"], + required: true, + button_label: "hi" + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + +const cp_loop_node_instructions_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cp_instructions_long, + cp_instructions_comp_long + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_instructions_key = trialData["conjunctive_positive"]["cp_instructions_key"]; + + console.log(trial_data); + console.log(cp_instructions_key); + if (JSON.stringify(trial_data) === JSON.stringify(cp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const cp_conditional_node_instructions_long = { + timeline: [ + cp_loop_node_instructions_long + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + + const cp_instructions_key = trialData["conjunctive_positive"]["cp_instructions_key"]; + + console.log(trial_data); + console.log(cp_instructions_key); + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(cp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const cp_loop_node_round_one_q1_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cp_round_one_long, + cp_round_one_comp_q1_long, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_participant_key = trialData["conjunctive_positive"]["cp_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(cp_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const cp_conditional_node_round_one_q1_long = { + timeline: [ + cp_loop_node_round_one_q1_long + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_participant_key = trialData["conjunctive_positive"]["cp_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(cp_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const cp_loop_node_round_one_q2_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cp_round_one_long, + cp_round_one_comp_q2_long, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_teammate_key = trialData["conjunctive_positive"]["cp_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(cp_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const cp_conditional_node_round_one_q2_long = { + timeline: [ + cp_loop_node_round_one_q2_long + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_teammate_key = trialData["conjunctive_positive"]["cp_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(cp_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const cp_intervention_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches ON manually. \ + The other switch will turn ON or OFF based on its probability.

" + + "", + questions: [ + { + name: 'cp_intervention_task', + prompt: trialData["conjunctive_positive"]["intervention_task"]["question"], + options: trialData["conjunctive_positive"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const cp_long = [ + cp_instructions_long, + cp_instructions_comp_long, + cp_conditional_node_instructions_long, + cp_intervention_task_long, + cp_round_one_long, + cp_round_one_comp_q1_long, + cp_conditional_node_round_one_q1_long, + cp_round_one_comp_q2_long, + cp_conditional_node_round_one_q2_long, + cp_round_two_long, + cp_selection_task_long +] \ No newline at end of file diff --git a/docs/experiment_1/js/conj_pos_short.js b/docs/experiment_1/js/conj_pos_short.js new file mode 100644 index 0000000..e2d2294 --- /dev/null +++ b/docs/experiment_1/js/conj_pos_short.js @@ -0,0 +1,349 @@ +/* +* File: conj_pos_short.js +* ----- +* Information for putting the pages for the CP trial together. +* This is the variation in which the participant runs through the DP trial first, then the CP trial. +*/ + +// CP short instructions +let cp_arr_short = []; +let cp_len_short = trialData["conjunctive_positive"]["instructions_short"].length; +for (let i = 0; i < cp_len_short; i++) { + cp_arr_short.push( + trialData["conjunctive_positive"]["instructions_short"][i] + ); +} + +var instruction_pages_short = []; +for (var i = 0; i < cp_len_short; i++) { + let slide = '
' + + '

' + cp_arr_short[i] + '

'; + instruction_pages_short.push(slide); +} + +const cp_instructions_short = { + type: jsPsychInstructions, + pages: instruction_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.4375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +cp_arr_short = []; +cp_len_short = trialData["conjunctive_positive"]["instructions_comp"].length; +for (let i = 0; i < cp_len_short; i++) { + cp_arr_short.push({ + name: 'cp_instructions_comp_' + [i], + prompt: trialData["conjunctive_positive"]["instructions_comp"][i]["question"], + options: trialData["conjunctive_positive"]["instructions_comp"][i]["options"], + required: true + }); +} +const cp_instructions_comp_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: cp_arr_short, + on_start: function() { + jsPsych.setProgressBar(0.5); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +cp_arr_short = []; +cp_len_short = trialData["conjunctive_positive"]["round_one_short"].length; +for (let i = 0; i < cp_len_short; i++) { + cp_arr_short.push( + trialData["conjunctive_positive"]["round_one_short"][i] + ); +} + +var round_one_pages_short = []; +for (var i = 0; i < cp_len_short; i++) { + let slide = '
' + + '

' + cp_arr_short[i] + '

'; + round_one_pages_short.push(slide); +} +const cp_round_one_short = { + type: jsPsychInstructions, + pages: round_one_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.5625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const cp_round_one_comp_q1_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cp_round_one_comp_q1_0', + prompt: trialData["conjunctive_positive"]["round_one_comp_q1"][0]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'cp_round_one_comp_q1_1', + prompt: trialData["conjunctive_positive"]["round_one_comp_q1"][1]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.625); + } +}; +const cp_round_one_comp_q2_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'cp_round_two_comp_q2_0', + prompt: trialData["conjunctive_positive"]["round_one_comp_q2"][0]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'cp_round_two_comp_q2_1', + prompt: trialData["conjunctive_positive"]["round_one_comp_q2"][1]["question"], + options: trialData["conjunctive_positive"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +cp_arr_short = []; +cp_len_short = trialData["conjunctive_positive"]["round_two_short"].length; +for (let i = 0; i < cp_len_short; i++) { + cp_arr_short.push( + trialData["conjunctive_positive"]["round_two_short"][i] + ); +} + +var round_two_pages_short = []; +for (var i = 0; i < cp_len_short; i++) { + let slide = '
' + + '

' + cp_arr_short[i] + '

'; + round_two_pages_short.push(slide); +} +const cp_round_two_short = { + type: jsPsychInstructions, + pages: round_two_pages_short, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.6875); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +const cp_selection_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Zarah gets to turn one of the switches ON. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn ON, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'cp_selection_task', + prompt: trialData["conjunctive_positive"]["selection_task"]["question"], + options: trialData["conjunctive_positive"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.75); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +} + +const cp_loop_node_instructions_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cp_instructions_short, + cp_instructions_comp_short + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_instructions_key = trialData["conjunctive_positive"]["cp_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(cp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const cp_conditional_node_instructions_short = { + timeline: [ + cp_loop_node_instructions_short + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_instructions_key = trialData["conjunctive_positive"]["cp_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(cp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const cp_loop_node_round_one_q1_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cp_round_one_short, + cp_round_one_comp_q1_short, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_participant_key = trialData["conjunctive_positive"]["cp_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(cp_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const cp_conditional_node_round_one_q1_short = { + timeline: [ + cp_loop_node_round_one_q1_short + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_participant_key = trialData["conjunctive_positive"]["cp_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(cp_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const cp_loop_node_round_one_q2_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + cp_round_one_short, + cp_round_one_comp_q2_short, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_teammate_key = trialData["conjunctive_positive"]["cp_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(cp_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const cp_conditional_node_round_one_q2_short = { + timeline: [ + cp_loop_node_round_one_q2_short + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const cp_teammate_key = trialData["conjunctive_positive"]["cp_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(cp_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const cp_intervention_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches ON manually. \ + The other switch will turn ON or OFF based on its probability.

" + + "", + questions: [ + { + name: 'cp_intervention_task', + prompt: trialData["conjunctive_positive"]["intervention_task"]["question"], + options: trialData["conjunctive_positive"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const cp_short = [ + cp_instructions_short, + cp_instructions_comp_short, + cp_conditional_node_instructions_short, + cp_intervention_task_short, + cp_round_one_short, + cp_round_one_comp_q1_short, + cp_conditional_node_round_one_q1_short, + cp_round_one_comp_q2_short, + cp_conditional_node_round_one_q2_short, + cp_round_two_short, + cp_selection_task_short +] \ No newline at end of file diff --git a/docs/experiment_1/js/consent.js b/docs/experiment_1/js/consent.js new file mode 100644 index 0000000..fb2e3d4 --- /dev/null +++ b/docs/experiment_1/js/consent.js @@ -0,0 +1,73 @@ +const consent = { + type: jsPsychHtmlButtonResponse, + stimulus: ` +

Consent Form

+
+

+ Description: + Welcome! You are invited to participate in a research study in cognitive psychology. + You will be asked to perform various tasks on a computer which may include: + looking at images or videos, listening to sounds, reading scenarios, or playing games. + You may be asked a number of different questions about making judgments and interpreting + people's actions. All information collected will remain confidential. +

+

+ Risks and benefits: + Risks involved in this study are the same as those normally associated + with using a computer (e.g., mild eye/arm strain). If you have any + pre-existing conditions that might make reading and completing a computer-based + survey strenuous for you, you should probably elect to not participate in this + study. If at any time during the study you feel unable to participate because + you are experiencing strain, you may end your participation without penalty. + We cannot and do not guarantee or promise that you will receive any benefits + from this study. Your decision whether or not to participate in this study will + not affect your employment, medical care, and/or grades in school. +

+

+ Time involvement: + Your involvement in this study should take about 10-15 minutes. +

+

+ Payment: + If recruitment materials indicate payment (e.g., Prolific or other recruitment), you + will receive compensation as indicated. +

+ Subject's rights: + If you have read this form and have decided to participate in this project, please + understand your participation is voluntary and you have the right to withdraw your + consent or discontinue participation at any time without penalty or loss of benefits + to which you are otherwise entitled. The alternative is not to participate. + You have the right to refuse to answer particular questions. Your individual privacy will + be maintained in all published and written data resulting from the study. No personally + identifying information is ever revealed to the researchers. +

+

+ Contact information: + If you have any questions, concerns or complaints about this research study, + its procedures, or risks and benefits, you should ask the Protocol Director, + (Professor Tobias Gerstenberg, Phone: (650) 725-2431; Email: gerstenberg@stanford.edu). + If you are not satisfied with how this study is being conducted, or if you have any + concerns, complaints, or general questions about the research or your rights as a participant, + please contact the Stanford Institutional Review Board (IRB) to speak to someone independent of + the research team at (650) 723-2480 or toll free at 1-866-680-2906. You can also write to + the Stanford IRB, Stanford University, 1705 El Camino Real, Palo Alto, CA 94306 or contact + the IRB by email at irbnonmed@stanford.edu. +
+

+

+ You may want to print a copy of this consent form to keep. By clicking the button below, + you acknowledge that you have read the above information, that you are 18 years of age, + or older and give your consent to participate in our internet-based study and consent for + us to analyze the resulting data. +

+
+

+ Do you agree with the terms of the experiment as explained above? +

+ `, + choices: ['I agree'], + on_start: function () { + jsPsych.setProgressBar(0); + } +}; \ No newline at end of file diff --git a/docs/experiment_1/js/demographics.js b/docs/experiment_1/js/demographics.js new file mode 100644 index 0000000..7f464c7 --- /dev/null +++ b/docs/experiment_1/js/demographics.js @@ -0,0 +1,61 @@ +const demographic_form = { + type: jsPsychSurveyHtmlForm, + data: { + "page_type": "participant_survey", + }, + html: '
Please provide the following' + + ' information to complete the study.

    ' + + '
  1. Age:

  2. ' + + '
  3. Gender:


  4. ' + + '
  5. Race:







  6. ' + + '
  7. Ethnicity:
  8. ' + + '
' + + '
' + + // age text box + '

' + + // gender options + ' ' + + ' ' + + '
' + + '

' + + // race options + '
' + + '
' + + '
' + + '
' + + '
' + + '
' + + '' + + '

' + + // ethnicity options + ' ' + + '
' + + '

Please press the finish button to complete the experiment.

', + button_label: 'Finish', + on_finish: function() { jsPsych.setProgressBar(1.0); } +}; \ No newline at end of file diff --git a/docs/experiment_1/js/disj_neg_long.js b/docs/experiment_1/js/disj_neg_long.js new file mode 100644 index 0000000..9a80d80 --- /dev/null +++ b/docs/experiment_1/js/disj_neg_long.js @@ -0,0 +1,352 @@ +/* +* File: disj_neg_long.js +* ----- +* Information for putting the pages for the DN trial together. +* This is the variation in which the participant runs through the DN trial first, then the CN trial. +*/ + +// DN long instructions +let dn_arr_long = []; +let dn_len_long = trialData["disjunctive_negative"]["instructions_long"].length; +for (let i = 0; i < dn_len_long; i++) { + dn_arr_long.push( + trialData["disjunctive_negative"]["instructions_long"][i] + ); +} + +var instruction_pages_long = []; +for (var i = 0; i < dn_len_long; i++) { + let slide = '
' + + '

' + dn_arr_long[i] + '

'; + instruction_pages_long.push(slide); +} + +const dn_instructions_long = { + type: jsPsychInstructions, + pages: instruction_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.0625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +dn_arr_long = []; +dn_len_long = trialData["disjunctive_negative"]["instructions_comp"].length; + +for (let i = 0; i < dn_len_long; i++) { + dn_arr_long.push({ + name: 'dn_instructions_comp_' + [i], + prompt: trialData["disjunctive_negative"]["instructions_comp"][i]["question"], + options: trialData["disjunctive_negative"]["instructions_comp"][i]["options"], + required: true + }); +} +const dn_instructions_comp_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: dn_arr_long, + on_start: function() { + jsPsych.setProgressBar(0.125); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +dn_arr_long = []; +dn_len_long = trialData["disjunctive_negative"]["round_one_long"].length; +for (let i = 0; i < dn_len_long; i++) { + dn_arr_long.push( + trialData["disjunctive_negative"]["round_one_long"][i] + ); +} + +var round_one_pages_long = []; +for (var i = 0; i < dn_len_long; i++) { + let slide = '
' + + '

' + dn_arr_long[i] + '

'; + round_one_pages_long.push(slide); +} +const dn_round_one_long = { + type: jsPsychInstructions, + pages: round_one_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.1875); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const dn_round_one_comp_q1_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dn_round_one_comp_q1_0', + prompt: trialData["disjunctive_negative"]["round_one_comp_q1"][0]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'dn_round_one_comp_q1_1', + prompt: trialData["disjunctive_negative"]["round_one_comp_q1"][1]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.25); + } +}; +const dn_round_one_comp_q2_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dn_round_two_comp_q2_0', + prompt: trialData["disjunctive_negative"]["round_one_comp_q2"][0]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'dn_round_two_comp_q2_1', + prompt: trialData["disjunctive_negative"]["round_one_comp_q2"][1]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +dn_arr_long = []; +dn_len_long = trialData["disjunctive_negative"]["round_two_long"].length; +for (let i = 0; i < dn_len_long; i++) { + dn_arr_long.push( + trialData["disjunctive_negative"]["round_two_long"][i] + ); +} + +var round_two_pages_long = []; +for (var i = 0; i < dn_len_long; i++) { + let slide = '
' + + '

' + dn_arr_long[i] + '

'; + round_two_pages_long.push(slide); +} +const dn_round_two_long = { + type: jsPsychInstructions, + pages: round_two_pages_long, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.3125); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +const dn_selection_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Alice gets to turn one of the switches OFF. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn OFF, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'dn_selection_task', + prompt: trialData["disjunctive_negative"]["selection_task"]["question"], + options: trialData["disjunctive_negative"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + +const dn_loop_node_instructions_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dn_instructions_long, + dn_instructions_comp_long + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_instructions_key = trialData["disjunctive_negative"]["dn_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(dn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const dn_conditional_node_instructions_long = { + timeline: [ + dn_loop_node_instructions_long + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + + const dn_instructions_key = trialData["disjunctive_negative"]["dn_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(dn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const dn_loop_node_round_one_q1_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dn_round_one_long, + dn_round_one_comp_q1_long, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_participant_key = trialData["disjunctive_negative"]["dn_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(dn_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const dn_conditional_node_round_one_q1_long = { + timeline: [ + dn_loop_node_round_one_q1_long + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_participant_key = trialData["disjunctive_negative"]["dn_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(dn_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const dn_loop_node_round_one_q2_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dn_round_one_long, + dn_round_one_comp_q2_long, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_teammate_key = trialData["disjunctive_negative"]["dn_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(dn_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const dn_conditional_node_round_one_q2_long = { + timeline: [ + dn_loop_node_round_one_q2_long + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_teammate_key = trialData["disjunctive_negative"]["dn_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(dn_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const dn_intervention_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches OFF manually. \ + The other switch will turn ON or OFF based on its probability." + + "", + questions: [ + { + name: 'dn_intervention_task', + prompt: trialData["disjunctive_negative"]["intervention_task"]["question"], + options: trialData["disjunctive_negative"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const dn_long = [ + dn_instructions_long, + dn_instructions_comp_long, + dn_conditional_node_instructions_long, + dn_intervention_task_long, + dn_round_one_long, + dn_round_one_comp_q1_long, + dn_conditional_node_round_one_q1_long, + dn_round_one_comp_q2_long, + dn_conditional_node_round_one_q2_long, + dn_round_two_long, + dn_selection_task_long +] \ No newline at end of file diff --git a/docs/experiment_1/js/disj_neg_short.js b/docs/experiment_1/js/disj_neg_short.js new file mode 100644 index 0000000..2c72884 --- /dev/null +++ b/docs/experiment_1/js/disj_neg_short.js @@ -0,0 +1,350 @@ +/* +* File: disj_neg_short.js +* ----- +* Information for putting the pages for the DN trial together. +* This is the variation in which the participant runs through the CN trial first, then the DN trial. +*/ + +// DN short instructions +let dn_arr_short = []; +let dn_len_short = trialData["disjunctive_negative"]["instructions_short"].length; +for (let i = 0; i < dn_len_short; i++) { + dn_arr_short.push( + trialData["disjunctive_negative"]["instructions_short"][i] + ); +} + +var instruction_pages_short = []; +for (var i = 0; i < dn_len_short; i++) { + let slide = '
' + + '

' + dn_arr_short[i] + '

'; + instruction_pages_short.push(slide); +} + +const dn_instructions_short = { + type: jsPsychInstructions, + pages: instruction_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.4375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +dn_arr_short = []; +dn_len_short = trialData["disjunctive_negative"]["instructions_comp"].length; +for (let i = 0; i < dn_len_short; i++) { + dn_arr_short.push({ + name: 'dn_instructions_comp_' + [i], + prompt: trialData["disjunctive_negative"]["instructions_comp"][i]["question"], + options: trialData["disjunctive_negative"]["instructions_comp"][i]["options"], + required: true + }); +} +const dn_instructions_comp_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: dn_arr_short, + on_start: function() { + jsPsych.setProgressBar(0.5); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +dn_arr_short = []; +dn_len_short = trialData["disjunctive_negative"]["round_one_short"].length; +for (let i = 0; i < dn_len_short; i++) { + dn_arr_short.push( + trialData["disjunctive_negative"]["round_one_short"][i] + ); +} + +var round_one_pages_short = []; +for (var i = 0; i < dn_len_short; i++) { + let slide = '
' + + '

' + dn_arr_short[i] + '

'; + round_one_pages_short.push(slide); +} +const dn_round_one_short = { + type: jsPsychInstructions, + pages: round_one_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.5625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const dn_round_one_comp_q1_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dn_round_one_comp_q1_0', + prompt: trialData["disjunctive_negative"]["round_one_comp_q1"][0]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'dn_round_one_comp_q1_1', + prompt: trialData["disjunctive_negative"]["round_one_comp_q1"][1]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.625); + } +}; +const dn_round_one_comp_q2_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dn_round_two_comp_q2_0', + prompt: trialData["disjunctive_negative"]["round_one_comp_q2"][0]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'dn_round_two_comp_q2_1', + prompt: trialData["disjunctive_negative"]["round_one_comp_q2"][1]["question"], + options: trialData["disjunctive_negative"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +dn_arr_short = []; +dn_len_short = trialData["disjunctive_negative"]["round_two_short"].length; +for (let i = 0; i < dn_len_short; i++) { + dn_arr_short.push( + trialData["disjunctive_negative"]["round_two_short"][i] + ); +} + +var round_two_pages_short = []; +for (var i = 0; i < dn_len_short; i++) { + let slide = '
' + + '

' + dn_arr_short[i] + '

'; + round_two_pages_short.push(slide); +} +const dn_round_two_short = { + type: jsPsychInstructions, + pages: round_two_pages_short, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.6875); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +const dn_selection_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Alice gets to turn one of the switches OFF. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn OFF, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'dn_selection_task', + prompt: trialData["disjunctive_negative"]["selection_task"]["question"], + options: trialData["disjunctive_negative"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.75); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +} + +const dn_loop_node_instructions_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dn_instructions_short, + dn_instructions_comp_short + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_instructions_key = trialData["disjunctive_negative"]["dn_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(dn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const dn_conditional_node_instructions_short = { + timeline: [ + dn_loop_node_instructions_short + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_instructions_key = trialData["disjunctive_negative"]["dn_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(dn_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const dn_loop_node_round_one_q1_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dn_round_one_short, + dn_round_one_comp_q1_short, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_participant_key = trialData["disjunctive_negative"]["dn_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(dn_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const dn_conditional_node_round_one_q1_short = { + timeline: [ + dn_loop_node_round_one_q1_short + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_participant_key = trialData["disjunctive_negative"]["dn_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(dn_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const dn_loop_node_round_one_q2_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dn_round_one_short, + dn_round_one_comp_q2_short, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_teammate_key = trialData["disjunctive_negative"]["dn_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(dn_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const dn_conditional_node_round_one_q2_short = { + timeline: [ + dn_loop_node_round_one_q2_short + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dn_teammate_key = trialData["disjunctive_negative"]["dn_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(dn_teammate_key))) { + return true; + } else { + return false; + } + }, +} + + +const dn_intervention_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches OFF manually. \ + The other switch will turn ON or OFF based on its probability." + + "", + questions: [ + { + name: 'dn_intervention_task', + prompt: trialData["disjunctive_negative"]["intervention_task"]["question"], + options: trialData["disjunctive_negative"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const dn_short = [ + dn_instructions_short, + dn_instructions_comp_short, + dn_conditional_node_instructions_short, + dn_intervention_task_short, + dn_round_one_short, + dn_round_one_comp_q1_short, + dn_conditional_node_round_one_q1_short, + dn_round_one_comp_q2_short, + dn_conditional_node_round_one_q2_short, + dn_round_two_short, + dn_selection_task_short +] \ No newline at end of file diff --git a/docs/experiment_1/js/disj_pos_long.js b/docs/experiment_1/js/disj_pos_long.js new file mode 100644 index 0000000..467b62a --- /dev/null +++ b/docs/experiment_1/js/disj_pos_long.js @@ -0,0 +1,352 @@ +/* +* File: disj_pos_long.js +* ----- +* Information for putting the pages for the DP trial together. +* This is the variation in which the participant runs through the DP trial first, then the CP trial. +*/ + +// DP long instructions +let dp_arr_long = []; +let dp_len_long = trialData["disjunctive_positive"]["instructions_long"].length; +for (let i = 0; i < dp_len_long; i++) { + dp_arr_long.push( + trialData["disjunctive_positive"]["instructions_long"][i] + ); +} + +var instruction_pages_long = []; +for (var i = 0; i < dp_len_long; i++) { + let slide = '
' + + '

' + dp_arr_long[i] + '

'; + instruction_pages_long.push(slide); +} + +const dp_instructions_long = { + type: jsPsychInstructions, + pages: instruction_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.0625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +dp_arr_long = []; +dp_len_long = trialData["disjunctive_positive"]["instructions_comp"].length; + +for (let i = 0; i < dp_len_long; i++) { + dp_arr_long.push({ + name: 'dp_instructions_comp_' + [i], + prompt: trialData["disjunctive_positive"]["instructions_comp"][i]["question"], + options: trialData["disjunctive_positive"]["instructions_comp"][i]["options"], + required: true + }); +} +const dp_instructions_comp_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: dp_arr_long, + on_start: function() { + jsPsych.setProgressBar(0.125); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +dp_arr_long = []; +dp_len_long = trialData["disjunctive_positive"]["round_one_long"].length; +for (let i = 0; i < dp_len_long; i++) { + dp_arr_long.push( + trialData["disjunctive_positive"]["round_one_long"][i] + ); +} + +var round_one_pages_long = []; +for (var i = 0; i < dp_len_long; i++) { + let slide = '
' + + '

' + dp_arr_long[i] + '

'; + round_one_pages_long.push(slide); +} +const dp_round_one_long = { + type: jsPsychInstructions, + pages: round_one_pages_long, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.1875); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const dp_round_one_comp_q1_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dp_round_one_comp_q1_0', + prompt: trialData["disjunctive_positive"]["round_one_comp_q1"][0]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'dp_round_one_comp_q1_1', + prompt: trialData["disjunctive_positive"]["round_one_comp_q1"][1]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.25); + } +}; +const dp_round_one_comp_q2_long = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dp_round_two_comp_q2_0', + prompt: trialData["disjunctive_positive"]["round_one_comp_q2"][0]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'dp_round_two_comp_q2_1', + prompt: trialData["disjunctive_positive"]["round_one_comp_q2"][1]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +dp_arr_long = []; +dp_len_long = trialData["disjunctive_positive"]["round_two_long"].length; +for (let i = 0; i < dp_len_long; i++) { + dp_arr_long.push( + trialData["disjunctive_positive"]["round_two_long"][i] + ); +} + +var round_two_pages_long = []; +for (var i = 0; i < dp_len_long; i++) { + let slide = '
' + + '

' + dp_arr_long[i] + '

'; + round_two_pages_long.push(slide); +} +const dp_round_two_long = { + type: jsPsychInstructions, + pages: round_two_pages_long, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.3125); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +const dp_selection_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Alice gets to turn one of the switches ON. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn ON, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'dp_selection_task', + prompt: trialData["disjunctive_positive"]["selection_task"]["question"], + options: trialData["disjunctive_positive"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + +const dp_loop_node_instructions_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dp_instructions_long, + dp_instructions_comp_long + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_instructions_key = trialData["disjunctive_positive"]["dp_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(dp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const dp_conditional_node_instructions_long = { + timeline: [ + dp_loop_node_instructions_long + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + + const dp_instructions_key = trialData["disjunctive_positive"]["dp_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(dp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const dp_loop_node_round_one_q1_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dp_round_one_long, + dp_round_one_comp_q1_long, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_participant_key = trialData["disjunctive_positive"]["dp_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(dp_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const dp_conditional_node_round_one_q1_long = { + timeline: [ + dp_loop_node_round_one_q1_long + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_participant_key = trialData["disjunctive_positive"]["dp_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(dp_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const dp_loop_node_round_one_q2_long = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dp_round_one_long, + dp_round_one_comp_q2_long, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_teammate_key = trialData["disjunctive_positive"]["dp_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(dp_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const dp_conditional_node_round_one_q2_long = { + timeline: [ + dp_loop_node_round_one_q2_long + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_teammate_key = trialData["disjunctive_positive"]["dp_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(dp_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const dp_intervention_task_long = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches ON manually. \ + The other switch will turn ON or OFF based on its probability." + + "", + questions: [ + { + name: 'dp_intervention_task', + prompt: trialData["disjunctive_positive"]["intervention_task"]["question"], + options: trialData["disjunctive_positive"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const dp_long = [ + dp_instructions_long, + dp_instructions_comp_long, + dp_conditional_node_instructions_long, + dp_intervention_task_long, + dp_round_one_long, + dp_round_one_comp_q1_long, + dp_conditional_node_round_one_q1_long, + dp_round_one_comp_q2_long, + dp_conditional_node_round_one_q2_long, + dp_round_two_long, + dp_selection_task_long +] \ No newline at end of file diff --git a/docs/experiment_1/js/disj_pos_short.js b/docs/experiment_1/js/disj_pos_short.js new file mode 100644 index 0000000..980c89d --- /dev/null +++ b/docs/experiment_1/js/disj_pos_short.js @@ -0,0 +1,349 @@ +/* +* File: disj_pos_short.js +* ----- +* Information for putting the pages for the DP trial together. +* This is the variation in which the participant runs through the CP trial first, then the DP trial. +*/ + +// DP short instructions +let dp_arr_short = []; +let dp_len_short = trialData["disjunctive_positive"]["instructions_short"].length; +for (let i = 0; i < dp_len_short; i++) { + dp_arr_short.push( + trialData["disjunctive_positive"]["instructions_short"][i] + ); +} + +var instruction_pages_short = []; +for (var i = 0; i < dp_len_short; i++) { + let slide = '
' + + '

' + dp_arr_short[i] + '

'; + instruction_pages_short.push(slide); +} + +const dp_instructions_short = { + type: jsPsychInstructions, + pages: instruction_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.4375); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// setup comprehension checks +dp_arr_short = []; +dp_len_short = trialData["disjunctive_positive"]["instructions_comp"].length; +for (let i = 0; i < dp_len_short; i++) { + dp_arr_short.push({ + name: 'dp_instructions_comp_' + [i], + prompt: trialData["disjunctive_positive"]["instructions_comp"][i]["question"], + options: trialData["disjunctive_positive"]["instructions_comp"][i]["options"], + required: true + }); +} +const dp_instructions_comp_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please answer the following comprehension check questions.", + questions: dp_arr_short, + on_start: function() { + jsPsych.setProgressBar(0.5); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + + +// round one +dp_arr_short = []; +dp_len_short = trialData["disjunctive_positive"]["round_one_short"].length; +for (let i = 0; i < dp_len_short; i++) { + dp_arr_short.push( + trialData["disjunctive_positive"]["round_one_short"][i] + ); +} + +var round_one_pages_short = []; +for (var i = 0; i < dp_len_short; i++) { + let slide = '
' + + '

' + dp_arr_short[i] + '

'; + round_one_pages_short.push(slide); +} +const dp_round_one_short = { + type: jsPsychInstructions, + pages: round_one_pages_short, + show_clickable_nav: true, + on_start: function() { + jsPsych.setProgressBar(0.5625); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round one comprehension checks +const dp_round_one_comp_q1_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dp_round_one_comp_q1_0', + prompt: trialData["disjunctive_positive"]["round_one_comp_q1"][0]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q1"][0]["options"], + required: true, + }, + { + name: 'dp_round_one_comp_q1_1', + prompt: trialData["disjunctive_positive"]["round_one_comp_q1"][1]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q1"][1]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.625); + } +}; +const dp_round_one_comp_q2_short = { + type: jsPsychSurveyMultiChoice, + preamble: "Please respond with whether or not the following statements are true.", + questions: [ + { + name: 'dp_round_two_comp_q2_0', + prompt: trialData["disjunctive_positive"]["round_one_comp_q2"][0]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q2"][0]["options"], + required: true, + }, + { + name: 'dp_round_two_comp_q2_1', + prompt: trialData["disjunctive_positive"]["round_one_comp_q2"][1]["question"], + options: trialData["disjunctive_positive"]["round_one_comp_q2"][1]["options"], + required: true, + }, + ], + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +// round two +dp_arr_short = []; +dp_len_short = trialData["disjunctive_positive"]["round_two_short"].length; +for (let i = 0; i < dp_len_short; i++) { + dp_arr_short.push( + trialData["disjunctive_positive"]["round_two_short"][i] + ); +} + +var round_two_pages_short = []; +for (var i = 0; i < dp_len_short; i++) { + let slide = '
' + + '

' + dp_arr_short[i] + '

'; + round_two_pages_short.push(slide); +} +const dp_round_two_short = { + type: jsPsychInstructions, + pages: round_two_pages_short, + show_clickable_nav: true, + on_start: function () { + jsPsych.setProgressBar(0.6875); + }, + on_finish: function () { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +}; + + +const dp_selection_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `
` + + "In the next round of the game, \ + Alice gets to turn one of the switches ON. \ + The other switch will turn ON or OFF \ + based on its probability. Before she decides which switch to turn ON, \ + you can explain why your team scored a point in the last round.

" + + "", + questions: [ + { + name: 'dp_selection_task', + prompt: trialData["disjunctive_positive"]["selection_task"]["question"], + options: trialData["disjunctive_positive"]["selection_task"]["options"], + required: true, + }, + ], + on_start: function() { + jsPsych.setProgressBar(0.75); + }, + on_finish: function() { + var prog = jsPsych.getProgressBarCompleted(); + jsPsych.setProgressBar(prog + 0.0625); + } +} + +const dp_loop_node_instructions_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dp_instructions_short, + dp_instructions_comp_short + ], + loop_function: function (data) { + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_instructions_key = trialData["disjunctive_positive"]["dp_instructions_key"]; + + if (JSON.stringify(trial_data) === JSON.stringify(dp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + +const dp_conditional_node_instructions_short = { + timeline: [ + dp_loop_node_instructions_short + ], + conditional_function: function(){ + // get the data from the previous trial + var trial_data = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_instructions_key = trialData["disjunctive_positive"]["dp_instructions_key"]; + + // if the participant's respone doesn't match the key, replay the instructions and comp checks + if (JSON.stringify(trial_data) === JSON.stringify(dp_instructions_key)) { + return false; + } else { + return true; + } + }, +} + + +const dp_loop_node_round_one_q1_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dp_round_one_short, + dp_round_one_comp_q1_short, + ], + loop_function: function (data) { + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_participant_key = trialData["disjunctive_positive"]["dp_gameplay_q1_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(participant_response) === JSON.stringify(dp_participant_key))) { + return false; + } else { + return true; + } + }, +} + +const dp_conditional_node_round_one_q1_short = { + timeline: [ + dp_loop_node_round_one_q1_short + ], + conditional_function: function(){ + const participant_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_participant_key = trialData["disjunctive_positive"]["dp_gameplay_q1_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(participant_response) === JSON.stringify(dp_participant_key))) { + return true; + } else { + return false; + } + }, +} + +const dp_loop_node_round_one_q2_short = { + timeline: [ + { + type: jsPsychHtmlButtonResponse, + stimulus: 'Unfortunately, you missed some of the comprehension ' + + 'questions.

Please review the instructions again.', + choices: ['Review'], + }, + dp_round_one_short, + dp_round_one_comp_q2_short, + ], + loop_function: function (data) { + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_teammate_key = trialData["disjunctive_positive"]["dp_gameplay_q2_key"]; + + // if participant and teammate responses match the key, return false, do not loop + if (!!(JSON.stringify(teammate_response) === JSON.stringify(dp_teammate_key))) { + return false; + } else { + return true; + } + }, +} + +const dp_conditional_node_round_one_q2_short = { + timeline: [ + dp_loop_node_round_one_q2_short + ], + conditional_function: function(){ + const teammate_response = jsPsych.data.getLastTrialData().filter({ trial_type: 'survey-multi-choice' }).trials[0]["response"]; + const dp_teammate_key = trialData["disjunctive_positive"]["dp_gameplay_q2_key"]; + + // if participant and teammate responses DO NOT match the key, return true to run the loop node + if (!(JSON.stringify(teammate_response) === JSON.stringify(dp_teammate_key))) { + return true; + } else { + return false; + } + }, +} + +const dp_intervention_task_short = { + type: jsPsychSurveyMultiChoice, + preamble: `

` + + "Now, imagine you had one trial round in which you were given the chance to turn one of the switches ON manually. \ + The other switch will turn ON or OFF based on its probability." + + "", + questions: [ + { + name: 'dp_intervention_task', + prompt: trialData["disjunctive_positive"]["intervention_task"]["question"], + options: trialData["disjunctive_positive"]["intervention_task"]["options"], + required: true, + }, + ], + // on_start: function() { + // jsPsych.setProgressBar(0.25); + // } +}; + +const dp_short = [ + dp_instructions_short, + dp_instructions_comp_short, + dp_conditional_node_instructions_short, + dp_intervention_task_short, + dp_round_one_short, + dp_round_one_comp_q1_short, + dp_conditional_node_round_one_q1_short, + dp_round_one_comp_q2_short, + dp_conditional_node_round_one_q2_short, + dp_round_two_short, + dp_selection_task_short +] \ No newline at end of file diff --git a/docs/experiment_1/js/feedback-demographics.js b/docs/experiment_1/js/feedback-demographics.js new file mode 100644 index 0000000..b81e703 --- /dev/null +++ b/docs/experiment_1/js/feedback-demographics.js @@ -0,0 +1,62 @@ +const feedback_demographics = { + type: jsPsychSurveyHtmlForm, + html: '

' + + 'What factors influenced how you decided to respond? Do you' + + ' have any questions or comments regarding the experiment?' + + // feedback box + '

Please provide the following' + + ' information to complete the study.

    ' + + '
  1. Age:


  2. ' + + '
  3. Gender:


  4. ' + + '
  5. Race:






  6. ' + + '
  7. Ethnicity:
  8. ' + + '
' + + '
' + + // age text box + '

' + + // gender options + ' ' + + ' ' + + '
' + + '

' + + // race options + '
' + + '
' + + '
' + + '
' + + '
' + + '
' + + '' + + '

' + + // ethnicity options + ' ' + + ' ' + + '
' + + '

Please press the finish button to complete the experiment.

', + button_label: 'Finish', + data: { + "page_type": 'exit_survey', + }, + }; \ No newline at end of file diff --git a/docs/experiment_1/js/jquery-ui.min.js b/docs/experiment_1/js/jquery-ui.min.js new file mode 100644 index 0000000..c52a3c6 --- /dev/null +++ b/docs/experiment_1/js/jquery-ui.min.js @@ -0,0 +1,12 @@ +/*! jQuery UI - v1.11.0 - 2014-08-07 +* http://jqueryui.com +* Includes: core.js, widget.js, mouse.js, position.js, draggable.js, droppable.js, resizable.js, selectable.js, sortable.js, accordion.js, autocomplete.js, button.js, datepicker.js, dialog.js, menu.js, progressbar.js, selectmenu.js, slider.js, spinner.js, tabs.js, tooltip.js +* Copyright 2014 jQuery Foundation and other contributors; Licensed MIT */ + +(function(e){"function"==typeof define&&define.amd?define(["jquery"],e):e(jQuery)})(function(e){function t(t,s){var n,a,o,r=t.nodeName.toLowerCase();return"area"===r?(n=t.parentNode,a=n.name,t.href&&a&&"map"===n.nodeName.toLowerCase()?(o=e("img[usemap=#"+a+"]")[0],!!o&&i(o)):!1):(/input|select|textarea|button|object/.test(r)?!t.disabled:"a"===r?t.href||s:s)&&i(t)}function i(t){return e.expr.filters.visible(t)&&!e(t).parents().addBack().filter(function(){return"hidden"===e.css(this,"visibility")}).length}function s(e){for(var t,i;e.length&&e[0]!==document;){if(t=e.css("position"),("absolute"===t||"relative"===t||"fixed"===t)&&(i=parseInt(e.css("zIndex"),10),!isNaN(i)&&0!==i))return i;e=e.parent()}return 0}function n(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:""},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},e.extend(this._defaults,this.regional[""]),this.regional.en=e.extend(!0,{},this.regional[""]),this.regional["en-US"]=e.extend(!0,{},this.regional.en),this.dpDiv=a(e("
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this.add(null==e?this.prevObject:this.prevObject.filter(e))}),e("").data("a-b","a").removeData("a-b").data("a-b")&&(e.fn.removeData=function(t){return function(i){return arguments.length?t.call(this,e.camelCase(i)):t.call(this)}}(e.fn.removeData)),e.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase()),e.fn.extend({focus:function(t){return function(i,s){return"number"==typeof i?this.each(function(){var t=this;setTimeout(function(){e(t).focus(),s&&s.call(t)},i)}):t.apply(this,arguments)}}(e.fn.focus),disableSelection:function(){var e="onselectstart"in document.createElement("div")?"selectstart":"mousedown";return function(){return this.bind(e+".ui-disableSelection",function(e){e.preventDefault()})}}(),enableSelection:function(){return this.unbind(".ui-disableSelection")},zIndex:function(t){if(void 0!==t)return this.css("zIndex",t);if(this.length)for(var 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i._mouseMove(e)},this._mouseUpDelegate=function(e){return i._mouseUp(e)},this.document.bind("mousemove."+this.widgetName,this._mouseMoveDelegate).bind("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),u=!0,!0)):!0}},_mouseMove:function(t){return e.ui.ie&&(!document.documentMode||9>document.documentMode)&&!t.button?this._mouseUp(t):t.which?this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=this._mouseStart(this._mouseDownEvent,t)!==!1,this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted):this._mouseUp(t)},_mouseUp:function(t){return this.document.unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&e.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),u=!1,!1},_mouseDistanceMet:function(e){return 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this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(),this.offsetParent=this.helper.offsetParent(),this.offsetParentCssPosition=this.offsetParent.css("position"),this.offset=this.positionAbs=this.element.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},this.offset.scroll=!1,e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_mouseDrag:function(t,i){if("fixed"===this.offsetParentCssPosition&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return e("div.ui-draggable-iframeFix").each(function(){this.parentNode.removeChild(this)}),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this._removeHandleClassName(),e(this.options.handle||this.element).addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.element.find(".ui-draggable-handle").addBack().removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return s.parents("body").length||s.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s[0]===this.element[0]||/(fixed|absolute)/.test(s.css("position"))||s.css("position","absolute"),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relative_container=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t="hidden"!==i.css("overflow"),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relative_container=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relative_container?(s=this.relative_container.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.lefti[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)} +},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),"drag"===t&&(this.positionAbs=this._convertPositionTo("absolute")),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=s.options,a=e.extend({},i,{item:s.element});s.sortables=[],e(n.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push({instance:i,shouldRevert:i.options.revert}),i.refreshPositions(),i._trigger("activate",t,a))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});e.each(s.sortables,function(){this.instance.isOver?(this.instance.isOver=0,s.cancelHelperRemoval=!0,this.instance.cancelHelperRemoval=!1,this.shouldRevert&&(this.instance.options.revert=this.shouldRevert),this.instance._mouseStop(t),this.instance.options.helper=this.instance.options._helper,"original"===s.options.helper&&this.instance.currentItem.css({top:"auto",left:"auto"})):(this.instance.cancelHelperRemoval=!1,this.instance._trigger("deactivate",t,n))})},drag:function(t,i,s){var n=this;e.each(s.sortables,function(){var a=!1,o=this;this.instance.positionAbs=s.positionAbs,this.instance.helperProportions=s.helperProportions,this.instance.offset.click=s.offset.click,this.instance._intersectsWith(this.instance.containerCache)&&(a=!0,e.each(s.sortables,function(){return this.instance.positionAbs=s.positionAbs,this.instance.helperProportions=s.helperProportions,this.instance.offset.click=s.offset.click,this!==o&&this.instance._intersectsWith(this.instance.containerCache)&&e.contains(o.instance.element[0],this.instance.element[0])&&(a=!1),a})),a?(this.instance.isOver||(this.instance.isOver=1,this.instance.currentItem=e(n).clone().removeAttr("id").appendTo(this.instance.element).data("ui-sortable-item",!0),this.instance.options._helper=this.instance.options.helper,this.instance.options.helper=function(){return i.helper[0]},t.target=this.instance.currentItem[0],this.instance._mouseCapture(t,!0),this.instance._mouseStart(t,!0,!0),this.instance.offset.click.top=s.offset.click.top,this.instance.offset.click.left=s.offset.click.left,this.instance.offset.parent.left-=s.offset.parent.left-this.instance.offset.parent.left,this.instance.offset.parent.top-=s.offset.parent.top-this.instance.offset.parent.top,s._trigger("toSortable",t),s.dropped=this.instance.element,s.currentItem=s.element,this.instance.fromOutside=s),this.instance.currentItem&&this.instance._mouseDrag(t)):this.instance.isOver&&(this.instance.isOver=0,this.instance.cancelHelperRemoval=!0,this.instance.options.revert=!1,this.instance._trigger("out",t,this.instance._uiHash(this.instance)),this.instance._mouseStop(t,!0),this.instance.options.helper=this.instance.options._helper,this.instance.currentItem.remove(),this.instance.placeholder&&this.instance.placeholder.remove(),s._trigger("fromSortable",t),s.dropped=!1)})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParent[0]!==i.document[0]&&"HTML"!==i.scrollParent[0].tagName&&(i.overflowOffset=i.scrollParent.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.document[0];s.scrollParent[0]!==o&&"HTML"!==s.scrollParent[0].tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+s.scrollParent[0].offsetHeight-t.pageY=0;c--)h=s.snapElements[c].left,l=h+s.snapElements[c].width,u=s.snapElements[c].top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top-s.margins.top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top-s.margins.top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left-s.margins.left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left-s.margins.left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top-s.margins.top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top-s.margins.top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left-s.margins.left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left-s.margins.left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.droppable",{version:"1.11.0",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var t=e(this).droppable("instance");return t.options.greedy&&!t.options.disabled&&t.options.scope===s.options.scope&&t.accept.call(t.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(t,{offset:t.element.offset()}),t.options.tolerance)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(s)),this.element):!1):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(){function e(e,t,i){return e>=t&&t+i>e}return function(t,i,s){if(!i.offset)return!1;var n,a,o=(t.positionAbs||t.position.absolute).left,r=(t.positionAbs||t.position.absolute).top,h=o+t.helperProportions.width,l=r+t.helperProportions.height,u=i.offset.left,d=i.offset.top,c=u+i.proportions().width,p=d+i.proportions().height;switch(s){case"fit":return o>=u&&c>=h&&r>=d&&p>=l;case"intersect":return o+t.helperProportions.width/2>u&&c>h-t.helperProportions.width/2&&r+t.helperProportions.height/2>d&&p>l-t.helperProportions.height/2;case"pointer":return n=(t.positionAbs||t.position.absolute).left+(t.clickOffset||t.offset.click).left,a=(t.positionAbs||t.position.absolute).top+(t.clickOffset||t.offset.click).top,e(a,d,i.proportions().height)&&e(n,u,i.proportions().width);case"touch":return(r>=d&&p>=r||l>=d&&p>=l||d>r&&l>p)&&(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return e(this).droppable("instance").options.scope===n}),a.length&&(s=e(a[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(t,i){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)}},e.ui.droppable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.0",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/canvas|textarea|input|select|button|img/i)&&(this.element.wrap(e("
").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("
"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String&&(this.handles[i]=this.element.children(this.handles[i]).first().show()),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/textarea|input|select|button/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),e(this.handles[i]).length},this._renderAxis(this.element),this._handles=e(".ui-resizable-handle",this.element).disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.originalPosition={left:i,top:s},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s=this.helper,n={},a=this.originalMousePosition,o=this.axis,r=t.pageX-a.left||0,h=t.pageY-a.top||0,l=this._change[o];return this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height},l?(i=l.apply(this,[t,r,h]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),this.position.top!==this.prevPosition.top&&(n.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(n.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(n.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(n.height=this.size.height+"px"),s.css(n),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(n)||this._trigger("resize",t,this.ui()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidthe.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_proportionallyResize:function(){if(this._proportionallyResizeElements.length){var e,t,i,s,n,a=this.helper||this.element;for(e=0;this._proportionallyResizeElements.length>e;e++){if(n=this._proportionallyResizeElements[e],!this.borderDif)for(this.borderDif=[],i=[n.css("borderTopWidth"),n.css("borderRightWidth"),n.css("borderBottomWidth"),n.css("borderLeftWidth")],s=[n.css("paddingTop"),n.css("paddingRight"),n.css("paddingBottom"),n.css("paddingLeft")],t=0;i.length>t;t++)this.borderDif[t]=(parseInt(i[t],10)||0)+(parseInt(s[t],10)||0);n.css({height:a.height()-this.borderDif[0]-this.borderDif[2]||0,width:a.width()-this.borderDif[1]-this.borderDif[3]||0})}}},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("
"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition,prevSize:this.prevSize,prevPosition:this.prevPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t,i){var s,n,a,o,r=e(this).resizable("instance"),h=r.options,l=r.containerOffset,u=r.position,d=r._aspectRatio||t.shiftKey,c={top:0,left:0},p=r.containerElement,f=!0;p[0]!==document&&/static/.test(p.css("position"))&&(c=l),u.left<(r._helper?l.left:0)&&(r.size.width=r.size.width+(r._helper?r.position.left-l.left:r.position.left-c.left),d&&(r.size.height=r.size.width/r.aspectRatio,f=!1),r.position.left=h.helper?l.left:0),u.top<(r._helper?l.top:0)&&(r.size.height=r.size.height+(r._helper?r.position.top-l.top:r.position.top),d&&(r.size.width=r.size.height*r.aspectRatio,f=!1),r.position.top=r._helper?l.top:0),r.offset.left=r.parentData.left+r.position.left,r.offset.top=r.parentData.top+r.position.top,s=Math.abs((r._helper?r.offset.left-c.left:r.offset.left-l.left)+r.sizeDiff.width),n=Math.abs((r._helper?r.offset.top-c.top:r.offset.top-l.top)+r.sizeDiff.height),a=r.containerElement.get(0)===r.element.parent().get(0),o=/relative|absolute/.test(r.containerElement.css("position")),a&&o&&(s-=Math.abs(r.parentData.left)),s+r.size.width>=r.parentData.width&&(r.size.width=r.parentData.width-s,d&&(r.size.height=r.size.width/r.aspectRatio,f=!1)),n+r.size.height>=r.parentData.height&&(r.size.height=r.parentData.height-n,d&&(r.size.width=r.size.height*r.aspectRatio,f=!1)),f||(r.position.left=i.prevPosition.left,r.position.top=i.prevPosition.top,r.size.width=i.prevSize.width,r.size.height=i.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=function(t){e(t).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})};"object"!=typeof i.alsoResize||i.alsoResize.parentNode?s(i.alsoResize):i.alsoResize.length?(i.alsoResize=i.alsoResize[0],s(i.alsoResize)):e.each(i.alsoResize,function(e){s(e)})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0},h=function(t,s){e(t).each(function(){var t=e(this),n=e(this).data("ui-resizable-alsoresize"),a={},o=s&&s.length?s:t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(o,function(e,t){var i=(n[t]||0)+(r[t]||0);i&&i>=0&&(a[t]=i||null)}),t.css(a)})};"object"!=typeof n.alsoResize||n.alsoResize.nodeType?h(n.alsoResize):e.each(n.alsoResize,function(e,t){h(e,t)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size,n=t.originalSize,a=t.originalPosition,o=t.axis,r="number"==typeof i.grid?[i.grid,i.grid]:i.grid,h=r[0]||1,l=r[1]||1,u=Math.round((s.width-n.width)/h)*h,d=Math.round((s.height-n.height)/l)*l,c=n.width+u,p=n.height+d,f=i.maxWidth&&c>i.maxWidth,m=i.maxHeight&&p>i.maxHeight,g=i.minWidth&&i.minWidth>c,v=i.minHeight&&i.minHeight>p;i.grid=r,g&&(c+=h),v&&(p+=l),f&&(c-=h),m&&(p-=l),/^(se|s|e)$/.test(o)?(t.size.width=c,t.size.height=p):/^(ne)$/.test(o)?(t.size.width=c,t.size.height=p,t.position.top=a.top-d):/^(sw)$/.test(o)?(t.size.width=c,t.size.height=p,t.position.left=a.left-u):(p-l>0?(t.size.height=p,t.position.top=a.top-d):(t.size.height=l,t.position.top=a.top+n.height-l),c-h>0?(t.size.width=c,t.position.left=a.left-u):(t.size.width=h,t.position.left=a.left+n.width-h))}}),e.ui.resizable,e.widget("ui.selectable",e.ui.mouse,{version:"1.11.0",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var t,i=this; +this.element.addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){t=e(i.options.filter,i.element[0]),t.addClass("ui-selectee"),t.each(function(){var t=e(this),i=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:i.left,top:i.top,right:i.left+t.outerWidth(),bottom:i.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=t.addClass("ui-selectee"),this._mouseInit(),this.helper=e("
")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger("start",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=e.data(this,"selectable-item");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass("ui-selected"),s.selected=!1,s.$element.addClass("ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,"selectable-item");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass("ui-selected"),n.$element.removeClass(s?"ui-unselecting":"ui-selected").addClass(s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",t,{selecting:n.element}):i._trigger("unselecting",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,"selectable-item"),l=!1;i&&i.element!==s.element[0]&&("touch"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):"fit"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass("ui-selected"),i.selected=!1),i.unselecting&&(i.$element.removeClass("ui-unselecting"),i.unselecting=!1),i.selecting||(i.$element.addClass("ui-selecting"),i.selecting=!0,s._trigger("selecting",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.$element.addClass("ui-selected"),i.selected=!0):(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.startselected&&(i.$element.addClass("ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass("ui-selected"),i.selected=!1,i.$element.addClass("ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(".ui-unselecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",t,{unselected:s.element})}),e(".ui-selecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-selecting").addClass("ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",t,{selected:s.element})}),this._trigger("stop",t),this.helper.remove(),!1}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.0",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){var e=this.options;this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.floating=this.items.length?"x"===e.axis||this._isFloating(this.items[0].item):!1,this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==document&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==document&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===document.body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===document.body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tr"===s?t.currentItem.children().each(function(){e(" ",t.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(n)}):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==document&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===document.body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,e("document"===n.containment?document:window).width()-this.helperProportions.width-this.margins.left,(e("document"===n.containment?document:window).height()||document.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==document&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==document&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==document&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.leftthis.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,this.cancelHelperRemoval){if(!t){for(this._trigger("beforeStop",e,this._uiHash()),s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!1}if(t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null,!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!0},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.accordion",{version:"1.11.0",options:{active:0,animate:{},collapsible:!1,event:"click",header:"> li > :first-child,> :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=e(),this.element.addClass("ui-accordion ui-widget ui-helper-reset").attr("role","tablist"),t.collapsible||t.active!==!1&&null!=t.active||(t.active=0),this._processPanels(),0>t.active&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():e()}},_createIcons:function(){var t=this.options.icons;t&&(e("").addClass("ui-accordion-header-icon ui-icon "+t.header).prependTo(this.headers),this.active.children(".ui-accordion-header-icon").removeClass(t.header).addClass(t.activeHeader),this.headers.addClass("ui-accordion-icons"))},_destroyIcons:function(){this.headers.removeClass("ui-accordion-icons").children(".ui-accordion-header-icon").remove()},_destroy:function(){var e;this.element.removeClass("ui-accordion ui-widget ui-helper-reset").removeAttr("role"),this.headers.removeClass("ui-accordion-header ui-accordion-header-active ui-state-default ui-corner-all ui-state-active ui-state-disabled ui-corner-top").removeAttr("role").removeAttr("aria-expanded").removeAttr("aria-selected").removeAttr("aria-controls").removeAttr("tabIndex").removeUniqueId(),this._destroyIcons(),e=this.headers.next().removeClass("ui-helper-reset ui-widget-content ui-corner-bottom ui-accordion-content ui-accordion-content-active ui-state-disabled").css("display","").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&e.css("height","")},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):("event"===e&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(t)),this._super(e,t),"collapsible"!==e||t||this.options.active!==!1||this._activate(0),"icons"===e&&(this._destroyIcons(),t&&this._createIcons()),"disabled"===e&&(this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this.headers.add(this.headers.next()).toggleClass("ui-state-disabled",!!t)),void 0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide()},_refresh:function(){var t,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active).addClass("ui-accordion-header-active ui-state-active ui-corner-top").removeClass("ui-corner-all"),this.active.next().addClass("ui-accordion-content-active").show(),this.headers.attr("role","tab").each(function(){var t=e(this),i=t.uniqueId().attr("id"),s=t.next(),n=s.uniqueId().attr("id"); +t.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(t=n.height(),this.element.siblings(":visible").each(function(){var i=e(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(t-=i.outerHeight(!0))}),this.headers.each(function(){t-=e(this).outerHeight(!0)}),this.headers.next().each(function(){e(this).height(Math.max(0,t-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===s&&(t=0,this.headers.next().each(function(){t=Math.max(t,e(this).css("height","").height())}).height(t))},_activate:function(t){var i=this._findActive(t)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):e()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n[0]===s[0],o=a&&i.collapsible,r=o?e():n.next(),h=s.next(),l={oldHeader:s,oldPanel:h,newHeader:o?e():n,newPanel:r};t.preventDefault(),a&&!i.collapsible||this._trigger("beforeActivate",t,l)===!1||(i.active=o?!1:this.headers.index(n),this.active=a?e():n,this._toggle(l),s.removeClass("ui-accordion-header-active ui-state-active"),i.icons&&s.children(".ui-accordion-header-icon").removeClass(i.icons.activeHeader).addClass(i.icons.header),a||(n.removeClass("ui-corner-all").addClass("ui-accordion-header-active ui-state-active ui-corner-top"),i.icons&&n.children(".ui-accordion-header-icon").removeClass(i.icons.header).addClass(i.icons.activeHeader),n.next().addClass("ui-accordion-content-active")))},_toggle:function(t){var i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr("aria-selected","false"),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===e(this).attr("tabIndex")}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true",tabIndex:0,"aria-expanded":"true"})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.length&&(!t.length||e.index()",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},items:"> *",menus:"ul",position:{my:"left-1 top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().addClass("ui-menu ui-widget ui-widget-content").toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length).attr({role:this.options.role,tabIndex:0}),this.options.disabled&&this.element.addClass("ui-state-disabled").attr("aria-disabled","true"),this._on({"mousedown .ui-menu-item":function(e){e.preventDefault()},"click .ui-menu-item":function(t){var i=e(t.target);!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&e(this.document[0].activeElement).closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(t){var i=e(t.currentTarget);i.siblings(".ui-state-active").removeClass("ui-state-active"),this.focus(t,i)},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-menu-icons ui-front").removeAttr("role").removeAttr("tabIndex").removeAttr("aria-labelledby").removeAttr("aria-expanded").removeAttr("aria-hidden").removeAttr("aria-disabled").removeUniqueId().show(),this.element.find(".ui-menu-item").removeClass("ui-menu-item").removeAttr("role").removeAttr("aria-disabled").removeUniqueId().removeClass("ui-state-hover").removeAttr("tabIndex").removeAttr("role").removeAttr("aria-haspopup").children().each(function(){var t=e(this);t.data("ui-menu-submenu-carat")&&t.remove()}),this.element.find(".ui-menu-divider").removeClass("ui-menu-divider ui-widget-content")},_keydown:function(t){function i(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")}var s,n,a,o,r,h=!0;switch(t.keyCode){case e.ui.keyCode.PAGE_UP:this.previousPage(t);break;case e.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case e.ui.keyCode.HOME:this._move("first","first",t);break;case e.ui.keyCode.END:this._move("last","last",t);break;case e.ui.keyCode.UP:this.previous(t);break;case e.ui.keyCode.DOWN:this.next(t);break;case e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:h=!1,n=this.previousFilter||"",a=String.fromCharCode(t.keyCode),o=!1,clearTimeout(this.filterTimer),a===n?o=!0:a=n+a,r=RegExp("^"+i(a),"i"),s=this.activeMenu.find(this.options.items).filter(function(){return r.test(e(this).text())}),s=o&&-1!==s.index(this.active.next())?this.active.nextAll(".ui-menu-item"):s,s.length||(a=String.fromCharCode(t.keyCode),r=RegExp("^"+i(a),"i"),s=this.activeMenu.find(this.options.items).filter(function(){return r.test(e(this).text())})),s.length?(this.focus(t,s),s.length>1?(this.previousFilter=a,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter):delete this.previousFilter}h&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, .ui-menu-divider").addClass("ui-menu-item").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(e,t){"icons"===e&&this.element.find(".ui-menu-icon").removeClass(this.options.icons.submenu).addClass(t.submenu),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move("previousPage",n);break;case a.PAGE_DOWN:t=!0,this._move("nextPage",n);break;case a.UP:t=!0,this._keyEvent("previous",n);break;case a.DOWN:t=!0,this._keyEvent("next",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(":visible")&&(this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e("
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    ").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var d,c="ui-button ui-widget ui-state-default ui-corner-all",p="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",f=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},m=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.0",defaultElement:"").addClass(this._triggerClass).html(a?e("").attr({src:a,alt:n,title:n}):n)),t[r?"before":"after"](i.trigger),i.trigger.click(function(){return e.datepicker._datepickerShowing&&e.datepicker._lastInput===t[0]?e.datepicker._hideDatepicker():e.datepicker._datepickerShowing&&e.datepicker._lastInput!==t[0]?(e.datepicker._hideDatepicker(),e.datepicker._showDatepicker(t[0])):e.datepicker._showDatepicker(t[0]),!1}))},_autoSize:function(e){if(this._get(e,"autoSize")&&!e.inline){var t,i,s,n,a=new Date(2009,11,20),o=this._get(e,"dateFormat");o.match(/[DM]/)&&(t=function(e){for(i=0,s=0,n=0;e.length>n;n++)e[n].length>i&&(i=e[n].length,s=n);return s},a.setMonth(t(this._get(e,o.match(/MM/)?"monthNames":"monthNamesShort"))),a.setDate(t(this._get(e,o.match(/DD/)?"dayNames":"dayNamesShort"))+20-a.getDay())),e.input.attr("size",this._formatDate(e,a).length)}},_inlineDatepicker:function(t,i){var s=e(t);s.hasClass(this.markerClassName)||(s.addClass(this.markerClassName).append(i.dpDiv),e.data(t,"datepicker",i),this._setDate(i,this._getDefaultDate(i),!0),this._updateDatepicker(i),this._updateAlternate(i),i.settings.disabled&&this._disableDatepicker(t),i.dpDiv.css("display","block"))},_dialogDatepicker:function(t,i,s,n,a){var r,h,l,u,d,c=this._dialogInst;return c||(this.uuid+=1,r="dp"+this.uuid,this._dialogInput=e(""),this._dialogInput.keydown(this._doKeyDown),e("body").append(this._dialogInput),c=this._dialogInst=this._newInst(this._dialogInput,!1),c.settings={},e.data(this._dialogInput[0],"datepicker",c)),o(c.settings,n||{}),i=i&&i.constructor===Date?this._formatDate(c,i):i,this._dialogInput.val(i),this._pos=a?a.length?a:[a.pageX,a.pageY]:null,this._pos||(h=document.documentElement.clientWidth,l=document.documentElement.clientHeight,u=document.documentElement.scrollLeft||document.body.scrollLeft,d=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[h/2-100+u,l/2-150+d]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),c.settings.onSelect=s,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),e.blockUI&&e.blockUI(this.dpDiv),e.data(this._dialogInput[0],"datepicker",c),this},_destroyDatepicker:function(t){var i,s=e(t),n=e.data(t,"datepicker");s.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),e.removeData(t,"datepicker"),"input"===i?(n.append.remove(),n.trigger.remove(),s.removeClass(this.markerClassName).unbind("focus",this._showDatepicker).unbind("keydown",this._doKeyDown).unbind("keypress",this._doKeyPress).unbind("keyup",this._doKeyUp)):("div"===i||"span"===i)&&s.removeClass(this.markerClassName).empty()) +},_enableDatepicker:function(t){var i,s,n=e(t),a=e.data(t,"datepicker");n.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),"input"===i?(t.disabled=!1,a.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):("div"===i||"span"===i)&&(s=n.children("."+this._inlineClass),s.children().removeClass("ui-state-disabled"),s.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=e.map(this._disabledInputs,function(e){return e===t?null:e}))},_disableDatepicker:function(t){var i,s,n=e(t),a=e.data(t,"datepicker");n.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),"input"===i?(t.disabled=!0,a.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):("div"===i||"span"===i)&&(s=n.children("."+this._inlineClass),s.children().addClass("ui-state-disabled"),s.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=e.map(this._disabledInputs,function(e){return e===t?null:e}),this._disabledInputs[this._disabledInputs.length]=t)},_isDisabledDatepicker:function(e){if(!e)return!1;for(var t=0;this._disabledInputs.length>t;t++)if(this._disabledInputs[t]===e)return!0;return!1},_getInst:function(t){try{return e.data(t,"datepicker")}catch(i){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,i,s){var n,a,r,h,l=this._getInst(t);return 2===arguments.length&&"string"==typeof i?"defaults"===i?e.extend({},e.datepicker._defaults):l?"all"===i?e.extend({},l.settings):this._get(l,i):null:(n=i||{},"string"==typeof i&&(n={},n[i]=s),l&&(this._curInst===l&&this._hideDatepicker(),a=this._getDateDatepicker(t,!0),r=this._getMinMaxDate(l,"min"),h=this._getMinMaxDate(l,"max"),o(l.settings,n),null!==r&&void 0!==n.dateFormat&&void 0===n.minDate&&(l.settings.minDate=this._formatDate(l,r)),null!==h&&void 0!==n.dateFormat&&void 0===n.maxDate&&(l.settings.maxDate=this._formatDate(l,h)),"disabled"in n&&(n.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(e(t),l),this._autoSize(l),this._setDate(l,a),this._updateAlternate(l),this._updateDatepicker(l)),void 0)},_changeDatepicker:function(e,t,i){this._optionDatepicker(e,t,i)},_refreshDatepicker:function(e){var t=this._getInst(e);t&&this._updateDatepicker(t)},_setDateDatepicker:function(e,t){var i=this._getInst(e);i&&(this._setDate(i,t),this._updateDatepicker(i),this._updateAlternate(i))},_getDateDatepicker:function(e,t){var i=this._getInst(e);return i&&!i.inline&&this._setDateFromField(i,t),i?this._getDate(i):null},_doKeyDown:function(t){var i,s,n,a=e.datepicker._getInst(t.target),o=!0,r=a.dpDiv.is(".ui-datepicker-rtl");if(a._keyEvent=!0,e.datepicker._datepickerShowing)switch(t.keyCode){case 9:e.datepicker._hideDatepicker(),o=!1;break;case 13:return n=e("td."+e.datepicker._dayOverClass+":not(."+e.datepicker._currentClass+")",a.dpDiv),n[0]&&e.datepicker._selectDay(t.target,a.selectedMonth,a.selectedYear,n[0]),i=e.datepicker._get(a,"onSelect"),i?(s=e.datepicker._formatDate(a),i.apply(a.input?a.input[0]:null,[s,a])):e.datepicker._hideDatepicker(),!1;case 27:e.datepicker._hideDatepicker();break;case 33:e.datepicker._adjustDate(t.target,t.ctrlKey?-e.datepicker._get(a,"stepBigMonths"):-e.datepicker._get(a,"stepMonths"),"M");break;case 34:e.datepicker._adjustDate(t.target,t.ctrlKey?+e.datepicker._get(a,"stepBigMonths"):+e.datepicker._get(a,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&e.datepicker._clearDate(t.target),o=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&e.datepicker._gotoToday(t.target),o=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,r?1:-1,"D"),o=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&e.datepicker._adjustDate(t.target,t.ctrlKey?-e.datepicker._get(a,"stepBigMonths"):-e.datepicker._get(a,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,-7,"D"),o=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,r?-1:1,"D"),o=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&e.datepicker._adjustDate(t.target,t.ctrlKey?+e.datepicker._get(a,"stepBigMonths"):+e.datepicker._get(a,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,7,"D"),o=t.ctrlKey||t.metaKey;break;default:o=!1}else 36===t.keyCode&&t.ctrlKey?e.datepicker._showDatepicker(this):o=!1;o&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var i,s,n=e.datepicker._getInst(t.target);return e.datepicker._get(n,"constrainInput")?(i=e.datepicker._possibleChars(e.datepicker._get(n,"dateFormat")),s=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||" ">s||!i||i.indexOf(s)>-1):void 0},_doKeyUp:function(t){var i,s=e.datepicker._getInst(t.target);if(s.input.val()!==s.lastVal)try{i=e.datepicker.parseDate(e.datepicker._get(s,"dateFormat"),s.input?s.input.val():null,e.datepicker._getFormatConfig(s)),i&&(e.datepicker._setDateFromField(s),e.datepicker._updateAlternate(s),e.datepicker._updateDatepicker(s))}catch(n){}return!0},_showDatepicker:function(t){if(t=t.target||t,"input"!==t.nodeName.toLowerCase()&&(t=e("input",t.parentNode)[0]),!e.datepicker._isDisabledDatepicker(t)&&e.datepicker._lastInput!==t){var i,n,a,r,h,l,u;i=e.datepicker._getInst(t),e.datepicker._curInst&&e.datepicker._curInst!==i&&(e.datepicker._curInst.dpDiv.stop(!0,!0),i&&e.datepicker._datepickerShowing&&e.datepicker._hideDatepicker(e.datepicker._curInst.input[0])),n=e.datepicker._get(i,"beforeShow"),a=n?n.apply(t,[t,i]):{},a!==!1&&(o(i.settings,a),i.lastVal=null,e.datepicker._lastInput=t,e.datepicker._setDateFromField(i),e.datepicker._inDialog&&(t.value=""),e.datepicker._pos||(e.datepicker._pos=e.datepicker._findPos(t),e.datepicker._pos[1]+=t.offsetHeight),r=!1,e(t).parents().each(function(){return r|="fixed"===e(this).css("position"),!r}),h={left:e.datepicker._pos[0],top:e.datepicker._pos[1]},e.datepicker._pos=null,i.dpDiv.empty(),i.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),e.datepicker._updateDatepicker(i),h=e.datepicker._checkOffset(i,h,r),i.dpDiv.css({position:e.datepicker._inDialog&&e.blockUI?"static":r?"fixed":"absolute",display:"none",left:h.left+"px",top:h.top+"px"}),i.inline||(l=e.datepicker._get(i,"showAnim"),u=e.datepicker._get(i,"duration"),i.dpDiv.css("z-index",s(e(t))+1),e.datepicker._datepickerShowing=!0,e.effects&&e.effects.effect[l]?i.dpDiv.show(l,e.datepicker._get(i,"showOptions"),u):i.dpDiv[l||"show"](l?u:null),e.datepicker._shouldFocusInput(i)&&i.input.focus(),e.datepicker._curInst=i))}},_updateDatepicker:function(t){this.maxRows=4,g=t,t.dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t),t.dpDiv.find("."+this._dayOverClass+" a");var i,s=this._getNumberOfMonths(t),n=s[1],a=17;t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),n>1&&t.dpDiv.addClass("ui-datepicker-multi-"+n).css("width",a*n+"em"),t.dpDiv[(1!==s[0]||1!==s[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===e.datepicker._curInst&&e.datepicker._datepickerShowing&&e.datepicker._shouldFocusInput(t)&&t.input.focus(),t.yearshtml&&(i=t.yearshtml,setTimeout(function(){i===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year:first").replaceWith(t.yearshtml),i=t.yearshtml=null},0))},_shouldFocusInput:function(e){return e.input&&e.input.is(":visible")&&!e.input.is(":disabled")&&!e.input.is(":focus")},_checkOffset:function(t,i,s){var n=t.dpDiv.outerWidth(),a=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,r=t.input?t.input.outerHeight():0,h=document.documentElement.clientWidth+(s?0:e(document).scrollLeft()),l=document.documentElement.clientHeight+(s?0:e(document).scrollTop());return i.left-=this._get(t,"isRTL")?n-o:0,i.left-=s&&i.left===t.input.offset().left?e(document).scrollLeft():0,i.top-=s&&i.top===t.input.offset().top+r?e(document).scrollTop():0,i.left-=Math.min(i.left,i.left+n>h&&h>n?Math.abs(i.left+n-h):0),i.top-=Math.min(i.top,i.top+a>l&&l>a?Math.abs(a+r):0),i},_findPos:function(t){for(var i,s=this._getInst(t),n=this._get(s,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||e.expr.filters.hidden(t));)t=t[n?"previousSibling":"nextSibling"];return i=e(t).offset(),[i.left,i.top]},_hideDatepicker:function(t){var i,s,n,a,o=this._curInst;!o||t&&o!==e.data(t,"datepicker")||this._datepickerShowing&&(i=this._get(o,"showAnim"),s=this._get(o,"duration"),n=function(){e.datepicker._tidyDialog(o)},e.effects&&(e.effects.effect[i]||e.effects[i])?o.dpDiv.hide(i,e.datepicker._get(o,"showOptions"),s,n):o.dpDiv["slideDown"===i?"slideUp":"fadeIn"===i?"fadeOut":"hide"](i?s:null,n),i||n(),this._datepickerShowing=!1,a=this._get(o,"onClose"),a&&a.apply(o.input?o.input[0]:null,[o.input?o.input.val():"",o]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),e.blockUI&&(e.unblockUI(),e("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(e){e.dpDiv.removeClass(this._dialogClass).unbind(".ui-datepicker-calendar")},_checkExternalClick:function(t){if(e.datepicker._curInst){var i=e(t.target),s=e.datepicker._getInst(i[0]);(i[0].id!==e.datepicker._mainDivId&&0===i.parents("#"+e.datepicker._mainDivId).length&&!i.hasClass(e.datepicker.markerClassName)&&!i.closest("."+e.datepicker._triggerClass).length&&e.datepicker._datepickerShowing&&(!e.datepicker._inDialog||!e.blockUI)||i.hasClass(e.datepicker.markerClassName)&&e.datepicker._curInst!==s)&&e.datepicker._hideDatepicker()}},_adjustDate:function(t,i,s){var n=e(t),a=this._getInst(n[0]);this._isDisabledDatepicker(n[0])||(this._adjustInstDate(a,i+("M"===s?this._get(a,"showCurrentAtPos"):0),s),this._updateDatepicker(a))},_gotoToday:function(t){var i,s=e(t),n=this._getInst(s[0]);this._get(n,"gotoCurrent")&&n.currentDay?(n.selectedDay=n.currentDay,n.drawMonth=n.selectedMonth=n.currentMonth,n.drawYear=n.selectedYear=n.currentYear):(i=new Date,n.selectedDay=i.getDate(),n.drawMonth=n.selectedMonth=i.getMonth(),n.drawYear=n.selectedYear=i.getFullYear()),this._notifyChange(n),this._adjustDate(s)},_selectMonthYear:function(t,i,s){var n=e(t),a=this._getInst(n[0]);a["selected"+("M"===s?"Month":"Year")]=a["draw"+("M"===s?"Month":"Year")]=parseInt(i.options[i.selectedIndex].value,10),this._notifyChange(a),this._adjustDate(n)},_selectDay:function(t,i,s,n){var a,o=e(t);e(n).hasClass(this._unselectableClass)||this._isDisabledDatepicker(o[0])||(a=this._getInst(o[0]),a.selectedDay=a.currentDay=e("a",n).html(),a.selectedMonth=a.currentMonth=i,a.selectedYear=a.currentYear=s,this._selectDate(t,this._formatDate(a,a.currentDay,a.currentMonth,a.currentYear)))},_clearDate:function(t){var i=e(t);this._selectDate(i,"")},_selectDate:function(t,i){var s,n=e(t),a=this._getInst(n[0]);i=null!=i?i:this._formatDate(a),a.input&&a.input.val(i),this._updateAlternate(a),s=this._get(a,"onSelect"),s?s.apply(a.input?a.input[0]:null,[i,a]):a.input&&a.input.trigger("change"),a.inline?this._updateDatepicker(a):(this._hideDatepicker(),this._lastInput=a.input[0],"object"!=typeof a.input[0]&&a.input.focus(),this._lastInput=null)},_updateAlternate:function(t){var i,s,n,a=this._get(t,"altField");a&&(i=this._get(t,"altFormat")||this._get(t,"dateFormat"),s=this._getDate(t),n=this.formatDate(i,s,this._getFormatConfig(t)),e(a).each(function(){e(this).val(n)}))},noWeekends:function(e){var t=e.getDay();return[t>0&&6>t,""]},iso8601Week:function(e){var t,i=new Date(e.getTime());return i.setDate(i.getDate()+4-(i.getDay()||7)),t=i.getTime(),i.setMonth(0),i.setDate(1),Math.floor(Math.round((t-i)/864e5)/7)+1},parseDate:function(t,i,s){if(null==t||null==i)throw"Invalid arguments";if(i="object"==typeof i?""+i:i+"",""===i)return null;var n,a,o,r,h=0,l=(s?s.shortYearCutoff:null)||this._defaults.shortYearCutoff,u="string"!=typeof l?l:(new Date).getFullYear()%100+parseInt(l,10),d=(s?s.dayNamesShort:null)||this._defaults.dayNamesShort,c=(s?s.dayNames:null)||this._defaults.dayNames,p=(s?s.monthNamesShort:null)||this._defaults.monthNamesShort,f=(s?s.monthNames:null)||this._defaults.monthNames,m=-1,g=-1,v=-1,y=-1,b=!1,_=function(e){var i=t.length>n+1&&t.charAt(n+1)===e;return i&&n++,i},x=function(e){var t=_(e),s="@"===e?14:"!"===e?20:"y"===e&&t?4:"o"===e?3:2,n=RegExp("^\\d{1,"+s+"}"),a=i.substring(h).match(n);if(!a)throw"Missing number at position "+h;return h+=a[0].length,parseInt(a[0],10)},w=function(t,s,n){var a=-1,o=e.map(_(t)?n:s,function(e,t){return[[t,e]]}).sort(function(e,t){return-(e[1].length-t[1].length)});if(e.each(o,function(e,t){var s=t[1];return i.substr(h,s.length).toLowerCase()===s.toLowerCase()?(a=t[0],h+=s.length,!1):void 0}),-1!==a)return a+1;throw"Unknown name at position "+h},k=function(){if(i.charAt(h)!==t.charAt(n))throw"Unexpected literal at position "+h;h++};for(n=0;t.length>n;n++)if(b)"'"!==t.charAt(n)||_("'")?k():b=!1;else switch(t.charAt(n)){case"d":v=x("d");break;case"D":w("D",d,c);break;case"o":y=x("o");break;case"m":g=x("m");break;case"M":g=w("M",p,f);break;case"y":m=x("y");break;case"@":r=new Date(x("@")),m=r.getFullYear(),g=r.getMonth()+1,v=r.getDate();break;case"!":r=new Date((x("!")-this._ticksTo1970)/1e4),m=r.getFullYear(),g=r.getMonth()+1,v=r.getDate();break;case"'":_("'")?k():b=!0;break;default:k()}if(i.length>h&&(o=i.substr(h),!/^\s+/.test(o)))throw"Extra/unparsed characters found in date: "+o;if(-1===m?m=(new Date).getFullYear():100>m&&(m+=(new Date).getFullYear()-(new Date).getFullYear()%100+(u>=m?0:-100)),y>-1)for(g=1,v=y;;){if(a=this._getDaysInMonth(m,g-1),a>=v)break;g++,v-=a}if(r=this._daylightSavingAdjust(new Date(m,g-1,v)),r.getFullYear()!==m||r.getMonth()+1!==g||r.getDate()!==v)throw"Invalid date";return r},ATOM:"yy-mm-dd",COOKIE:"D, dd M yy",ISO_8601:"yy-mm-dd",RFC_822:"D, d M y",RFC_850:"DD, dd-M-y",RFC_1036:"D, d M y",RFC_1123:"D, d M yy",RFC_2822:"D, d M yy",RSS:"D, d M y",TICKS:"!",TIMESTAMP:"@",W3C:"yy-mm-dd",_ticksTo1970:1e7*60*60*24*(718685+Math.floor(492.5)-Math.floor(19.7)+Math.floor(4.925)),formatDate:function(e,t,i){if(!t)return"";var s,n=(i?i.dayNamesShort:null)||this._defaults.dayNamesShort,a=(i?i.dayNames:null)||this._defaults.dayNames,o=(i?i.monthNamesShort:null)||this._defaults.monthNamesShort,r=(i?i.monthNames:null)||this._defaults.monthNames,h=function(t){var i=e.length>s+1&&e.charAt(s+1)===t;return i&&s++,i},l=function(e,t,i){var s=""+t;if(h(e))for(;i>s.length;)s="0"+s;return s},u=function(e,t,i,s){return h(e)?s[t]:i[t]},d="",c=!1;if(t)for(s=0;e.length>s;s++)if(c)"'"!==e.charAt(s)||h("'")?d+=e.charAt(s):c=!1;else switch(e.charAt(s)){case"d":d+=l("d",t.getDate(),2);break;case"D":d+=u("D",t.getDay(),n,a);break;case"o":d+=l("o",Math.round((new Date(t.getFullYear(),t.getMonth(),t.getDate()).getTime()-new Date(t.getFullYear(),0,0).getTime())/864e5),3);break;case"m":d+=l("m",t.getMonth()+1,2);break;case"M":d+=u("M",t.getMonth(),o,r);break;case"y":d+=h("y")?t.getFullYear():(10>t.getYear()%100?"0":"")+t.getYear()%100;break;case"@":d+=t.getTime();break;case"!":d+=1e4*t.getTime()+this._ticksTo1970;break;case"'":h("'")?d+="'":c=!0;break;default:d+=e.charAt(s)}return d},_possibleChars:function(e){var t,i="",s=!1,n=function(i){var s=e.length>t+1&&e.charAt(t+1)===i;return s&&t++,s};for(t=0;e.length>t;t++)if(s)"'"!==e.charAt(t)||n("'")?i+=e.charAt(t):s=!1;else switch(e.charAt(t)){case"d":case"m":case"y":case"@":i+="0123456789";break;case"D":case"M":return null;case"'":n("'")?i+="'":s=!0;break;default:i+=e.charAt(t)}return i},_get:function(e,t){return void 0!==e.settings[t]?e.settings[t]:this._defaults[t]},_setDateFromField:function(e,t){if(e.input.val()!==e.lastVal){var i=this._get(e,"dateFormat"),s=e.lastVal=e.input?e.input.val():null,n=this._getDefaultDate(e),a=n,o=this._getFormatConfig(e);try{a=this.parseDate(i,s,o)||n}catch(r){s=t?"":s}e.selectedDay=a.getDate(),e.drawMonth=e.selectedMonth=a.getMonth(),e.drawYear=e.selectedYear=a.getFullYear(),e.currentDay=s?a.getDate():0,e.currentMonth=s?a.getMonth():0,e.currentYear=s?a.getFullYear():0,this._adjustInstDate(e)}},_getDefaultDate:function(e){return this._restrictMinMax(e,this._determineDate(e,this._get(e,"defaultDate"),new Date))},_determineDate:function(t,i,s){var n=function(e){var t=new Date;return t.setDate(t.getDate()+e),t},a=function(i){try{return e.datepicker.parseDate(e.datepicker._get(t,"dateFormat"),i,e.datepicker._getFormatConfig(t))}catch(s){}for(var n=(i.toLowerCase().match(/^c/)?e.datepicker._getDate(t):null)||new Date,a=n.getFullYear(),o=n.getMonth(),r=n.getDate(),h=/([+\-]?[0-9]+)\s*(d|D|w|W|m|M|y|Y)?/g,l=h.exec(i);l;){switch(l[2]||"d"){case"d":case"D":r+=parseInt(l[1],10);break;case"w":case"W":r+=7*parseInt(l[1],10);break;case"m":case"M":o+=parseInt(l[1],10),r=Math.min(r,e.datepicker._getDaysInMonth(a,o));break;case"y":case"Y":a+=parseInt(l[1],10),r=Math.min(r,e.datepicker._getDaysInMonth(a,o))}l=h.exec(i)}return new Date(a,o,r)},o=null==i||""===i?s:"string"==typeof i?a(i):"number"==typeof i?isNaN(i)?s:n(i):new Date(i.getTime());return o=o&&"Invalid Date"==""+o?s:o,o&&(o.setHours(0),o.setMinutes(0),o.setSeconds(0),o.setMilliseconds(0)),this._daylightSavingAdjust(o)},_daylightSavingAdjust:function(e){return e?(e.setHours(e.getHours()>12?e.getHours()+2:0),e):null},_setDate:function(e,t,i){var s=!t,n=e.selectedMonth,a=e.selectedYear,o=this._restrictMinMax(e,this._determineDate(e,t,new Date));e.selectedDay=e.currentDay=o.getDate(),e.drawMonth=e.selectedMonth=e.currentMonth=o.getMonth(),e.drawYear=e.selectedYear=e.currentYear=o.getFullYear(),n===e.selectedMonth&&a===e.selectedYear||i||this._notifyChange(e),this._adjustInstDate(e),e.input&&e.input.val(s?"":this._formatDate(e))},_getDate:function(e){var t=!e.currentYear||e.input&&""===e.input.val()?null:this._daylightSavingAdjust(new Date(e.currentYear,e.currentMonth,e.currentDay));return t},_attachHandlers:function(t){var i=this._get(t,"stepMonths"),s="#"+t.id.replace(/\\\\/g,"\\");t.dpDiv.find("[data-handler]").map(function(){var t={prev:function(){e.datepicker._adjustDate(s,-i,"M")},next:function(){e.datepicker._adjustDate(s,+i,"M")},hide:function(){e.datepicker._hideDatepicker()},today:function(){e.datepicker._gotoToday(s)},selectDay:function(){return e.datepicker._selectDay(s,+this.getAttribute("data-month"),+this.getAttribute("data-year"),this),!1},selectMonth:function(){return e.datepicker._selectMonthYear(s,this,"M"),!1},selectYear:function(){return e.datepicker._selectMonthYear(s,this,"Y"),!1}};e(this).bind(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(e){var t,i,s,n,a,o,r,h,l,u,d,c,p,f,m,g,v,y,b,_,x,w,k,T,D,S,N,M,C,A,P,I,z,H,F,E,W,L,O,j=new Date,R=this._daylightSavingAdjust(new Date(j.getFullYear(),j.getMonth(),j.getDate())),Y=this._get(e,"isRTL"),J=this._get(e,"showButtonPanel"),B=this._get(e,"hideIfNoPrevNext"),K=this._get(e,"navigationAsDateFormat"),V=this._getNumberOfMonths(e),U=this._get(e,"showCurrentAtPos"),q=this._get(e,"stepMonths"),G=1!==V[0]||1!==V[1],X=this._daylightSavingAdjust(e.currentDay?new Date(e.currentYear,e.currentMonth,e.currentDay):new Date(9999,9,9)),Q=this._getMinMaxDate(e,"min"),$=this._getMinMaxDate(e,"max"),Z=e.drawMonth-U,et=e.drawYear;if(0>Z&&(Z+=12,et--),$)for(t=this._daylightSavingAdjust(new Date($.getFullYear(),$.getMonth()-V[0]*V[1]+1,$.getDate())),t=Q&&Q>t?Q:t;this._daylightSavingAdjust(new Date(et,Z,1))>t;)Z--,0>Z&&(Z=11,et--);for(e.drawMonth=Z,e.drawYear=et,i=this._get(e,"prevText"),i=K?this.formatDate(i,this._daylightSavingAdjust(new Date(et,Z-q,1)),this._getFormatConfig(e)):i,s=this._canAdjustMonth(e,-1,et,Z)?""+i+"":B?"":""+i+"",n=this._get(e,"nextText"),n=K?this.formatDate(n,this._daylightSavingAdjust(new Date(et,Z+q,1)),this._getFormatConfig(e)):n,a=this._canAdjustMonth(e,1,et,Z)?""+n+"":B?"":""+n+"",o=this._get(e,"currentText"),r=this._get(e,"gotoCurrent")&&e.currentDay?X:R,o=K?this.formatDate(o,r,this._getFormatConfig(e)):o,h=e.inline?"":"",l=J?"
    "+(Y?h:"")+(this._isInRange(e,r)?"":"")+(Y?"":h)+"
    ":"",u=parseInt(this._get(e,"firstDay"),10),u=isNaN(u)?0:u,d=this._get(e,"showWeek"),c=this._get(e,"dayNames"),p=this._get(e,"dayNamesMin"),f=this._get(e,"monthNames"),m=this._get(e,"monthNamesShort"),g=this._get(e,"beforeShowDay"),v=this._get(e,"showOtherMonths"),y=this._get(e,"selectOtherMonths"),b=this._getDefaultDate(e),_="",w=0;V[0]>w;w++){for(k="",this.maxRows=4,T=0;V[1]>T;T++){if(D=this._daylightSavingAdjust(new Date(et,Z,e.selectedDay)),S=" ui-corner-all",N="",G){if(N+="
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    "+(/all|left/.test(S)&&0===w?Y?a:s:"")+(/all|right/.test(S)&&0===w?Y?s:a:"")+this._generateMonthYearHeader(e,Z,et,Q,$,w>0||T>0,f,m)+"
    "+"",M=d?"":"",x=0;7>x;x++)C=(x+u)%7,M+="";for(N+=M+"",A=this._getDaysInMonth(et,Z),et===e.selectedYear&&Z===e.selectedMonth&&(e.selectedDay=Math.min(e.selectedDay,A)),P=(this._getFirstDayOfMonth(et,Z)-u+7)%7,I=Math.ceil((P+A)/7),z=G?this.maxRows>I?this.maxRows:I:I,this.maxRows=z,H=this._daylightSavingAdjust(new Date(et,Z,1-P)),F=0;z>F;F++){for(N+="",E=d?"":"",x=0;7>x;x++)W=g?g.apply(e.input?e.input[0]:null,[H]):[!0,""],L=H.getMonth()!==Z,O=L&&!y||!W[0]||Q&&Q>H||$&&H>$,E+="",H.setDate(H.getDate()+1),H=this._daylightSavingAdjust(H);N+=E+""}Z++,Z>11&&(Z=0,et++),N+="
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    "+this._get(e,"calculateWeek")(H)+""+(L&&!v?" ":O?""+H.getDate()+"":""+H.getDate()+"")+"
    "+(G?"
    "+(V[0]>0&&T===V[1]-1?"
    ":""):""),k+=N}_+=k}return _+=l,e._keyEvent=!1,_},_generateMonthYearHeader:function(e,t,i,s,n,a,o,r){var h,l,u,d,c,p,f,m,g=this._get(e,"changeMonth"),v=this._get(e,"changeYear"),y=this._get(e,"showMonthAfterYear"),b="
    ",_="";if(a||!g)_+=""+o[t]+"";else{for(h=s&&s.getFullYear()===i,l=n&&n.getFullYear()===i,_+=""}if(y||(b+=_+(!a&&g&&v?"":" ")),!e.yearshtml)if(e.yearshtml="",a||!v)b+=""+i+"";else{for(d=this._get(e,"yearRange").split(":"),c=(new Date).getFullYear(),p=function(e){var t=e.match(/c[+\-].*/)?i+parseInt(e.substring(1),10):e.match(/[+\-].*/)?c+parseInt(e,10):parseInt(e,10);return isNaN(t)?c:t},f=p(d[0]),m=Math.max(f,p(d[1]||"")),f=s?Math.max(f,s.getFullYear()):f,m=n?Math.min(m,n.getFullYear()):m,e.yearshtml+="",b+=e.yearshtml,e.yearshtml=null}return b+=this._get(e,"yearSuffix"),y&&(b+=(!a&&g&&v?"":" ")+_),b+="
    "},_adjustInstDate:function(e,t,i){var s=e.drawYear+("Y"===i?t:0),n=e.drawMonth+("M"===i?t:0),a=Math.min(e.selectedDay,this._getDaysInMonth(s,n))+("D"===i?t:0),o=this._restrictMinMax(e,this._daylightSavingAdjust(new Date(s,n,a)));e.selectedDay=o.getDate(),e.drawMonth=e.selectedMonth=o.getMonth(),e.drawYear=e.selectedYear=o.getFullYear(),("M"===i||"Y"===i)&&this._notifyChange(e)},_restrictMinMax:function(e,t){var i=this._getMinMaxDate(e,"min"),s=this._getMinMaxDate(e,"max"),n=i&&i>t?i:t;return s&&n>s?s:n},_notifyChange:function(e){var t=this._get(e,"onChangeMonthYear");t&&t.apply(e.input?e.input[0]:null,[e.selectedYear,e.selectedMonth+1,e])},_getNumberOfMonths:function(e){var t=this._get(e,"numberOfMonths");return null==t?[1,1]:"number"==typeof t?[1,t]:t},_getMinMaxDate:function(e,t){return this._determineDate(e,this._get(e,t+"Date"),null)},_getDaysInMonth:function(e,t){return 32-this._daylightSavingAdjust(new Date(e,t,32)).getDate()},_getFirstDayOfMonth:function(e,t){return new Date(e,t,1).getDay()},_canAdjustMonth:function(e,t,i,s){var n=this._getNumberOfMonths(e),a=this._daylightSavingAdjust(new Date(i,s+(0>t?t:n[0]*n[1]),1));return 0>t&&a.setDate(this._getDaysInMonth(a.getFullYear(),a.getMonth())),this._isInRange(e,a)},_isInRange:function(e,t){var i,s,n=this._getMinMaxDate(e,"min"),a=this._getMinMaxDate(e,"max"),o=null,r=null,h=this._get(e,"yearRange");return h&&(i=h.split(":"),s=(new Date).getFullYear(),o=parseInt(i[0],10),r=parseInt(i[1],10),i[0].match(/[+\-].*/)&&(o+=s),i[1].match(/[+\-].*/)&&(r+=s)),(!n||t.getTime()>=n.getTime())&&(!a||t.getTime()<=a.getTime())&&(!o||t.getFullYear()>=o)&&(!r||r>=t.getFullYear())},_getFormatConfig:function(e){var t=this._get(e,"shortYearCutoff");return t="string"!=typeof t?t:(new Date).getFullYear()%100+parseInt(t,10),{shortYearCutoff:t,dayNamesShort:this._get(e,"dayNamesShort"),dayNames:this._get(e,"dayNames"),monthNamesShort:this._get(e,"monthNamesShort"),monthNames:this._get(e,"monthNames")}},_formatDate:function(e,t,i,s){t||(e.currentDay=e.selectedDay,e.currentMonth=e.selectedMonth,e.currentYear=e.selectedYear);var n=t?"object"==typeof t?t:this._daylightSavingAdjust(new Date(s,i,t)):this._daylightSavingAdjust(new Date(e.currentYear,e.currentMonth,e.currentDay));return this.formatDate(this._get(e,"dateFormat"),n,this._getFormatConfig(e))}}),e.fn.datepicker=function(t){if(!this.length)return this;e.datepicker.initialized||(e(document).mousedown(e.datepicker._checkExternalClick),e.datepicker.initialized=!0),0===e("#"+e.datepicker._mainDivId).length&&e("body").append(e.datepicker.dpDiv);var i=Array.prototype.slice.call(arguments,1);return"string"!=typeof t||"isDisabled"!==t&&"getDate"!==t&&"widget"!==t?"option"===t&&2===arguments.length&&"string"==typeof arguments[1]?e.datepicker["_"+t+"Datepicker"].apply(e.datepicker,[this[0]].concat(i)):this.each(function(){"string"==typeof t?e.datepicker["_"+t+"Datepicker"].apply(e.datepicker,[this].concat(i)):e.datepicker._attachDatepicker(this,t)}):e.datepicker["_"+t+"Datepicker"].apply(e.datepicker,[this[0]].concat(i))},e.datepicker=new n,e.datepicker.initialized=!1,e.datepicker.uuid=(new Date).getTime(),e.datepicker.version="1.11.0",e.datepicker,e.widget("ui.dialog",{version:"1.11.0",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._trigger("open"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find("[autofocus]")),e.length||(e=this.element.find(":tabbable")),e.length||(e=this.uiDialogButtonPane.find(":tabbable")),e.length||(e=this.uiDialogTitlebarClose.filter(":tabbable")),e.length||(e=this.uiDialog),e.eq(0).focus() +},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e("
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e.each(["min","max","step"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},"mousedown .ui-spinner-button":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){return e(t.currentTarget).hasClass("ui-state-active")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_draw:function(){var 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