From 18acec641aec7340c022be405849867fde324744 Mon Sep 17 00:00:00 2001 From: David Rose Date: Mon, 5 Aug 2024 14:11:32 -0700 Subject: [PATCH] updated R code --- code/R/teleological_properties.Rmd | 58 ++++++++++++++++-- ...y_selections_for_property_type_ratings.pdf | Bin 93097 -> 93213 bytes 2 files changed, 53 insertions(+), 5 deletions(-) diff --git a/code/R/teleological_properties.Rmd b/code/R/teleological_properties.Rmd index 95b536b..924c666 100644 --- a/code/R/teleological_properties.Rmd +++ b/code/R/teleological_properties.Rmd @@ -23,6 +23,7 @@ library("emmeans") # for comparing models library("knitr") # for knitting library("RSQLite") # for reading in participants.db file library("tidyjson") # for reading in json data +library("entropy"). # for computing entropy library("brms") # for Bayesian data analysis library("tidyverse") # for everything else @@ -415,7 +416,7 @@ ggsave(filename = "../../figures/experiment2/exp2_heat_map.pdf", ``` -### Expected Property Selections for Each Property Type on Catgeorization Rating +### Catgeorization Ratings for Each Individual Property ```{r fig.height=6, fig.width=10, message=FALSE, warning=FALSE} @@ -468,11 +469,11 @@ df.exp2.property_categorization = df.exp2.behavior %>% df.exp2.purpose, df.exp2.social) -df.plot = df.exp2.property_categorization %>% +df.each_property = df.exp2.property_categorization %>% filter(categorization == "behavioral" & property_selected %in% c("jump", "chew", "swim", "run") | categorization == "biological" & property_selected %in% c("have hair", "have long legs", "are warm blooded", "have pointy ears") | categorization == "purpose" & property_selected %in% c("purify water", "aerate soil", "enable decomposition", "make honey") | categorization == "social" & property_selected %in% c("pair bond", "share food with group members", "follow the dominant group member", "cooperate with group members")) #give short labels to properties for plotting -df.plot = df.plot %>% +df.each_property = df.each_property %>% mutate(property_selected = str_replace_all(property_selected, "are warm blooded", "blood"), property_selected = str_replace_all(property_selected, "have hair", "hair"), property_selected = str_replace_all(property_selected, "have pointy ears", "ears"), @@ -487,12 +488,12 @@ df.plot = df.plot %>% property_selected = str_replace_all(property_selected, "pair bond", "bond")) # rename property types -df.plot = df.plot %>% +df.each_property = df.each_property %>% mutate(property_changed = str_replace_all(property_changed, "behavioral", "behavior"), property_changed = str_replace_all(property_changed, "biological", "biology")) -ggplot(data = df.plot, +ggplot(data = df.each_property, mapping = aes(x = property_selected, y = response, group = property_changed, @@ -535,6 +536,8 @@ ggplot(data = df.plot, ggsave(width = 10, height = 6, "../../figures/experiment2/exp2_expected_property_selections_for_property_type_ratings.pdf") ``` + + ## Stats ### Bayesian Linear Mixed Model @@ -589,6 +592,51 @@ df.llm = df.llm %>% model = str_replace_all(model, "roberta_large", "RoBERTa-large")) ``` + +### Stats + +#### Correlate participant ratings for individual properties with LLM completions + +```{r} + +df.each_property = df.each_property %>% + select(property_selected, categorization, response) %>% + rename(property = property_selected, category = categorization) %>% mutate(category = str_replace_all(category, "behavioral", "behavior"), + category = str_replace_all(category, "biological", "biology")) %>% + + group_by(category, property) %>% + summarise(mean = mean(response, na.rm = TRUE)) + +df.llm = df.llm %>% + #give short labels to properties for plotting + mutate(property = str_replace_all(property, "warm blooded", "blood"), + property = str_replace_all(property, "pointy ears", "ears"), + property = str_replace_all(property, "long legs", "legs"), + property = str_replace_all(property, "enables decomposition", "decompose"), + property = str_replace_all(property, "purifies water", "purify"), + property = str_replace_all(property, "makes honey", "honey"), + property = str_replace_all(property, "aerates soil", "aerate"), + property = str_replace_all(property, "share food with group members", "share"), + property = str_replace_all(property, "cooperates with group members", "cooperate"), + property = str_replace_all(property, "follow dominant group member", "follow"), + property = str_replace_all(property, "pair bond", "bond")) %>% + group_by(category, property, model) %>% + summarise(entropy_value = entropy(probability)) %>% + pivot_wider(names_from = model, + values_from = entropy_value) + +df.diagnostic = df.each_property %>% + left_join(df.llm, by = c("category", "property")) %>% + ungroup() + +##how to do the correlation? + + +``` + + + + ### Plots #### Overall Property Means by Model diff --git a/figures/experiment2/exp2_expected_property_selections_for_property_type_ratings.pdf 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