Ari Dyckovsky
-Construct a list of the extracted behavioral CSV file targets by participant.
-extracted_behavioral_csv_files <- participants %>%
- unlist(use.names = FALSE) %>%
- map(~ str_c("extracted_behavioral_csv_file_", .x))Read CSVs into dataframe combined_df after using tar_read_raw to get the file path for participants from targets. Will include all participants and include an id column to identify the participant individually.
withr::with_dir(here::here(), {
- combined_df <- extracted_behavioral_csv_files %>%
- map_df(~ read_csv(tar_read_raw(.)))
-})
-
-# Transform step and response types to 0 or 1 integer values to simulate boolean behavior.
-combined_df <- combined_df %>%
- mutate(
- is_signal = as.integer(step_type > 1),
- is_response = as.integer(resp_type)
- ) %>%
- select(-c(resp_type, step_type))Count of participants by unique id.
- -## [1] 50
-combined_df %>%
- select(trial, id, is_response, is_signal, resp_time, step_time) %>%
- group_by(id) %>%
- tally(is_response) %>%
- summarise(mean(n), sd(n), min(n), max(n))## # A tibble: 1 x 4
-## `mean(n)` `sd(n)` `min(n)` `max(n)`
-## <dbl> <dbl> <int> <int>
-## 1 37.3 32.9 4 170
-The function to get each participant’s hit times vector is itrackvalr::get_hit_times, and the function to get a dataframe of all participants’ hits is itrackvalr::get_all_hits_with_reaction_times, including each hit time and reaction time between that hit time and the nearest signal prompting a response within a fixed interval.
Get the combined hits using the function:
- -| trial | -id | -image_index | -signal_time | -hit_time | -reaction_time | -
|---|---|---|---|---|---|
| 81 | -CSN001 | -801 | -80.0604399294 | -NA | -NA | -
| 117 | -CSN001 | -941 | -116.0665163944 | -116.984091845 | -0.917575450165 | -
| 119 | -CSN001 | -3131 | -118.0679634164 | -NA | -NA | -
| 211 | -CSN001 | -1325 | -210.0571848214 | -NA | -NA | -
| 235 | -CSN001 | -752 | -234.0716901990 | -NA | -NA | -
| 361 | -CSN001 | -103 | -360.0647047530 | -NA | -NA | -
| 461 | -CSN001 | -2804 | -460.0590288559 | -NA | -NA | -
| 591 | -CSN001 | -28 | -590.0714484362 | -NA | -NA | -
| 823 | -CSN001 | -929 | -822.0630183384 | -822.772524834 | -0.709506495599 | -
| 845 | -CSN001 | -517 | -844.0589960678 | -845.923186561 | -1.864190493347 | -
knitr::kable(head(
- combined_hits_df %>%
- drop_na() %>%
- group_by(id) %>%
- summarise(
- reaction_time_mean = mean(reaction_time),
- reaction_time_min = min(reaction_time),
- reaction_time_max = max(reaction_time),
- reaction_time_sd = sd(reaction_time)
- ),
- 10
-))| id | -reaction_time_mean | -reaction_time_min | -reaction_time_max | -reaction_time_sd | -
|---|---|---|---|---|
| CSN001 | -1.71981596174 | -0.030177300678 | -4.73494848317 | -1.185127808038 | -
| CSN002 | -1.99359213268 | -0.439399655617 | -7.77171126677 | -1.727003771770 | -
| CSN004 | -1.70646593409 | -0.996316162899 | -2.84090208776 | -0.543133623999 | -
| CSN005 | -3.06080474118 | -1.533621048930 | -7.64323121076 | -2.120246304387 | -
| CSN006 | -1.47237477858 | -0.093593949678 | -7.99434786988 | -1.763412125625 | -
| CSN007 | -1.79454994735 | -0.759996981775 | -3.99019392886 | -0.965011326176 | -
| CSN008 | -3.09417513841 | -0.850421200456 | -7.92466269398 | -1.627078951623 | -
| CSN009 | -1.31759492969 | -0.599157401573 | -2.19912816937 | -0.469031982754 | -
| CSN010 | -2.15590923006 | -0.246194777312 | -7.99283455560 | -2.249618738692 | -
| CSN011 | -1.36405598454 | -0.692828005016 | -3.64924517583 | -0.651777575973 | -
Note the 1.000s to 1.100s gap where hits were not recorded.
-combined_hits_df %>%
- drop_na() %>%
- ggplot(aes(x = signal_time, y = reaction_time)) +
- geom_point(color = 'orange') +
- geom_smooth(method=lm) +
- theme_classic()
combined_hits_df %>%
- drop_na() %>%
- arrange(reaction_time) %>%
- ggplot(aes(x = reorder(id, reaction_time, FUN = median), y = reaction_time)) +
- geom_point(alpha = 0.8, size = 0.7, color = 'darkblue', position = 'jitter') +
- geom_boxplot(alpha = 0) +
- geom_hline(yintercept = 1, color = 'coral') +
- theme_minimal() +
- theme(
- aspect.ratio = 1,
- text = element_text(size = 15),
- plot.margin = margin(18, 18, 18, 18, 'pt')
- ) +
- labs(
- title = 'Reaction times of participants by id',
- subtitle = 'Boxplot per participant anchored at median of reaction time',
- y = 'Reaction time for HIT after double-tick signal',
- x = 'Participant'
- ) +
- coord_flip()