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Xiang2023 Replication Project

Made from template: https://github.com/psyc-201/replication_template

Note

For more information:

Usage

  • First, set up environment using pixi install then pixi run install_non_conda
  • Can preview writeup, presentation, or both using pixi run preview writeup/pixi run preview presentation/pixi run preview
  • Can render writeup, presentation, or both using pixi run render writeup/pixi run render presentation/pixi run render
  • To deploy rendered documents, render locally then deploy the rendered files

Important

This repository contains key submodules for the experiment code and modeling code. To load them, run git submodule update --init --recursive after cloning the repository. (To update submodule(s) to remote changes, run git submodule update --remote [path].)

Warning

To fetch data from OSF, need to set OSF_PAT (e.g., using a .Renviron file). If OSF_PAT is specified, the data will be fetched and resaved as data/...-anon.csv, assuming their contents are already non-identifiable (see writeup/index.qmd). Double-check that they are before committing them. If OSF_PAT is not specified, the anonymized data will be assumed to be already accessible in data/.

Codebook

Raw Data (in data/)

Note that some columns are only specified for some levels of Stage

Column Description
rt Reaction time in ms
url Url of the experiment
trial_type JsPsych trial type
trial_index Trial index
time_elapsed Time elapsed in ms
internal_node_id JsPsych internal node ID
encryptedProlificId RSA-encrypted Prolific Participant ID
studyId Prolific Study ID
sessionId Prolific Session ID
expIndex Experiment ID (index1 for this study)
roundN_outcomes HTML Stubs for each contestant's Round-N Outcomes across contests, separated by commas (see original code for how they're ordered)
roundN_o Each contestant's Round-N Outcomes across contests (numerical, where 1 = Lift, 0 = Fail), separated by commas (see original code for how they're ordered)
roundN_reward Lift reward for round N
success Did the participant successfully enter fullscreen (Only present on row with trial_type="fullscreen")
view_history View history from JsPsych, for trial types where relevant
stimulus Stimulus presented to participant
key_press Unused
responses Comprehension check responses
question_order Order of comprehension check questions (constant)
button_pressed Was the button on the screen pressed?
j Contest index
Stage Round stage as label
stage Round stage as number
contest Contest index
round Round index
strength_a/b Participant's estimated strength for contestant a/b
effort_a/b Participant's estimated effort for contestant a/b
outcome_a/b Actual outcome for contestant a/b
reward Current round reward
rN_strength_a/b Participant's round-N estimated strength for contestant a/b
rN_effort_a/b Participant's round-N estimated effort for contestant a/b
rN_outcome_a/b Actual outcome in round-N for contestant a/b
probability_a/b Participant's estimated lift probability for contestant a/b
rN_prob_a/b Participant's estimated lift probability for contestant a/b at round N (2 or 3)
attention_check Whether passed the attention check (1 = passed, 0 = failed)
count Unclear; related to attention checks
screen Unclear; seems to be a fullscreen enter event, but it's at the end of the study
pick_contest Which contest was selected for the bonus
pick_round Which round was selected for the bonus
pick_side Which contestant was selected for the bonus
trial_probability The estimated lift probability for the selected contestant (0 = A (left), 1 = B (right))
trial_outcome The actual outcome for the selected contestant
bonus The computed bonus
attention_sum The total number of attention checks passed
attention Did the participant pass both attention checks? (1 = passed both, 0 = failed at least one)
datapipe_meta.attributes.date_modified The date the file on OSF was last modified (corresponds to when it was saved)

Clean data

See original_code/competence_effort/Data/README.txt for description of the derived columns (in replication.dat in writeup/index.qmd)

More information about the collected data

  • The prolific IDs are encrypted on the client-side so OSF does not receive that identifying information
  • As of 2025-11-13, on OSF, the first three csv files are pilots (6bt3juzg6gt3 was not a genuine pilot and just was moving through the task to test it technically)

Computational models

Note

new_code/memo-sandbox is an environment for building up memo models alongside reference examples (both in memo and in webppl). To run the code specifically for reproducing the computational modeling done in this replication, one file is sufficient: new_code/memo-sandbox/webppl vs memo/xiang2023-exp1-round3-memo.qmd.

See new_code/memo-sandbox for more information, and https://jczimm.com/memo-sandbox/ for renders of relevant modeling code.

References

Xiang, Y., Vélez, N., & Gershman, S. J. (2023). Collaborative decision making is grounded in representations of other people’s competence and effort. J. Exp. Psychol. Gen., 152(6), 1565–1579.