This README file was generated on 2023-08-31 by Ido Ben-Artzi.

GENERAL INFORMATION

1. Title of Dataset:
   Computational mechanisms underlying latent value updating of unchosen actions
2. Author Information:
   A. Principal Investigator Contact Information:
   Name: Nitzan Shahar
   Institution: Tel Aviv University
   Email: nitzansh@tauex.tau.ac.il

DATA & FILE OVERVIEW

1. File List:

DATA-SPECIFIC INFORMATION

Task description

Human participants completed an online multi-armed bandit reinforcement learning task where they were asked to choose cards to gain monetary rewards. The task included four cards, and in each trial, the computer randomly selected and offered two for participants to choose from. Each card led to a reward according to an expected value that drifted across the trials (generated using a random walk with a noise of N(0,.03)). The task included two conditions (win vs. loss block) manipulated between four interleaved blocks (whether the first block was win or loss was counterbalanced between participants). In a 'win' block, the only possible outcomes were winning 1 or 0 play dollars, and in the 'loss' condition, the only possible outcomes were losing 0 or 1 play dollars. Each block consisted of different cards.
Give a brief summary of dataset contents, contextualized in experimental procedures and results. Participants were told that they need to do their best to earn as much money as possible. Participants completed four blocks, with 50 trials each and at the end of the experiment were paid a fixed amount (£2.5) plus a bonus (of £1 or £1.5) based on their performance. Further information and trial sequence is described in Figure 1 and SI.

Data Treatment

The first trial on each block, trials with implausibly quick RTs (<200ms), or exceptionally slow RTs (>4000ms) were omitted (1.79% of all trials). Participants with more than 10% excluded trials (21 participants) or higher than 5% no-response rate (4 participants), in total 25 participants (12.3% of subjects; age mean = 22.8, range 18 to 36; 22 males, 3 females) were excluded altogether. To conduct our main behavioral analysis, we selected a subset of trials in which the previously unoffered card was reoffered, and the previously offered card was not. This resulted in an average of 63.6 trials per participant (SD=6.7), with the number of trials ranging from 46 to 81 across subjects.

Description of the data and file structure: df_raw.csv

1. Number of variables:
   14
2. Number of rows:
   40601
3. Variable List:

Description of the data and file structure: df.rdata

This file can be loaded using R as a rdata file. It includes 22 further variables for a total of 36 variables. It excludes any trials described in the data treatment section (see preprocessing script for specific implementation).

1. Added variables:
   *acc - Accuracy estimate calculated based on whether the participant chose the card with the higher probability of giving a reward (1) or not (0).
   *trial.total - A running counter for the trials of each participant
   *delta_exp_value - The difference in expected values (i.e., probabilities to give reward) of the two cards. Specifically, chosen minus unchosen.
   *offer1 - The same as frcA
   *offer2 - The same as frcB
   *choice - The same as ch
   *unchosen - The identity of the card which was not chosen (ranges from 0 to 3)
   *reward - The same as rw
   *subject - The same as subj
   *delta_exp_value_oneback - The delta_exp_value in the previous trial (notice that this trial could not be shown in this dataframe as it is filtered out)
   *reoffer_ch - Describes whether the chosen card from the previous trial is reoffered at the current trial
   *reoffer_unch - Describes whether the unchosen card from the previous trial is reoffered at the current trial
   *stay_frc_ch - Refers to whether the card (fractal) which was chosen on the previous trial, was chosen again at the current trial
   *stay_frc_unch - Refers to whether the card which was unchosen on the previous trial, was chosen at the current trial
   *reward_oneback - Refers to the outcome of the previous trial (notice this trial could not be appearing in this dataset)
   *acc_oneback - Same as acc, but for the previous trial
   *prob1_oneback - Same as prob1 (chosen card EV), but for the previous trial
   *prob2_oneback - Same as prob2 (unchosen card EV), but for the previous trial
   *rt_oneback - same as rt, but for the previous trial
   *condition - same as cond
   *delta_exp_value_oneback_abs - same as "delta_exp_value_oneback", but as an absolute value.
   *trial_scaled - scaling of the trial number

Sharing/Access information

A replication of the effect described in the current dataset can be found here:
https://osf.io/xyrhe/

Code/Software

The preprocessing.R file was used to generate the df.rdata file from the df_raw.csv file.
The regression.zip file contains R files in which Bayesian regression analyses were conducted using the "brms" R package.
For each model, an .R file exists for data simulation purposes. Two further stan files were used for parameter estimation and model comparison.