Research data supporting "Association of Environmental Uncertainty With Altered Decision-making and Learning Mechanisms in Youths With Obsessive-Compulsive Disorder"
Aziz Marzuki, Aleya
Ip, Samantha Hiu Yan
Kanen, Jonathan W
Sahakian, Barbara J
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Aziz Marzuki, A., Tomić, I., Ip, S. H. Y., Gottwald, J., Kanen, J. W., Kaser, M., Sule, A., et al. (2022). Research data supporting "Association of Environmental Uncertainty With Altered Decision-making and Learning Mechanisms in Youths With Obsessive-Compulsive Disorder" [Dataset]. https://doi.org/10.17863/CAM.82186
In the 'PRL' folder, there is a 'core' and a 'support' folder. The raw data are saved in 'core' as 'probrev_forstan.csv'. The data in this file are collected from 103 adolescents (50 with OCD and 53 without) who completed a probabilistic reversal learning task (details in paper). The file contains information about subject ID (patient_num), trials, chosen stimulus per trial (chosen_stim), whether stimulus 1 was chosen (stim1_chosen), whether subject chose correct stimulus (chosen_correctly), task feedback (1 - positive, 0 - negative; fdbk), group (1 - Control, 0 - OCD). This data are in a format ready to undergo computational modelling. To run the models, use the Runner.R file in the 'core' folder. The models themselves are in .stan format. Next, in the 'WCST' folder, the raw data (in the wcst_data.txt) file are obtained from 73 adolescents (27 with OCD 46 without) who completed a Wisconsin Card Sorting Task (details in paper). Relevant columns in the wcst_data file are subject ID (subnum), trial (trial), whether the card chosen matches the test card on colour (corr_col), shape (corr_shape) and number (corr_name), group (0 -OCD, 1 - CTL), whether card was chosen correctly (corr). The main script for fitting the computational models to data is WCST_JAGS_2021.R. Models themselves are in .txt files (files that end in _dnormsd_groupdiff).
Required software for running scripts and models: R, RStudio, STAN, JAGS
OCD, Adolescence, Reversal-Learning, Cognitive Flexibility, Computational Models
Publication Reference: https://doi.org/10.1001/jamanetworkopen.2021.36195
Wellcome Trust grant 104631/Z/14/Z/
This record's DOI: https://doi.org/10.17863/CAM.82186
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/