Disrupted decision making in adult male rats after prolonged withdrawal from ethanol vapor exposure

# Data and code for Cheng et al. (2025) This repository contains the data and MATLAB code used for the manuscript: Cheng Y, Kalapatapu N, Janak PH. *Disrupted decision making in adult male rats after prolonged withdrawal from ethanol vapor exposure*. Alcohol Clin Exp Res. 2025. doi:10.1111/acer.70143 For the Zenodo upload, first unzip the archived files and then run the MATLAB scripts from the extracted top-level folder. ## Overview - Behavioral figures and statistics can be regenerated from the MATLAB scripts in the repository root directory. - Reinforcement learning (RL) model fitting, model comparison, and parameter-distribution plots can be reproduced from the MATLAB code in the `RLfitting/` folder. - This release is intended for open data and code sharing associated with the published paper above. ## Repository contents - `Analysis_Figure1_BEC.m`: blood ethanol concentration summary plot from `RawData/BECs.xlsx` - `Analysis_Figure2_100.m`: analyses for the deterministic 100/0 condition - `Analysis_Figure4_9010.m`: analyses for the 90/10 condition - `Analysis_Figure5_8020.m`: analyses for the 80/20 condition - `RawData/`: behavioral data files used by the root analysis scripts - `Utlities/`: helper functions used by the behavioral analysis scripts - `RLfitting/`: RL model-fitting code, Stan model files, plotting scripts, utilities, and saved fitting outputs ## Software used ### Behavioral analyses - Validated with MATLAB R2025b - Validated on macOS Tahoe 26.3.1 ### RL fitting - Originally run with MATLAB R2020a - Originally run on Windows Server 2019 - Requires the MATLAB Stan interface (`matlab-stan`): https://mc-stan.org/users/interfaces/matlab-stan The paper also cites the MATLAB Stan interface in the Methods section. Users can consult the paper for the modeling context and fitting strategy. ## Reproducing the behavioral analyses From the repository root, run the scripts directly in MATLAB: - `Analysis_Figure1_BEC.m` - `Analysis_Figure2_100.m` - `Analysis_Figure4_9010.m` - `Analysis_Figure5_8020.m` These scripts load the packaged files in `RawData/`, add the helper functions in `Utlities/`, and generate the behavioral figures and associated statistics used in the manuscript. Output files are written by the scripts as `.pdf` and `.csv` files in the current working directory unless you edit the paths in the code. ## Reproducing the RL analyses RL-related code is located in `RLfitting/`. Main entry points include: - `RLfitting/StanFit_subj_group_level.m`: hierarchical Stan model fitting - `RLfitting/ModelComp_psisloo.m`: model comparison using PSIS-LOO - `RLfitting/PlotParamDistri_DFRL.m`: parameter-distribution plots for the DFRL model - `RLfitting/PlotParamDistri_QDF2Lr.m`: parameter-distribution plots for the Q_DF2LR model Before running the RL fitting code: 1. Install `matlab-stan`. 2. Update the path configuration at the top of the RL scripts to match your local installation. 3. If needed, also add any required local process-manager utilities referenced in the commented `addpath(...)` lines. The RL input data used by the fitting scripts are included in `RLfitting/Data4RL/`. Stan model definitions and saved fitting outputs are included under `RLfitting/StanModels/`. ## Notes - The behavioral analysis scripts are self-contained with the packaged data provided here. - The RL code reflects the original computing environment used for the manuscript and may require small path edits for reuse on a different machine or operating system. - If you use these data or code, please cite the published paper.