This repository holds data for the paper entitled: **"Learning to synchronize: midfrontal theta dynamics during reversal learning"** *Paper available at:* https://doi.org/10.1523/JNEUROSCI.1874-20.2020 *Analyses code available at :* https://github.com/CogComNeuroSci/PieterV_public/tree/master/Reversal_learning Below I explain the folder structure and its contents ---------- **Behavioral_data** --------------- 34 csv files Each file contains the behavioral data for one subject in the study. ---------- **Behavioral_fit_data** ------------------- 3 subfolders for each of the three behavioral models that were fitted. ALR_model: 27 csv files, one for each subject that survived cleaning 1 extra csv file with general model fit metrics (log-likelihood, AIC,...) for each subject Note: filenames have "Hybrid" annotation because during writing model terminology changed from Hybrid to ALR. bSync_model: 27 csv files, one for each subject that survived cleaning 1 extra csv file with general model fit metrics (log-likelihood, AIC,...) for each subject RW_model: 27 csv files, one for each subject that survived cleaning 1 extra csv file with general model fit metrics (log-likelihood, AIC,...) for each subject ---------- **EEG_data** -------- 2 subfolders with either raw data or cleaned data Raw_data: 34 bdf (biosemi data file) files, one for each subject. This is divided over 4 compressed folders due to upload limit. Cleaned_data: 27 set files and 27 fdt files locked to feedback onset. This isdivided over 3 compressed folders due to upload limit We only analysed subjects that survived behavioral cleaning and had no technical problems. ---------- **Task_simulation** --------------- 27 mat-files including the data of each one of the 27 model simulations.