v1.0.0 initial release # Computational and neural mechanisms of statistical pain learning Suyi Zhang, Ben Seymour, Flavia Mancini In press in Nature Communications An older version of the paper: doi: https://doi.org/10.1101/2021.10.21.465270 ## Usage The code in folder exp_code is used to generate the sequence of stimuli. The experiment is launched by the matlab function exp_MR_1500ms(sub,sess,stimCurrent,MR_state). See detailed comments inside the exp_MR_1500ms.m file. For behavioural data analysis, the following directories contain code for specific use. * data (behavioural data from fMRI sessions) * model_fit (fit models to behavioural data) * model_comparison (performs model comparison) * model_gen (generate parametric modulators for fMRI analyses using fitted model parameters) For imaging analysis, * imaging (1st and 2nd level analysis scripts based on nipype) * imaging_plot (result visualisation using nilearn) Please change data paths and parameter settings within the scripts. The analysis code is written by Suyi Zhang. The raw MRI data are available on [OpenNeuro](https://openneuro.org/datasets/ds003836). ## Requirements To run the code for sequence generation, you will need: * MATLAB * [Psychotoolbox 3](http://psychtoolbox.org) * a DAQ * a stimulus generator To run the code for behavioural analyses, you will need the following: * MATLAB * [Minimal Transition Probs Model package](https://github.com/florentmeyniel/MinimalTransitionProbsModel) * [VBA toolbox](https://mbb-team.github.io/VBA-toolbox/) For imaging analyses, the required python packages are listed in `requirements.txt`. Nipype scripts are best run inside its docker/singularity container, a useful tutorial can be found [here](https://miykael.github.io/nipype_tutorial/).