Statistical learning shapes pain perception and prediction independently of external cues

<h1>Dataset and code for the relevant analysis and results:</h1> <h3>"Statistical learning shapes pain perception and prediction independently of external cues"</h3> <p>Onysk, J., Whitefield, M., Gregory, N., Jain, M., Turner, G., Seymour, B., Mancini, F. (2024). eLife. <a href="https://doi.org/10.7554/eLife.90634.2">https://doi.org/10.7554/eLife.90634.2</a></p> <h2>1 - data_collection</h2> <p>Contains the code for the psychophysical experiment (PsychToolBox), including the sequence generations scripts.</p> <h2>2 - preprocessing</h2> <p>Contains code that preprocesses behavioural data from PsychToolBox. This includes linear transformation of inputs, exporting data to stan readable format and plotting Supplement figures.</p> <p>- The raw behavioural data can be found in <strong><em>preprocessing/data</em></strong>. Stan ready ready for each condition is found in <strong><em>preprocessing/stan_data</em></strong></p> <h2>3 - model_fit_analysis</h2> <p>Contains stan models used in the paper ('models/'), model fitting code ('fit_models_cs.R) (inlcuding HPC setup in 'hpc/'), initial analysis script for processing stan samples ('primary_analysis_cs.R'), as well as additional analyis scripts ('extra_analysis_cs.R', 'correlation_beh_model_cs.ipynb') that generate figures from the paper and supplement.</p> <p>- The posterior draws for parameters can be found in <em><strong>model_fit_analysis/output/cs_results</strong></em>&nbsp;</p> <h2>4 - model_recovery</h2> <p>Contains code that execute model and parameter recovery analysis ('mp_recovery.R'), including HPC setup ('hpc/'). The 'mp_rec_analyse.R' reproduces model and parameter recovery results from the supplement.</p> <h2>5 - Figures</h2> <p>Contains all the figures from the manuscript and the supplement.</p> <h2>6 - RDS_fits</h2> <p>Contains RStan fit objects for each condition for each model</p>