Code and data for the Bayesian strategy analysis paper
This repository contains the code used to generate the results in our paper on a new Bayesian algorithm for tracking a subject's behavioural strategy on each trial. Read the paper at eLife: https://doi.org/10.7554/eLife.86491
It also copies of the four datasets used in the paper:
- Y-maze task (rats): all behavioural sessions we used. From Peyrache et al (2009) Nature Neuroscience. Full data are available at https://crcns.org/data-sets/pfc/pfc-6/about-pfc-6
- Lever-press task (rats). Not previously published, collected by Rebecca Hock in the lab of Tobias Bast.
- Stimulus-association task (monkey). Not previously published, a single session collected in the lab of Mark Buckley.
- Gain-loss task (humans). Data collected by Musa Sami.
This is research code, with all its flaws and messiness. To use the algorithn, download our tested and documented toolbox:
- MATLAB: https://github.com/Humphries-Lab/Bayesian_Strategy_Analysis_MATLAB
- Python: https://github.com/Humphries-Lab/Bayesian_Strategy_Analysis_Python
Any further development of the algorithm's capabilities will be published in the toolbox.