PsyTrack_Learning is an extension of the PsyTrack Python package, originally designed for fitting a dynamic psychophysical model to behavioral data as proposed in our 2018 NeurIPS paper, 'Efficient inference for time-varying behavior during learning.' PsyTrack_Learning extends this model to include a deterministic learning component to the model. The model is presented and described in detail in our 2020 NeurIPS paper, 'Inferring learning rules from animal decision-making.'
Documentation and examples can be found in PsyTrack_Learning_Examples.ipynb where many of the figures from our manuscript can be reproduced.
Zoe Ashwood, Nick Roy, Ji Hyun Bak, and Jonathan Pillow
Please cite as:
Ashwood, Zoe C., et al. 'Inferring learning rules from animal decision-making.' Advances in Neural Information Processing Systems. 2020.