/2021_RouhaniNiv

data and code for analysis and figures in "Signed and unsigned reward prediction errors dynamically enhance learning and memory"

Primary LanguageJupyter NotebookMIT LicenseMIT

2021_RouhaniNiv

data and code for analysis and figures in "Signed and unsigned reward prediction errors dynamically enhance learning and memory"

datafiles: includes all data, files should be loaded to "analysis&figures.ipynb"

models_RL_matlabCode: matlab drivers and models to fit reinforcement learning models to learning behavior in Experiment 1 ('exp1'), Experiment 2 ('exp2'), and to perform model recovery ('sims')

models_memory_stanCode: R driver and stan models for Bayesian hierarchical modeling of memory data (to be run separately for Experiment 1, and the instructed and incidental memory versions of Experiment 2; 3 models)

analysis&figures.ipynb: jupyter notebook including all analyses and figures in paper