Spontaneous eye blink rate predicts individual differences in exploration and exploitation during reinforcement learning

doi:10.17605/OSF.IO/4PQ9C

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All the analyses and behavioural data used in the manuscript can be found in the files directory of this project page. We modeled the behavioural data using a Bayesian hierarchical model that was implemented in pySTAN, version 2.14.0.0 The file RLSession.py contains all per-subject analyses. The file group_level.py contains all across-subject analyses. The file stan_model.py contains two models: 1) the Bayesian hierarchical Q-learning model that we used to fit behaviour of low and high sEBR groups. 2) the Bayesian latent mixture model that we used to predict sEBR group membership on the basis of learning behaviour.

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