Hierarchical Bayesian modeling can be used to extract parameters that can reliably predict out-of-sample data. We show that using hierarchical models to simultaneously extract and impose empirical priors leads to better out- of-sample prediction.
Reinforcement learning models have been used extensively to capture learning and decision-making processes in humans and other organisms. One essential goal of these computational models is the generalization to new sets of observations. Extracting parameters that can reliably predict out-of-sample data can be difficult, however. The use of prior distributions to regularize parameter estimates has been shown to help remedy this issue. While previous research has suggested that empirical priors estimated from a separate dataset improve predictive accuracy, this paper outlines an alternate method for the derivation of empirical priors: hierarchical Bayesian modeling. We provide a detailed introduction to this method, and show that using hierarchical models to simultaneously extract and impose empirical priors leads to better out-of-sample prediction while being more data efficient.