Supplementary Figure 9: Model comparison in the dynamic foraging task

(a) Computational models of reinforcement-learning and decision-making used to analyze the behavioral data, summarizing whether sensitivity to uncertain outcomes arises from learning, decision, or both processes. (b,c) Bayesian Information Criterion (BIC) for the standard reinforcement learning model and alternative models: standard model with asymmetric learning (L) rates for positive and negative outcomes, uncertainty model with a single learning rate for value and uncertainty (bonus), uncertainty model with separate learning rates for value and uncertainty, uncertainty model with three learning rates (for positive and negative outcomes, and for uncertainty). Smaller BIC value indicates better fit, which was the uncertainty model with separate learning rates for value and uncertainty for WT mice (b) and the standard reinforcement learning model for β2KO mice (c).