Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning
Fig 5
Strategic balance of two learning mechanisms.
(A) The contribution of serial hypothesis testing (SHT) was inversely correlated with reaction time such that participants who responded faster used SHT to a greater extent. (B) The contribution of reinforcement learning (RL) was correlated with average reward rate: participants for whom adding the RL component improved the model fit to a greater extent earned more rewards on the task, on average. Each dot represents one participant. (C, D) Contribution of RL and SHT for each game type. The contribution of each component was measured as the difference in likelihood per trial between the hybrid value-based SHT model and the other component model (SHT: the feature RL with decay model; RL: the random-switch SHT model). Error bars represent ±1 s.e.m. across participants.