Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning
Fig 4
Model comparison supports both reinforcement learning (RL) and serial hypothesis testing (SHT) strategies.
(A) Geometric average likelihood per trial for each model (i.e., average total log likelihood divided by number of trials and exponentiated). Higher values indicate better model fits. Dashed lines indicate chance. Error bars represent ±1 s.e.m. across participants. (B, C) Simulation of the best-fitting value-based SHT model. The same learning curves as in Fig 2 but for model simulation.