Pupil-linked arousal encodes uncertainty-weighted prediction errors

Learning to predict future outcomes is essential for successful decision-making. One importantmechanism governing such learning is the reward prediction error. In many real-worldscenarios, sensory information about stimuli and choice options is ambiguous, leading to uncertaintyabout the environment’s underlying states that guide learning and choice behavior.In such cases, learning from prediction errors should be modulated by the probabilities ofthese hypothetical states, known as the belief state. We hypothesized that prediction errorsmight be weighted by the belief state during learning under perceptual uncertainty, and thatthis modulation is governed by pupil-linked arousal systems. Combining pupillometry andan uncertainty-augmented reward-learning task (N = 47), we found that pupil responses tooutcomes scaled with prediction errors and were down-weighted under higher uncertainty.This suggests that the brain’s arousal systems combine newly arriving perceptual and rewardinformation to dynamically regulate how much to learn in an uncertain world.