Pupil Dynamics Reflect Uncertainty-Driven Adjustments of Probability Learning

Adaptive learning in dynamic environments depends on the brain's ability to represent and respond to uncertainty. Changes in arousal-mediated pupil diameter provide insight into the neural basis of this process. Prior work has focused on magnitude-based inference tasks and revealed that learning is primarily influenced by the detection of change points and the corresponding modulation of arousal. In comparison, the role of arousal systems in learning contexts where changes are harder to detect, such as in probability learning, remains poorly understood. Here, we combined pupillometry with a probability learning task in which human participants estimated a hidden generative probability of stimuli that changed abruptly and unpredictably. A Bayesian ideal observer model was used to estimate trial-by-trial learning rates and two uncertainty-associated factors: change point probability (the probability that the generative process has abruptly changed) and prior uncertainty (about the current beliefs on this generative process). In line with normative theory, participants relied on prior uncertainty to adjust learning rates. Pupil analyses revealed temporally distinct representations of uncertainty, with phasic dilations tracking change point probability and tonic pupil dilations tracking prior uncertainty. Mediation analyses further indicated that both phasic and tonic pupil signals partially carried the effects of change point probability and prior uncertainty on learning, respectively. Critically, high tonic arousal weakened the coupling between phasic pupil responses and change point probability, accompanied by a reduced influence of change point probability on learning. Together, our results demonstrate that probability learning in a dynamic environment is underpinned by computationally rational integration of latent uncertainty factors and its contribution to belief updating,regulated through arousal-associated responses.