Auditory perception is subject to ambiguity caused by sensory noise and volatility of dynamic environments. Bayesian inference postulates the use of perceptual priors to increase perceptual acuity in the face of uncertainty. Importantly, dynamic environments require a flexible use of priors to enable quick adaptation to environmental changes.The present study investigates Bayesian perceptual adaptation in dynamic environments through EEG and pupillometry. We had 26 young adults perform an auditory motion discrimination task, indicating the final directions of auditory motion sequences with random lengths and change points (CP). The task was designed to induce utilization of perceptual priors to reduce uncertainty. Strategically rendering them obsolete through CPs forced adaptation. Participants’ accuracy decreased sharply directly following a CP, followed by a fast and steady increase for multiple motions in the same direction. This indicates strong influence of perceptual priors on motion direction and quick adaptation to a changed environment. Cluster-based permutation analysis of the EEG data revealed stronger and longer sustained P3b activity for motions directly following a CP. Cluster amplitudes on a single sound level were further significantly predicted by momentary estimates of surprisal extracted from a Bayesian CP model.We are currently analyzing the pupillometric data, and expect it to show indications of changes in arousal during perceptual adaptation, reflecting the utilization of priors, best visible directly following a CP.