Perceivers benefit from integrating their prior beliefs with sensory signals based on their relative reliability to mitigate the effects of stochastic noise. However, in volatile environments, one should avoid integrating these signals when changes in the signal source render their prior beliefs irrelevant. Bayesian causal inference could offer the brain an optimal strategy for adaptively adjusting its reliance on priors. Mechanistically, it has been suggested that the arousal system modulates the weights between prior beliefs and sensory signals accordingly. We tested these hypotheses using an auditory localization task. Participants listened to noisy sound sequences of unknown length, with sudden changes in source location. They provided estimates of the last sound's location and a spatial uncertainty interval, while we tracked their pupil size throughout the stimulation as a proxy for arousal. We then fitted a Bayesian model to their behavioral responses and a deconvolution model to their ongoing pupil responses. Behavioral analysis indicated response biases towards predicted prior locations. Consistent with Bayesian inference, adaptive effects of prior reliability and relevance were clearly observed. Pupillometry analyses revealed single-event correlations between evoked dilation and both modeled surprisal as well as posterior runlength, the effective number of integrated sensory observations. These results confirm that the brain employs principles of Bayesian inference to simultaneously manage stochasticity and volatility in perceptual decision-making. Unlike previous studies, the priors were formed without prompting explicit predictions during ongoing, purely auditory stimulation. Moreover, our pupillometry results support the modulating role of the arousal system during implicit perceptual belief updating.