Predicting the timing of external stimuli is a key function in sensory processing. In noisy environments, the importance of prior information increases due to reduced reliability of sensory information. At the same time, sudden changes in the environment often render prior information invalid. In such cases, observers should update beliefs and establish new priors using current sensory evidence. To study these dynamic processes, our current EEG experiment investigates the behavioral and neural patterns associated with auditory temporal discrimination in a noisy and dynamically changing environment, using a Bayesian inference framework.Participants listened to a sequence of sounds with varying stimulus onset asynchronies, in which each sound had a 1/5 chance of being a change point at which the temporal pattern changed from acceleration to deceleration or vice versa. At the end of each sequence, they indicated whether the final sound was part of an accelerating or decelerating pattern. As a different number of sounds was presented in each trial, participants were expected to constantly update their temporal estimations. We hypothesize that electrophysiological responses to each sound will reflect a reliability-based weighting between prior and likelihood parameters, which we will capture using a computational model.We have collected a full dataset of 28 participants, and will report behavioral outcomes and results of a linear regression of event-related potentials against our computational model variables reflecting both prior and likelihood.