Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments.

ABSTRACT: Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stab

SUBMITTER: Bayram B 

PROVIDER: S-EPMC11885517 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature