Task-induced neural covariability as a signature of approximate Bayesian learning and inference.

ABSTRACT: Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The prediction

SUBMITTER: Lange RD 

PROVIDER: S-EPMC8963539 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature