Computational framework for investigating predictive processing in auditory perception.

ABSTRACT:

Background

The brain tracks sound sources as they evolve in time, collecting contextual information to predict future sensory inputs. Previous work in predictive coding typically focuses on the perception of predictable stimuli, leaving the implementation of these same neural processes in more complex, real-world environments containing randomness and uncertainty up for debate.

New method

To facilitate investigation into the perception of less tightly-controlled listening scenarios, we present a computational model as a tool to ask targeted questions about the underlying predictive processes that connect complex sensory inputs to listener behavior and neural responses. In the modeling framework, observed sound features (e.g. pitch) are tracked sequentially using Bayesian inf

SUBMITTER: Skerritt-Davis B 

PROVIDER: S-EPMC9017011 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature