Bayesian Efficient Coding as a Theory of Perception: Progress, Controversies and Prospects

Abstract

Bayesian efficient coding unifies two foundational theories of sensory processing: efficient coding and Bayesian inference. Central to this account is the idea that natural environmental statistics shape both how sensory information is encoded and how it is perceptually interpreted. By unifying these principles, the framework accounts for counterintuitive perceptual biases and establishes lawful relationships between environmental statistics, bias, and discrimination thresholds. Here, we review behavioural and neural evidence for this theory in perception and cognition, as well as how short- and long-term adaptation to the environment may be expressed within the framework. We further review theoretical developments that extend the original framework, focusing on how response biases can be decomposed into encoding- and decoding-related components. A decade after its introduction, Bayesian efficient coding continues to evolve as a powerful theory, with recent extensions addressing early limitations and opening new directions for investigating perception and cognition.