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

Bayesian efficient coding unifies two foundational theories of sensory processing: efficient codingand Bayesian inference. Central to this account is the idea that natural environmental statisticsshape both how sensory information is encoded and how it is perceptually interpreted. By unifyingthese principles, the framework accounts for counterintuitive perceptual biases and establishes lawfulrelationships between environmental statistics, bias, and discrimination thresholds. Here, we reviewbehavioural 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 furtherreview theoretical developments that extend the original framework, focusing on how response biasescan 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 addressingearly limitations and opening new directions for investigating perception and cognition.