A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts

• Published in: Nature neuroscience Vol. 18; no. 10; pp. 1509 - 1517
• Main Authors: Wei, Xue-Xin, Stocker, Alan A
• Format: Journal Article
• Language: English
• Published: United States Nature Publishing Group 01.10.2015
• Subjects: Animals; Bayes Theorem; Bayesian statistical decision theory; Brain research; Humans; Hypotheses; Methods; Models, Neurological; Perception; Perception - physiology; Perceptions; United States > US; Pennsylvania
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/nn.4105

Bayesian observer models provide a principled account of the fact that our perception of the world rarely matches physical reality. The standard explanation is that our percepts are biased toward our prior beliefs. However, reported psychophysical data suggest that this view may be simplistic. We propose a new model formulation based on efficient coding that is fully specified for any given natural stimulus distribution. The model makes two new and seemingly anti-Bayesian predictions. First, it predicts that perception is often biased away from an observer's prior beliefs. Second, it predicts that stimulus uncertainty differentially affects perceptual bias depending on whether the uncertainty is induced by internal or external noise. We found that both model predictions match reported perceptual biases in perceived visual orientation and spatial frequency, and were able to explain data that have not been explained before. The model is general and should prove applicable to other perceptual variables and tasks.