A unifying theory explains seemingly contradictory biases in perceptual estimation

• Published in: Nature neuroscience Vol. 27; no. 4; pp. 793 - 804
• Main Authors: Hahn, Michael, Wei, Xue-Xin
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2024; Nature Publishing Group
• Subjects: 631/378/116/2394; 631/378/116/2395; 631/378/2613; 631/378/2649/1723; Animal Genetics and Genomics; Bayes Theorem; Bayesian analysis; Behavioral Sciences; Bias; Biological Techniques; Biomedical and Life Sciences; Biomedicine; Decomposition; First principles; Neural coding; Neurobiology; Neurosciences; Perception; Visual Perception
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/s41593-024-01574-x

Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations. Despite a long history of studying perceptual biases in neuroscience, many of the biases remain difficult to explain and even appear to be contradictory. The authors propose a unifying theory that sheds new light on such puzzling perceptual biases.