Abstract

This week we will discuss and debate a very recent paper by Hahn and Wei, published in Nature Neuroscience (2024).

Abstract: “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” (Hahn & Wei, 2024).

Reference: Hahn, M., & Wei, X.X. (2024). A unifying theory explains seemingly contradictory biases in perceptual estimation. Nature Neuroscience, 27(4), 793–804. https://doi.org/10.1038/s41593-024-01574-x