A Bayesian observer model reveals a prior for natural daylights in hue perception

Abstract

Incorporating statistical characteristics of stimuli in perceptual processing can be highly beneficial for reliable estimation from noisy sensory measurements but may generate perceptual bias. According to Bayesian inference, perceptual biases arise from integrating internal priors with noisy sensory inputs. We used a Bayesian observer model to derive biases and priors in hue perception based on discrimination data for hue ensembles with varying levels of chromatic noise. For isoluminant stimuli with hue defined by azimuth angle in cone-opponent color space, discrimination thresholds showed a bimodal pattern, with lowest thresholds near a non-cardinal blue-yellow axis that aligns closely with the variation of natural daylights. Perceptual biases showed zero crossings around this axis, indicating repulsion away from yellow and attraction towards blue. The biases could be explained by the Bayesian observer model through a non-uniform prior with a preference for blue. Our results suggest that visual processing exploits knowledge of the distribution of colors in natural environments for hue perception.