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

•Hue matching experiments using stimuli with different levels of noise reveal internal biases in hue perception.•Observers show lowest discrimination thresholds and smallest biases near a non-cardinal color space axis that aligns with the variation of natural daylights, suggesting that the statistics of colors in nature shape how we perceive hues.•A Bayesian observer model explains the hue biases through a non-uniform prior that favors blue. 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 the integration of internal priors with noisy sensory inputs. In this study, 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. Our results showed that discrimination thresholds for isoluminant stimuli with hue defined by azimuth angle in cone-opponent color space exhibited 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. These biases could be explained by the Bayesian observer model through a non-uniform prior with a preference for blue. Our findings suggest that visual processing takes advantage of knowledge of the distribution of colors in natural environments for hue perception.