Optimal decisions for contrast discrimination.

Contrast discrimination functions for simple gratings famously look like a dipper. Discrimination thresholds are lower than detection thresholds for moderate pedestal contrasts, and the rate of growth of thresholds as the pedestal contrast gets larger typically lies between the values implied by two popular treatments of noise. Here, we suggest a new normative treatment of the dipper, showing how it emerges from Bayesian inference based on the responses of a population of orientation-tuned units. Our central assumption concerns the noise corrupting the outputs of these units as a function of the contrast: We suggest that it has the shape of a hinge. We show the match to the psychophysical data and discuss the neurobiological and statistical rationales for this form of noise. Finally, we relate our model to other major accounts of contrast discrimination.