title: Does a Bayesian model of V1 contrast coding offer a neurophysiological account of human contrast discrimination?
abstract: The dipper effect for contrast discrimination provides strong evidence that the underlying neural response is accelerating at low contrasts and saturating at high contrasts. The contrast-response functions of V1 neurons do have this sigmoidal shape, but individual neurons do not generally have a dynamic range wide enough to account for the dipper effect. This paper presents a Bayesian model of neurons in monkey V1, whose contrast-response function is described by a modified Naka-Rushton with multiplicative noise. It is shown that a model of groups of twelve or more neurons gives a reasonable explanation of the psychophysical data of two observers, but there is a large systematic error which is apparently due to the shape of the distribution of the monkey's sensitivity parameter, c50. A further model provides a better fit to the data by sacrificing strict adherence to V1 neuronal parameters and, instead using an arbitrary bimodal c50 distribution, perhaps reflecting differences between M- and P-cells.
authors.affiliation: Department of Physiology, Cambridge CB2 3EG, UK.
authors.forename: M
authors.initials: M
authors.lastname: Chirimuuta
authors.forename: D J
authors.initials: DJ
authors.lastname: Tolhurst
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D015350:Contrast Sensitivity / Q000502:physiology*; D004192:Discrimination, Psychological; D006801:Humans; D008960:Models, Psychological; D009474:Neurons / Q000502:physiology; D011601:Psychophysics