title: A bayesian model of sensory adaptation.
abstract: Recent studies reported two opposite types of adaptation in temporal perception. Here, we propose a bayesian model of sensory adaptation that exhibits both types of adaptation. We regard adaptation as the adaptive updating of estimations of time-evolving variables, which determine the mean value of the likelihood function and that of the prior distribution in a bayesian model of temporal perception. On the basis of certain assumptions, we can analytically determine the mean behavior in our model and identify the parameters that determine the type of adaptation that actually occurs. The results of our model suggest that we can control the type of adaptation by controlling the statistical properties of the stimuli presented.
authors.affiliation: Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan.
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Sato
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D000222:Adaptation, Physiological; D001499:Bayes Theorem; D003198:Computer Simulation; D008960:Models, Psychological; D009716:Numerical Analysis, Computer-Assisted; D010465:Perception / Q000502:physiology; D010812:Physical Stimulation; D011594:Psychometrics; D012679:Sense Organs / Q000502:physiology*; D013997:Time Factors