title: Sensorimotor priors in nonstationary environments.
abstract: In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known, however, about how priors develop in environments with nonstationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various nonadaptive and adaptive models. Simulations show that models that rapidly adapt to nonstationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment.
authors.affiliation: MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University, Amsterdam, The Netherlands. d.narain@vu.nl
authors.forename: Devika
authors.initials: D
authors.lastname: Narain
authors.forename: Robert J
authors.initials: RJ
authors.lastname: van Beers
authors.forename: Jeroen B J
authors.initials: JB
authors.lastname: Smeets
authors.forename: Eli
authors.initials: E
authors.lastname: Brenner
mesh_terms: D000222:Adaptation, Physiological; D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D010775:Photic Stimulation; D011597:Psychomotor Performance / Q000502:physiology*; D012677:Sensation