title: Visual learning in multisensory environments.
abstract: We study the claim that multisensory environments are useful for visual learning because nonvisual percepts can be processed to produce error signals that people can use to adapt their visual systems. This hypothesis is motivated by a Bayesian network framework. The framework is useful because it ties together three observations that have appeared in the literature: (a) signals from nonvisual modalities can "teach" the visual system; (b) signals from nonvisual modalities can facilitate learning in the visual system; and (c) visual signals can become associated with (or be predicted by) signals from nonvisual modalities. Experimental data consistent with each of these observations are reviewed.
authors.affiliation: Department of Brain and Cognitive Sciences, University of RochesterDepartment of Psychology, University of California, Los Angeles.
authors.forename: Robert A
authors.initials: RA
authors.lastname: Jacobs
authors.forename: Ladan
authors.initials: L
authors.lastname: Shams
mesh_terms: D000161:Acoustic Stimulation; D001499:Bayes Theorem; D004777:Environment; D006801:Humans; D007858:Learning; D008960:Models, Psychological; D010465:Perception; D010775:Photic Stimulation; D014796:Visual Perception
keywords: Bayesian modeling; Learning; Multisensory perception; Visual perception