title: Bayesian models of binocular 3-D motion perception.
abstract: Psychophysical studies on three-dimensional (3-D) motion perception have shown that perceived trajectory angles of a small target traveling in depth are systematically biased. Here, predictions from Bayesian models, which extend existing models of motion-first and stereo-first processing, are investigated. These statistical models are based on stochastic representations of monocular velocity and binocular disparity input in a binocular viewing geometry. The assumption of noise in these inputs together with a plausible prior for 3-D motion leads to testable predictions of perceived trajectory angle and velocity. Results from two experiments are reported, suggesting that disparity rather than motion processing introduces perceptual bias.
authors.affiliation: Department of Psychology, University of Glasgow, Glasgow, Scotland, UK. m.lages@psy.gla.ac.uk.
authors.forename: Martin
authors.initials: M
authors.lastname: Lages
mesh_terms: D001499:Bayes Theorem; D003867:Depth Perception; D006801:Humans; D008960:Models, Psychological; D009039:Motion Perception; D010775:Photic Stimulation / Q000379:methods; D011601:Psychophysics; D013997:Time Factors; D015357:Vision Disparity; D015348:Vision, Binocular; D015349:Vision, Monocular