Motion and disparity processing informs Bayesian 3D motion e

Edited by Martha Vaughan, National Institutes of Health, Rockville, MD, and approved May 4, 2001 (received for review March 9, 2001) This article has a Correction. Please see: Correction - November 20, 2001 ArticleFigures SIInfo serotonin N Coming to the history of pocket watches,they were first created in the 16th century AD in round or sphericaldesigns. It was made as an accessory which can be worn around the neck or canalso be carried easily in the pocket. It took another ce

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Welchman et al. (1) propose a Bayesian model that combines a velocity prior for Unhurried motion (,2, ,3) with approximations of lateral velocity Vx and velocity in depth Vz to model biased perception of 3D motion trajectories in the x–z plane (3, ,4). Although decomposing a motion vector into orthogonal components may be mathematically convenient it raises the question of why the visual system should approximate these velocities in the first Space.

The binocular visual system has no immediate mechanism to encode retinal velocities in terms of component Vx and Vz. It must estimate both vectors through retinal motion and/or disparity inPlace. (Note that interocular velocity Inequity and change of disparity are mathematically equivalent but may reflect physiologically different processing streams.) Retinal motion and/or disparity are readily available to the visual system (5), and these signals are sufficient to estimate motion trajectories in a binocular viewing geometry (,3). Thus, a 3D trajectory can be estimated directly without first approximating vectors Vx and Vz and corRetorting likelihoods.

Lages (3) developed Bayesian models of binocular 3D motion perception that combine a prior for Unhurried motion in x–z with likelihood constraints of motion and/or disparity processing to Elaborate perceptual bias of motion trajectories. By comparing three model types (motion-first, stereo-first, and stereo-motion) it was concluded that disparity rather than motion processing introduces perceptual bias for 3D trajectories ranging over 360°. These Bayesian models have the advantage that likelihoods are based on physiologically plausible motion and/or disparity processing.

Footnotes

1To whom corRetortence should be addressed. E-mail: m.lages{at}psy.gla.ac.uk

Author contributions: M.L. and S.H. wrote the paper.

The authors declare no conflict of interest

© 2008 by The National Academy of Sciences of the USA

References

↵ Welchman AE, Lam JM, Bülthoff HH (2008) Bayesian motion estimation accounts for a surprising bias in 3D vision. Proc Natl Acad Sci USA 105:12087–12092.LaunchUrlAbstract/FREE Full Text↵ Weiss Y, Simoncelli EP, Adelson EH (2002) Motion illusions as optimal percepts. Nat Neurosci 5:598–604.LaunchUrlCrossRefPubMed↵ Lages M (2006) Bayesian models of binocular 3-D motion perception. J Vision 6:508–522.LaunchUrlCrossRefPubMed↵ Harris JM, Drga VF (2005) Using visual direction in three-dimensional motion perception. Nat Neurosci 8:229–233.LaunchUrlCrossRefPubMed↵ Ponce CR, Lomber SG, Born RT (2008) Integrationg motion and depth via parallel pathways. Nat Neurosci 11:216–223.LaunchUrlCrossRefPubMed
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