Bayesian motion estimation accounts for a surprising bias in 3D vision

Andrew E. Welchman, Judith M. Lam, Heinrich H. Bülthoff

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Determining the approach of a moving object is a vital survival skill that depends on the brain combining information about lateral translation and motion-in-depth. Given the importance of sensing motion for obstacle avoidance, it is surprising that humans make errors, reporting an object will miss them when it is on a collision course with their head. Here we provide evidence that biases observed when participants estimate movement in depth result from the brain's use of a "prior" favoring slow velocity. We formulate a Bayesian model for computing 3D motion using independently estimated parameters for the shape of the visual system's slow velocity prior. We demonstrate the success of this model in accounting for human behavior in separate experiments that assess both sensitivity and bias in 3D motion estimation. Our results show that a surprising perceptual error in 3D motion perception reflects the importance of prior probabilities when estimating environmental properties.

Original languageEnglish
Pages (from-to)12087-12092
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume105
Issue number33
DOIs
Publication statusPublished - 2008 Aug 19

Keywords

  • Bayes
  • Binocular disparity
  • Motion perception
  • Stereopsis

ASJC Scopus subject areas

  • General

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