Systematic misperceptions of 3-D motion explained by Bayesian inference

Bas Rokers, Jacqueline M. Fulvio, Jonathan William Pillow, Emily A. Cooper

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

People make surprising but reliable perceptual errors. Here, we provide a unified explanation for systematic errors in the perception of three-dimensional (3-D) motion. To do so, we characterized the binocular retinal motion signals produced by objects moving through arbitrary locations in 3-D. Next, we developed a Bayesian model, treating 3-D motion perception as optimal inference given sensory noise in the measurement of retinal motion. The model predicts a set of systematic perceptual errors, which depend on stimulus distance, contrast, and eccentricity. We then used a virtual-reality headset as well as a standard 3-D desktop stereoscopic display to test these predictions in a series of perceptual experiments. As predicted, we found evidence that errors in 3-D motion perception depend on the contrast, viewing distance, and eccentricity of a stimulus. These errors include a lateral bias in perceived motion direction and a surprising tendency to misreport approaching motion as receding and vice versa. In sum, we present a Bayesian model that provides a parsimonious account for a range of systematic misperceptions of motion in naturalistic environments.

Original languageEnglish (US)
Article number23
Pages (from-to)1-23
Number of pages23
JournalJournal of vision
Volume18
Issue number3
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Ophthalmology
  • Sensory Systems

Keywords

  • Computational modeling
  • Motion perception
  • Motion-3D
  • Virtual reality

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