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Perceptual multistability predicted by search model for Bayesian decisions.

Rashmi Sundareswara1, Paul R Schrater

  • 1Department of Computer Science and Engineering, University of Minnesota, Minnesota, MN 55455, USA. sundares@cs.umn.edu

Journal of Vision
|October 10, 2008
PubMed
Summary
This summary is machine-generated.

Perceptual multistability, or spontaneous switching between image interpretations, can be explained by Bayesian inference. This study shows scene regularities influence Necker cube perception, suggesting multistability is a byproduct of normal visual decision-making.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Vision

Background:

  • Perceptual multistability involves spontaneous switching between multiple interpretations of ambiguous stimuli.
  • Existing explanations often cite adaptation or hysteresis.
  • The role of probabilistic inference in multistability is less understood.

Purpose of the Study:

  • To investigate if perceptual switching arises from Bayesian inference in decision-making.
  • To examine the influence of scene regularities on Necker cube bistability.
  • To introduce a novel mathematical framework for analyzing perceptual switching dynamics.

Main Methods:

  • Empirical study using Necker cubes flanked by unambiguous background cubes.
  • Introduction of Markov Renewal Processes (MRPs) to model temporal dynamics.
  • Development of a Bayesian-inspired computational model of visual perception.

Main Results:

  • Background cubes biased Necker cube perception towards congruent interpretations.
  • MRPs effectively characterized the temporal dynamics of perceptual alternations.
  • The computational model quantitatively matched human behavioral data.

Conclusions:

  • Perceptual switching in bistable figures may be an inherent outcome of Bayesian visual inference.
  • Scene context significantly influences the dynamics of perceptual choice.
  • The proposed model provides a unified framework for understanding multistability and Bayesian vision.