Multistability and Perceptual Inference
Author(s)
Gershman, Samuel J.; Vul, Edward; Tenenbaum, Joshua B.
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Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many real-world situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability—stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic sample-generating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena—gamma-like stochastic switching, patchy percepts, fusion, and traveling waves—can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing sample-based posterior approximations in the brain.
Date issued
2012-01Department
Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
Neural Computation
Publisher
MIT Press
Citation
Gershman, Samuel J., Edward Vul, and Joshua B. Tenenbaum. “Multistability and Perceptual Inference.” Neural Computation 24.1 (2012): 1–24. Web.© 2012 Massachusetts Institute of Technology.
Version: Final published version
ISSN
0899-7667
1530-888X