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Multistability and Perceptual Inference
Open accessAbstract
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.
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“and Bayesian samplers produce human-like autocorrelations in perceptual and reasoning tasks [18,25].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [18]
In the citing paper
“Explicit models of autocorrelated sampling have been used to account for perceptual switching times of multistable stimuli [18] and anchoring effects in reasoning tasks [25].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [18]
In the citing paper
“In the Bayesian framework, the perceptual uncertainty that characterizes the “misleading” stimuli arises from weak or conflicting sensory evidence (Sundareswara and Schrater, 2008; Gershman et al., 2012), with 2 principal mechanisms.”
From What visual illusions teach us about schizophrenia (2014) · cites this paper as Gershman et al., 2012
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Metadata
- Authors
- Gershman, S. J., Vul, E., & Tenenbaum, J. B.
- Affiliations
- Princeton University (US)
- University of California San Diego (US)
- Massachusetts Institute of Technology (US)
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2012-01
- Volume
- 24
- Issue
- 1
- Pages
- 1-24
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W1972769376
- Cited by
- 140
- References
- 79
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 12 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 10 · 2019: 11 · 2018: 11 · 2017: 16 · 2016: 5 · 2015: 10 · 2014: 17 · 2013: 7 · 2012: 10
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- green
- handle.uba.uva.nl
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Funders
- National Science Foundation (1216045)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Arts and Humanities › Philosophy › Philosophy and Theoretical Science
- Keywords
- perceptual multistability, binocular rivalry, approximate Bayesian inference, Markov chain Monte Carlo, stochastic sampling, posterior distribution, perceptual uncertainty, traveling waves, fusion, spiking neurons, probabilistic graphical models, visual perception
- MeSH terms
- Algorithms, Bayes Theorem, Brain, Humans, Markov Chains, Models, Neurological, Monte Carlo Method, Nerve Net, Neurons, Signal Processing, Computer-Assisted, Visual Perception, Neural Networks, Computer, Uncertainty
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