Multistability and Perceptual Inference
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. Ho...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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MIT Press
2012
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| Online Access: | http://hdl.handle.net/1721.1/70125 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1811097103671230464 |
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| author | Gershman, Samuel J. Vul, Edward Tenenbaum, Joshua B. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Gershman, Samuel J. Vul, Edward Tenenbaum, Joshua B. |
| author_sort | Gershman, Samuel J. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:54:04Z |
| format | Article |
| id | mit-1721.1/70125 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:54:04Z |
| publishDate | 2012 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/701252022-10-03T09:02:45Z Multistability and Perceptual Inference Gershman, Samuel J. Vul, Edward Tenenbaum, Joshua B. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. Tenenbaum, Joshua B. 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. National Institutes of Health (U.S.) (Quantitative Computational Neuroscience fellowship) United States. Office of Naval Research (ONR MURI: Complex Learning and Skill Transfer with Video Games N00014-07-1-0937) United States. Dept. of Defense (National Defense Science and Engineering Graduate Fellowship) National Science Foundation (U.S.) (NSF DRMS Dissertation grant) 2012-04-25T14:22:30Z 2012-04-25T14:22:30Z 2012-01 2011-04 Article http://purl.org/eprint/type/JournalArticle 0899-7667 1530-888X http://hdl.handle.net/1721.1/70125 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. https://orcid.org/0000-0002-1925-2035 en_US http://dx.doi.org/10.1162/NECO_a_00226 Neural Computation Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press MIT Press |
| spellingShingle | Gershman, Samuel J. Vul, Edward Tenenbaum, Joshua B. Multistability and Perceptual Inference |
| title | Multistability and Perceptual Inference |
| title_full | Multistability and Perceptual Inference |
| title_fullStr | Multistability and Perceptual Inference |
| title_full_unstemmed | Multistability and Perceptual Inference |
| title_short | Multistability and Perceptual Inference |
| title_sort | multistability and perceptual inference |
| url | http://hdl.handle.net/1721.1/70125 https://orcid.org/0000-0002-1925-2035 |
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