The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
(2009) · Cited by 6,153
Monte Carlo Statistical Methods
Robert, C., & Casella, G. (2004) · Springer Texts in Statistics · doi:10.1007/978-1-4757-4145-2 · Cited by 5,871
Monte Carlo Statistical Methods
Kim, H., Robert, C. P., & Casella, G. (2000) · Technometrics · doi:10.2307/1270959 · Cited by 5,596
Time-Dependent Statistics of the Ising Model
Glauber, R. J. (1963) · Journal of Mathematical Physics · doi:10.1063/1.1703954 · Cited by 3,649
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588
Amit, D. J. (1989) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623257 · Cited by 2,261
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
Hierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · cited 1×
“…Griffiths, & Tenenbaum, 2009) and animal (Daw & Courville, 2007) cognition, and several authors have proposed that the spiking activity of neurons might be interpreted as a set of samples (Hoyer & Hyvärinen, 2003; Lee & Mumford, 2003; Shi & Griffiths, 2009; Fiser, Berkes, Orbán, & Lengyel, 2010).”
BackgroundPerception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425
Blake, R., & Logothetis, N. K. (2002) · Nature reviews. Neuroscience · doi:10.1038/nrn701 · Cited by 1,113
Motion illusions as optimal percepts
Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002) · Nature Neuroscience · doi:10.1038/nn0602-858 · Cited by 1,076
Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex
Kiani, R., & Shadlen, M. N. (2009) · Science · doi:10.1126/science.1169405 · Cited by 1,027
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Robert, C. P., & Casella, G. (2005) · Springer eBooks · Cited by 991
Activity changes in early visual cortex reflect monkeys' percepts during binocular rivalry
Leopold, D., & Logothetis, N. (1996) · Nature · doi:10.1038/379549a0 · Cited by 961
The Anchoring-and-Adjustment Heuristic: Why the Adjustments Are Insufficient
Epley, N., & Gilovich, T. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01704.x · Cited by 920 · cited 2×
“Anchoring and adjustment effects, in which people seem to incrementally adjust their numerical estimates away from an anchor (Epley & Gilovich, 2006 ), could arise from a similar process.”
Vision as Bayesian inference: analysis by synthesis?
Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.002 · Cited by 849
Levelt, W. J. M. (1968) · MPG.PuRe (Max Planck Society) · Cited by 809
Neural bases of binocular rivalry
Tong, F., Meng, M., & Blake, R. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.09.003 · Cited by 719
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672
Optimal Predictions in Everyday Cognition
Griffiths, T. L., & Tenenbaum, J. B. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01780.x · Cited by 661
Do We Know What the Early Visual System Does?
Carandini, M., Demb, J. B., Mante, V., Tolhurst, D. J., Dan, Y., Olshausen, B. A., Gallant, J. L., & Rust, N. C. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3726-05.2005 · Cited by 604
Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry
Polonsky, A., Blake, R., Braun, J., & Heeger, D. J. (2000) · Nature Neuroscience · doi:10.1038/80676 · Cited by 589
Gold, J. I., & Shadlen, M. N. (2002) · Neuron · doi:10.1016/s0896-6273(02)00971-6 · Cited by 561
What is rivalling during binocular rivalry?
Logothetis, N. K., Leopold, D. A., & Sheinberg, D. L. (1996) · Nature · doi:10.1038/380621a0 · Cited by 560
Predictive coding explains binocular rivalry: an epistemological review
Hohwy, J., Roepstorff, A., & Friston, K. (2008) · Cognition · doi:10.1016/j.cognition.2008.05.010 · Cited by 546
Brainard, D. H., & Freeman, W. T. (1997) · Journal of the Optical Society of America A · doi:10.1364/josaa.14.001393 · Cited by 525
Spike-triggered neural characterization
Schwartz, O., Pillow, J. W., Rust, N. C., & Simoncelli, E. P. (2006) · Journal of Vision · doi:10.1167/6.4.13 · Cited by 441
Computational evidence for a rivalry hierarchy in vision
Wilson, H. R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2333622100 · Cited by 412
Noise-Induced Alternations in an Attractor Network Model of Perceptual Bistability
Moreno‐Bote, R., Rinzel, J., & Rubin, N. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00116.2007 · Cited by 389
Rational approximations to rational models: alternative algorithms for category learning
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010) · Psychological Review · doi:10.1037/a0020511 · Cited by 369
A Primer on Binocular Rivalry, Including Current Controversies
Blake, R. (2001) · Brain and Mind · doi:10.1023/a:1017925416289 · Cited by 339
A Neural Representation of Categorization Uncertainty in the Human Brain
Grinband, J., Hirsch, J., & Ferrera, V. P. (2006) · Neuron · doi:10.1016/j.neuron.2006.01.032 · Cited by 337
A Spiking Neuron Model for Binocular Rivalry
Laing, C. R., & Chow, C. C. (2002) · Journal of Computational Neuroscience · doi:10.1023/a:1014942129705 · Cited by 333
Topography of contextual modulations mediated by short-range interactions in primary visual cortex
Das, A., & Gilbert, C. D. (1999) · Nature · doi:10.1038/21371 · Cited by 313
Dynamics of travelling waves in visual perception
Wilson, H. R., Blake, R., & Lee, S.-H. (2001) · Nature · doi:10.1038/35091066 · Cited by 287
Traveling waves of activity in primary visual cortex during binocular rivalry
Lee, S., Blake, R., & Heeger, D. J. (2004) · Nature Neuroscience · doi:10.1038/nn1365 · Cited by 258
The time course of binocular rivalry reveals a fundamental role of noise
Brascamp, J. W., van Ee, R., Noest, A. J., Jacobs, R. H. A. H., & van den Berg, A. V. (2006) · Journal of Vision · doi:10.1167/6.11.8 · Cited by 234
Percept-choice sequences driven by interrupted ambiguous stimuli: A low-level neural model
Noest, A. J., van Ee, R., Nijs, M. M., & Wezel, R. J. A. V. (2007) · Journal of Vision · doi:10.1167/7.8.10 · Cited by 224
Spatial zones of binocular rivalry in central and peripheral vision
Blake, R., O’Shea, R. P., & Mueller, T. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800004971 · Cited by 217
What is Suppressed during Binocular Rivalry?
Blake, R., Westendorf, D., & Overton, R. (1980) · Perception · doi:10.1068/p090223 · Cited by 205
Visual sensitivity underlying changes in visual consciousness
Alais, D., Cass, J., O'Shea, R. P., & Blake, R. (2010) · Current Biology · doi:10.1016/j.cub.2010.06.015 · Cited by 166
Multistage Model for Binocular Rivalry
Freeman, A. W. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00557.2005 · Cited by 165
Minimal physiological conditions for binocular rivalry and rivalry memory
Wilson, H. R. (2007) · Vision Research · doi:10.1016/j.visres.2007.07.007 · Cited by 159
Real world occlusion constraints and binocular rivalry
Shimojo, S., & Nakayama, K. (1990) · Vision Research · doi:10.1016/0042-6989(90)90128-8 · Cited by 153
A Hierarchical Model of Binocular Rivalry
Dayan, P. (1998) · Neural Computation · doi:10.1162/089976698300017377 · Cited by 145
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior
Hoyer, P. O., & Hyvärinen, A. (2002) · Cited by 144
Grouping visual features during binocular rivalry
Alais, D., & Blake, R. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00146-7 · Cited by 142
A fresh look at the temporal dynamics of binocular rivalry
Mueller, T. J., & Blake, R. (1989) · Biological Cybernetics · doi:10.1007/bf00198769 · Cited by 142
Failure of rivalry at low contrast: Evidence of a suprathreshold binocular summation process
Liu, L., Tyler, C. W., & Schor, C. M. (1992) · Vision Research · doi:10.1016/0042-6989(92)90203-u · Cited by 127
Increment detection thresholds during binocular rivalry suppression
Wales, R., & Fox, R. A. (1970) · Perception & Psychophysics · doi:10.3758/bf03210180 · Cited by 120
Perceptual multistability predicted by search model for bayesian decisions.
Sundareswara, R., & Schrater, P. R. (2008) · Journal of Vision · doi:10.1167/8.5.12 · Cited by 112
A more rational model of categorization
Sanborn, A. N., Griffiths, T. L., & Navarro, D. (2006) · Warwick Research Archive Portal (University of Warwick) · Cited by 109
van Ee, R. (2009) · Journal of the Optical Society of America A · doi:10.1364/josaa.26.002612 · Cited by 104
Detection of motion during binocular rivalry suppression.
Fox, R. A., & Check, R. (1968) · Journal of Experimental Psychology · doi:10.1037/h0026440 · Cited by 103
The effect of contrast on the completeness of binocular rivalry suppression
Hollins, M. (1980) · Perception & Psychophysics · doi:10.3758/bf03198684 · Cited by 103
Temporal dynamics in bistable perception
Mamassian, P., & Goutcher, R. (2005) · Journal of Vision · doi:10.1167/5.4.7 · Cited by 103
O’Shea, R. P., Sims, A., & Govan, D. G. (1997) · Vision Research · doi:10.1016/s0042-6989(96)00113-7 · Cited by 90
Unraveling adaptation and mutual inhibition in perceptual rivalry
Lankheet, M. J. (2006) · Journal of Vision · doi:10.1167/6.4.1 · Cited by 89
Modeling the effects of memory on human online sentence processing with particle filters
Lévy, R., Reali, F., & Griffiths, T. L. (2008) · neural information processing systems · Cited by 79
Daw, N. D., & Courville, A. C. (2007) · neural information processing systems · Cited by 77
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
Shi, L., & Griffiths, T. L. (2009) · Cited by 72
One and Done? Optimal Decisions from Very Few Samples
Goodman, N. D., Griffiths, T. L., Tenenbaum, J. B., & Vul, E. (2009) · eScholarship (California Digital Library) · Cited by 68
Sanders, A. (1970) · Medical Entomology and Zoology · Cited by 64
Swersky, K., Chen, B., Marlin, B. M., & de Freitas, N. (2010) · Information Theory and Applications Workshop (ITA) · doi:10.1109/ita.2010.5454138 · Cited by 60
How Optimal Depth Cue Integration Depends on the Task
Schrater, P., & Kersten, D. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026557704054 · Cited by 48
Size matters: A study of binocular rivalry dynamics
Kang, M. S. (2009) · Journal of Vision · doi:10.1167/9.1.17 · Cited by 42
Chronometric analysis supports fusion rather than suppression theory of binocular vision
O’Shea, R. P. (1987) · Vision Research · doi:10.1016/0042-6989(87)90075-7 · Cited by 40
Random Switching and Optimal Processing in the Perception of Ambiguous Signals
Bialek, W., & DeWeese, M. R. (1995) · Physical Review Letters · doi:10.1103/physrevlett.74.3077 · Cited by 37
Theory Acquisition as Stochastic Search
Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 23
Distance in feature space determines exclusivity in visual rivalry
Knapen, T. H. J., Ryota Kanai, C., Brascamp, J. W., van Boxtel, J. J. A., & van Ee, R. (2007) · Vision Research · doi:10.1016/j.visres.2007.09.005 · Cited by 23
Determinants of Fusion of Dichoptically Presented Orthogonal Gratings
Burke, D., Alais, D., & Wenderoth, P. (1999) · Perception · doi:10.1068/p2694 · Cited by 13
Theory and Dynamics of Perceptual Bistability
Schrater, P., & Sundareswara, R. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0157 · Cited by 13
Magnitude of Suppression of Binocular Rivalry within the Invisible Pattern
Fukuda, H. (1981) · Perceptual and Motor Skills · doi:10.2466/pms.1981.53.2.371 · Cited by 7
Research works printed but not linked: 2
- Levy R., 2009, Advances in neural information processing systems, 21, 937
- Shi L., 2009, Advances in neural information processing systems, 22, 1669
Cited by
Sorted by citations
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Self-citationComputational rationality: A converging paradigm for intelligence in brains, minds, and machines
Gershman, S. J., Horvitz, E., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aac6076 · Cited by 722
Metacognition in human decision-making: confidence and error monitoring
Yeung, N., & Summerfield, C. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2011.0416 · Cited by 667
Vul (2014) · doi:10.1111/cogs.12101 · Cited by 502
Bayesian Brains without Probabilities
Sanborn, A. N., & Chater, N. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.10.003 · Cited by 307
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
