Bayesian sampling in visual perception
Open accessAbstract
It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sampling. To test this idea, we manipulated pairs of sensory cues in a bistable display consisting of two superimposed moving drifting gratings, and we asked subjects to report their perceived changes in depth ordering. We report that the fractions of dominance of each percept follow the multiplicative rule predicted by Bayesian sampling. Furthermore, we show that attractor neural networks can sample probability distributions if input currents add linearly and encode probability distributions with probabilistic population codes.
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In binocular rivalry, conscious experience alternates between the two images and cannot simultaneously represent inconsistent visual information, suggesting a bottleneck on conscious experience.
“In binocular rivalry, a subject is shown a different image in each of their eyes. The subject's conscious experience flips back and forth between the two images (Moreno-Bote et al. 2011). Crucially, the subject cannot have simultaneous conscious experiences of inconsistent visual information. This suggests that there is a bottleneck on conscious experience.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Moreno-Bote et al. 2011
The sampling viewpoint fits with current thinking in computational neuroscience, where an influential proposal is that transitions between brain states correspond to sampling from a probability distribution or multiple samples are represented simultaneously.
“The sampling viewpoint also fits with current thinking in computational neuroscience, where an influential proposal is that transitions between brain states correspond to sampling from a probability distribution [26,27,81,82] or multiple samples are represented simultaneously (e.g., using particle filters [83][84][85][86]).”
From Bayesian Brains without Probabilities (2016) · cites this paper as [27]
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“The subject’s conscious experience flips back and forth between the two images (Moreno-Bote et al. 2011).”
- Authors
- Moreno-Bote, R., Knill, D. C., & Pouget, A.
- Affiliations
- Foundation Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, Esplugues de Llobregat, 08950 Barcelona, Spain; and
- University of Rochester (US)
- Department of Brain and Cognitive Sciences and
- ORCID
- Pouget, Alexandre
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-07-08
- Volume
- 108
- Issue
- 30
- Pages
- 12491-12496
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3145684
- OpenAlex
- W1986194633
- Cited by
- 182
- References
- 48
- Citations by year
- 2026: 23 · 2025: 4 · 2024: 8 · 2023: 11 · 2022: 6 · 2021: 11 · 2020: 10 · 2019: 6 · 2018: 14 · 2017: 18 · 2016: 13 · 2015: 11 · 2014: 26 · 2013: 10 · 2012: 10
- FWCI
- 7.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Multidisciplinary University Research Initiative
- National Eye Institute (EY-017939, R01EY017939, P30EY001319)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- Bayesian sampling, visual perception, probabilistic inference, bistable perception, depth ordering, perceptual dominance, attractor networks, drifting gratings, sensory cues
- MeSH terms
- Bayes Theorem, Depth Perception, Female, Humans, Male, Models, Neurological, Nerve Net, Photic Stimulation, Visual Perception, Models, Statistical, Dominance, Ocular
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