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Object Perception as Bayesian Inference
Open accessAbstract
We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.
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“GNW neurons, lying at the top of a deep feedforward network, provide a compressed, high-level symbolic model of an aspect of the external world; top-down signals broadcast the predictions that this model makes to lower-level areas; and bottom-up signals convey sensory signals and may either amplify the signals predicted by top-down input”
From Conscious Processing and the Global Neuronal Workspace Hypothesis (2020) · cites this paper as Kersten et al., 2004
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“The product of the likelihood and prior-the posterior distribution over scenes, given the image y 0 -thus strongly favors some scene interpretations over others”
From The Interface Theory of Perception (2015) · cites this paper as Kersten, Mamassian & Yuille, 2004
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“Interestingly, human observers often approach such an 'ideal observer' ([102], but see [103]), inspiring the idea that the human perceptual system may implement (approximate) Bayesian inference [104].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [104]
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“These two mechanisms, which have been referred to as 'shutting up' and 'stop gossiping' [4,104] (see also previous paragraph on dampening vs sharpening) are both accommodated within predictive coding, and operate on distinct populations (error and prediction units, respectively).”
From How Do Expectations Shape Perception? (2018) · cites this paper as [104]
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“Disentangling the many external states that could underlie any given sensory input is an ill-posed inverse problem that requires constraints or regularization (e.g., by the priors of a generative model; Lee and Mumford, 2003; Kersten et al., 2004).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Kersten et al., 2004
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Self-citationMechanisms of visual motion detection
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Self-citationThe Importance of Being Convex: An Advantage for Convexity when Judging Position
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Self-citationBayesian modeling of visual perception
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Self-citationHow Optimal Depth Cue Integration Depends on the Task
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Self-citationA Bayesian model of stereopsis depth and motion direction discrimination
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Self-citationThe KGBR viewpoint–lighting ambiguity
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Vision, Psychophysics and Bayes
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Self-citationThe KGBR viewpoint-lighting ambiguity and its resolution by generic constraints
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Metadata
- Authors
- Kersten, D., Mamassian, P., & Yuille, A.
- Affiliations
- University of Minnesota (US)
- University of Glasgow (GB)
- University of California, Los Angeles (US)
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2004-02-01
- Volume
- 55
- Issue
- 1
- Pages
- 271-304
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- OpenAlex
- W1497935377
- Cited by
- 1,269
- References
- 159
- Citations by year
- 2026: 33 · 2025: 55 · 2024: 45 · 2023: 49 · 2022: 63 · 2021: 53 · 2020: 71 · 2019: 97 · 2018: 71 · 2017: 60 · 2016: 78 · 2015: 70 · 2014: 69 · 2013: 57 · 2012: 52
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Annual Review of Psychology (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Department of Statistics, UCLA
- Funders
- National Institutes of Health (5r01ey011507-04, 5r01ey012691-03)
- National Eye Institute (R01EY013875, EY12691, R01 EY11507-001, EY013875, EY02587)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- object perception, Bayesian inference, prior knowledge, probabilistic integration, object shape perception, occlusion, background clutter, illumination effects
- MeSH terms
- Bayes Theorem, Decision Making, Humans, Learning, Psychological Theory, Psychophysics, Visual Perception
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.
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