The DOI matches its Crossref record; 158 of the 159 references it cites were found in a registry or an index.
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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“…depth, or background clutter) may require an internal generative process that in some sense mirrors aspects of the external generative model used for inference (Grenander 1996, Mumford 1992,Rao & Ballard 1999,Tu & Zhu 2002a,Tu et al 2003) or for learning (Dayan et al 1995,Hinton & Ghahramani 1997).”
Background
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Cited by
Sorted by citations
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
The trouble with overconfidence.
Moore, D. A., & Healy, P. J. (2008) · Psychological Review · doi:10.1037/0033-295x.115.2.502 · Cited by 2,358
Why do humans reason? Arguments for an argumentative theory
Mercier, H., & Sperber, D. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10000968 · Cited by 2,254
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
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