The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Motion illusions as optimal percepts
Abstract
The pattern of local image velocities on the retina encodes important environmental information. Although humans are generally able to extract this information, they can easily be deceived into seeing incorrect velocities. We show that these 'illusions' arise naturally in a system that attempts to estimate local image velocity. We formulated a model of visual motion perception using standard estimation theory, under the assumptions that (i) there is noise in the initial measurements and (ii) slower motions are more likely to occur than faster ones. We found that specific instantiation of such a velocity estimator can account for a wide variety of psychophysical phenomena.
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In the citing paper
“Many motion ' illusions ' are not the result of sloppy computation by various components in the visual system, but rather a result of a coherent computational strategy that is optimal under reasonable assumptions. (Weiss et al. 2002, p. 603)”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Weiss et al. 2002
In the citing paper
“Similar results have been obtained for motion perception, neatly accounting for various illusions of motion perception by invoking statistically valid priors that favor slower and smoother motions -see Weiss et al. (2002) and Ernst (2010).”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Weiss et al. 2002
In the citing paper
“One success story is that of Bayesian cognitive models, which optimally combine prior knowledge about the world with sensory evidence21–23.”
From Cognitive computational neuroscience (2018) · cites this paper as [23]
In the citing paper
“which is inconsistent with the proposition that strong priors would be at the root of perceptual illusions.141,142”
From Are hallucinations due to an imbalance between excitatory and inhibitory influences on the brain? (2016) · cites this paper as [142]
In the citing paper
“Importantly, several authors (Geisler and Kersten, 2002; Weiss et al., 2002) suggested that a whole range of illusory percepts, even those that are idiosyncratic in appearance (such as motion illusions), correspond to what an optimal Bayesian observer would perceive in similar conditions.”
From What visual illusions teach us about schizophrenia (2014) · cites this paper as Weiss et al., 2002
Citations
Cites
(1981) · Cited by 11,963
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312
Horn, B. K. P., & Schunck, B. G. (1981) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.965761 · Cited by 7,443
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771
Treatise on Physiological Optics
von Helmholtz, H., & Southall, P. C. (1962) · Medical Entomology and Zoology · Cited by 2,196
The Interpretation of Visual Motion
Burt, P. J., & Ullman, S. (1980) · The American Journal of Psychology · doi:10.2307/1422246 · Cited by 1,762
The Interpretation of Visual Motion
Ullman, S. (1979) · The MIT Press eBooks · doi:10.7551/mitpress/3877.001.0001 · Cited by 1,648
Perception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425
Phenomenal coherence of moving visual patterns
Adelson, E. H., & Movshon, J. A. (1982) · Nature · doi:10.1038/300523a0 · Cited by 1,091
Self-citationComputational Models of Visual Processing
(1991) · The MIT Press eBooks · doi:10.7551/mitpress/2002.001.0001 · Cited by 1,086
Research works printed but not linked: 1
- Simoncelli, E., Adelson, E. & Heeger, D. in Proc. IEEE Conf. Comput. Vision Pattern Recog. 310–315 (IEEE, Washington DC, 1991).
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Metadata
- Authors
- Weiss, Y., Simoncelli, E. P., & Adelson, E. H.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Howard Hughes Medical Institute (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Weiss, Yair
- ORCID
- Simoncelli, Eero P.
- Venue
- Nature Neuroscience
- Published
- 2002-05-20
- Volume
- 5
- Issue
- 6
- Pages
- 598-604
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2148596731
- Cited by
- 1,076
- References
- 49
- Citations by year
- 2026: 33 · 2025: 48 · 2024: 42 · 2023: 31 · 2022: 38 · 2021: 52 · 2020: 43 · 2019: 59 · 2018: 55 · 2017: 57 · 2016: 44 · 2015: 50 · 2014: 65 · 2013: 59 · 2012: 51
- FWCI
- 16.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ey011005-05)
- National Eye Institute (R01 EY11005)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- illusory motion, visual motion perception, velocity estimation, Bayesian estimation, psychophysics, measurement noise
- MeSH terms
- Humans, Models, Neurological, Motion Perception, Optical Illusions, Time Factors, Contrast Sensitivity
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