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.
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
“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
Flagged first, then by citations
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).
Cited by
Sorted by citations
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
SlowFast Networks for Video Recognition
Feichtenhofer, C., Fan, H., Malik, J., & He, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00630 · Cited by 4,007
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
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
Error Correction, Sensory Prediction, and Adaptation in Motor Control
Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-153135 · Cited by 1,869
The emulation theory of representation: Motor control, imagery, and perception
Grush, R. (2004) · Behavioral and Brain Sciences · doi:10.1017/s0140525x04000093 · Cited by 1,387
Object perception as Bayesian inference
Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269
When the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156
Topics in semantic representation
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.211 · Cited by 1,122
STRUCTURE AND FUNCTION OF VISUAL AREA MT
Born, R. T., & Bradley, D. C. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131052 · Cited by 1,023
How Do Expectations Shape Perception?
de Lange, F. P., Heilbron, M., & Kok, P. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.06.002 · Cited by 975
Action understanding as inverse planning
Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009) · Cognition · doi:10.1016/j.cognition.2009.07.005 · Cited by 920
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782
Noise characteristics and prior expectations in human visual speed perception
Stocker, A. A., & Simoncelli, E. P. (2006) · Nature Neuroscience · doi:10.1038/nn1669 · Cited by 809
Self-citationVisual Perception and the Statistical Properties of Natural Scenes
Geisler, W. S. (2007) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085632 · Cited by 732
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
Cardinal rules: visual orientation perception reflects knowledge of environmental statistics
Girshick, A. R., Landy, M. S., & Simoncelli, E. P. (2011) · Nature Neuroscience · doi:10.1038/nn.2831 · Cited by 613
Self-citationProbabilistic models of cognition: exploring representations and inductive biases
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568
A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations.
Wagemans, J. P., Feldman, J., Gepshtein, S., Kimchi, R., Pomerantz, J. R., van der Helm, P. A., & van Leeuwen, C. (2012) · Psychological Bulletin · doi:10.1037/a0029334 · Cited by 595
Probabilistic models of cognition: Conceptual foundations
Chater, N., Tenenbaum, J. B., & Yuille, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.007 · Cited by 573
Cognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535
Hallucinations and strong priors
Corlett, P. R., Horga, G., Fletcher, P. C., Alderson-Day, B., Schmack, K., & Powers, A. R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.001 · Cited by 534
Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
Optimal representation of sensory information by neural populations
Jazayeri, M., & Movshon, J. A. (2006) · Nature Neuroscience · doi:10.1038/nn1691 · Cited by 548
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0064 · Cited by 515
How prediction errors shape perception, attention, and motivation
den Ouden, H. E. M., Kok, P., & de Lange, F. P. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00548 · Cited by 512
Blind Prediction of Natural Video Quality
Saad, M. A., Bovik, A. C., & Charrier, C. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2299154 · Cited by 471
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436
I NFERENCE AND C OMPUTATION WITH P OPULATION C ODES
Pouget, A., Dayan, P., & Zemel, R. S. (2003) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131112 · Cited by 446
The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process.
Norris, D. G. (2006) · Psychological Review · doi:10.1037/0033-295x.113.2.327 · Cited by 447
Fleet, D. J., & Weiss, Y. (2005) · Springer eBooks · doi:10.1007/0-387-28831-7_15 · Cited by 406
Self-citationExperience can change the 'light-from-above' prior
Adams, W. J., Graf, E. W., & Ernst, M. O. (2004) · Nature Neuroscience · doi:10.1038/nn1312 · Cited by 377
Space and time in visual context
Schwartz, O., Hsu, A., & Dayan, P. (2007) · Nature reviews. Neuroscience · doi:10.1038/nrn2155 · Cited by 374
Suboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342
Bayesian integration of spatial information.
Cheng, K., Shettleworth, S. J., Huttenlocher, J., & Rieser, J. J. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.4.625 · Cited by 326
Estimating the sources of motor errors for adaptation and generalization
Berniker, M., & Kording, K. (2008) · Nature Neuroscience · doi:10.1038/nn.2229 · Cited by 316
Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.016 · Cited by 316
Decision theory: what “should” the nervous system do?
Körding, K. (2007) · Science · doi:10.1126/science.1142998 · Cited by 313
With or without you: predictive coding and Bayesian inference in the brain
Aitchison, L., & Lengyel, M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.010 · Cited by 308
Bayesian models of object perception
Kersten, D., & Yuille, A. (2003) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(03)00042-4 · Cited by 320
Integer Ratio Priors on Musical Rhythm Revealed Cross-culturally by Iterated Reproduction
Jacoby, N., & McDermott, J. H. (2017) · Current Biology · doi:10.1016/j.cub.2016.12.031 · Cited by 279
Children balance theories and evidence in exploration, explanation, and learning
Bonawitz, E. B., van Schijndel, T. J. P., Friel, D., & Schulz, L. (2012) · Cognitive Psychology · doi:10.1016/j.cogpsych.2011.12.002 · Cited by 262
Prior expectations bias sensory representations in visual cortex
Kok, P., Brouwer, G. J., van Gerven, M. A. J., & de Lange, F. P. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.0742-13.2013 · Cited by 260
Stich, S. P., Laurence, S., & Carruthers, P. (2005) · doi:10.1093/acprof:oso/9780195179675.001.0001 · Cited by 265
Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex
Orbán, G., Berkes, P., Fiser, J., & Lengyel, M. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.038 · Cited by 275
Bayesian learning of visual chunks by human observers
Orbán, G., Fiser, J., Aslin, R. Ν., & Lengyel, M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0708424105 · Cited by 266
Trommershäuser, J., Kording, K. P., & Landy, M. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.001.0001 · Cited by 256
Bayesian Computation in Recurrent Neural Circuits
Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252
Natural auditory scene statistics shapes human spatial hearing
Parise, C., Knorre, K., & Ernst, M. O. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322705111 · Cited by 252
Contributions of ideal observer theory to vision research
Geisler, W. S. (2010) · Vision Research · doi:10.1016/j.visres.2010.09.027 · Cited by 272
Illusions, perception and Bayes
Geisler, W. S., & Kersten, D. (2002) · Nature Neuroscience · doi:10.1038/nn0602-508 · Cited by 249
Winkler, I., & Czigler, I. (2011) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2011.10.001 · Cited by 264
Sperber, D. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195179675.003.0004 · Cited by 245
Etchells, P. J., Benton, C. P., Ludwig, C. J. H., & Gilchrist, I. D. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00115 · Cited by 234
Verstynen, T., & Sabes, P. N. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6525-10.2011 · Cited by 227
Internal models in biological control
McNamee, D., & Wolpert, D. M. (2019) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105206 · Cited by 225
Probabilistic models in human sensorimotor control
Wolpert, D. M. (2007) · Human Movement Science · doi:10.1016/j.humov.2007.05.005 · Cited by 212
Video quality assessment using a statistical model of human visual speed perception
Wang, Z., & Li, Q. (2007) · Journal of the Optical Society of America A · doi:10.1364/josaa.24.000b61 · Cited by 210
A Bayesian View on Multimodal Cue Integration
Ernst, M. O. (2005) · Human Body Perception From The Inside Out · doi:10.1093/oso/9780195178371.003.0006 · Cited by 209
A Bayesian model of the disambiguation of gravitoinertial force by visual cues
MacNeilage, P. R., Banks, M. S., Berger, D. R., & Bülthoff, H. (2006) · Experimental Brain Research · doi:10.1007/s00221-006-0792-0 · Cited by 218
Clark, A. (2015) · The Southern Journal of Philosophy · doi:10.1111/sjp.12120 · Cited by 230
Bayesian generic priors for causal learning.
Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2008) · Psychological Review · doi:10.1037/a0013256 · Cited by 207
Combining Priors and Noisy Visual Cues in a Rapid Pointing Task
Tassinari, H., Hudson, T. E., & Landy, M. S. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.2779-06.2006 · Cited by 213
Circular inferences in schizophrenia
Jardri, R., & Denève, S. (2013) · Brain · doi:10.1093/brain/awt257 · Cited by 201
Lupyan, G. (2015) · Review of Philosophy and Psychology · doi:10.1007/s13164-015-0253-4 · Cited by 212
Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
Sun, D., Yang, X., Liu, M.-Y., & Kautz, J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2894353 · Cited by 208
Contrast Dependence of Suppressive Influences in Cortical Area MT of Alert Macaque
Pack, C. C., Hunter, J. N., & Born, R. T. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00629.2004 · Cited by 199
What and where: A Bayesian inference theory of attention
Chikkerur, S. S., Serre, T., Tan, C., & Poggio, T. (2010) · Vision Research · doi:10.1016/j.visres.2010.05.013 · Cited by 202
Learning what to expect (in visual perception)
Seriès, P., & Seitz, A. R. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00668 · Cited by 194
Flanagan, J. R., Bittner, J. P., & Johansson, R. S. (2008) · Current Biology · doi:10.1016/j.cub.2008.09.042 · Cited by 194
Zago, M., Bosco, G., Maffei, V., Iosa, M., Ivanenko, Y. P., & Lacquaniti, F. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00862.2003 · Cited by 193
A statistical explanation of visual space
Yang, Z., & Purves, D. (2003) · Nature Neuroscience · doi:10.1038/nn1059 · Cited by 195
Reconciling intuitive physics and Newtonian mechanics for colliding objects
Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013) · Psychological Review · doi:10.1037/a0031912 · Cited by 189
Neural tuning and representational geometry
Kriegeskorte, N., & Wei, X.-X. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00502-3 · Cited by 187
Perception as evidence accumulation and Bayesian inference: Insights from masked priming.
Norris, D. G., & Kinoshita, S. (2008) · Journal of Experimental Psychology General · doi:10.1037/a0012799 · Cited by 199
Adaptable history biases in human perceptual decisions
Abrahamyan, A., Silva, L. L., Dakin, S. C., Carandini, M., & Gardner, J. L. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1518786113 · Cited by 194
Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality
Drugowitsch, J., Wyart, V., Devauchelle, A.-D., & Koechlin, É. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.005 · Cited by 192
Motion psychophysics: 1985-2010.
Burr, D., & Thompson, P. (2011) · Vision Research · doi:10.1016/j.visres.2011.02.008 · Cited by 178
What visual illusions teach us about schizophrenia
Notredame, C.-E., Pins, D., Deneve, S., & Jardri, R. (2014) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2014.00063 · Cited by 177
Bayesian processing of vestibular information
Laurens, J., & Jacques Droulez, A. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0133-1 · Cited by 182
Petzschner, F. H., & Glasauer, S. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2028-11.2011 · Cited by 183
Observing the observer (I): meta-bayesian models of learning and decision-making
Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015554 · Cited by 167
Organizing probabilistic models of perception
Ma, W. J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.010 · Cited by 169
Bayes in the Brain—On Bayesian Modelling in Neuroscience
Colombo, M., & Seriès, P. (2012) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axr043 · Cited by 164
Zhaoping, L. (2014) · doi:10.1093/acprof:oso/9780199564668.001.0001 · Cited by 160
Neurophysiology of Skin Thermal Sensations
Filingeri, D. (2016) · Comprehensive physiology · doi:10.1002/cphy.c150040 · Cited by 168
Are hallucinations due to an imbalance between excitatory and inhibitory influences on the brain?
Jardri, R., Hugdahl, K., Hughes, M., Brunelin, J., Waters, F., Alderson-Day, B., Smailes, D., Sterzer, P., Corlett, P. R., Leptourgos, P., et al. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw075 · Cited by 157
Spering, M., & Montagnini, A. (2010) · Vision Research · doi:10.1016/j.visres.2010.10.017 · Cited by 157
Fetsch, C. R., DeAngelis, G. C., & Angelaki, D. E. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07207.x · Cited by 154
“…behavioral performance by incorporating top-down expectations or biases about the stimuli, whether they are learned during the course of a task (Körding & Wolpert, 2004) or otherwise established during development or through life experience (e.g., Weiss et al., 2002; Stocker & Simoncelli, 2006).”
BackgroundTroubles with Bayesianism: An introduction to the psychological immune system
Mandelbaum, E. (2018) · Mind & Language · doi:10.1111/mila.12205 · Cited by 161
Bayesian Inference Underlies the Contraction Bias in Delayed Comparison Tasks
Ashourian, P., & Loewenstein, Y. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019551 · Cited by 149
Learning Priors for Bayesian Computations in the Nervous System
Berniker, M., Voss, M., & Kording, K. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012686 · Cited by 154
Computational Neuropsychology and Bayesian Inference
Parr, T., Rees, G. E., & Friston, K. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00061 · Cited by 158
Advancement of motion psychophysics: Review 2001-2010
Nishida, S. (2011) · Journal of Vision · doi:10.1167/11.5.11 · Cited by 144
Multistability and perceptual inference
Gershman, S. J., Vul, E., & Tenenbaum, J. B. (2012) · Neural Computation · doi:10.1162/neco_a_00226 · Cited by 140
Owl’s behavior and neural representation predicted by Bayesian inference
Fischer, B. J., & Peña, J. L. (2011) · Nature Neuroscience · doi:10.1038/nn.2872 · Cited by 139
Probabilistic Representation in Human Visual Cortex Reflects Uncertainty in Serial Decisions
van Bergen, R. S., & Jehee, J. F. M. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3212-18.2019 · Cited by 156
Bayesian Perceptual Psychology
Rescorla, M. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199600472.013.010 · Cited by 137
On the Origins of Suboptimality in Human Probabilistic Inference
Acerbi, L., Vijayakumar, S., & Wolpert, D. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003661 · Cited by 145
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
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