The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Slant from texture and disparity cues: Optimal cue combination
Open accessAbstract
How does the visual system combine information from different depth cues to estimate three-dimensional scene parameters? We tested a maximum-likelihood estimation (MLE) model of cue combination for perspective (texture) and binocular disparity cues to surface slant. By factoring the reliability of each cue into the combination process, MLE provides more reliable estimates of slant than would be available from either cue alone. We measured the reliability of each cue in isolation across a range of slants and distances using a slant-discrimination task. The reliability of the texture cue increases as |slant| increases and does not change with distance. The reliability of the disparity cue decreases as distance increases and varies with slant in a way that also depends on viewing distance. The trends in the single-cue data can be understood in terms of the information available in the retinal images and issues related to solving the binocular correspondence problem. To test the MLE model, we measured perceived slant of two-cue stimuli when disparity and texture were in conflict and the reliability of slant estimation when both cues were available. Results from the two-cue study indicate, consistent with the MLE model, that observers weight each cue according to its relative reliability: Disparity weight decreased as distance and |slant| increased. We also observed the expected improvement in slant estimation when both cues were available. With few discrepancies, our data indicate that observers combine cues in a statistically optimal fashion and thereby reduce the variance of slant estimates below that which could be achieved from either cue alone. These results are consistent with other studies that quantitatively examined the MLE model of cue combination. Thus, there is a growing empirical consensus that MLE provides a good quantitative account of cue combination and that sensory information is used in a manner that maximizes the precision of perceptual estimates.
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
“Adults integrate texture and disparity cues to reduce the uncertainty of their estimates of slant, adjusting their relative weightings as they change in reliability (3).”
From Fusion of visual cues is not mandatory in children (2010) · cites this paper as [3]
In the citing paper
“Human adults can reduce sensory uncertainty by integrating estimates, both across modalities (1, 2) (e.g., integrating vision and touch to judge size) and within a modality (3) (e.g., integrating visual stereoscopic and texture information to judge surface slant).”
From Fusion of visual cues is not mandatory in children (2010) · cites this paper as [3]
In the citing paper
“Our assumptions and approach are based on previous more detailed validation of similar stimuli (3).”
From Fusion of visual cues is not mandatory in children (2010) · cites this paper as [3]
Citations
Cites
(1966) · Medical Entomology and Zoology · Cited by 10,293
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844
Self-citationComputational Geometry: Algorithms and Applications
de Berg, M., Cheong, O., van Kreveld, M. J., & Overmars, M. H. (1997) · CERN Document Server (European Organization for Nuclear Research) · Cited by 4,450
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
The Ventriloquist Effect Results from Near-Optimal Bimodal Integration
Alais, D., & Burr, D. (2004) · Current Biology · doi:10.1016/j.cub.2004.01.029 · Cited by 1,936
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
Measurement and modeling of depth cue combination: in defense of weak fusion
Landy, M. S., Maloney, L. T., Johnston, E. B., & Young, M. (1995) · Vision Research · doi:10.1016/0042-6989(94)00176-m · Cited by 996
Self-citationGoodwin, C. (1995) · Social Studies of Science · doi:10.1177/030631295025002002 · Cited by 620
When Feeling Is More Important Than Seeing in Sensorimotor Adaptation
van Beers, R. J., Wolpert, D. M., & Haggard, P. (2002) · Current Biology · doi:10.1016/s0960-9822(02)00836-9 · Cited by 560
Researches in Binocular Vision
(1951) · JAMA · doi:10.1001/jama.1951.02920280080027 · Cited by 537
Cited by
Sorted by citations
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
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
Vergence–accommodation conflicts hinder visual performance and cause visual fatigue
Hoffman, D. M., Girshick, A. R., Akeley, K., & Banks, M. S. (2008) · Journal of Vision · doi:10.1167/8.3.33 · Cited by 1,641
Self-citationVisual Discomfort and Visual Fatigue of Stereoscopic Displays: A Review
Lambooij, M., Fortuin, M. F., Heynderickx, I. E. J., & IJsselsteijn, W. A. (2009) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2009.53.3.030201 · Cited by 1,006
Bayesian decision theory in sensorimotor control
Kording, K. P., & Wolpert, D. M. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.003 · Cited by 916
Neural correlates of multisensory cue integration in macaque MSTd
Gu, Y., Angelaki, D. E., & DeAngelis, G. C. (2008) · Nature Neuroscience · doi:10.1038/nn.2191 · Cited by 554
Optimal representation of sensory information by neural populations
Jazayeri, M., & Movshon, J. A. (2006) · Nature Neuroscience · doi:10.1038/nn1691 · Cited by 548
Neural correlates of reliability-based cue weighting during multisensory integration
Fetsch, C. R., Pouget, A., DeAngelis, G. C., & Angelaki, D. E. (2012) · Nature Neuroscience · doi:10.1038/nn.2983 · Cited by 459
Dynamic Reweighting of Visual and Vestibular Cues during Self-Motion Perception
Fetsch, C. R., Turner, A. H., DeAngelis, G. C., & Angelaki, D. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2574-09.2009 · Cited by 436
Stereopsis and amblyopia: A mini-review
Levi, D. M., Knill, D. C., & Bavelier, D. (2015) · Vision Research · doi:10.1016/j.visres.2015.01.002 · Cited by 427
Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience
Brenner, E., Smeets, J. B. J., & Wixted, J. T. (2018) · Wiley eBooks · doi:10.1002/9781119170174 · Cited by 530
Multisensory integration: psychophysics, neurophysiology, and computation
Angelaki, D. E., Gu, Y., & DeAngelis, G. C. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.06.008 · Cited by 383
Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception
Rohe, T., & Noppeney, U. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002073 · Cited by 344
Knowing how much you don't know: a neural organization of uncertainty estimates
Bach, D. R., & Dolan, R. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3289 · Cited by 339
Learning to integrate arbitrary signals from vision and touch
Ernst, M. O. (2007) · Journal of Vision · doi:10.1167/7.5.7 · Cited by 332
Fetsch, C. R., DeAngelis, G. C., & Angelaki, D. E. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3503 · Cited by 322
Focus cues affect perceived depth
Watt, S. J., Akeley, K., Ernst, M. O., & Banks, M. S. (2005) · Journal of Vision · doi:10.1167/5.10.7 · Cited by 309
Self-citationOptimal integration of shape information from vision and touch
Helbig, H. B., & Ernst, M. O. (2007) · Experimental Brain Research · doi:10.1007/s00221-006-0814-y · Cited by 277
Probability matching as a computational strategy used in perception
Wozny, D. R., Beierholm, U. R., & Shams, L. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000871 · Cited by 265
A review of virtual environments for training in ball sports
Miles, H. C., Pop, S. R., Watt, S. J., Lawrence, G. P., & John, N. W. (2012) · Computers & Graphics · doi:10.1016/j.cag.2012.04.007 · Cited by 270
Self-citationTrommershäuser, J., Kording, K. P., & Landy, M. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.001.0001 · Cited by 256
The Perception and Misperception of Specular Surface Reflectance
Marlow, P. J., Kim, J., & Anderson, B. L. (2012) · Current Biology · doi:10.1016/j.cub.2012.08.009 · Cited by 227
Bayesian models: the structure of the world, uncertainty, behavior, and the brain
Vilares, I., & Kording, K. P. (2011) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.05965.x · Cited by 237
Probabilistic models in human sensorimotor control
Wolpert, D. M. (2007) · Human Movement Science · doi:10.1016/j.humov.2007.05.005 · Cited by 212
Roach, N. W., Heron, J., & McGraw, P. V. (2006) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2006.3578 · Cited by 220
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
Multisensory Decision-Making in Rats and Humans
Raposo, D. C., Sheppard, J. P., Schrater, P., & Churchland, A. K. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4998-11.2012 · Cited by 215
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
Self-citationSteinbach, E., Hirche, S., Ernst, M., Brandi, F., Chaudhari, R., Kammerl, J., & Vittorias, I. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2182100 · Cited by 198
Knill, D. C. (2007) · Journal of Vision · doi:10.1167/7.7.5 · Cited by 194
Why pictures look right when viewed from the wrong place
Vishwanath, D., Girshick, A. R., & Banks, M. S. (2005) · Nature Neuroscience · doi:10.1038/nn1553 · Cited by 187
Self-citationBayesian sampling in visual perception
Moreno-Bote, R., Knill, D. C., & Pouget, A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1101430108 · Cited by 182
A Major Human White Matter Pathway Between Dorsal and Ventral Visual Cortex
Takemura, H., Rokem, A., Winawer, J. A., Yeatman, J. D., Wandell, B. A., & Pestilli, F. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv064 · Cited by 184
Fusion of visual cues is not mandatory in children
Nardini, M., Bedford, R., & Mareschal, D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001699107 · Cited by 161
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
“However, in many cases the prior is assumed to be approximately uniform (e.g., Ernst & Banks, 2002; Alais & Burr, 2004; Hillis et al., 2004).”
BackgroundMultiply-constrained semantic search in the Remote Associates Test
Smith, K. A., Huber, D. E., & Vul, E. (2013) · Cognition · doi:10.1016/j.cognition.2013.03.001 · Cited by 158
Gori, M. (2015) · Multisensory Research · doi:10.1163/22134808-00002478 · Cited by 155
Ideal-Observer Models of Cue Integration
Landy, M. S., Banks, M. S., & Knill, D. C. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.003.0001 · Cited by 145
Self-citationRagan, E. D., Kopper, R., Schuchardt, P., & Bowman, D. A. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.163 · Cited by 138
Stereoscopy and the Human Visual System
Banks, M. S., Read, J. C. A., Allison, R. S., & Watt, S. J. (2012) · SMPTE Motion Imaging Journal · doi:10.5594/j18173 · Cited by 127
Self-citationVirtual reality to assess and train team ball sports performance: A scoping review
Faure, C., Limballe, A., Bideau, B., & Kulpa, R. (2019) · Journal of Sports Sciences · doi:10.1080/02640414.2019.1689807 · Cited by 132
A Vestibular Sensation: Probabilistic Approaches to Spatial Perception
Angelaki, D. E., Klier, E. M., & Snyder, L. H. (2009) · Neuron · doi:10.1016/j.neuron.2009.11.010 · Cited by 130
Shape and the Stereo Correspondence Problem
Ogale, A. S., & Aloimonos, Y. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3672-3 · Cited by 118
Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes
Whiteley, L., & Sahani, M. (2008) · Journal of Vision · doi:10.1167/8.3.2 · Cited by 120
Faisal, A. A., & Wolpert, D. M. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90974.2008 · Cited by 120
Trans-saccadic integration of peripheral and foveal feature information is close to optimal
Wolf, C., & Schütz, A. C. (2015) · Journal of Vision · doi:10.1167/15.16.1 · Cited by 117
Binocular integration and disparity selectivity in mouse primary visual cortex
Scholl, B., Burge, J., & Priebe, N. J. (2013) · Journal of Neurophysiology · doi:10.1152/jn.01021.2012 · Cited by 112
Acerbi, L., Dokka, K., Angelaki, D. E., & Ma, W. J. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006110 · Cited by 111
Toward a new theory of stereopsis.
Vishwanath, D. (2014) · Psychological Review · doi:10.1037/a0035233 · Cited by 109
Statistically Optimal Multisensory Cue Integration: A Practical Tutorial
Rohde, M., van Dam, L. C. J., & Ernst, M. O. (2015) · Multisensory Research · doi:10.1163/22134808-00002510 · Cited by 119
Burge, J., Fowlkes, C. C., & Banks, M. S. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5551-09.2010 · Cited by 106
Self-citationThe Perception of a Face Is No More Than the Sum of Its Parts
Gold, J. M., Mundy, P. J., & Tjan, B. S. (2012) · Psychological Science · doi:10.1177/0956797611427407 · Cited by 108
MacNeilage, P. R., Ganesan, N., & Angelaki, D. E. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90677.2008 · Cited by 113
The role of memory in visually guided reaching
Brouwer, A.-M., & Knill, D. C. (2007) · Journal of Vision · doi:10.1167/7.5.6 · Cited by 104
Visual and vestibular cue integration for heading perception in extrastriate visual cortex
Angelaki, D. E., Gu, Y., & DeAngelis, G. C. (2010) · The Journal of Physiology · doi:10.1113/jphysiol.2010.194720 · Cited by 113
Knill, D. C. (2005) · Journal of Vision · doi:10.1167/5.2.2 · Cited by 99
Self-motion leads to mandatory cue fusion across sensory modalities
Prsa, M., Gale, S., & Blanke, O. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00439.2012 · Cited by 100
Combination and Integration in the Perception of Visual-Haptic Compliance Information
Kuschel, M., Luca, M. D., BUSS, M. J., & Klatzky, R. L. (2010) · IEEE Transactions on Haptics · doi:10.1109/toh.2010.9 · Cited by 92
Learning Bayesian priors for depth perception
Knill, D. C. (2007) · Journal of Vision · doi:10.1167/7.8.13 · Cited by 91
The precision of binocular and monocular depth judgments in natural settings
McKee, S. P., & Taylor, D. G. (2010) · Journal of Vision · doi:10.1167/10.10.5 · Cited by 91
Humans Ignore Motion and Stereo Cues in Favor of a Fictional Stable World
Glennerster, A., Tcheang, L., Gilson, S. J., Fitzgibbon, A. W., & Parker, A. J. (2006) · Current Biology · doi:10.1016/j.cub.2006.01.019 · Cited by 94
On the minimum audible difference in direct-to-reverberant energy ratio
Larsen, E., Iyer, N., Lansing, C. R., & Feng, A. S. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2936368 · Cited by 94
Limits of stereopsis explained by local cross-correlation
Filippini, H. R., & Banks, M. S. (2009) · Journal of Vision · doi:10.1167/9.1.8 · Cited by 84
Self-citationDynamic weighting of multisensory stimuli shapes decision-making in rats and humans
Sheppard, J. P., Raposo, D., & Churchland, A. K. (2013) · Journal of Vision · doi:10.1167/13.6.4 · Cited by 87
Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
Orhan, A. E., & Ma, W. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00181-8 · Cited by 77
Bayesian motion estimation accounts for a surprising bias in 3D vision
Welchman, A. E., Lam, J. M., & Bülthoff, H. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804378105 · Cited by 71
Presbypropria: the effects of physiological ageing on proprioceptive control
Boisgontier, M. P., Olivier, I., Olivier Chenu, A., & Nougier, V. (2011) · AGE · doi:10.1007/s11357-011-9300-y · Cited by 85
Girshick, A. R., & Banks, M. S. (2009) · Journal of Vision · doi:10.1167/9.9.8 · Cited by 82
Self-citationReliability-Weighted Integration of Audiovisual Signals Can Be Modulated by Top-down Attention
Rohe, T., & Noppeney, U. (2018) · eNeuro · doi:10.1523/eneuro.0315-17.2018 · Cited by 77
Development of auditory selective attention: Why children struggle to hear in noisy environments.
Jones, P. R., Moore, D. R., & Amitay, S. (2015) · Developmental Psychology · doi:10.1037/a0038570 · Cited by 79
Bayesian approaches to sensory integration for motor control
Berniker, M., & Kording, K. P. (2011) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.125 · Cited by 73
The neural mechanisms of reliability weighted integration of shape information from vision and touch
Helbig, H. B., Ernst, M. O., Ricciardi, E., Pietrini, P., Thielscher, A., Mayer, K. M., Schultz, J., & Noppeney, U. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.072 · Cited by 70
Retinal blur and the perception of egocentric distance
Vishwanath, D., & Blaser, E. (2010) · Journal of Vision · doi:10.1167/10.10.26 · Cited by 66
Late Development of Cue Integration Is Linked to Sensory Fusion in Cortex
Dekker, T. M., Ban, H., van der Velde, B., Sereno, M. I., Welchman, A. E., & Marko Nardini, A. (2015) · Current Biology · doi:10.1016/j.cub.2015.09.043 · Cited by 66
Estimation of 3D shape from image orientations
Fleming, R. W., Holtmann-Rice, D., & Bülthoff, H. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114619109 · Cited by 64
Optimal combination of environmental cues and path integration during navigation
Sjolund, L. A., Kelly, J. W., & McNamara, T. P. (2017) · Memory & Cognition · doi:10.3758/s13421-017-0747-7 · Cited by 67
The Development of Audio-Visual Integration for Temporal Judgements
Adams, W. J. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004865 · Cited by 66
Visual size perception and haptic calibration during development
Gori, M., Giuliana, L., Sandini, G., & Burr, D. C. (2012) · Developmental Science · doi:10.1111/j.1467-7687.2012.2012.01183.x · Cited by 65
Risso, G., Valle, G., Iberite, F., Strauss, I., Stieglitz, T., Controzzi, M., Clemente, F., Granata, G., Rossini, P. M., Micera, S., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-43815-1 · Cited by 62
Visual and Haptic Integration in the Estimation of Softness of Deformable Objects
Cellini, C., Kaim, L. R., & Drewing, K. (2013) · i-Perception · doi:10.1068/i0598 · Cited by 59
Pollock, B., Burton, M., Kelly, J. W., Gilbert, S. B., & Winer, E. H. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.58 · Cited by 58
Stereo and motion information are not independently processed by the visual system
Domini, F., Caudek, C., & Tassinari, H. (2006) · Vision Research · doi:10.1016/j.visres.2005.11.018 · Cited by 59
Integrating visual cues for motor control: A matter of time
Greenwald, H. S., Knill, D. C., & Saunders, J. A. (2005) · Vision Research · doi:10.1016/j.visres.2005.01.025 · Cited by 57
Inferring the stiffness of unfamiliar objects from optical, shape, and motion cues
Schmidt, F., Paulun, V. C., van Assen, J. J. R., & Fleming, R. W. (2017) · Journal of Vision · doi:10.1167/17.3.18 · Cited by 57
Smelling directions: Olfaction modulates ambiguous visual motion perception
Kuang, S., & Zhang, T. (2014) · Scientific Reports · doi:10.1038/srep05796 · Cited by 56
Reuschel, J., Drewing, K., Henriques, D. Y. P., Rösler, F., & Fiehler, K. (2009) · Experimental Brain Research · doi:10.1007/s00221-009-2099-4 · Cited by 52
Integration of vision and haptics during tool use
Takahashi, C., Diedrichsen, J., & Watt, S. J. (2009) · Journal of Vision · doi:10.1167/9.6.3 · Cited by 53
Self-citationAre discrimination thresholds a valid measure of variance for judgments of slant from texture?
Todd, J. T. (2010) · Journal of Vision · doi:10.1167/10.2.20 · Cited by 54
Attention in a Bayesian Framework
Whiteley, L., & Sahani, M. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00100 · Cited by 56
Probabilistic Computations for Attention, Eye Movements, and Search
Eckstein, M. P. (2017) · Annual Review of Vision Science · doi:10.1146/annurev-vision-102016-061220 · Cited by 49
Cue combination for 3D location judgements
Svarverud, E., Gilson, S. J., & Glennerster, A. (2010) · Journal of Vision · doi:10.1167/10.1.5 · Cited by 52
Common Bayesian Models for Common Cognitive Issues
Colas, F., Diard, J., & Bessìère, P. (2010) · Acta Biotheoretica · doi:10.1007/s10441-010-9101-1 · Cited by 59
Combination of texture and color cues in visual segmentation
Saarela, T., & Landy, M. S. (2012) · Vision Research · doi:10.1016/j.visres.2012.01.019 · Cited by 49
Self-citationCue Combination Within a Bayesian Framework
Alais, D., & Burr, D. C. (2019) · Springer handbook of auditory research · doi:10.1007/978-3-030-10461-0_2 · Cited by 49
Integration of texture and disparity cues to surface slant in dorsal visual cortex
Murphy, A. P., Ban, H., & Welchman, A. E. (2013) · Journal of Neurophysiology · doi:10.1152/jn.01055.2012 · Cited by 46
Perception of surface slant from oriented textures
Saunders, J. A., & Backus, B. T. (2006) · Journal of Vision · doi:10.1167/6.9.3 · Cited by 46
Display adaptive 3D content remapping
Masiá, B., Wetzstein, G., Aliaga, C., Raskar, R., & Gutiérrez, D. (2013) · Computers & Graphics · doi:10.1016/j.cag.2013.06.004 · Cited by 45
Depth cue combination in spontaneous eye movements
Wismeijer, D. A., Erkelens, C. J., van Ee, R., & Wexler, M. (2010) · Journal of Vision · doi:10.1167/10.6.25 · Cited by 45
Statistically optimal integration of biased sensory estimates
Scarfe, P., & Hibbard, P. B. (2011) · Journal of Vision · doi:10.1167/11.7.12 · Cited by 44
Cognitive, Systems, and Computational Neurosciences of the Self in Motion
Noel, J., & Angelaki, D. E. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-021021-103038 · Cited by 43
Metadata
- Authors
- Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S.
- Affiliations
- University of Pennsylvania (US)
- Bangor University (GB)
- New York University (US)
- University of California, Berkeley (US)
- Venue
- Journal of Vision
- Published
- 2004-12-01
- Volume
- 4
- Issue
- 12
- Pages
- 1
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2158527951
- Cited by
- 421
- References
- 48
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 13 · 2023: 12 · 2022: 18 · 2021: 13 · 2020: 13 · 2019: 21 · 2018: 25 · 2017: 20 · 2016: 16 · 2015: 28 · 2014: 16 · 2013: 27 · 2012: 28
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Vision (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (R01EY012851, EY 08266, EY12851, R01EY008266)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- surface slant perception, cue combination, binocular disparity, maximum likelihood estimation, cue reliability, perceptual precision, depth perception
- MeSH terms
- Cues, Depth Perception, Form Perception, Humans, Pattern Recognition, Visual, Vision, Binocular, Vision Disparity, Likelihood Functions
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
