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Bayesian decision theory and psychophysics
Open accessAbstract
We argue that Bayesian decision theory provides a good theoretical framework for visual perception. Such a theory involves a likelihood function specifying how the scene generates the image(s), a prior assumption about the scene, and a decision rule to determine the scene interpretation. This is illustrated by describing Bayesian theories for individual visual cues and showing that perceptual biases found in psychophysical experiments can be interpreted as biases towards prior assumptions made by the visual system. We then describe the implications of this framework for the integration of different cues. We argue that the dependence of cues on prior assumptions means that care must be taken to model these dependencies during integration. This suggests that a number of proposed schemes for cue integration, which only allow weak interaction between cues, are not adequate and instead stronger coupling is often required. These theories require the choice of decision rules and we argue that this choice is important since these rules help capture the task dependent nature of vision. This is illustrated by analysing the generic viewpoint assumption. Finally, we suggest that the visual system uses a set of competing prior assumptions, rather than the single generic priors, or natural constraints, commonly used in computational theories of vision.
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“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 [141]
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How Optimal Depth Cue Integration Depends on the Task
Schrater, P., & Kersten, D. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026557704054 · Cited by 48
Actions, policies, values, and the basal ganglia
Daw, N. D., Niv, Y., & Dayan, P. (2006) · MPG.PuRe (Max Planck Society) · Cited by 42
van den Berg, R., Vogel, M., Josić́, K., & Ma, W. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1108790109 · Cited by 38
Cue combination and color edge detection in natural scenes
Zhou, C., & Mel, B. W. (2008) · Journal of Vision · doi:10.1167/8.4.4 · Cited by 40
A Bayesian Framework for Sensory Adaptation
Grzywacz, N. M., & Balboa, R. M. (2002) · Neural Computation · doi:10.1162/089976602317250898 · Cited by 38
“Therefore, one should not assume automatically that the goal of the early sensory system is to maximize transmission of information (Haft & van Hemmen, 1998; Wainwright, 1999 ).”
BackgroundCue combination on the circle and the sphere
Murray, R. F., & Morgenstern, Y. (2010) · Journal of Vision · doi:10.1167/10.11.15 · Cited by 32
“…several cues to object properties like depth and shape, and many studies have investigated how the human visual system combines multiple cues to arrive at a single estimate of such properties (Landy, Maloney, Johnston, & Young, 1995; MacKenzie, Wilcox, & Murray, 2008; Yuille & Bülthoff, 1996).”
BackgroundOptimal inference explains the perceptual coherence of visual motion stimuli
Hedges, J., Stocker, A. A., & Simoncelli, E. P. (2011) · Journal of Vision · doi:10.1167/11.6.14 · Cited by 35
Signal Integration in Human Visual Speed Perception
Jogan, M., & Stocker, A. A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4801-14.2015 · Cited by 35
“…the predictions of a novel Bayesian channel model that optimally integrates speed information across all channels with those of a model that assumed only the most reliable channel (max model) or performed a weak form of integration (Landy et al., 1995; Yuille and Bülthoff, 1996; averaging model).”
BackgroundFast Temporal Dynamics of Visual Cue Integration
Triesch, J., Ballard, D. H., & Jacobs, R. A. (2002) · Perception · doi:10.1068/p3314 · Cited by 34
Transcranial magnetic stimulation to visual cortex induces suboptimal introspection
Peters, M. A. K., Fesi, J. D., Amendi, N., Knotts, J. D., Lau, H. C., & Ro, T. (2017) · Cortex · doi:10.1016/j.cortex.2017.05.017 · Cited by 34
“…& Lau, 2016; Peters & Lau, 2015; Stocker and Simoncelli 2006, 2008; Vilares, Howard, Fernandes, Gottfried, & K€ording, 2012; Vilares & K€ording, 2011; Yuille & Bülthoff, 1996), we assume S is a standardized representation of the combination of internal and external noise that the observer has…”
BackgroundMonkeys and humans take local uncertainty into account when localizing a change
Devkar, D. T., Wright, A. A., & Ma, W. J. (2017) · Journal of Vision · doi:10.1167/17.11.4 · Cited by 31
“…to manipulate the reliability of the stimulus information, as is often done in the study of cue combination (Knill & Pouget, 2004; Trommershauser, Kording, & Landy, 2011; Yuille & Bulthoff, 1996) and recently also in the study of visual search (Ma, Navalpakkam, Beck, van den Berg, & Pouget, 2011).”
BackgroundExploiting the generic viewpoint assumption
Freeman, W. (1996) · International Journal of Computer Vision · doi:10.1007/bf00208721 · Cited by 33
“This method also applies to other vision problems, such as motion (Freeman, 1994) and stereo (Yuille and Bulthoff, 1996).”
BackgroundMethodPark, I. M., & Pillow, J. W. (2026) · PLoS Computational Biology · doi:10.1101/178418 · Cited by 31
“Although this provides a insightful connection, such relations do not always exist, and more generally it is not clear why a bound on mutual information would be a biologically relevant constraint on a sensory encoder.”
Rational inattention and tonic dopamine
Mikhael, J. G., Lai, L., & Gershman, S. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008659 · Cited by 34
“Bayes’ rule formalizes this intuition [91–95] and states that 159 an optimal agent will take the product of the likelihood and prior to compute the posterior distribution over 160”
BackgroundDepth-cue integration in grasp programming: No evidence for a binocular specialism
Keefe, B. D., Hibbard, P. B., & Watt, S. J. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2011.02.047 · Cited by 32
Number of perceptually distinct surface colors in natural scenes
Marín‐Franch, I., & Foster, D. H. (2010) · Journal of Vision · doi:10.1167/10.11.20 · Cited by 31
Is prior knowledge of object geometry used in visually guided reaching?
Hartung, B., Schrater, P., Bülthoff, H., Kersten, D., & Franz, V. H. (2005) · Journal of Vision · doi:10.1167/5.6.2 · Cited by 31
Self-citationMao, J., Vosoughi, A., & Carney, L. H. (2013) · The Journal of the Acoustical Society of America · doi:10.1121/1.4807815 · Cited by 30
“…of energy and temporal cues in the detection task, this linear combination would yield an optimal estimation of the combined cue value if the energy and temporal cues were uncorrelated (Yuille and Bulthoff, 1996); however, energy and temporal cues are typically correlated (Davidson et al., 2009a).”
BackgroundExplicit estimation of visual uncertainty in human motion processing
Graf, E. W., Warren, P. A., & Maloney, L. T. (2005) · Vision Research · doi:10.1016/j.visres.2005.08.007 · Cited by 29
“In an Appendix A, we illustrate how an existing model of motion perception ( Weiss, Simoncelli, & Adelson, 2002 ) may be extended and used to recover an estimate of the uncertainty of visual motion, a quantity which we have shown to be sufficient to predict observers extrapolation data.”
MethodPattern Inference Theory: A Probabilistic Approach to Vision
Kersten, D., & Schrater, P. (2002) · Wiley eBooks · doi:10.1002/0470013427.ch7 · Cited by 31
“The example of ideal observer analysis of light detection further illustrates a fundamental strategy for studying perception, consisting of three modeling domains.”
BackgroundBayesian decision theory and navigation
McNamara, T. P., & Chen, X. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-021-01988-9 · Cited by 28
“The MAP estimate is implied by the use of Dirac-delta loss (Yuille & Bülthoff, 1996), This loss function, which is based on the Dirac delta function, produces constant loss except if the action is correct ( θ − a = 0), where loss is infinitely small.”
BackgroundMethodInfluentialAuditory Localisation Biases Increase with Sensory Uncertainty
Garcia, S. E., Jones, P. R., Rubin, G. S., & Marko Nardini, A. (2017) · Scientific Reports · doi:10.1038/srep40567 · Cited by 26
Proactive Information Sampling in Value-Based Decision-Making: Deciding When and Where to Saccade
Song, M., Wang, X., Zhang, H., & Li, J. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00035 · Cited by 31
Multisensory decisions from self to world
Zaidel, A., & Salomon, R. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0335 · Cited by 32
“Furthermore, Bayesian theories of perceptual inference explain how the brain integrates separate measurements from the individual sensory cues into a unified percept (multisensory integration) [27 – 33].”
BackgroundHogervorst, M. A. J., & Eagle, R. A. (2000) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.26.3.934 · Cited by 26
Computational Approaches for Acute Traumatic Brain Injury Image Recognition
Lin, E., & Yuh, E. L. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.791816 · Cited by 28
“Bayesian decision theory quantifies the tradeoffs between multiple choices in a probabilistic manner (65, 66).”
BackgroundSegil, J. L., Cuberovic, I., Graczyk, E. L., Weir, R. F. F., & Tyler, D. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-62970-4 · Cited by 28
Towards characterizing the canonical computations generating phenomenal experience
Peters, M. A. K. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104903 · Cited by 25
The interaction of vision and audition in two-dimensional space
Godfroy-Cooper, M., Sandor, P. M. B., Miller, J. D., & Welch, R. B. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00311 · Cited by 24
“…of Sydney, Australia *Correspondence: Martine Godfroy-Cooper, NASA Ames Research Center, PO Box 1, Mail Stop 262-4, Moffett Field, CA 94035-0001, USA martine.godfroy-1@nasa.gov Specialty section: This article was submitted to Auditory Cognitive Neuroscience, a section of the journal…”
BackgroundThe role of attention on the integration of visual and inertial cues
Berger, D. R., & Bülthoff, H. (2009) · Experimental Brain Research · doi:10.1007/s00221-009-1767-8 · Cited by 25
“estimate, for example, if they are incorporated into the integration process as ‘‘priors’’ in a Bayesian framework (Yuille and Bülthoff 1996).”
BackgroundSelf-citationThe economics of motion perception and invariants of visual sensitivity
Gepshtein, S., Tyukin, I., & Kubovy, M. (2007) · Journal of Vision · doi:10.1167/7.8.8 · Cited by 22
“The “weak fusion” theory of cue combination (Clark & Yuille, 1990; Landy, Maloney, Johnsten, & Young, 1995; Maloney & Landy, 1989; Yuille & Bülthoff, 1996) predicts that the nervous system combines different estimates of a parameter of interest using a linear weighting rule, similar to the linear…”
BackgroundMethodThe Epistemological Status of Vision and its Implications for Design
Vishwanath, D. (2005) · Axiomathes · doi:10.1007/s10516-004-5445-y · Cited by 21
Perceptual categories for spatial layout
Kersten, D. (1997) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1997.0099 · Cited by 21
Foster, R. M., Fantoni, C., Caudek, C., & Fulvio Domini, A. (2011) · Acta Psychologica · doi:10.1016/j.actpsy.2010.12.003 · Cited by 21
“This combination rule also satisfies other statistical criteria of optimality: it is the maximum likelihood estimator and also the MAP estimator (Yuille and Bülthoff, 1996).”
Audio-Visual Speech Cue Combination
Arnold, D. H., Tear, M. J., Schindel, R., & Roseboom, W. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010217 · Cited by 20
“The magnitude of facilitation was well predicted by a linear summation, but could not be predicted on the basis of a probability summation [10,11–12], or a Bayesian MLE [8,19].”
BackgroundThe Role of Visuohaptic Experience in Visually Perceived Depth
Ho, Y.-X., Serwe, S., Trommershäuser, J., Maloney, L. T., & Landy, M. S. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91129.2008 · Cited by 20
Jantvik, T., Gustafsson, L., & Papliński, A. P. (2011) · Neural Computation · doi:10.1162/neco_a_00149 · Cited by 20
“In short the maximum likelihood estimation (MLE), which can be derived from Bayes’ theorem ( 1763 ), is a specification of how to combine multiple sources of information in a way that minimizes the variance of the combined result, under certain assumptions (see, eg, the appendix of the book chapter by Yuille & Bulthof…”
BackgroundA wholly empirical explanation of perceived motion
Yang, Z., Shimpi, A., & Purves, D. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091095298 · Cited by 19
Information space is action space: perceiving the partial lengths of rods rotated on an axle
Michaels, C. F., & Isenhower, R. W. (2010) · Attention Perception & Psychophysics · doi:10.3758/s13414-010-0024-3 · Cited by 21
“Bayesian theory has several versions of how to compute the likelihood functions of cue combinations (see Yuille & Bülthoff, 1996, for an enumeration).”
BackgroundTactile length contraction as Bayesian inference
Tong, J., Ngo, V., & Goldreich, D. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00029.2016 · Cited by 20
Multisensory Integration in Bionics: Relevance and Perspectives
Risso, G., & Valle, G. (2022) · Current Physical Medicine and Rehabilitation Reports · doi:10.1007/s40141-022-00350-x · Cited by 20
Liang, L., & Acuna, D. E. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3375623 · Cited by 18
“This is largely similar to the Bayesian treatment of the 2AFC task (see [19, 31, 32, 33, 34, 35]).”
MethodBayesian methods for image fusion
Beyerer, J., Heizmann, M., Sander, J., et al. (2008) · Elsevier eBooks · doi:10.1016/b978-0-12-372529-5.00003-2 · Cited by 18
Winter, C. J., & Peters, M. A. K. (2022) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-021-02358-2 · Cited by 17
Integrating multiple cues to depth order at object boundaries
Hildreth, E. C., & Royden, C. S. (2011) · Attention Perception & Psychophysics · doi:10.3758/s13414-011-0172-0 · Cited by 16
Disparity and Shading Cues Cooperate for Surface Interpolation
Vuong, Q. C., Domini, F., & Caudek, C. (2006) · Perception · doi:10.1068/p5315 · Cited by 17
Does tilt/translation ratio affect perception of deceleration in driving simulators?
Stratulat, A., Roussarie, V., Vercher, J., & Bourdin, C. (2011) · Journal of Vestibular Research · doi:10.3233/ves-2011-0399 · Cited by 15
“Viewed from the perspective of the Bayesian approach [26,27,34], the contribution of each sensorial cue carries a certain weight, which represents a probability density function conditioned by “a priori” information.”
Computational Models of Hallucinations
Jardri, R., & Denève, S. (2013) · The Neuroscience of Hallucinations · doi:10.1007/978-1-4614-4121-2_16 · Cited by 16
Validating model-based Bayesian integration using prior–cost metamers
Sohn, H., & Jazayeri, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021531118 · Cited by 18
“Bayesian integration | internal model | sensorimotor learning B ayesian decision theory (BDT) provides a normative framework for how to make optimal decisions under uncertainty (1 – 4).”
Mahani, M.-A. N., Sheybani, S., Bausenhart, K. M., Ulrich, R., & Ahmadabadi, M. N. (2017) · Scientific Reports · doi:10.1038/s41598-017-03521-2 · Cited by 17
Learning optimal integration of arbitrary features in a perceptual discrimination task
Michel, M. M., & Jacobs, R. A. (2008) · Journal of Vision · doi:10.1167/8.2.3 · Cited by 14
“Yuille and Bülthoff (1996) demonstrated that, given certain mathematical assumptions, the optimal strategy for combining estimates Ê1, I, Ên from a set of class-conditionally independent cues (i.”
BackgroundEarly Integration and Bayesian Causal Inference in Multisensory Perception
Shams, L. B. (2011) · Frontiers in neuroscience · doi:10.1201/9781439812174-16 · Cited by 21
Combining probabilistic population codes
Zemel, R. S., & Dayan, P. (1997) · Cited by 13
Vision based wildfire and natural risk observers
Stipaničev, D., Šerić, L., Braović, M., Krstinić, D., Jakovčević, T., Štula, M., Bugarić, M., & Maras, J. (2012) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2012.6469518 · Cited by 15
Multisensory contributions to spatial perception.
Mohler, B. J., Luca, M. D., & Bülthoff, H. (2012) · American Psychological Association eBooks · doi:10.1037/13936-005 · Cited by 14
Self-citationA Novel Framework for the Analysis of Eye Movements during Visual Search for Knowledge Gathering
Dempere‐Marco, L., Hu, X., & Yang, G. (2010) · Cognitive Computation · doi:10.1007/s12559-010-9085-9 · Cited by 13
“computer vision community lately (see for instance [35, 55, 63]).”
BackgroundThe Role of Stereopsis, Motion Parallax, Perspective and Angle Polarity in Perceiving 3-D Shape
Sherman, A., Papathomas, T. V., Jain, A., & Keane, B. P. (2012) · Seeing and Perceiving · doi:10.1163/187847511x576802 · Cited by 12
Does evolution favor true perceptions?
Hoffman, D. D., Singh, M., & Mark, J. T. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2011609 · Cited by 12
Sensory cue-combination in the context of newly learned categories
Bankieris, K., Bejjanki, V. R., & Aslin, R. Ν. (2017) · Scientific Reports · doi:10.1038/s41598-017-11341-7 · Cited by 12
Integration of Sensory Force Feedback Is Disturbed in CRPS-Related Dystonia
Mugge, W., van der Helm, F. C. T., & Schouten, A. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060293 · Cited by 11
“In the central nervous system the sensory feedback channels are integrated and weighted to improve the state estimate [3,4].”
BackgroundBayesian combination of two-dimensional location estimates
Alinda Friedman, A., Ludvig, E. A., Legge, E. L. G., & Vuong, Q. C. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0241-x · Cited by 12
Bimodal sensory discrimination is finer than dual single modality discrimination
Koene, A. R., Arnold, D. H., & Johnston, A. (2007) · Journal of Vision · doi:10.1167/7.11.14 · Cited by 11
“Performance in both the simultaneous and the sequential bimodal conditions was statistically indistinguishable from discrimination performance predicted by maximum likelihood estimation (Alais & Burr, 2004; Ernst & Banks, 2002; Hillis et al., 2004; Landy et al., 1995; Yuille & Buelthoff, 1996).”
BackgroundInfluentialLinton, P., Morgan, M. J., Read, J. C. A., Vishwanath, D., Creem-Regehr, S. H., & Domini, F. (2022) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2021.0443 · Cited by 11
“Non-linear cue combination was originally more popular in the early 1990s, and ranges from the addition (rather than averaging) of cues [271,272], through to highly sophisticated interdependencies between cues [244,273].”
BackgroundOn the Rules of Integration of Crowded Orientation Signals
Põder, E. (2012) · i-Perception · doi:10.1068/i0412 · Cited by 10
“In essence, it is a simplified version of the Bayesian inference with competitive priors (e.g., Stocker & Simoncelli, 2008; Yuille & Bülthoff, 1996).”
MethodThe material-weight illusion is a Bayes-optimal percept under competing density priors
Peters, M. A. K., Zhang, L.-Q., & Shams, L. B. (2018) · PeerJ · doi:10.7717/peerj.5760 · Cited by 12
Treisman, M., & Lages, M. (2010) · Seeing and Perceiving · doi:10.1163/187847510x541126 · Cited by 9
Bejjanki, V. R., Randrup, E. R., & Aslin, R. Ν. (2019) · Developmental Science · doi:10.1111/desc.12912 · Cited by 13
The material-weight illusion disappears or inverts in objects made of two materials
Paulun, V. C., Buckingham, G., Goodale, M. A., & Fleming, R. W. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00199.2018 · Cited by 12
“Peters et al. (2016) proposed a model that predicts the illusion as the result of Bayesian integration in a framework of multiple competing density priors (as proposed by Yuille and Bülthoff 1996) and the likelihood of incoming haptic information.”
BackgroundKhumalo, B., & Hendlin, Y. H. (2024) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-024-10013-y · Cited by 8
“Within Bayesian estimation, veridical perception depends on the assump - tion that evolution has shaped perceptual systems to accurately estimate, based on sensory information, the true structure of the objective world (Yuille & Bülthoff, 1996 ).”
BackgroundMultisensory Interactions in Head and Body Centered Perception of Verticality
de Winkel, K. N., Edel, E., Happee, R., & Bülthoff, H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.599226 · Cited by 11
Self-citationCircadian-Time Sickness: Time-of-Day Cue-Conflicts Directly Affect Health
van Ee, R., de Cruys, S. V., Schlangen, L. J. M., & Vlaskamp, B. N. S. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2016.09.004 · Cited by 9
“If a current circadian rhythm corresponds to the local environmental (solar) rhythm, the natural way to deal with a correct prediction in a probabilistic framework is to increase the assumed precision of the prior distribution one circadian cycle later by making the prior distribution more peaked [28,29].”
BackgroundVision, Psychophysics and Bayes
Schrater, P., & Kersten, D. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/5583.003.0006 · Cited by 8
Learning to Combine Arbitrary Signals from Vision and Touch
Jäkel, F., & Ernst, M. O. (2003) · TUbilio (Technical University of Darmstadt) · Cited by 8
Interview with Michael Gazzaniga
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Bayesian Causal Inference: A Unifying Neuroscience TheoryPreprint
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“e) The generative model of shape perception in the shape-from-shading paradigm (Yuille and Bulthoff, 1996).”
BackgroundMethodInfluentialFusion of Perceptions in Architectural Design
Ciftcioglu, Ö., & Bittermann, M. S. (2013) · eCAADe proceedings · doi:10.52842/conf.ecaade.2013.2.335 · Cited by 6
“In the field of computer vision perception is considered to be a mere image processing and ensuing pattern recognition process, where Bayesian methods are appropriate (Knill et al., 2008; Knill and Richards, 2008; Yuille and Bulthoff, 2008).”
MethodBayesian feature evaluation for visual saliency estimation
Hu, X.-P., Dempere‐Marco, L., & Davies, E. R. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.05.002 · Cited by 6
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Keefe, B. D., Suray, P.-A., & Watt, S. J. (2019) · Experimental Brain Research · doi:10.1007/s00221-019-05489-z · Cited by 6
Statistical Structure and Task Dependence in Visual Cue Integration 1
Schrater, P., & Kersten, D. (1999) · Cited by 6
Leib, R., Howard, I. S., Millard, M., & Franklin, D. W. (2023) · Comprehensive physiology · doi:10.1002/cphy.c220032 · Cited by 24
“On the behavioral level, Clark and Yuille suggested that multisensory combination might be formalized using a Bayesian approach (strong coupling) in which we combine a prior assumption regarding the estimated property with a combined likelihood function of the sensory inputs (16, 46, 413).”
Limits of the Application of Bayesian Modeling to Perception
Luccio, R. (2019) · Perception · doi:10.1177/0301006619868125 · Cited by 4
Probability as an Alternative to Logic for Rational Sensory–Motor Reasoning and Decision
Bessìère, P. (2008) · Springer tracts in advanced robotics · doi:10.1007/978-3-540-79007-5_1 · Cited by 5
A Survey on Probabilistic Models in Human Perception and Machines
Li, L., Rehr, R., Bruns, P., Gerkmann, T., & Röder, B. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00085 · Cited by 5
Object Perception: Generative Image Models and Bayesian Inference
Kersten, D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36181-2_21 · Cited by 4
“Generative models help to identify the key information used by human visual perception, and thus provide a basis for modeling vision as Bayesian statistical inference [27,16,34].”
Background3D shape discrimination using relative disparity derivatives
Vreven, D. L. (2006) · Vision Research · doi:10.1016/j.visres.2006.08.014 · Cited by 5
“Several authors have argued that these biases are due to resolution limits early in visual processing, when 2D information is extracted (Hogervorst & Eagle, 1998; Yuille & Bultho V , 1996).”
BackgroundDepth and luminance edges attract
Robinson, A. E., & MacLeod, D. I. A. (2013) · Journal of Vision · doi:10.1167/13.11.3 · Cited by 5
Three comments on Teller’s “bridge locus”
Movshon, J. A. (2013) · Visual Neuroscience · doi:10.1017/s0952523813000527 · Cited by 5
“…and these assumptions are captured by Bayesian statistics in the form of distributions of prior probabilities, which capture the observer's knowledge or belief about the possible states of the world that might have given rise to a particular set of sensory data (Yuille & Bülthoff, 1996 ).”
Managing Uncertainty in Cue Combination
Yang, Z., & Zemel, R. S. (1999) · Cited by 5
3D Media and the Human Visual System
Watt, S. J., & MacKenzie, K. J. (2013) · Wiley eBooks · doi:10.1002/9781118583593.ch18 · Cited by 6
Sensory weighting of position and force feedback during pinching
Geelen, J. E., van der Helm, F. C. T., Schouten, A. C., & Mugge, W. (2023) · Experimental Brain Research · doi:10.1007/s00221-023-06654-1 · Cited by 4
“Reaching studies have proposed that the integration of multiple sensory modalities, mostly between vision and proprioception, follows Bayesian theory (Yuille and Bülthoff 1996; van Beers et al. 1999; Körding and Wolpert 2004).”
BackgroundCausal Context Presented in Subsequent Event Modifies the Perceived Timing of Cause and Effect
Umemura, H. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00314 · Cited by 4
“The Bayesian model has explained a variety of phenomena in vision (Yuille and Bülthoff, 1996; Weiss et al., 2002; Welchman et al., 2008), sensory motor control (Körding and Wolpert, 2004, 2006), multimodal integration (Ernst and Banks, 2002; Ernst and Bülthoff, 2004) and temporal perception…”
BackgroundInfluentialSensory cue integration of visual and vestibular stimuli: a case study for 4D rides
Lee, J., Han, S. H., & Choi, S. (2023) · Virtual Reality · doi:10.1007/s10055-023-00762-7 · Cited by 4
Psychophysical Models of Sound Localisation with Audiovisual Interactions
Mendonça, C. (2020) · Modern acoustics and signal processing · doi:10.1007/978-3-030-00386-9_11 · Cited by 4
Bayesian decision making in two-alternative forced choices
Denève, S. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.00021-2 · Cited by 5
Combining cues in contour orientation discrimination
Popple, A. V., & Levi, D. M. (2004) · Vision Research · doi:10.1016/j.visres.2004.07.013 · Cited by 4
Sun, H., Wang, J., Li, L., Liu, X., Che, Y., & Ma, Y. (2022) · International Journal of Developmental Neuroscience · doi:10.1002/jdn.10169 · Cited by 3
“The response corresponds to the weighted average of cues, with the more reliable cue that yields lessvariable responses having a greater weight (Yuille & Bülthoff, 1996).”
BackgroundGepshtein, S. (2009) · Philosophical Psychology · doi:10.1080/09515080802703695 · Cited by 3
Metadata
- Authors
- Yuille, A. L., & Bülthoff, H. H.
- Affiliations
- Harvard University (US)
- Max Planck Society (DE)
- Venue
- Perception as Bayesian Inference
- Publisher
- Cambridge University Press
- Published
- 1996-09-13
- Pages
- 123-162
- OpenAlex
- W1604979084
- Cited by
- 250
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 5 · 2022: 8 · 2021: 8 · 2020: 11 · 2019: 8 · 2018: 7 · 2017: 11 · 2016: 11 · 2015: 8 · 2014: 8 · 2013: 15 · 2012: 10
- FWCI
- 7.83
- Type
- book chapter
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Cambridge University Press eBooks (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- Bayesian decision theory, visual perception, perceptual bias, cue integration, prior assumptions, likelihood function, decision rules, natural constraints, psychophysics
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