The DOI matches its Crossref record.
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.
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
“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]
Citations
Cited by
Sorted by citations
Items 201–247 of 247
The generic viewpoint assumption and planar bias
Yuille, A., Coughlan, J. M., & Konishi, S. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1201826 · Cited by 3
“Alternative results will be obtained if we use another formulation of the generic view assumption based on decision theory [5], [15].”
MethodSelf-citationThe Role of Task Specification in Optimal Cue Integration
Schrater, P., & Kersten, D. (1999) · Cited by 3
A Comparative Study on Decision Support Approaches Under Uncertainty
Christias, P. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-04849-5_45 · Cited by 3
The Focus-Aspect-Value Model for Explainable Prediction of Subjective Visual Interpretation
Karayil, T., Blandfort, P., Hees, J., & Dengel, A. R. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3325026 · Cited by 3
“For example, the meaning that we infer from an image can depend on: our internal templates about the stimuli [30], expectations and learned biases about the visual object [9], context / prior visual input [8], random neural fluctuations in cortex [18] and other factors like personality of the interpreting individual.”
BackgroundControlling Attention with Noise: The Cue-Combination Model of Visual Search
Baldwin, D., & Mozer, M. C. (2006) · eScholarship (California Digital Library) · Cited by 3
Towards Characterizing the Canonical Computations Generating Phenomenal ExperiencePreprint
Peters, M. A. K. (2021) · doi:10.31234/osf.io/bqfr6 · Cited by 3
Motor Cognition and Decision Theory in Sensorimotor Control
Jelačić, Z. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95194-7_39 · Cited by 2
Bees flexibly adjust decision strategies to information content in a foraging task
Spaethe, J., Hutzenthaler, S., Dietz, A., Gehrig, K., Foster, J. J., & Stöckl, A. (2026) · Science Advances · doi:10.1126/sciadv.adw9320 · Cited by 2
Perception Theory: Perception Representation and Analysis in Cybernetics
Ciftcioglu, Ö., & Bittermann, M. S. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3522360 · Cited by 2
“Consequently, vision is defined as perceptual inference subjected to the estimation of a scene, or scene properties from an image or a sequence of images [26].”
From uncertainty and entropy to coherence and consciousness
Beshkar, M. (2025) · Brain‐X · doi:10.1002/brx2.70027 · Cited by 2
Effects of Haptic Information on the Perception of Dynamic 3-D Movement
Umemura, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106633 · Cited by 2
Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies
Gepperth, A., & Lefort, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11179-7_90 · Cited by 2
“W ith the advent of Bayesian inference accounts of biological information processing [2], not only the question of neural coding received renewed interest, but also the issue how neural populations can implement the required mathematical operations for inference, most notably populationlevel multiplication which seems…”
BackgroundFlowers in the Attic: Lateralization of the detection of meaning in visual noise
Cropper, S. J., McCauley, A., Gwinn, O. S., Bartlett, M. L., & Nicholls, M. E. R. (2020) · Journal of Vision · doi:10.1167/jov.20.10.11 · Cited by 2
“…& Yuille, 2004; Lau, 2008; Reed et al., 2008; Rieth et al., 2011; Schmack et al., 2013), contextual cues or prior visual input (Rensink, 2000; Yuille & Bulthoff, 1996) and on random neural fluctuations in functionally relevant areas of cortex (Hesselmann, Kell, & Kleinschmidt, 2008;…”
BackgroundThe Development of Bayesian Theory and Its Applications in Business and Bioinformatics
Zhang, Y. (2018) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/128/1/012120 · Cited by 2
“In 1996, it was used to explain perceptual bias in psychophysical [9].”
MethodThe Focus–Aspect–Value model for predicting subjective visual attributes
Blandfort, P., Karayil, T., Hees, J., & Dengel, A. R. (2020) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00188-5 · Cited by 3
Bayesian Decision Theory and NavigationPreprint
McNamara, T. P., & Chen, X. (2020) · doi:10.31234/osf.io/zduca · Cited by 2
BTP: automatic identification and prediction of tasks in data center networks
Zou, S., Ji, W. D., & Huang, J. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00312-7 · Cited by 1
“Then we leverage bayesian decision [4] to predict the task category based on the size of the task, which can provide the high accuracy of task-size prediction.”
MethodWestwig, A., Kim, Y., & Bushong, W. (2026) · Attention Perception & Psychophysics · doi:10.3758/s13414-026-03280-1 · Cited by 1
“Additionally, when considering more than one cue, the reliability of each cue relative to other cues in the environment becomes relevant: Yuille and Bülthoff (1996) derive that in ideal observer models, absolute cue weights are normalized by the sum of all cue weights in the environment.”
InfluentialLatency-based probabilistic information processing in a learning feedback hierarchy
Gepperth, A. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889919 · Cited by 1
“W ith the advent of Bayesian inference accounts of biological information processing [2], not only the question”
BackgroundAngelucci, A., Fano, V., Ferretti, G., Macrelli, R., & Tarozzi, G. (2021) · Philosophia Scientae · doi:10.4000/philosophiascientiae.2960 · Cited by 1
“…currently mainstream view among scientists studying perception is that, in normal conditions, our perceptual representations are largely accurate.1 Had this not been the case—it is usually argued—our species would have long been driven to extinction [Geisler & Diehl 2003], [Yuille & Bülthoff 1996].”
BackgroundComputational Modeling of Multisensory Object Perception
Rothkopf, C. A., Weisswange, T. H., & Triesch, J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-5615-6_3 · Cited by 1
“Yuille and Bülthoff (1996) consider the case where the posterior distribution of a scene variable given two cues is proportional to the product of the two posterior distributions given the individual cues, i.e.”
BackgroundAudiovisual Processing and Integration in Amblyopia
Richards, M. (2018) · TSpace (University of Toronto) · Cited by 1
What neuroimaging methods can tell us about perceptual developmentPreprint
Aslin, R. Ν., & Bejjanki, V. R. (2024) · doi:10.31234/osf.io/qabwt · Cited by 1
How relevant is the prior? Bayesian causal inference for dynamic perception in volatile environmentsPreprint
Meijer, D., Barumerli, R., & Baumgartner, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620874 · Cited by 1
Monocular depth estimation in images and sequences using occlusion cues
Visa, G. P. (2014) · doi:10.5821/dissertation-2117-95302 · Cited by 1
Supplement: The uncertainty principle of measurement in visionPreprint
Gepshtein, S., Tyukin, I., & Albright, T. D. (2010) · arXiv (Cornell University)
Visual-haptic integration during tool use
Takahashi, C. (2012) · Bangor University Research Portal (Bangor University)
Primary Visual Cortex Within the Cortico-corticothalamic Network
Scannell, J. W., & Young, M. P. (2002) · Elsevier eBooks · doi:10.1016/b978-012552104-8/50016-x · Cited by 1
Noise Induced Perceptual Completion. 1,2
Schrater, P., Khuu, T., & Kersten, D. (2000)
Explaining Human Causal Learning using a Dynamic Probabilistic Model
Angeles, L. (2010)
Spatial Proximity and Similarity
Kubovy, M. (2014) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199829347.013.7
Modèles probabilistes formels pour problèmes cognitifs usuels
Bessìère, P., Diard, J., & Colas, F. (2016) · Intellectica Revue de l Association pour la Recherche Cognitive · doi:10.3406/intel.2016.1792 · Cited by 1
How neuronal oscillations code for temporal statistics
ten Oever, S. (2016) · doi:10.26481/dis.20160609so
The neural basis of audio-visual integration and adaptation
Mihalik, Á. (2017) · University of Birmingham Institutional Research Archive (University of Birmingham)
Beierholm, U. (2015) · Durham Research Online (Durham University) · doi:10.1007/978-1-4614-6675-8_451 · Cited by 1
The image recognition based on neural network and Bayesian decision
Wang, C. (2018) · AIP conference proceedings · doi:10.1063/1.5033684
Surface curvature from kinetic depth can affect lightness.
Peterson, L. M., Kersten, D., & Mannion, D. J. (2018) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0000575
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in ImagesPreprint
Karayil, T., Blandfort, P., Hees, J., & Dengel, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06219
“y that is detected typically is about which entities are visible, while subjective information is rather about characterizing how these entities or the picture as a whole differ from some expectation [6,7]. To attenuate these issues, previous methods typically consider holistic aspects of subjectivity (e.g. in visual…”
BackgroundMethodThe effect of active hand movement on visually perceived depth direction
Umemura, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245000
Metamers of Bayesian computationPreprint
Sohn, H., & Jazayeri, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.11.246355
Surface curvature from kinetic depth can affect lightnessPreprint
Peterson, L., Kersten, D., & Mannion, D. J. (2018) · doi:10.31234/osf.io/csr4y
Bayesian Decision-Making Under Uncertainty in Borderline Personality DisorderPreprint
Manavalan, M., Song, X., Nolte, T., et al. (2022) · doi:10.31234/osf.io/nsyh6
A Comparison of Visual Cue Combination Models
SHARKEY, A. J. C. (1999) · Perspectives in neural computing · doi:10.1007/978-1-4471-0793-4_8
Bayesian Decision Theory and the Action-Perception Loop
Ferreira, J. F., & Dias, J. M. M. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-02006-8_5
Role of goal-orientated attention and expectations in visual processing and perception
Chalk, M. (2013) · ERA
Panel Summary: Knowledge Model Representations
Andreani-Dentici, O., Ferraro, M., & Gaglio, S. (1997) · Springer eBooks · doi:10.1007/978-1-4615-5965-8_19
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
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
