The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.
Neural Coding of Uncertainty and Probability
Open accessAbstract
Organisms must act in the face of sensory, motor, and reward uncertainty stemming from a pandemonium of stochasticity and missing information. In many tasks, organisms can make better decisions if they have at their disposal a representation of the uncertainty associated with task-relevant variables. We formalize this problem using Bayesian decision theory and review recent behavioral and neural evidence that the brain may use knowledge of uncertainty, confidence, and probability.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks [16–18].”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [17]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
(1966) · Medical Entomology and Zoology · Cited by 10,293
Signal detection theory and psychophysics
Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
A framework for studying the neurobiology of value-based decision making
Rangel, A., Camerer, C., & Montague, P. R. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2357 · Cited by 2,184
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790
Neural correlates of decision variables in parietal cortex
Platt, M. L., & Glimcher, P. W. (1999) · Nature · doi:10.1038/22268 · Cited by 1,721
Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689
Hierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · cited 1×
“Explicit probability codes compose a third class of neural codes for uncertainty (Barlow & Levick 1969, Anderson 1994, Anastasio et al. 2000, Barber et al. 2003, Lee & Mumford 2003, Rao 2004, Deneve 2008).”
BackgroundBayesian inference with probabilistic population codes
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457
Self-citationMultiple reward signals in the brain
Schultz, W. (2000) · Nature Reviews Neuroscience · doi:10.1038/35044563 · Cited by 1,303
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
Causal Inference in Multisensory Perception
Körding, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000943 · Cited by 1,069
Self-citationMatching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030
Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex
Kiani, R., & Shadlen, M. N. (2009) · Science · doi:10.1126/science.1169405 · Cited by 1,027
Generalization, similarity, and Bayesian inference
Tenenbaum, J. B., & Griffiths, T. L. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000061 · Cited by 878
Choice, uncertainty and value in prefrontal and cingulate cortex
Rushworth, M. F. S., & Behrens, T. E. J. (2008) · Nature Neuroscience · doi:10.1038/nn2066 · Cited by 866
Optimal eye movement strategies in visual search
Najemnik, J., & Geisler, W. S. (2005) · Nature · doi:10.1038/nature03390 · Cited by 841
The statistical reliability of signals in single neurons in cat and monkey visual cortex
Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983) · Vision Research · doi:10.1016/0042-6989(83)90200-6 · Cited by 829
Temporal context calibrates interval timing
Jazayeri, M., & Shadlen, M. N. (2010) · Nature Neuroscience · doi:10.1038/nn.2590 · Cited by 827
Self-citationNoise 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
Neural correlates, computation and behavioural impact of decision confidence
Kepecs, A., Uchida, N., Zariwala, H. A., & Mainen, Z. F. (2008) · Nature · doi:10.1038/nature07200 · Cited by 809
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
(2009) · The MIT Press eBooks · doi:10.7551/mitpress/8029.001.0001 · Cited by 753
The neural basis of metacognitive ability
Fleming, S. M., & Dolan, R. J. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2011.0417 · Cited by 747 · cited 1×
“Confidence generally correlates with task performance, and the strength of this correlation—measuring the accuracy of one's knowledge of the quality of one's decisions—is itself typically correlated with, although sometimes dissociable from, performance (Fleming & Dolan 2012).”
ResultNeural differentiation of expected reward and risk in human subcortical structures
Preuschoff, K., Bossaerts, P., & Quartz, S. R. (2006) · Neuron · doi:10.1016/j.neuron.2006.06.024 · Cited by 692
Simulation as an engine of physical scene understanding
Battaglia, P. W., Hamrick, J. B., & Tenenbaum, J. B. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1306572110 · Cited by 681
Prefrontal cortex and decision making in a mixed-strategy game
Barraclough, D. J., Conroy, M. L., & Lee, D. (2004) · Nature Neuroscience · doi:10.1038/nn1209 · Cited by 678
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
Decision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671
Neuroeconomics: Decision Making and the Brain
Rushworth, M. F. S., & Walton, M. E. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.005 · Cited by 669
The discovery of structural form
Kemp, C., & Tenenbaum, J. B. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802631105 · Cited by 666
Probabilistic population codes for Bayesian decision making
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645
Self-citationProbabilistic brains: knowns and unknowns
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3495 · Cited by 615 · cited 2×
“A higher-order form of inference that has barely been studied at the neural level is structure learning (Tenenbaum & Griffiths 2001, Kemp & Tenenbaum 2008, Braun et al. 2010, Pouget et al. 2013), the process of learning generalizable rules for categorizing stimuli or performing actions.”
BackgroundSelf-citationCardinal 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
A representation of the hazard rate of elapsed time in macaque area LIP
Janssen, P., & Shadlen, M. N. (2005) · Nature Neuroscience · doi:10.1038/nn1386 · Cited by 601
Probabilistic reasoning by neurons
Yang, T., & Shadlen, M. N. (2007) · Nature · doi:10.1038/nature05852 · Cited by 597
Responses of neurons in macaque MT to stochastic motion signals
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1993) · Visual Neuroscience · doi:10.1017/s0952523800010269 · Cited by 594
Neural Basis of Reinforcement Learning and Decision Making
Lee, D., Seo, H., & Jung, M. W. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150512 · Cited by 581
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
Self-citationComputational Intelligence: Imitating Life
Marks, R. J., Żurada, J. M., & Robinson, C. J. (1994) · IEEE Press eBooks · Cited by 537
Confidence in value-based choice
De Martino, B., Fleming, S. M., Garrett, N., & Dolan, R. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3279 · Cited by 534
Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area
Law, C.-T., & Gold, J. I. (2008) · Nature Neuroscience · doi:10.1038/nn2070 · Cited by 516
Combining Sensory Information: Mandatory Fusion Within, but Not Between, Senses
Hillis, J. M., Ernst, M. O., Banks, M. S., & Landy, M. S. (2002) · Science · doi:10.1126/science.1075396 · Cited by 486
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
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
Dorris, M. C., & Glimcher, P. W. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.009 · Cited by 440
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
Value Representations in the Primate Striatum during Matching Behavior
Lau, B., & Glimcher, P. W. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.021 · Cited by 419
Modulation of neuronal activity by target uncertainty
Basso, M. A., & Wurtz, R. H. (1997) · Nature · doi:10.1038/37975 · Cited by 345
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
Self-citationDecision making, movement planning and statistical decision theory
Trommershäuser, J., Maloney, L. T., & Landy, M. S. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.04.010 · Cited by 314
Bayesian Spiking Neurons I: Inference
Deneve, S. (2008) · Neural Computation · doi:10.1162/neco.2008.20.1.91 · Cited by 313
Attentional modulation in visual cortex depends on task timing
Ghose, G. M., & Maunsell, J. H. R. (2002) · Nature · doi:10.1038/nature01057 · Cited by 301
Optimal Encoding of Interval Timing in Expert PercussionistsPreprint
Burr, D., Chicchini, G., Arrighi, R., Giusti, M., & Cecchetti, L. (2011) · Nature Precedings · doi:10.1523/jneurosci.3411-11.2012 · Cited by 276
Trommershäuser, J., Kording, K. P., & Landy, M. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.001.0001 · Cited by 256
Decoding the activity of neuronal populations in macaque primary visual cortex
Graf, A. B. A., Kohn, A., Jazayeri, M., & Movshon, J. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2733 · Cited by 254
Self-citationBayesian Computation in Recurrent Neural Circuits
Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252
Three factors limiting the reliable detection of light by retinal ganglion cells of the cat
Barlow, H. B., & Levick, W. R. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008679 · Cited by 247
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
Attentional limits on the perception and memory of visual information.
Palmer, J. C. (1990) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.16.2.332 · Cited by 233
The variability of discharge of simple cells in the cat striate cortex
Dean, A. F. (1981) · Experimental Brain Research · doi:10.1007/bf00238837 · Cited by 231
Responses of pulvinar neurons reflect a subject's confidence in visual categorization
Komura, Y., Nikkuni, A., Hirashima, N., Uetake, T., & Miyamoto, A. (2013) · Nature Neuroscience · doi:10.1038/nn.3393 · Cited by 226
Probability density estimation for the interpretation of neural population codes
Sanger, T. D. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.4.2790 · Cited by 216
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
Braun, D. A., Mehring, C., & Wolpert, D. M. (2010) · Behavioural Brain Research · doi:10.1016/j.bbr.2009.08.031 · Cited by 210
Multisensory Integration in Macaque Visual Cortex Depends on Cue Reliability
Morgan, M. L., DeAngelis, G. C., & Angelaki, D. E. (2008) · Neuron · doi:10.1016/j.neuron.2008.06.024 · Cited by 208
Ma, W. J., Zhou, X., Ross, L. A., Foxe, J. J., & Parra, L. C. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004638 · Cited by 200
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
Bayesian decision theory as a model of human visual perception: Testing Bayesian transfer
MALONEY, L. T., & MAMASSIAN, P. (2009) · Visual Neuroscience · doi:10.1017/s0952523808080905 · Cited by 180
Differential Representations of Prior and Likelihood Uncertainty in the Human Brain
Vilares, I., Howard, J. D., Fernandes, H. L., Gottfried, J. A., & Kording, K. P. (2012) · Current Biology · doi:10.1016/j.cub.2012.07.010 · Cited by 176
Organizing probabilistic models of perception
Ma, W. J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.010 · Cited by 169
Self-citationExemplar models as a mechanism for performing Bayesian inference
Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010) · Psychonomic Bulletin & Review · doi:10.3758/pbr.17.4.443 · Cited by 168
Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing
Acerbi, L., Wolpert, D. M., & Vijayakumar, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002771 · Cited by 168
Bridging Levels of Analysis for Probabilistic Models of Cognition
Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012) · Current Directions in Psychological Science · doi:10.1177/0963721412447619 · Cited by 159
Using Bayes' Rule to Model Multisensory Enhancement in the Superior Colliculus
Anastasio, T. J., Patton, P. E., & Belkacem-Boussaid, K. (2000) · Neural Computation · doi:10.1162/089976600300015547 · Cited by 151
New insights on the subcortical representation of reward
Hikosaka, O., Bromberg-Martin, E. S., Hong, S., & Matsumoto, M. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.07.002 · Cited by 149
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
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior
Hoyer, P. O., & Hyvärinen, A. (2002) · 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
Marginalization in Neural Circuits with Divisive Normalization
Beck, J. M., Latham, P. E., & Pouget, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1706-11.2011 · Cited by 135
Testing Bayesian Models of Human Coincidence Timing
Miyazaki, M., Nozaki, D., & Nakajima, Y. (2005) · Journal of Neurophysiology · doi:10.1152/jn.01168.2004 · Cited by 130
Neuronal Correlates of Metacognition in Primate Frontal Cortex
Middlebrooks, P. G., & Sommer, M. A. (2012) · Neuron · doi:10.1016/j.neuron.2012.05.028 · Cited by 124
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
Humans Have an Expectation That Gaze Is Directed Toward Them
Mareschal, I., Calder, A. J., & Clifford, C. W. G. (2013) · Current Biology · doi:10.1016/j.cub.2013.03.030 · Cited by 118
Behavior and neural basis of near-optimal visual search
Ma, W. J., Navalpakkam, V., Beck, J. M., van den Berg, R., & Pouget, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2814 · Cited by 112
Self-citationBattaglia, P., & Schrater, P. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1309-07.2007 · Cited by 110
A Fast and Simple Population Code for Orientation in Primate V1
Berens, P., Ecker, A. S., Cotton, R. J., Ma, W. J., Bethge, M., & Tolias, A. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1335-12.2012 · Cited by 109
Self-citationCan Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards?
Feng, S. F., Holmes, P. J., Rorie, A. E., & Newsome, W. T. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000284 · Cited by 108
Spiking networks for Bayesian inference and choice
Ma, W. J., Beck, J. M., & Pouget, A. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.07.004 · Cited by 103
Self-citationCausal inference of asynchronous audiovisual speech
Magnotti, J. F., Ma, W. J., & Beauchamp, M. S. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00798 · Cited by 103
Self-citationSupplementary Eye Field Encodes Option and Action Value for Saccades With Variable Reward
So, N., & Stuphorn, V. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00430.2010 · Cited by 96
Evaluation of Objective Uncertainty in the Visual System
Barthelmé, S., & Mamassian, P. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000504 · Cited by 92
Movement Planning With Probabilistic Target Information
Hudson, T. E., Maloney, L. T., & Landy, M. S. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00858.2007 · Cited by 90
Evolution of the cerebellum as a neuronal machine for Bayesian state estimation
Paulin, M. G. (2005) · Journal of Neural Engineering · doi:10.1088/1741-2560/2/3/s06 · Cited by 87
The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses
Földiák, P. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3254-5_9 · Cited by 83
Implicit encoding of prior probabilities in optimal neural populations.
Ganguli, D., & Simoncelli, E. P. (2010) · PubMed · Cited by 81
Least Squares Estimation Without Priors or Supervision
Raphan, M., & Simoncelli, E. P. (2010) · Neural Computation · doi:10.1162/neco_a_00076 · Cited by 70
Observers Exploit Stochastic Models of Sensory Change to Help Judge the Passage of Time
Ahrens, M. B., & Sahani, M. (2011) · Current Biology · doi:10.1016/j.cub.2010.12.043 · Cited by 68
Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization
Qamar, A. T., Cotton, R. J., George, R. G., Beck, J. M., Prezhdo, E., Laudano, A., Tolias, A. S., & Ma, W. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219756110 · Cited by 64
Self-citationProbabilistic Computation in Human Perception under Variability in Encoding Precision
Keshvari, S., van den Berg, R., & Ma, W. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040216 · Cited by 62
Self-citationCue Integration in Categorical Tasks: Insights from Audio-Visual Speech Perception
Bejjanki, V. R., Clayards, M. A., Knill, D. C., & Aslin, R. Ν. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019812 · Cited by 61
Hausmann, L., von Campenhausen, M., Endler, F., Singheiser, M., & Wagner, H. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007721 · Cited by 58
Attention in a Bayesian Framework
Whiteley, L., & Sahani, M. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00100 · Cited by 56
Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
O’Reilly, J. X., Jbabdi, S., Rushworth, M. F. S., & Behrens, T. E. J. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001662 · Cited by 49
Neural Representation of Probabilistic Information
Barber, M. J., Clark, J. W., & Anderson, C. H. (2003) · Neural Computation · doi:10.1162/08997660360675062 · Cited by 47
Optimal Estimation in Sensory Systems
Simoncelli, E. P. (2009) · The MIT Press eBooks · doi:10.7551/mitpress/8029.003.0046 · Cited by 45
The Adaptive Nature of Visual Working Memory
Orhan, A. E., Sims, C. R., Jacobs, R. A., & Knill, D. C. (2014) · Current Directions in Psychological Science · doi:10.1177/0963721414529144 · Cited by 45
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
Self-citationThe Irrationality of Categorical Perception
Fleming, S. M., Maloney, L. T., & Daw, N. D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1263-13.2013 · Cited by 36
Dynamic Estimation of Task-Relevant Variance in Movement under Risk
Landy, M. S., Trommershäuser, J., & Daw, N. D. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.6160-11.2012 · Cited by 35
Multisensory Oddity Detection as Bayesian Inference
Hospedales, T. M., & Vijayakumar, S. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004205 · Cited by 33
Learning Stochastic Reward Distributions in a Speeded Pointing Task
Seydell, A., McCann, B. C., Trommershäuser, J., & Knill, D. C. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0647-08.2008 · Cited by 31
Inferring Visuomotor Priors for Sensorimotor Learning
Turnham, E. J. A., Braun, D. A., & Wolpert, D. M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001112 · Cited by 29
Mechanisms underlying the influence of saliency on value-based decisions
Chen, X., Mihalaş, Ş., Niebur, E., & Stuphorn, V. (2013) · Journal of Vision · doi:10.1167/13.12.18 · Cited by 28
Independence is elusive: Set size effects on encoding precision in visual search
Mazyar, H., van den Berg, R., Seilheimer, R. L., & Ma, W. J. (2013) · Journal of Vision · doi:10.1167/13.5.8 · Cited by 27
Self-citationProbabilistic sensory recoding
Jazayeri, M. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.09.004 · Cited by 24
Self-citationHumans make efficient use of natural image statistics when performing spatial interpolation
D'Antona, A. D., Perry, J. S., & Geisler, W. S. (2013) · Journal of Vision · doi:10.1167/13.14.11 · Cited by 14
Integrative Processing of Perception and Reward in an Auditory Localization Paradigm
Maiworm, M., Peter König, B., & Röder, B. (2010) · Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · doi:10.1027/1618-3169/a000088 · Cited by 5
Research works printed but not linked: 1
- Peirce CS, 1884, Mem. Natl. Acad. Sci., 3, 73
Cited by
Sorted by citations
Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation
Fleming, S. M., & Daw, N. D. (2017) · Psychological Review · doi:10.1037/rev0000045 · Cited by 584
Confidence and certainty: Distinct probabilistic quantities for different goals
Pouget, A., Drugowitsch, J., & Kepecs, A. (2016) · Nature Neuroscience · doi:10.1038/nn.4240 · Cited by 528
Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias
Urai, A. E., Braun, A., & Donner, T. H. (2017) · Nature Communications · doi:10.1038/ncomms14637 · Cited by 466
Confidence as Bayesian probability: From neural origins to behavior
Meyniel, F., Sigman, M., & Mainen, Z. F. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.039 · Cited by 349
Perceptual Decision Making in Rodents, Monkeys, and Humans
Hanks, T. D., & Summerfield, C. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.003 · Cited by 409
Metadata
- Authors
- Ma, W. J., & Jazayeri, M.
- Affiliations
- New York University (US)
- McGovern Institute for Brain Research (US)
- Corresponding author
- Ma, Wei Ji
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2014-07-08
- Volume
- 37
- Issue
- 1
- Pages
- 205-220
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2157745749
- Cited by
- 290
- References
- 128
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 24 · 2023: 23 · 2022: 24 · 2021: 33 · 2020: 36 · 2019: 22 · 2018: 29 · 2017: 21 · 2016: 25 · 2015: 9 · 2014: 1
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- probability representation, Bayesian decision theory, confidence, decision making under uncertainty, sensory uncertainty, reward uncertainty, Bayesian brain
- MeSH terms
- Animals, Bayes Theorem, Brain, Decision Making, Humans, Models, Neurological, Neurons, Probability, Uncertainty
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
