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
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
Gershman, S. J., & Uchida, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0220-7 · Cited by 236
Bayesian Computation through Cortical Latent Dynamics
Sohn, H., Narain, D., Meirhaeghe, N., & Jazayeri, M. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.012 · Cited by 235
Self-citationDistinct encoding of decision confidence in human medial prefrontal cortex
Bang, D., & Fleming, S. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800795115 · Cited by 207
Communicating Uncertainty: a Narrative Review and Framework for Future Research
Simpkin, A. L., & Armstrong, K. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-04860-8 · Cited by 203
Normative evidence accumulation in unpredictable environments
Glaze, C. M., Kable, J. W., & Gold, J. I. (2015) · eLife · doi:10.7554/elife.08825 · Cited by 190
A touch with words: Dynamic synergies between manual actions and language
García, A. M., & Ibáñez, A. M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.04.022 · Cited by 157
Comparing Bayesian and non-Bayesian accounts of human confidence reports
Adler, W. T., & Ma, W. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006572 · Cited by 118
Self-citationTime Slices: What Is the Duration of a Percept?
Herzog, M. H., Kammer, T., & Scharnowski, F. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002433 · Cited by 157
A neural basis of probabilistic computation in visual cortex
Walker, E. Y., Cotton, R. J., Ma, W. J., & Tolias, A. S. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0554-5 · Cited by 146
Self-citationHuman Inferences about Sequences: A Minimal Transition Probability Model
Meyniel, F., Maheu, M., & Dehaene, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005260 · Cited by 145
Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making
Aitchison, L., Bang, D., Bahrami, B., & Latham, P. E. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004519 · Cited by 131
Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains
de Gee, J. W., Tsetsos, K., Schwabe, L., Urai, A. E., McCormick, D. A., McGinley, M. J., & Donner, T. H. (2020) · eLife · doi:10.7554/elife.54014 · Cited by 135
Perceptual Inference, Learning, and Attention in a Multisensory World
Noppeney, U. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100120-085519 · Cited by 147
Quantum reinforcement learning during human decision-making
Li, J., Dong, D., Wei, Z., Liu, Y., Pan, Y., Nori, F., & Zhang, X. (2020) · Nature Human Behaviour · doi:10.1038/s41562-019-0804-2 · Cited by 126
Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence
Denison, R. N., Adler, W. T., Carrasco, M., & Ma, W. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717720115 · Cited by 122
Self-citationConfidence modulates exploration and exploitation in value-based learning
Boldt, A., Blundell, C., & Martino, B. D. (2019) · Neuroscience of Consciousness · doi:10.1093/nc/niz004 · Cited by 120
Questioning the role of sparse coding in the brain
Spanne, A., & Jörntell, H. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.05.005 · Cited by 125
An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition
Rasouli, M., Yi, C., Basu, A., Kukreja, S. L., & Thakor, N. V. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2805721 · Cited by 110
Computational principles and models of multisensory integration
Chandrasekaran, C. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.11.002 · Cited by 117
Bayesian Decision Models: A Primer
Ma, W. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.037 · Cited by 111
Self-citationGoing in circles is the way forward: the role of recurrence in visual inference
van Bergen, R. S., & Kriegeskorte, N. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.11.009 · Cited by 95
Relating Pupil Dilation and Metacognitive Confidence during Auditory Decision-Making
Lempert, K. M., Chen, Y. L., & Fleming, S. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126588 · Cited by 94
Steering intermediate courses: desert ants combine information from various navigational routines
Wehner, R., Hoinville, T., Cruse, H., & Cheng, K. (2016) · Journal of Comparative Physiology A · doi:10.1007/s00359-016-1094-z · Cited by 96
Expectancy-based rhythmic entrainment as continuous Bayesian inference
Cannon, J. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009025 · Cited by 100
How Outcome Uncertainty Mediates Attention, Learning, and Decision-Making
Monosov, I. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.009 · Cited by 95
Boldt, A., Schiffer, A.-M., Waszak, F., & Yeung, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-40681-9 · Cited by 86
Fechner’s law in metacognition: A quantitative model of visual working memory confidence.
van den Berg, R., Yoo, A. H., & Ma, W. J. (2017) · Psychological Review · doi:10.1037/rev0000060 · Cited by 83
Self-citationUncertainty-based inference of a common cause for body ownership
Chancel, M., Ehrsson, H. H., & Ma, W. J. (2022) · eLife · doi:10.7554/elife.77221 · Cited by 97
Self-citationThe neurobiology of uncertainty: implications for statistical learning
Hasson, U. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0048 · Cited by 77
Serial dependence revealed in history-dependent perceptual templates
Murai, Y., & Whitney, D. C. (2021) · Current Biology · doi:10.1016/j.cub.2021.05.006 · Cited by 73
Being Realist about Bayes, and the Predictive Processing Theory of Mind
Colombo, M., Elkin, L., & Hartmann, S. (2018) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axy059 · Cited by 74
Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions
Honig, M., Ma, W. J., & Fougnie, D. L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1918143117 · Cited by 63
Self-citationInternal models for interpreting neural population activity during sensorimotor control
Golub, M. D., Yu, B. M., & Chase, S. M. (2015) · eLife · doi:10.7554/elife.10015 · Cited by 61
Studying the neural representations of uncertainty
Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01444-y · Cited by 56
Self-citationDistinct oxytocin effects on belief updating in response to desirable and undesirable feedback
Ma, Y., Li, S., Wang, C., Liu, Y., Li, W., Yan, X., Chen, Q., & Han, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604285113 · Cited by 54
Generalization in Category Learning: The Roles of Representational and Decisional Uncertainty
Seger, C. A., Braunlich, K., Wehe, H. S., & Liu, Z. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0654-15.2015 · Cited by 56
Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty
Kobayashi, K., & Hsu, M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0535-17.2017 · Cited by 52
Representations of uncertainty: where art thou?
Koblinger, Á., Fiser, J., & Lengyel, M. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.03.009 · Cited by 52
The perceptual and social components of metacognition.
Pescetelli, N., Rees, G. E., & Bahrami, B. (2016) · Journal of Experimental Psychology General · doi:10.1037/xge0000180 · Cited by 58
Venture Idea Assessment (VIA): Development of a needed concept, measure, and research agenda
Davidsson, P., Grégoire, D. A., & Lex, M. (2021) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2021.106130 · Cited by 50
Threat Prediction from Schemas as a Source of Bias in Pain Perception
Lim, M., O'Grady, C. B., Cane, D. B., Goyal, A., Lynch, M. E., Beyea, S., & Hashmi, J. A. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2104-19.2019 · Cited by 52
A detailed comparison of optimality and simplicity in perceptual decision making.
Shen, S., & Ma, W. J. (2016) · Psychological Review · doi:10.1037/rev0000028 · Cited by 46
Self-citationA two-phase model of resource allocation in visual working memory.
Ye, C., Hu, Z., Li, H., Ristaniemi, T., Liu, Q., & Liu, T. (2017) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000376 · Cited by 46
Limitations of Proposed Signatures of Bayesian Confidence
Adler, W. T., & Ma, W. J. (2018) · Neural Computation · doi:10.1162/neco_a_01141 · Cited by 44
Self-citationCuriosity: primate neural circuits for novelty and information seeking
Monosov, I. E. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00784-9 · Cited by 44
Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges
Dennison, J. B., Sazhin, D., & Smith, D. V. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1589 · Cited by 50
Gross, S. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0343 · Cited by 38
Neurocomputational mechanisms of confidence in self and others
Bang, D., Moran, R., Daw, N. D., & Fleming, S. M. (2022) · Nature Communications · doi:10.1038/s41467-022-31674-w · Cited by 39
Modeling correlated noise is necessary to decode uncertainty
van Bergen, R. S., & Jehee, J. F. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.015 · Cited by 34
Prefrontal transthalamic uncertainty processing drives flexible switching
Lam, N. H., Mukherjee, A., Wimmer, R. D., Nassar, M. R., Chen, Z. S., & Halassa, M. M. (2024) · Nature · doi:10.1038/s41586-024-08180-8 · Cited by 43
Neural implementations of Bayesian inference
Sohn, H., & Narain, D. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.09.008 · Cited by 37
How Radical Is Predictive Processing?
Orlandi, N., & Lee, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190662813.003.0016 · Cited by 34
Uncertainty in learning, choice, and visual fixation
Stojić, H., Orquin, J. L., Dayan, P., Dolan, R. J., & Speekenbrink, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911348117 · Cited by 34
Task-induced neural covariability as a signature of approximate Bayesian learning and inference
Lange, R. D., & Haefner, R. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009557 · Cited by 45
The suboptimality of perceptual decision making with multiple alternatives
Yeon, J., & Rahnev, D. (2020) · Nature Communications · doi:10.1038/s41467-020-17661-z · Cited by 31
Post-decision wagering after perceptual judgments reveals bi-directional certainty readouts
Moreira, C. M., Rollwage, M., Kaduk, K., Wilke, M., & Kagan, I. (2018) · Cognition · doi:10.1016/j.cognition.2018.02.026 · Cited by 37
Human noise blindness drives suboptimal cognitive inference
Castañón, S. H., Moran, R., Ding, J., Egner, T., Bang, D., & Summerfield, C. (2019) · Nature Communications · doi:10.1038/s41467-019-09330-7 · Cited by 34
An uncertainty-based model of the effects of fixation on choice
Li, Z., & Ma, W. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009190 · Cited by 39
Self-citationMonkeys 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
Self-citationEmergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All
Yu, Z., Guo, S., Deng, F., Yan, Q., Huang, K., Liu, J. K., & Chen, F. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2871144 · Cited by 31
A Computational Perspective of the Role of the Thalamus in Cognition
Dehghani, N., & Wimmer, R. D. (2019) · Neural Computation · doi:10.1162/neco_a_01197 · Cited by 33
Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System
Cazettes, F., Fischer, B. J., & Peña, J. L. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3753-15.2016 · Cited by 29
A neural implementation of Bayesian inference based on predictive coding
Spratling, M. (2016) · Connection Science · doi:10.1080/09540091.2016.1243655 · Cited by 32
Jeong, J., & Chong, S. C. (2020) · Vision Research · doi:10.1016/j.visres.2020.01.002 · Cited by 27
Uncertainty is maintained and used in working memory
Yoo, A. H., Acerbi, L., & Ma, W. J. (2021) · Journal of Vision · doi:10.1167/jov.21.8.13 · Cited by 26
Self-citationGarcía, A. M., Hesse, E., Birba, A., Adolfi, F., Mikulan, E., Caro, M. M., Petroni, A., Bekinschtein, T. A., García, M. D. C., Silva, W., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa178 · Cited by 27
Can the Brain Build Probability Distributions?
Lindskog, M., Nyström, P., & Gredebäck, G. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.596231 · Cited by 25
Inverse Bayesian inference in swarming behaviour of soldier crabs
Gunji, Y., Murakami, H., Tomaru, T., & Basios, V. (2018) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2017.0370 · Cited by 25
Using the past to estimate sensory uncertainty
Beierholm, U., Rohe, T., Ferrari, A., Stegle, O., & Noppeney, U. (2020) · eLife · doi:10.7554/elife.54172 · Cited by 31
Dynamic task-belief is an integral part of decision-making
Xue, C., Kramer, L. E., & Cohen, M. R. (2022) · Neuron · doi:10.1016/j.neuron.2022.05.010 · Cited by 25
Prior information differentially affects discrimination decisions and subjective confidence reports
Constant, M., Pereira, M., Faivre, N., & Filevich, E. (2023) · Nature Communications · doi:10.1038/s41467-023-41112-0 · Cited by 27
Beliefs: A challenge in neuropsychological disorders
Seitz, R. J. (2021) · Journal of Neuropsychology · doi:10.1111/jnp.12249 · Cited by 24
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
Uncertainty information that is irrelevant for report impacts confidence judgments.
Spence, M. L., Mattingley, J. B., & Dux, P. E. (2018) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0000584 · Cited by 23
Fitting predictive coding to the neurophysiological data
Spratling, M. W. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146313 · Cited by 23
Attentional priority is determined by predicted feature distributions.
Witkowski, P. P., & Geng, J. J. (2022) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0001041 · Cited by 22
Striatal Activity and Reward Relativity: Neural Signals Encoding Dynamic Outcome Valuation
Webber, E. S., Mankin, D. E., & Cromwell, H. C. (2016) · eNeuro · doi:10.1523/eneuro.0022-16.2016 · Cited by 23
Hétu, S., Luo, Y., D’Ardenne, K., Lohrenz, T. M., & Montague, P. R. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx097 · Cited by 23
The role of the hippocampus in weighting expectations during inference under uncertainty
Rigoli, F., Michely, J., Friston, K. J., & Dolan, R. J. (2019) · Cortex · doi:10.1016/j.cortex.2019.01.005 · Cited by 20
Su, Y.-S., Chen, J.-T., Tang, Y.-J., Yuan, S.-Y., McCarrey, A. C., & Goh, J. O. S. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.05.019 · Cited by 22
Computational roles of plastic probabilistic synapses
Montero, M. L., Sacramento, J., & Costa, R. P. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.09.002 · Cited by 23
Neural Population Dynamics Underlying Expected Value Computation
Yamada, H., Imaizumi, Y., & Matsumoto, M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1987-20.2020 · Cited by 24
TAFKAP: An improved method for probabilistic decoding of cortical activityPreprint
van Bergen, R. S., & Jehee, J. F. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.04.433946 · Cited by 20
Modelling Bayesian Computation in the Brain: Unification, Explanation, and Constraints
Kaplan, D. M., & Hewitson, C. L. (2020) · Studies in brain and mind · doi:10.1007/978-3-030-54092-0_2 · Cited by 21
Sarno, S., Beirán, M., Falcó-Roget, J., Diaz-deLeon, G., Rossi-Pool, R., Romo, R., & Parga, N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113311119 · Cited by 22
Humans adapt their anticipatory eye movements to the volatility of visual motion properties
Pasturel, C., Montagnini, A., & Perrinet, L. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007438 · Cited by 19
Zobeiri, O. A., Ostrander, B. T., Roat, J., Agrawal, Y., & Cullen, K. E. (2021) · The Journal of Physiology · doi:10.1113/jp281183 · Cited by 20
Day, M. L., & Delgutte, B. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00643.2015 · Cited by 20
The speed prior account: A new theory to explain multiple phenomena regarding dynamic information.
Merz, S., Soballa, P., Spence, C. J., & Frings, C. (2022) · Journal of Experimental Psychology General · doi:10.1037/xge0001212 · Cited by 19
Computational roles of intrinsic synaptic dynamics
Shimizu, G., Yoshida, K., Kasai, H., & Toyoizumi, T. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.06.002 · Cited by 17
Stochastic satisficing account of confidence in uncertain value-based decisions
Hertz, U., Bahrami, B., & Keramati, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195399 · Cited by 22
Ez‐zizi, A., Farrell, S., Leslie, D. S., Malhotra, G., & Ludwig, C. J. H. (2023) · Computational Brain & Behavior · doi:10.1007/s42113-022-00165-y · Cited by 17
Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model
Li, Y., Nakae, K., Ishii, S., & Naoki, H. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005099 · Cited by 20
Freestone, D. M., & Church, R. M. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.02.031 · Cited by 18
A Biased Bayesian Inference for Decision-Making and Cognitive Control
Matsumori, K., Koike, Y., & Matsumoto, K. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00734 · Cited by 19
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
