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Decision theory, reinforcement learning, and the brain
Open accessAbstract
Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.
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“One framework to address this challenge is Bayesian decision theory (Körding, 2007; Dayan and Daw, 2008; Daunizeau et al., 2010). In a nutshell, this identifies an optimal action as one that maximizes the “expected utility” (where “expected” refers to a weighted average; i.e., the predicted outcomes are weighted by their relative uncertainty).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Dayan and Daw, 2008
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
(1995) · Cited by 10,558
(1966) · Medical Entomology and Zoology · Cited by 10,293
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Self-citationSignal detection theory and psychophysics
Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
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Learning to predict or predicting to learn?
Rabagliati, H., Gambi, C., & Pickering, M. J. (2015) · Language Cognition and Neuroscience · doi:10.1080/23273798.2015.1077979 · Cited by 99
Training Visual Imagery: Improvements of Metacognition, but not Imagery Strength
Rademaker, R. L., & Pearson, J. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00224 · Cited by 97
Making Decisions with Unknown Sensory Reliability
Denève, S. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00075 · Cited by 91
Neural Mechanisms and Psychology of Psychedelic Ego Dissolution
Stoliker, D., Egan, G. F., Friston, K. J., & Razi, A. (2022) · Pharmacological Reviews · doi:10.1124/pharmrev.121.000508 · Cited by 96
Neural Mechanisms Underlying Human Consensus Decision-Making
Suzuki, S., Adachi, R., Dunne, S., Bossaerts, P. L., & O’Doherty, J. P. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.019 · Cited by 91
The associative basis of cue-elicited drug taking in humans
Hogarth, L., Dickinson, A., & Duka, T. (2009) · Psychopharmacology · doi:10.1007/s00213-009-1735-9 · Cited by 89
Integration of Reinforcement Learning and Optimal Decision-Making Theories of the Basal Ganglia
Bogacz, R., & Larsen, T. (2011) · Neural Computation · doi:10.1162/neco_a_00103 · Cited by 86
Modulation of value-based decision making behavior by subregions of the rat prefrontal cortex
Verharen, J. P. H., den Ouden, H. E. M., Adan, R. A. H., & Vanderschuren, L. J. M. J. (2020) · Psychopharmacology · doi:10.1007/s00213-020-05454-7 · Cited by 81
Behavioural thermal regulation explains pedestrian path choices in hot urban environments
Melnikov, V. R., Christopoulos, G. I., Krzhizhanovskaya, V. V., Lees, M., & Sloot, P. M. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-06383-5 · Cited by 90
Huys, Q. J. M., Guitart‐Masip, M., Dolan, R. J., & Dayan, P. (2015) · Clinical Psychological Science · doi:10.1177/2167702614562040 · Cited by 85
Self-citationBasic functional trade-offs in cognition: An integrative framework
Giudice, M. D., & Crespi, B. J. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.008 · Cited by 86
Parietal neurons encode expected gains in instrumental information
Foley, N. C., Kelly, S. P., Mhatre, H., Lopes, M. C., & Gottlieb, J. P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613844114 · Cited by 76
Evidence for model-based computations in the human amygdala during Pavlovian conditioning
Prévost, C., McNamee, D., Jessup, R. K., Bossaerts, P., & O'Doherty, J. P. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002918 · Cited by 79
Brain Plasticity in Human Lifespan Development: The Exploration–Selection–Refinement Model
Lindenberger, U., & Lövdén, M. (2019) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-121318-085229 · Cited by 89
Lucantonio, F., Caprioli, D., & Schoenbaum, G. M. (2013) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.05.033 · Cited by 80
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Hertzmann, A. (2010) · International Symposium on Non-Photorealistic Animation and Rendering (NPAR) · doi:10.1145/1809939.1809957 · Cited by 78
More Than the Sum of Its Parts: A Role for the Hippocampus in Configural Reinforcement Learning
Duncan, K. D., Doll, B. B., Daw, N. D., & Shohamy, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.042 · Cited by 83
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Wassum, K. M. (2022) · eLife · doi:10.7554/elife.80926 · Cited by 88
Learning relative values in the striatum induces violations of normative decision making
Klein, T. A., Ullsperger, M., & Jocham, G. (2017) · Nature Communications · doi:10.1038/ncomms16033 · Cited by 79
Dogmatism manifests in lowered information search under uncertainty
Schulz, L., Rollwage, M., Dolan, R. J., & Fleming, S. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2009641117 · Cited by 76
Neural systems analysis of decision making during goal-directed navigation
Penner, M. R., & Mizumori, S. J. Y. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.08.010 · Cited by 80
Sias, A. C., Jafar, Y., Goodpaster, C. M., Ramírez-Armenta, K., Wrenn, T. M., Griffin, N. K., Patel, K., Lamparelli, A. C., Sharpe, M. J., & Wassum, K. M. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01586-7 · Cited by 86
Kheifets, A., & Gallistel, C. R. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1205131109 · Cited by 73
Vicarious Reinforcement Learning Signals When Instructing Others
Apps, M. A. J., Lesage, E., & Ramnani, N. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3669-14.2015 · Cited by 71
Singer, N., Sommer, M., Döhnel, K., Zänkert, S., Wüst, S., & Kudielka, B. M. (2017) · Hormones and Behavior · doi:10.1016/j.yhbeh.2017.05.002 · Cited by 73
Distributional Reinforcement Learning in the Brain
Lowet, A. S., Qiao, Z., Matias, S., Drugowitsch, J., & Uchida, N. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.004 · Cited by 75
Computational models of reinforcement learning: the role of dopamine as a reward signal
Samson, R. D., Frank, M. J., & Fellous, J. (2010) · Cognitive Neurodynamics · doi:10.1007/s11571-010-9109-x · Cited by 75
A new model of decision processing in instrumental learning tasks
Miletić, S., Boag, R. J., Trutti, A. C., Stevenson, N., Forstmann, B. U., & Heathcote, A. (2021) · eLife · doi:10.7554/elife.63055 · Cited by 77
What is value—accumulated reward or evidence?
Friston, K. J., Adams, R. A., & Montague, P. R. (2012) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2012.00011 · Cited by 69
Pedersen, M. L., & Frank, M. J. (2020) · Computational Brain & Behavior · doi:10.1007/s42113-020-00084-w · Cited by 67
Motor-Sensory Confluence in Tactile Perception
Saig, A., Gordon, G., Assa, E., Arieli, A., & Ahissar, E. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2432-12.2012 · Cited by 68
Predicting explorative motor learning using decision-making and motor noise
Chen, X., Mohr, K. S., & Galea, J. M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005503 · Cited by 63
Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology
Stephan, K. E., Binder, E. B., Breakspear, M., Dayan, P., Johnstone, E. C., Meyer-Lindenberg, A., Schnyder, U., Wang, X.-J., Bach, D. R., Fletcher, P. C., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00360-0 · Cited by 64
Self-citationKhalvati, K., Kiani, R., & Rao, R. P. N. (2021) · Nature Communications · doi:10.1038/s41467-021-25419-4 · Cited by 64
Task-evoked pupil responses reflect internal belief states
Colizoli, O., de Gee, J. W., Urai, A. E., & Donner, T. H. (2018) · Scientific Reports · doi:10.1038/s41598-018-31985-3 · Cited by 67
What does the free energy principle tell us about the brain?
Gershman, S. J. (2019) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.10839 · Cited by 63
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Ebitz, R. B., Sleezer, B. J., Jedema, H. P., Bradberry, C. W., & Hayden, B. Y. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007475 · Cited by 58
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Gustafson, N. J., & Daw, N. D. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002235 · Cited by 67
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Trimmer, P. C., Paul, E. S., Mendl, M., McNamara, J. M., & Houston, A. I. (2013) · Behavioral Sciences · doi:10.3390/bs3030501 · Cited by 61
Hierarchical models of pain: Inference, information-seeking, and adaptive control.
Seymour, B., & Mancini, F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117212 · Cited by 59
Mice exhibit stochastic and efficient action switching during probabilistic decision making
Beron, C. C., Neufeld, S. Q., Linderman, S. W., & Sabatini, B. L. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113961119 · Cited by 60
Modeling other minds: Bayesian inference explains human choices in group decision-making
Khalvati, K., Park, S. A., Mirbagheri, S., Philippe, R., Sestito, M., Dreher, J., & Rao, R. P. N. (2019) · Science Advances · doi:10.1126/sciadv.aax8783 · Cited by 61
Costs of Learning and the Evolution of Mimetic Signals
Kikuchi, D. W., & Sherratt, T. N. (2015) · The American Naturalist · doi:10.1086/682371 · Cited by 60
Verharen, J. P. H., Adan, R. A. H., & Vanderschuren, L. J. M. J. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0454-0 · Cited by 57
Model-free decision making is prioritized when learning to avoid harming others
Lockwood, P. L., Klein-Flügge, M. C., Abdurahman, A., & Crockett, M. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2010890117 · Cited by 67
Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
Liu, Y., Dolan, R. J., Higgins, C., Penagos, H., Woolrich, M. W., Ólafsdóttir, H. F., Barry, C., Kurth-Nelson, Z. L., & Behrens, T. E. J. (2021) · eLife · doi:10.7554/elife.66917 · Cited by 65
Emotion prediction errors guide socially adaptive behaviour
Heffner, J., Son, J.-Y., & FeldmanHall, O. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01213-6 · Cited by 67
Lee, C. R., & Margolis, D. J. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00200 · Cited by 59
A State Representation for Reinforcement Learning and Decision-Making in the Orbitofrontal Cortex
Schuck, N. W., Wilson, R., & Niv, Y. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812098-9.00012-7 · Cited by 66
Prefrontal mechanisms combining rewards and beliefs in human decision-making
Rouault, M., Drugowitsch, J., & Koechlin, É. (2019) · Nature Communications · doi:10.1038/s41467-018-08121-w · Cited by 61
Harnessing behavioral diversity to understand neural computations for cognition
Musall, S., Urai, A. E., Sussillo, D., & Churchland, A. K. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.011 · Cited by 59
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Matthews, J., Pisauro, M. A., Jurgelis, M., Müller, T., Vassena, E., Chong, T. T. ‐., & Apps, M. A. J. (2023) · Cognition · doi:10.1016/j.cognition.2023.105603 · Cited by 62
The KIV model of intentional dynamics and decision making
Kozma, R., & Freeman, W. J. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.03.019 · Cited by 54
Morris, S. E., Holroyd, C. B., Mann-Wrobel, M. C., & Gold, J. M. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00123 · Cited by 54
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Stouraitis, T., Chatzinikolaidis, I., Gienger, M., & Vijayakumar, S. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.2992987 · Cited by 51
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Morehead, J. R., & de Xivry, J. O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.14.435278 · Cited by 55
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Lane, R. D., Anderson, F. S., & Smith, R. S. (2018) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000640 · Cited by 51
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Corlett, P. R. (2009) · Frontiers in Behavioral Neuroscience · doi:10.3389/neuro.08.053.2009 · Cited by 50
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Differences in perceptual learning transfer as a function of training task
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Harm to self outweighs benefit to others in moral decision making
Volz, L. J., Welborn, B. L., Gobel, M. S., Gazzaniga, M. S., & Grafton, S. T. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706693114 · Cited by 46
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Nassar, M. R., & Gold, J. I. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003015 · Cited by 41
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Navigating complex decision spaces: Problems and paradigms in sequential choice.
Walsh, M. M., & Anderson, J. R. (2013) · Psychological Bulletin · doi:10.1037/a0033455 · Cited by 41
Spektor, M. S., & Kellen, D. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1446-5 · Cited by 40
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Gershman, S. J., & Daw, N. D. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/9108.003.0015 · Cited by 38
Self-citationSmith, R. S., Alkozei, A., Killgore, W. D. S., & Lane, R. D. (2017) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2017.09.011 · Cited by 39
Reward Optimization in the Primate Brain: A Probabilistic Model of Decision Making under Uncertainty
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Dissolving the self:Active inference, psychedelics, and ego-dissolution
Deane, G. (2020) · Philosophy and the Mind Sciences · doi:10.33735/phimisci.2020.i · Cited by 37
Peivaste, I., Belouettar, S., Mercuri, F., Fantuzzi, N., Dehghani, H., Izadi, R., Ibrahim, H., Lengiewicz, J., Belouettar-Mathis, M., Bendine, K., et al. (2025) · Composite Structures · doi:10.1016/j.compstruct.2025.119419 · Cited by 51
Reward-Modulated Hebbian Learning of Decision Making
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Ousdal, O. T., Huys, Q. J. M., Milde, A. M., Craven, A. R., Ersland, L., Endestad, T., Melinder, A. M. D., Hugdahl, K., & Dolan, R. J. (2017) · Psychological Medicine · doi:10.1017/s003329171700174x · Cited by 36
Zhao, F., Zeng, Y., Wang, G., Bai, J., & Xu, B. (2017) · Cognitive Computation · doi:10.1007/s12559-017-9511-3 · Cited by 37
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Lövdén, M., Garzón, B., & Lindenberger, U. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.11.002 · Cited by 43
Degrees of algorithmic equivalence between the brain and its DNN models
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Reward maximization through discrete active inference
Da Costa, L., Sajid, N., Parr, T., Friston, K., & Smith, R. (2023) · Neural Computation · doi:10.1162/neco_a_01574 · Cited by 35
FitzGerald, T. H. B., Schwartenbeck, P., & Dolan, R. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4389-13.2014 · Cited by 36
The sensory valuation account of aesthetic experience
Nadal, M., & Skov, M. (2024) · Nature Reviews Psychology · doi:10.1038/s44159-024-00385-y · Cited by 42
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Story, G. W., Moutoussis, M., & Dolan, R. J. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01948 · Cited by 35
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Hong, Z. (2022) · Cognitive Science · doi:10.1111/cogs.13088 · Cited by 34
Diederich, A., & Busemeyer, J. R. (2012) · Wiley eBooks · doi:10.1002/9781118133880.hop204024 · Cited by 32
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Sarno, S., de Lafuente, V., Romo, R., & Parga, N. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1712479114 · Cited by 32
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Montague, P. R., & Kishida, K. T. (2018) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2018.83.038166 · Cited by 27
Distinct dynamics of ramping activity in the frontal cortex and caudate nucleus in monkeys
Ding, L. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00395.2015 · Cited by 34
Metadata
- Authors
- Dayan, P., & Daw, N. D.
- Affiliations
- University College London (GB)
- New York University (US)
- Corresponding author
- Dayan, P.
- Daw, N. D.
- ORCID
- Venue
- Cognitive, Affective, & Behavioral Neuroscience
- Published
- 2008-12-01
- Volume
- 8
- Issue
- 4
- Pages
- 429-453
- ISSN
- 1530-7026 (ISSN-L)
- 1531-135X
- OpenAlex
- W2037917209
- Cited by
- 580
- References
- 131
- Citations by year
- 2026: 25 · 2025: 52 · 2024: 46 · 2023: 33 · 2022: 43 · 2021: 34 · 2020: 55 · 2019: 38 · 2018: 26 · 2017: 37 · 2016: 25 · 2015: 27 · 2014: 25 · 2013: 26 · 2012: 37
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cognitive Affective & Behavioral Neuroscience (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- decision-making, Bayesian decision theory, Markov decision process, signal detection theory, sequential sampling, model-based learning, model-free learning, reinforcement learning, computational models of decision making
- MeSH terms
- Algorithms, Animals, Bayes Theorem, Brain, Cognition, Cost-Benefit Analysis, Decision Making, Decision Theory, Exploratory Behavior, Humans, Markov Chains, Models, Psychological, Problem Solving, Reinforcement, Psychology, Models, Statistical, Signal Detection, Psychological, 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 ›
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