Inferring on the intentions of others by hierarchical Bayesian learning
Open accessAbstract
Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to "player" or "adviser" roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.
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
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 HGF explains learning and decision-making under volatility better than other commonly used models.
“This formulation has found successful application in several recent studies with healthy participants, showing that the HGF explains learning and decision-making under volatility better than other commonly used models (111–113).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [113]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195 · see citing works
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558 · see citing works
Measuring individual differences in empathy: Evidence for a multidimensional approach.
Davis, M. H. (1983) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.44.1.113 · Cited by 10,272 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 1× · see citing works
“This model is related to the ‘‘Bayesian brain’’ hypothesis [26–30], which postulates that evolutionary selection should have resulted in neural and cognitive processing principles that approximate a statistical optimum.”
“Risk, Ambiguity, and the Savage Axioms.”
Ellsberg, D. (1961) · The Quarterly Journal of Economics · doi:10.2307/1884324 · Cited by 7,891 · see citing works
Rescorla, R. A. (1972) · Medical Entomology and Zoology · Cited by 7,268 · see citing works
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,535 · see citing works
A psychobiological model of temperament and character
Cloninger, C. R. (1993) · Archives of General Psychiatry · doi:10.1001/archpsyc.1993.01820240059008 · Cited by 5,458 · cited 1× · see citing works
“This included the Temperament and Character Inventory (TCI-K) [20] to measure personality traits and the Interpersonal Reactivity Index (IRI) [21] to measure empathy, perspectivetaking, and theory of mind traits.”
MethodA new approach to variable metric algorithms
Fletcher, R. (1970) · The Computer Journal · doi:10.1093/comjnl/13.3.317 · Cited by 4,146 · see citing works
Meeting of minds: the medial frontal cortex and social cognition
Amodio, D. M., & Frith, C. D. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1884 · Cited by 3,782 · see citing works
Conditioning of quasi-Newton methods for function minimization
Shanno, D. F. (1970) · Mathematics of Computation · doi:10.1090/s0025-5718-1970-0274029-x · Cited by 3,752 · see citing works
A family of variable-metric methods derived by variational means
Goldfarb, D. (1970) · Mathematics of Computation · doi:10.1090/s0025-5718-1970-0258249-6 · Cited by 3,223 · see citing works
The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
Broyden, C. G. (1970) · IMA Journal of Applied Mathematics · doi:10.1093/imamat/6.1.76 · Cited by 2,934 · see citing works
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 · see citing works
The Neural Basis of Mentalizing
Frith, C. D., & Frith, U. (2006) · Neuron · doi:10.1016/j.neuron.2006.05.001 · Cited by 1,855 · see citing works
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815 · see citing works
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423 · see citing works
Self-citationGetting to Know You: Reputation and Trust in a Two-Person Economic Exchange
King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., & Montague, P. R. (2005) · Science · doi:10.1126/science.1108062 · Cited by 1,345 · see citing works
Understanding others' actions and goals by mirror and mentalizing systems: a meta-analysis
Van Overwalle, F., & Baetens, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.009 · Cited by 1,329 · see citing works
Computational roles for dopamine in behavioural control
Montague, P. R., Hyman, S. E., & Cohen, J. D. (2004) · Nature · doi:10.1038/nature03015 · Cited by 966 · see citing works
Associative learning of social value
Behrens, T. E. J., Hunt, L. T., Woolrich, M. W., & Rushworth, M. F. S. (2008) · Nature · doi:10.1038/nature07538 · Cited by 925 · see citing works
PERSECUTORY DELUSIONS: A REVIEW AND THEORETICAL INTEGRATION
Bentall, R. P., Corcoran, R., Howard, R. J., Blackwood, N., & Kinderman, P. (2001) · Clinical Psychology Review · doi:10.1016/s0272-7358(01)00106-4 · Cited by 876 · see citing works
Bayesian brain : probabilistic approaches to neural coding
賢治, 銅., 信, 石., Pouget, A., & Rao, R. P. N. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262042383.001.0001 · Cited by 823 · see citing works
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809 · see citing works
Comparing families of dynamic causal models
Penny, W. D., Stephan, K. E., Daunizeau, J., Rosa, M. J., Friston, K. J., Schofield, T. M., & Leff, A. P. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000709 · Cited by 688 · see citing works
Self-citationA Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655 · see citing works
Self-citationThe Rupture and Repair of Cooperation in Borderline Personality Disorder
King‐Casas, B., Sharp, C., Lomax-Bream, L., Lohrenz, T. M., Fonagy, P., & Montague, P. R. (2008) · Science · doi:10.1126/science.1156902 · Cited by 603 · see citing works
Trust and gender: An examination of behavior and beliefs in the Investment Game
Buchan, N. R., Croson, R. T. A., & Solnick, S. J. (2008) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2007.10.006 · Cited by 559 · see citing works
Integrating simulation and theory of mind: from self to social cognition
Keysers, C., & Gazzola, V. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.02.002 · Cited by 530 · see citing works
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.006 · Cited by 514 · see citing works
Strategies for revising judgment: How (and how well) people use others’ opinions.
Soll, J. B., & Larrick, R. P. (2009) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0015145 · Cited by 420 · see citing works
The Computation of Social Behavior
Behrens, T. E. J., Hunt, L. T., & Rushworth, M. F. S. (2009) · Science · doi:10.1126/science.1169694 · Cited by 407 · see citing works
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342 · see citing works
Self-citationCortical Microcircuit Dynamics Mediating Binocular Rivalry: The Role of Adaptation in Inhibition
Theodoni, P., Panagiotaropoulos, T. I., Kapoor, V., Logothetis, N. K., & Deco, G. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00145 · Cited by 338 · see citing works
Yoshida, W., Dolan, R. J., & Friston, K. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000254 · Cited by 333 · see citing works
Decision theory: what “should” the nervous system do?
Körding, K. (2007) · Science · doi:10.1126/science.1142998 · Cited by 313 · see citing works
The intentional network: How the brain reads varieties of intentions
Ciaramidaro, A., Adenzato, M., Enrici, I., Erk, S., Pia, L., Bara, B. G., & Walter, H. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.05.011 · Cited by 277 · see citing works
Computational approaches to psychiatry
Stephan, K. E., & Mathys, C. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.12.007 · Cited by 260 · see citing works
Self-citationNeural Mechanisms of Belief Inference during Cooperative Games
Yoshida, W., Seymour, B., Friston, K. J., & Dolan, R. J. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5895-09.2010 · Cited by 212 · see citing works
Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution
Baker, C. L., Saxe, R. R., & Tenenbaum, J. B. (2011) · Cited by 199 · see citing works
Known, Unknown, and Unknowable Uncertainties
Chow, C. C., & Sarin, R. K. (2002) · Theory and Decision · doi:10.1023/a:1015544715608 · Cited by 169 · see citing works
Observing the observer (I): meta-bayesian models of learning and decision-making
Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015554 · Cited by 167 · see citing works
Self-citationThe Neural Basis of Following Advice
Biele, G., Rieskamp, J., Krugel, L. K., & Heekeren, H. R. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001089 · Cited by 157 · see citing works
Help or Hinder: Bayesian Models of Social Goal Inference
Ullman, T. D., Baker, C. L., Macindoe, O., Evans, O., Goodman, N. D., & Tenenbaum, J. B. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 156 · see citing works
Spatial attention, precision, and Bayesian inference: a study of saccadic response speed
Vossel, S., Mathys, C., Daunizeau, J., Bauer, M., Driver, J., Friston, K. J., & Stephan, K. E. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs418 · Cited by 146 · see citing works
Self-citationTo trust or not to trust: the dynamics of social interaction in psychosis
Fett, A.-K. J., Shergill, S. S., Joyce, D. W., Riedl, A. M., Strobel, M., Gromann, P. M., & A, L. K. M. (2012) · Brain · doi:10.1093/brain/awr359 · Cited by 136 · see citing works
Adding prediction risk to the theory of reward learning
PREUSCHOFF, K., & BOSSAERTS, P. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1390.005 · Cited by 133 · cited 1× · see citing works
“As mentioned above, the form of these update equations is similar to those of RW learning, providing a Bayesian perspective on reinforcement learning theory [32].”
ResultSimple Heuristics in a Social World
Hertwig, R., Hoffrage, U., & the, A. R. G. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388435.001.0001 · Cited by 129 · see citing works
Ambiguity attitudes and social interactions: An experimental investigation
Charness, G., Karni, E., & Levin, D. (2013) · Journal of Risk and Uncertainty · doi:10.1007/s11166-012-9157-1 · Cited by 113 · see citing works
Langdon, R. A., Corner, T., McLaren, J., Ward, P. B., & Coltheart, M. (2005) · Behaviour Research and Therapy · doi:10.1016/j.brat.2005.03.012 · Cited by 107 · see citing works
Cooperation and Heterogeneity of the Autistic Mind
Yoshida, W., Dziobek, I., Kliemann, D., Heekeren, H. R., Friston, K. J., & Dolan, R. J. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0400-10.2010 · Cited by 104 · see citing works
Theory of Mind: Did Evolution Fool Us?
Devaine, M., Hollard, G., & Jean Daunizeau, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087619 · Cited by 98 · see citing works
Xiang, T., Ray, D., Lohrenz, T. M., Dayan, P., & Montague, P. R. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002841 · Cited by 78 · see citing works
The Neural Bases of Social Intention Understanding: The Role of Interaction Goals
Canessa, N., Alemanno, F., Riva, F., Zani, A., Proverbio, A. M., Mannara, N., Perani, D., & Cappa, S. F. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042347 · Cited by 69 · see citing works
Talking nets: A multiagent connectionist approach to communication and trust between individuals.
Overwalle, F. V., & Heylighen, F. (2006) · Psychological Review · doi:10.1037/0033-295x.113.3.606 · Cited by 64 · see citing works
Computational psychiatry (vol 16, pg 72, 2012)
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · UCL Discovery (University College London) · Cited by 64 · see citing works
Observing the Observer (II): Deciding When to Decide
Jean Daunizeau, A., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Friston, K. J., & Stephan, K. E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015555 · Cited by 58 · see citing works
Self-citationCause and Intent: Social Reasoning in Causal Learning
Baker, C., Goodman, N. D., & Tenenbaum, J. B. (2009) · eScholarship (California Digital Library) · Cited by 51 · see citing works
Decision Making and Confidence Given Uncertain Advice
Lee, M. D., & Dry, M. J. (2006) · Cognitive Science · doi:10.1207/s15516709cog0000_71 · Cited by 48 · see citing works
Integrating advice and experience: Learning and decision making with social and nonsocial cues.
Collins, E. C., Percy, E. J., Smith, E. R., & Kruschke, J. K. (2011) · Journal of Personality and Social Psychology · doi:10.1037/a0022982 · Cited by 42 · see citing works
Imaging Models of Valuation During Social Interaction in Humans
Kishida, K. T., & Montague, P. R. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.02.037 · Cited by 40 · see citing works
Mind Reading by Machine Learning: A Doubly Bayesian Method for Inferring Mental Representations
Huszár, F., Noppeney, U., & Lengyel, M. (2010) · eScholarship (California Digital Library) · Cited by 15 · see citing works
Lee, M. D. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000343 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [37]Osborne MA, Garnett R, Roberts SJ (2009) Gaussian processes for global optimization. 3rd international conference on learning and intelligent optimization (LION3). pp. 1–15.
Cited by
Sorted by citations
Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions
Hasson, U., & Frith, C. D. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2015.0366 · Cited by 371 · see citing works
Focus of attention modulates the heartbeat evoked potential
Petzschner, F. H., Weber, L. A., Wellstein, K. V., Paolini, G., Do, C. T., & Stephan, K. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.037 · Cited by 248 · see citing works
Self-citationPetzschner, F. H., Weber, L. A. E., Gard, T., & Stephan, K. E. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.05.012 · Cited by 181 · see citing works
Self-citationPharmacological Fingerprints of Contextual Uncertainty
Marshall, L., Mathys, C., Ruge, D., de Berker, A. O., Dayan, P., Stephan, K. E., & Bestmann, S. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002575 · Cited by 158 · see citing works
Self-citationBrain networks for confidence weighting and hierarchical inference during probabilistic learning
Meyniel, F., & Dehaene, S. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615773114 · Cited by 138 · see citing works
Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
Haker, H., Schneebeli, M., & Stephan, K. E. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00107 · Cited by 130 · see citing works
Self-citationDopamine, reward learning, and active inference
FitzGerald, T. H. B., Dolan, R. J., & Friston, K. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00136 · Cited by 112 · see citing works
Palmer, C. J., Seth, A. K., & Hohwy, J. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2015.04.007 · Cited by 101 · see citing works
Disrupted prediction errors index social deficits in autism spectrum disorder
Balsters, J. H., Apps, M. A. J., Bolis, D., Lehner, R., Gallagher, L., & Wenderoth, N. (2017) · Brain · doi:10.1093/brain/aww287 · Cited by 85 · see citing works
Vinckier, F., Gaillard, R., Palminteri, S., Rigoux, L., Salvador, A., Fornito, A., Adapa, R., Krebs, M. O., Pessiglione, M., & Fletcher, P. C. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.73 · Cited by 87 · see citing works
Reciprocity and alignment: quantifying coupling in dynamic interactions
Dumas, G., & Fairhurst, M. T. (2021) · Royal Society Open Science · doi:10.1098/rsos.210138 · Cited by 88 · see citing works
Attractor-like dynamics in belief updating in schizophrenia
Adams, R. A., Napier, G., Roiser, J. P., Mathys, C., & Gilleen, J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.3163-17.2018 · Cited by 77 · see citing works
Self-citationThe empirical status of predictive coding and active inference
Hodson, R., Mehta, M., & Smith, R. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105473 · Cited by 69 · see citing works
Unifying treatments for depression: an application of the free energy principle
Chekroud, A. M. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00153 · Cited by 55 · see citing works
Nine quick tips for efficient bioinformatics curriculum development and training
McClatchy, S., Bass, K. M., Gatti, D. M., Moylan, A., & Churchill, G. (2020) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1008007 · Cited by 8 · see citing works
- Authors
- Diaconescu, A. O., Mathys, C., Weber, L. A. E., Daunizeau, J., Kasper, L., Lomakina, E. I., Fehr, E., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Diaconescu, Andreea O.
- ORCID
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-09-04
- Volume
- 10
- Issue
- 9
- Pages
- e1003810
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4154656
- OpenAlex
- W2076243793
- Cited by
- 219
- References
- 63
- Citations by year
- 2026: 26 · 2025: 19 · 2024: 14 · 2023: 15 · 2022: 15 · 2021: 22 · 2020: 18 · 2019: 26 · 2018: 24 · 2017: 8 · 2016: 16 · 2015: 13 · 2014: 3
- FWCI
- 31.29
- Type
- journal article
- Language
- en
- Update notices
- correction (2014-10-16)
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- SDL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (091593)
- University College London
- Max-Planck-Gesellschaft
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- intention inference, advice taking, volatility estimation, social cognition, Interpersonal Reactivity Index, probabilistic reinforcement learning, economic games, individual differences, social decision-making
- MeSH terms
- Adult, Bayes Theorem, Decision Making, Games, Experimental, Humans, Learning, Male, Models, Psychological, Motivation, Social Behavior, Intention, Young Adult
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
