The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Probabilistic models of cognition: Conceptual foundations
Open accessOverview
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.
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
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
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
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Motion illusions as optimal percepts
Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002) · Nature Neuroscience · doi:10.1038/nn0602-858 · Cited by 1,076
Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880 · cited 1×
“the regularities linking cause and effect (see Courville et al. [46] and Tenebaum et al. [ 47 ], in this issue).”
Self-citationVision as Bayesian inference: analysis by synthesis?
Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.002 · Cited by 849
Self-citationStewart, N., Chater, N., & Brown, G. D. A. (2006) · Cognitive Psychology · doi:10.1016/j.cogpsych.2005.10.003 · Cited by 737
Self-citationCausal Models: How People Think about the World and Its Alternatives
Sloman, S. (2005) · doi:10.1093/acprof:oso/9780195183115.001.0001 · Cited by 677
Probabilistic models of language processing and acquisition
Chater, N., & Manning, C. D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.006 · Cited by 411
The Probability Heuristics Model of Syllogistic Reasoning
Chater, N., & Oaksford, M. (1999) · Cognitive Psychology · doi:10.1006/cogp.1998.0696 · Cited by 324
Self-citation
Cited by
(2013) · Cited by 1,351
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Piqueras-Fiszman, B., & Spence, C. (2015) · Food Quality and Preference · doi:10.1016/j.foodqual.2014.09.013 · Cited by 855
The Now-or-Never bottleneck: A fundamental constraint on language
Christiansen, M. H., & Chater, N. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1500031x · Cited by 733
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
“These experiments have shown that human behavior is highly consistent with probabilistic reasoning not only in the sensory domain 8–12 , but also in the motor 13–15 and cognitive 16–22 domains.”
Background
Metadata
- Authors
- Chater, N., Tenenbaum, J. B., & Yuille, A.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2006-07
- Volume
- 10
- Issue
- 7
- Pages
- 287-291
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W167351231
- Cited by
- 549
- References
- 13
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
