The DOI matches its Crossref record; 102 of the 103 references it cites were found in a registry or an index.
Structured statistical models of inductive reasoning
Open accessAbstract
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.
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“Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [8]
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
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
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
(1989) · Cited by 4,498
Madden, E. H., Goodman, N., & van Melsen, A. G. (1955) · Philosophy and Phenomenological Research · doi:10.2307/2103789 · Cited by 4,395
Connectionism and cognitive architecture: A critical analysis
Fodor, J. A., & Pylyshyn, Z. W. (1988) · Cognition · doi:10.1016/0010-0277(88)90031-5 · Cited by 4,022
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/3080.003.0017 · Cited by 3,974
A First Course in Stochastic Processes
(1975) · Elsevier eBooks · doi:10.1016/c2009-1-28569-8 · Cited by 3,959
The empirical case for two systems of reasoning.
Sloman, S. A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.3 · Cited by 3,942
Cited by
Sorted by citations
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
Self-citationProbabilistic models of cognition: exploring representations and inductive biases
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568
Self-citationA review of visual memory capacity: Beyond individual items and toward structured representations
Brady, T. F., Konkle, T., & Alvarez, G. A. (2011) · Journal of Vision · doi:10.1167/11.5.4 · Cited by 556
Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
A sensorimotor paradigm for Bayesian model selection
Genewein, T., & Braun, D. A. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00291 · Cited by 488
Metadata
- Authors
- Kemp, C., & Tenenbaum, J. B.
- Affiliations
- Carnegie Mellon University (US)
- Massachusetts Institute of Technology (US)
- Venue
- Psychological Review
- Published
- 2009
- Volume
- 116
- Issue
- 1
- Pages
- 20-58
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W1994114516
- Cited by
- 259
- References
- 103
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 19 · 2023: 12 · 2022: 11 · 2021: 7 · 2020: 14 · 2019: 17 · 2018: 15 · 2017: 11 · 2016: 13 · 2015: 18 · 2014: 20 · 2013: 13 · 2012: 24
- FWCI
- 29.15
- Type
- journal article
- Language
- en
- Update notices
- correction (2009-04-06)
- Open access
- green
- figshare.com
- Licence
- cc-by
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- KiltHub Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Funders
- US Air Force Office of Scientific Research
- William Asbjornsen Albert Memorial Fellowship
- Paul E. Newton Chair
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- inductive reasoning, prior knowledge, structured priors, taxonomic model, spatial modeling, threshold model, causal models, probabilistic inference, category structure, human reasoning
- MeSH terms
- Association Learning, Bayes Theorem, Concept Formation, Decision Making, Humans, Judgment, Problem Solving, Psychological Theory, Mental Recall, Models, Statistical, Neural Networks, Computer, Knowledge
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