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A rational analysis of rule-based concept learning
Open accessAbstract
This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space-a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts-a more natural setting in several ways-and again finds that the model explains human performance.
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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]”
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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
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
Women, Fire, and Dangerous Things
Lakoff, G. P. (1987) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226471013.001.0001 · Cited by 13,358
Ambrose, A., Wittgenstein, L., & Anscombe, G. E. M. (1954) · Philosophy and Phenomenological Research · doi:10.2307/2103745 · Cited by 12,289
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
Sequential Monte Carlo Methods in Practice
Arnaud Doucet, B. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,516
Probability theory the logic of science
Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,600
Fodor, J. A. (1975) · Medical Entomology and Zoology · Cited by 4,588
Research works printed but not linked: 8
- Briscoe E., 2006, Proceedings of the Conference of the Cognitive Science Society, 1038
- Bruner J. S., 1956, A study of thinking
- Gentner D., 2005, Learning and using relational categories, 151
- Goodman N. D., The probabilistic mind: Prospects for Bayesian cognitive science
- Marr D., 1982, Vision
- Sanborn A., 2006, Proceedings of the 28th Annual Conference of the Cognitive Science Society
- Tenenbaum J. B., 2000, Advances in neural information processing systems 12, 59
- Von Humboldt W., 1863, On language
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Metadata
- Authors
- Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Massachusetts Institute of Technology (US)
- University of California, Berkeley (US)
- Corresponding author
- Goodman, Noah D.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2008-01
- Volume
- 32
- Issue
- 1
- Pages
- 108-154
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2142336306
- Cited by
- 469
- References
- 96
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 51 · 2023: 43 · 2022: 74 · 2021: 35 · 2020: 15 · 2019: 30 · 2018: 31 · 2017: 21 · 2016: 25 · 2015: 16 · 2014: 11 · 2013: 6 · 2012: 29
- FWCI
- 23.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Funders
- National Science Foundation (0339062)
- James S. McDonnell Foundation
- Air Force Office of Scientific Research
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- Bayesian inference, category learning, rational analysis, individual differences in learning
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