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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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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
Madden, E. H., Goodman, N., & van Melsen, A. G. (1955) · Philosophy and Phenomenological Research · doi:10.2307/2103789 · Cited by 4,395
Probability Theory: The Logic of Science
Jaynes, E. T. (2003) · doi:10.1017/cbo9780511790423 · Cited by 4,045
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
PINKER, S. (1999) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1999.tb08538.x · Cited by 3,738
Advances in neural information processing systems 11
Peter Sollich, A. (1999) · Cited by 3,168
Context theory of classification learning
Medin, D. L., & Schaffer, M. M. (1978) · Psychological Review · doi:10.1037/0033-295x.85.3.207 · Cited by 3,061
Smith, E. E., & Medin, D. L. (1981) · Harvard University Press eBooks · doi:10.4159/harvard.9780674866270 · Cited by 2,653
Murphy, G. L. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1602.001.0001 · Cited by 2,650
Luce, R. (1959) · Econometrica · doi:10.2307/1911299 · Cited by 2,603
Toward a Universal Law of Generalization for Psychological Science
Shepard, R. N. (1987) · Science · doi:10.1126/science.3629243 · Cited by 2,416
A Mathematical Introduction to Logic
(2001) · Elsevier eBooks · doi:10.1016/c2009-0-22107-6 · Cited by 2,398
Attention, similarity, and the identification-categorization relationship.
Nosofsky, R. M. (1986) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.115.1.39 · Cited by 2,207
The Adaptive Character of Thought
Massaro, D. W., Friedman, D., & Anderson, J. R. (1991) · The American Journal of Psychology · doi:10.2307/1423253 · Cited by 2,174
Attention, similarity, and the identification–categorization relationship.
Nosofsky, R. M. (1986) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.115.1.39 · Cited by 2,136
(1986) · Cited by 2,022
Categorization of Natural Objects
Mervis, C. Β., & Rosch, E. H. (1981) · Annual Review of Psychology · doi:10.1146/annurev.ps.32.020181.000513 · Cited by 1,990
On the genesis of abstract ideas.
Posner, M. I., & Keele, S. W. (1968) · Journal of Experimental Psychology · doi:10.1037/h0025953 · Cited by 1,983
Women, Fire, and Dangerous Things: What Categories Reveal about the Mind
Gibbs, R. W., & Lakoff, G. P. (1989) · The American Journal of Psychology · doi:10.2307/1422958 · Cited by 1,886
Proceedings of the 29th Annual Conference of the Cognitive Science Society
Rohde, H., Kehler, A., & Elman, J. L. (2007) · Cognitive Science · Cited by 1,782
ALCOVE: An exemplar-based connectionist model of category learning.
Kruschke, J. K. (1992) · Psychological Review · doi:10.1037/0033-295x.99.1.22 · Cited by 1,582
Fodor, J. A. (1998) · doi:10.1093/0198236360.001.0001 · Cited by 1,278
The adaptive nature of human categorization.
Anderson, J. R. (1991) · Psychological Review · doi:10.1037/0033-295x.98.3.409 · Cited by 1,069
Word learning as Bayesian inference.
Xu, F., & Tenenbaum, J. B. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.245 · Cited by 1,061
What some concepts might not be
Armstrong, S. L., Gleitman, L. R., & Gleitman, H. (1983) · Cognition · doi:10.1016/0010-0277(83)90012-4 · Cited by 1,055
Pattern recognition and categorization
Reed, S. K. (1972) · Cognitive Psychology · doi:10.1016/0010-0285(72)90014-x · Cited by 1,002
Molecular diversity of arbuscular mycorrhizal fungi colonising arable crops
Daniell, T. J., Husband, R., FITTER, A. H., & Young, J. P. W. (2001) · FEMS Microbiology Ecology · doi:10.1111/j.1574-6941.2001.tb00841.x · Cited by 991
Concepts: Where Cognitive Science Went Wrong
Bach, K., & Fodor, J. A. (2000) · The Philosophical Review · doi:10.2307/2693638 · Cited by 936
Generalization, similarity, and Bayesian inference
Tenenbaum, J. B., & Griffiths, T. L. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000061 · Cited by 878
Learning and memorization of classifications.
Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961) · The Psychological Monographs · doi:10.1037/h0093825 · Cited by 844
On the adequacy of prototype theory as a theory of concepts
Osherson, D. N., & Smith, E. E. (1981) · Cognition · doi:10.1016/0010-0277(81)90013-5 · Cited by 830
Rule-plus-exception model of classification learning.
Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994) · Psychological Review · doi:10.1037/0033-295x.101.1.53 · Cited by 794
SUSTAIN: A Network Model of Category Learning.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.309 · Cited by 789
A sequential particle filter method for static models
Chopin, N. (2002) · Biometrika · doi:10.1093/biomet/89.3.539 · Cited by 709
Structure and strength in causal induction☆
Griffiths, T. L., & Tenenbaum, J. B. (2005) · Cognitive Psychology · doi:10.1016/j.cogpsych.2005.05.004 · Cited by 626
The development of features in object concepts
Schyns, P. G., Goldstone, R. L., & Thibaut, J. (1998) · Behavioral and Brain Sciences · doi:10.1017/s0140525x98000107 · Cited by 598
Connectionist and diffusion models of reaction time.
Ratcliff, R., Zandt, T. V., & McKoon, G. (1999) · Psychological Review · doi:10.1037/0033-295x.106.2.261 · Cited by 588
Rules and exemplars in category learning.
Erickson, M. A., & Kruschke, J. K. (1998) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.127.2.107 · Cited by 561
Prototypes in the mist: The early epochs of category learning.
Smith, J. D., & Minda, J. P. (1998) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.24.6.1411 · Cited by 491
Minimization of Boolean complexity in human concept learning
Feldman, J. (2000) · Nature · doi:10.1038/35036586 · Cited by 475
Family resemblance, conceptual cohesiveness, and category construction
Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987) · Cognitive Psychology · doi:10.1016/0010-0285(87)90012-0 · Cited by 445
Feature Centrality and Conceptual Coherence
Sloman, S. A., Love, B. C., & Ahn, W. (1998) · Cognitive Science · doi:10.1207/s15516709cog2202_2 · Cited by 392
Proceedings of the 22nd Annual Conference of the Cognitive Science Society
Scheepers, C., & Corley, M. M. B. (2000) · Cited by 386
Comparing decision bound and exemplar models of categorization
Maddox, W. T., & Ashby, F. G. (1993) · Perception & Psychophysics · doi:10.3758/bf03211715 · Cited by 373
Gobet, F. R. (2000) · Swiss Journal of Psychology · doi:10.1024//1421-0185.59.3.211b · Cited by 288
(1953) · Medical Entomology and Zoology · Cited by 281
Ten years of the rational analysis of cognition
Chater, N., & Oaksford, M. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01273-x · Cited by 280
Categorization creates functional features.
Schyns, P. G., & Rodet, L. (1997) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.23.3.681 · Cited by 265
Chater, N., & Oaksford, M. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00603 · Cited by 252
Linear separability in classification learning.
Medin, D. L., & Schwanenflugel, P. J. (1981) · Journal of Experimental Psychology Human Learning & Memory · doi:10.1037/0278-7393.7.5.355 · Cited by 251
Comparing supervised and unsupervised category learning
Love, B. C. (2002) · Psychonomic Bulletin & Review · doi:10.3758/bf03196342 · Cited by 242
Causes and Explanations: A Structural-Model Approach. Part II: Explanations
Halpern, J. Y., & Pearl, J. (2005) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axi148 · Cited by 242
Correlated symptoms and simulated medical classification.
Medin, D. L., Altom, M. W., Edelson, S. M., & Freko, D. (1982) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.8.1.37 · Cited by 241
Rules and exemplars in categorization, identification, and recognition.
Nosofsky, R. M., Clark, S. E., & Shin, H. J. (1989) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.15.2.282 · Cited by 237
Information-accumulation theory of speeded categorization.
Lamberts, K. (2000) · Psychological Review · doi:10.1037/0033-295x.107.2.227 · Cited by 210
A Causal-Model Theory of Conceptual Representation and Categorization.
Rehder, B. (2003) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.29.6.1141 · Cited by 201
The Simplicity Principle in Human Concept Learning
Feldman, J. (2003) · Current Directions in Psychological Science · doi:10.1046/j.0963-7214.2003.01267.x · Cited by 188
Are there representational shifts during category learning?
Johansen, M. (2002) · Cognitive Psychology · doi:10.1016/s0010-0285(02)00505-4 · Cited by 185
The rules versus similarity distinction
Pothos, E. M. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000014 · Cited by 184
Isolated and interrelated concepts
Goldstone, R. L. (1996) · Memory & Cognition · doi:10.3758/bf03201087 · Cited by 182
Markman, A. B., & Stilwell, C. H. (2001) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528130110100252 · Cited by 180
Bayesian Modeling of Human Concept Learning
Tenenbaum, J. B. (1998) · Cited by 175
Conceptual complexity and the bias/variance tradeoff
Briscoe, E., & Feldman, J. (2010) · Cognition · doi:10.1016/j.cognition.2010.10.004 · Cited by 169
Jaeger, D., & Jung, R. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_100494 · Cited by 168
Wolff, K. H., Cassirer, E., & Langer, S. K. (1947) · American Sociological Review · doi:10.2307/2086541 · Cited by 163
Geisler, W. S. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/7131.003.0061 · Cited by 149
Human Category Learning: Implications for Backpropagation Models
Kruschke, J. K. (1993) · Connection Science · doi:10.1080/09540099308915683 · Cited by 145
Feldman, J. (2001) · Perception & Psychophysics · doi:10.3758/bf03194532 · Cited by 141
Further tests of an exemplar-similarity approach to relating identification and categorization
Nosofsky, R. M. (1989) · Perception & Psychophysics · doi:10.3758/bf03204942 · Cited by 137
A rule-plus-exception model for classifying objects in continuous-dimension spaces
Nosofsky, R. M., & Palmeri, T. J. (1998) · Psychonomic Bulletin & Review · doi:10.3758/bf03208813 · Cited by 137
Stimulus generalization in the learning of classifications.
Shepard, R. N., & Chang, J.-J. (1963) · Journal of Experimental Psychology · doi:10.1037/h0043732 · Cited by 132
Similarity as an explanatory construct
Sloman, S. A., & Rips, L. J. (1998) · Cognition · doi:10.1016/s0010-0277(97)00048-6 · Cited by 118
Learning to classify integral-dimension stimuli
Nosofsky, R. M., & Palmeri, T. J. (1996) · Psychonomic Bulletin & Review · doi:10.3758/bf03212422 · Cited by 116
A more rational model of categorization
Sanborn, A. N., Griffiths, T. L., & Navarro, D. (2006) · Warwick Research Archive Portal (University of Warwick) · Cited by 109
Locally Bayesian learning with applications to retrospective revaluation and highlighting.
Kruschke, J. K. (2006) · Psychological Review · doi:10.1037/0033-295x.113.4.677 · Cited by 105
A Bayesian framework for concept learning
Tenenbaum, J. B. (1999) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 101
Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks.
Smith, J. D., Minda, J. P., & Washburn, D. A. (2004) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.133.3.398 · Cited by 100
An algebra of human concept learning
Feldman, J. (2006) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2006.03.002 · Cited by 90
Word learning as Bayesian inference
Tenenbaum, J. B., & Xu, F. (2000) · eScholarship (California Digital Library) · Cited by 80
Jaeger, D., & Jung, R. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_300520 · Cited by 70
Rules and Similarity in Concept LearningPreprint
Tenenbaum, J. B. (1999) · Cited by 65
How surprising is a simple pattern? Quantifying ?Eureka!?
FELDMAN, J. (2004) · Cognition · doi:10.1016/j.cognition.2003.09.013 · Cited by 37
An introduction to rational models of cognition
Oaksford, M., & Chater, N. (2000) · doi:10.1093/oso/9780198524151.003.0001 · Cited by 24
Word Learning as Bayesian Inference: Evidence from Preschoolers
Tenenbaums, J. B., & Xu, F. (2005) · eScholarship (California Digital Library) · Cited by 20
From natural kinds to complex categories
Navarro, D. (2006) · Cited by 15
Compositionality in rational analysis: grammar-based induction for concept learning
Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., & Feldman, J. (2008) · Cited by 10
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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Bayesian Brains without Probabilities
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Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011) · Cognition · doi:10.1016/j.cognition.2010.11.015 · Cited by 300
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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