A rational analysis of rule-based concept learning
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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.
“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 [7]
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Compositional Inductive Biases in Function Learning
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Theory learning as stochastic search in the language of thought
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Learning the Form of Causal Relationships Using Hierarchical Bayesian Models
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A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans
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Hypothesis generation, sparse categories, and the positive test strategy.
Navarro, D., & Perfors, A. (2010) · Psychological Review · doi:10.1037/a0021110 · Cited by 86 · see citing works
The simplicity principle in perception and cognition
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Bayes and the simplicity principle in perception.
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Learning Programs: A Hierarchical Bayesian Approach
Liang, P., Jordan, M. I., & Klein, D. (2010) · Cited by 80 · see citing works
The Oxford Handbook of the Mental Lexicon
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Building machines that learn and think with people
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Burn-in, bias, and the rationality of anchoring
Lieder, F., Griffiths, T. L., & Goodman, N. D. (2012) · Cited by 76 · see citing works
Response-time tests of logical-rule models of categorization.
Little, D. R., Nosofsky, R. M., & Denton, S. E. (2010) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0021330 · Cited by 74 · see citing works
Rule, J., Tenenbaum, J. B., & Piantadosi, S. T. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.07.005 · Cited by 68 · see citing works
Three ideal observer models for rule learning in simple languages.
Frank, M. C., & Tenenbaum, J. (2011) · Cognition · doi:10.1016/j.cognition.2010.10.005 · Cited by 64 · see citing works
The limits of learning: Exploration, generalization, and the development of learning traps.
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Moreton, E., Pater, J., & Pertsova, K. (2015) · Cognitive Science · doi:10.1111/cogs.12319 · Cited by 60 · see citing works
The Computational Origin of Representation
Piantadosi, S. T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09540-9 · Cited by 62 · see citing works
Taniguchi, H., Satō, H., & Shirakawa, T. (2018) · Scientific Reports · doi:10.1038/s41598-018-25679-z · Cited by 57 · see citing works
A probabilistic model of cross-categorization
Shafto, P., Kemp, C., Mansinghka, V. K., & Tenenbaum, J. B. (2011) · Cognition · doi:10.1016/j.cognition.2011.02.010 · Cited by 55 · see citing works
One model for the learning of language
Yang, Y., & Piantadosi, S. T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021865119 · Cited by 50 · see citing works
Ease of learning explains semantic universals
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Rational analysis, intractability, and the prospects of ‘as if’-explanations
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Models of attentional learning
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Compositional Reasoning in Early Childhood
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Kurtz, K. J., Levering, K. R., Stanton, R. D., Romero, J., & Morris, S. N. (2012) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0029178 · Cited by 50 · see citing works
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Time in causal structure learning.
Bramley, N. R., Gerstenberg, T., Mayrhofer, R., & Lagnado, D. A. (2018) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000548 · Cited by 49 · see citing works
Four Problems Solved by the Probabilistic Language of Thought
Piantadosi, S. T., & Jacobs, R. A. (2016) · Current Directions in Psychological Science · doi:10.1177/0963721415609581 · Cited by 47 · see citing works
Logical rules and the classification of integral-dimension stimuli.
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Independent Sampling of Features Enables Conscious Perception of Bound Objects
Vul, E., & Rich, A. N. (2010) · Psychological Science · doi:10.1177/0956797610377341 · Cited by 45 · see citing works
Learning to Overexert Cognitive Control in a Stroop Task
Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., & Cohen, J. (2020) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-020-00845-x · Cited by 37 · see citing works
Structural Learning in a Visuomotor Adaptation Task Is Explicitly Accessible
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Structure Learning in a Sensorimotor Association Task
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A rule-based presentation order facilitates category learning
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The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science
Nick Chater, A., Goodman, N. D., Griffiths, T. L., Kemp, C., Oaksford, M., & Tenenbaum, J. B. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000239 · Cited by 38 · see citing works
The evolutionary origin of Bayesian heuristics and finite memory
Lo, A. W., & Zhang, R. (2021) · iScience · doi:10.1016/j.isci.2021.102853 · Cited by 36 · see citing works
Towards the design principles of neural population codes
Schneidman, E. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.03.001 · Cited by 37 · see citing works
Statistical Learning in Vision
Fiser, J., & Lengyel, G. (2022) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100720-103343 · Cited by 37 · see citing works
Why concepts are (probably) vectors
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Mental models of Boolean concepts
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Rational arbitration between statistics and rules in human sequence processing
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Connecting Content and Logical Words
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Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning
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Battaglia, F. P., & Pennartz, C. M. A. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00036 · Cited by 34 · see citing works
Metacognitive resources for adaptive learning⋆
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Ethical Learning, Natural and Artificial
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Computational Models of Conditioning
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Learning and using relational theories
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A taxonomy of inductive problems
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Similarity, feature discovery, and the size principle
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Complex Probabilistic Inference
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What does the mind learn? A comparison of human and machine learning representations
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A model of conceptual bootstrapping in human cognition
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A Simple Sequential Algorithm for Approximating Bayesian Inference.
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Unifying Principles of Generalization: Past, Present, and Future
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Ingredients of intelligence: From classic debates to an engineering roadmap
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STRUCTURALLY BIASED PHONOLOGY: COMPLEXITY IN LEARNING AND TYPOLOGY
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Learning abstract visual concepts via probabilistic program induction in a Language of Thought
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- 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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