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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]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [7]
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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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