The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
SUSTAIN: A Network Model of Category Learning.
Abstract
SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.
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Cites
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Neural Networks and the Bias/Variance Dilemma
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BackgroundToward an instance theory of automatization.
Logan, G. D. (1988) · Psychological Review · doi:10.1037/0033-295x.95.4.492 · Cited by 3,466
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A rational analysis of rule-based concept learning
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008) · Cognitive Science · doi:10.1080/03640210701802071 · Cited by 469
Cognitive control over learning: Creating, clustering, and generalizing task-set structure.
Collins, A. G. E., & Frank, M. J. (2013) · Psychological Review · doi:10.1037/a0030852 · Cited by 426
Metadata
- Authors
- Love, B. C., Medin, D. L., & Gureckis, T. M.
- Venue
- Psychological Review
- Published
- 2004
- Volume
- 111
- Issue
- 2
- Pages
- 309-332
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2170014483
- Cited by
- 775
- References
- 15
- FWCI
- 34.36
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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