The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Human Category Learning
Abstract
Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular—rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.
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Cites
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Cited by
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Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms
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Metadata
- Authors
- Ashby, F. G., & Maddox, W. T.
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2005-02-01
- Volume
- 56
- Issue
- 1
- Pages
- 149-178
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- OpenAlex
- W2129668249
- Cited by
- 938
- References
- 9
- FWCI
- 8.51
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
