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  Generalization and Similarity in Exemplar Models of Categorization: Insights from Machine Learning

Jäkel, F., Schölkopf, B., & Wichmann, F. (2008). Generalization and Similarity in Exemplar Models of Categorization: Insights from Machine Learning. Psychonomic Bulletin & Review, 15(2), 256-271. doi:10.3758/PBR.15.2.256.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C9C1-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-30F8-F
Genre: Journal Article

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 Creators:
Jäkel, F, Author              
Schölkopf, B1, 2, Author              
Wichmann, FA, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Exemplar theories of categorization depend on similarity for explaining subjects’ ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction mechanisms and thus, seemingly, generalization ability. Here, we use insights from machine learning to demonstrate that exemplar models can actually generalize very well. Kernel methods in machine learning are akin to exemplar models and very successful in real-world applications. Their generalization performance depends crucially on the chosen similaritymeasure. While similarity plays an important role in describing generalization behavior it is not the only factor that controls generalization performance. In machine learning, kernel methods are often combined with regularization techniques to ensure good generalization. These same techniques are easily incorporated in exemplar models. We show that the Generalized Context Model (Nosofsky, 1986) and ALCOVE (Kruschke, 1992) are closely related to a statistical model called kernel logistic regression. We argue that generalization is central to the enterprise of understanding categorization behavior and suggest how insights from machine learning can offer some guidance. Keywords: kernel, similarity, regularization, generalization, categorization.

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 Dates: 2008-04
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.3758/PBR.15.2.256
BibTex Citekey: 4783
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Title: Psychonomic Bulletin & Review
Source Genre: Journal
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Publ. Info: Austin, TX : Psychonomic Society
Pages: - Volume / Issue: 15 (2) Sequence Number: - Start / End Page: 256 - 271 Identifier: ISSN: 1069-9384
CoNE: https://pure.mpg.de/cone/journals/resource/954928526942