Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. (2008)
Generalization and Similarity in Exemplar Models of Categorization: Insights from Machine Learning.
In: Psychonomic Bulletin & Review, 15 (2)
doi: 10.3758/PBR.15.2.256
Artikel, Bibliographie
Kurzbeschreibung (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, of 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 are very successful in real-world applications. Their generalization performance depends crucially on the chosen similarity measure. Although 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 in order 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 we suggest some ways in which insights from machine learning can offer guidance.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2008 |
| Autor(en): | Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. |
| Art des Eintrags: | Bibliographie |
| Titel: | Generalization and Similarity in Exemplar Models of Categorization: Insights from Machine Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2008 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Psychonomic Bulletin & Review |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 2 |
| DOI: | 10.3758/PBR.15.2.256 |
| URL / URN: | https://doi.org/10.3758/PBR.15.2.256 |
| Kurzbeschreibung (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, of 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 are very successful in real-world applications. Their generalization performance depends crucially on the chosen similarity measure. Although 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 in order 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 we suggest some ways in which insights from machine learning can offer guidance. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition |
| Hinterlegungsdatum: | 09 Jul 2018 09:14 |
| Letzte Änderung: | 12 Okt 2020 11:21 |
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