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要旨:
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.
