Does Cognitive Science Need Kernels?
F. Jäkel, B. Schölkopf and F.A. Wichmann
Trends in Cognitive Sciences Volume 13, Number 9, pp. 381-388, 2009.

Abstract

Kernel methods are among the most successful tools in machine learning and are used in challenging data-analysis problems in many disciplines. Here we provide examples where kernel methods have proven to be powerful tools for analyzing behavioral data, especially for identifying features in categorization experiments. We also demonstrate that kernel methods relate to perceptrons and exemplar models of categorization. Hence, we argue that kernel methods have neural and psychological plausibility and theoretical results about their behavior are therefore potentially relevant for human category learning. In particular, we think that kernel methods show the prospect of providing explanations ranging from the implementational via the algorithmic to the computational level.

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