title: Does cognitive science need kernels?
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 concerning their behavior are therefore potentially relevant for human category learning. In particular, we believe kernel methods have the potential to provide explanations ranging from the implementational via the algorithmic to the computational level.
authors.affiliation: Massachusetts Institute of Technology, Brain and Cognitive Sciences, 43 Vassar St, Cambridge, MA 02139, USA. fjaekel@mit.edu
authors.forename: Frank
authors.initials: F
authors.lastname: Jäkel
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
authors.forename: Felix A
authors.initials: FA
authors.lastname: Wichmann
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003071:Cognition / Q000502:physiology*; D019336:Cognitive Science; D003198:Computer Simulation; D006801:Humans; D010465:Perception / Q000502:physiology