Header logo is

Does Cognitive Science Need Kernels?




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.

Author(s): Jäkel, F. and Schölkopf, B. and Wichmann, FA.
Journal: Trends in Cognitive Sciences
Volume: 13
Number (issue): 9
Pages: 381-388
Year: 2009
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.tics.2009.06.002
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Does Cognitive Science Need Kernels?},
  author = {J{\"a}kel, F. and Sch{\"o}lkopf, B. and Wichmann, FA.},
  journal = {Trends in Cognitive Sciences},
  volume = {13},
  number = {9},
  pages = {381-388},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2009},
  month_numeric = {9}