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A Connectionist Model of Categorization Response Times

  • David Peebles
  • Koen Lamberts
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

Although perceptual categorisation has been studied extensively in psychology, response times in categorisation tasks have only recently become an important research topic (e.g., [1,2,3,4,5,6,7,8,9]). In this article, we propose a connectionist model of categorisation RT, called CONCAT, which aims to provide a joint account of response times and choice proportions in binary classification tasks. First, we outline the basic principles of the model. Next, we present a perceptual categorisation experiment and apply CONCAT to the results.

Keywords

Input Node Category Learning Connectionist Model Training Stage Error Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • David Peebles
    • 1
  • Koen Lamberts
    • 2
  1. 1.University of NottinghamUK
  2. 2.University of WarwickUK

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