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Fitting Decision Bound Models to Identification or Categorization Data Daniel M. Ennis
 

Summary: Fitting Decision Bound Models to Identification or Categorization Data
Daniel M. Ennis
The Institute for Perception
F. Gregory Ashby
University of California at Santa Barbara
August 25, 2003
Correspondence: F. Gregory Ashby
Department of Psychology
University of California
Santa Barbara, CA 93106
Phone: 805-893-2130
Fax: 805-893-4303
Email: ashby@psych.ucsb.edu
Running Head: Fitting the Decision Bound Model
Abstract
Decision bound models are derived from general recognition theory, which is a multidimensional
generalization of signal detection theory. This broad class of models has been remarkably
successful at accounting for data from categorization and identification experiments. Fitting
decision bound models however, requires the numerical evaluation of multiple integrals over the
multidimensional normal distribution. This article describes an extremely general algorithm for

  

Source: Ashby, F. Gregory - Department of Psychology, University of California at Santa Barbara

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences