Moments of transition-additive random variables defined on finite, regenerative random processes
Journal of Mathematical Psychology
Journal of Mathematical Psychology
Dynamic stochastic models for decision making under time constraints
Journal of Mathematical Psychology
Journal of Mathematical Psychology
A Stochastic version of general recognition theory
Journal of Mathematical Psychology
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An extension of Nosofsky and Palmeri's (Psychol. Rev. 104 (1997a) 266) exemplar-based random-walk (EBRW) model of categorization is presented as a model of the time course of categorization of separable-dimension stimuli. Nosofsky and Palmeri (1997a) assumed that the perceptual encoding of all stimuli was identical. However, in the current model, we assume as in Lamberts (J. Exp. Psychol: General 124 (1995) 161) that the inclusion of individual stimulus dimension into the similarity calculations is a stochastic process with the probability of inclusion based or the perceptual salience of the dimensions. Thus, the exemplars that enter into the random-walk changes dynamically during the time course of processing. This model is implemented as a Markov chain. Its predictions are compared with alternative models in a speeded categorization experiment with separable-dimension stimuli.