An extension of the exemplar-based random-walk model to separable-dimension stimuli

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 dimensions into the similarity calculations is a stochastic process with the probability of inclusion based on the perceptual salience of the dimensions. Thus, the exemplars that enter into the random-walk change 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.