An exemplar-based random walk model of speeded classification.

Abstract

The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R.M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within-and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.Models of multidimensional perceptual classification have grown increasingly powerful and sophisticated in recent years, providing detailed quantitative accounts of patterns of classification learning, transfer, and generalization (e.g., Anderson, 1991;Ashby, 1992;Estes, 1986Estes, , 1994Nosofsky, 1992b;Shanks & Gluck, 1994). However, a fundamental limitation of all the major competing models in the field today is that they offer no processing account of the time course of classification. Because response times provide a window into understanding the nature of cognitive representations and decision processes, it is vital to move in the direction of models that account for this form of data. In this article we propose and test a process-oriented model for predicting response times in tasks of speeded perceptual classification.Our proposed model follows in the spirit of some leading extant models of categorization by assuming that people represent categories in terms of stored exemplars (Hintzman, 1986;Medin & Schaffer, 1978;Nosofsky, 1986). Classification decisions are made by retrieving these stored exemplars from memory. In the newly proposed model, retrieved exemplars are used to drive a random walk process (e.g., Luce, 1986;Townsend & Ashby, 1983) in which evidence accrues to alternative categories over time. Random-walk models have been successful at accounting for performance in tasks of memory, decision making, sensory discrimination, and unidimensional absolute judgment (e.g., Busemeyer, 1985; Karpiuk, Lacouture, & Marley, in press; This work was supported by Grant PHS RO1 MH48494-05 from the National Institute of Mental Health.Jerome Busemeyer, A. A. J. Marley, Richard Shiffrin, and James Townsend provided extensive commentary and discussion. We also wish to thank John Anderson, Rob Goldstone, John Kruschke, Roger Ratcliff, Roger Shepard, and Trisha van Zandt for their comments, discussion, and advice.Correspondence concerning this article should be addressed to Robert M. Nosofsky, Department of Psychology, Indiana University, Bloomington, Indiana 47405; or to Thomas J. Palmeri, Department of Psychology, Vanderbilt University, Nashville, Tennessee 37240. Electronic mail may be sent vi...

On the ability to inhibit thought and action: General and special theories of an act of control.

“…On the one hand, several successful models of RT assume a race between independent runners (e.g., Brown & Heathcote, 2005Logan, 1988; P. L. Smith & Van Zandt, 2000;Van Zandt, 2000b;Van Zandt, Colonius, & Proctor, 2000). On the other hand, other successful models assume competition between alternative responses, including random walk (e.g., Nosofsky & Palmeri, 1997), diffusion (e.g., Ratcliff, Van Zandt, & McKoon, 1999), and competitive leaky accumulator models (Usher & McClelland, 2001). In direct comparisons, some specific race models have not accounted for behavioral data as well as some specific competitive models (Ratcliff & Smith, 2004;Teodorescu & Usher, 2013), but in other contexts, race models sometimes do a better job of accounting for behavioral (Leite & Ratcliff, 2010) and physiological data (Ratcliff, Cherian, & Segraves, 2003; but see Purcell et al, 2010).…”

“…We assume that thresholds are determined mostly by strategic factors, like expectancies of events and rewards (Ratcliff, 2006;Ratcliff & Smith, 2004). We assume that drift rates are determined partly by structural factors, like capacity limitations, the quality of stimulus information, and the quality of memory representations (Nosofsky, Little, Donkin, & Fific, 2011;Ratcliff et al, 1999), and partly by strategic factors, like division of attention among stimuli (Logan, 1996;Logan & Gordon, 2001; P. L. Smith & Ratcliff, 2009) or stimulus dimensions (Logan & Gordon, 2001;Nosofsky & Palmeri, 1997). When there is no competition for attention, we predict selective influence of experimental manipulations on model parameters: Structural factors should affect drift rates, and strategic factors should affect thresholds.…”

“…A theory of drift rate would allow us to separate structural factors, like similarity and discriminability (Logan, 2002;Nosofsky & Palmeri, 1997), from strategic factors, like attention and task set (Logan & Gordon, 2001; P. L. Smith & This document is copyrighted by the American Psychological Association or one of its allied publishers.…”

Comparison and Contrast in Perceptual Categorization.

“…According to exemplar models, a category is represented by storing exemplars as they are experienced, and categorization is a function of the similarity of the target instance to some set of those stored exemplars that are retrieved for comparison (Estes, 1994;Lamberts, 1995Lamberts, , 2000Medin & Schaffer, 1978;Nosofsky, 1986;Nosofsky & Palmeri, 1997). So, to continue our example, the target hue is compared with a set of blue exemplars and a set of purple exemplars, and the target is included in the category to which its summed similarity is greatest.…”

“…If the prototype is a running average of previously categorized stimuli, then one can assume that the category representation will be biased toward weighting recent stimulus values more heavily. And likewise in an exemplar model, recent exemplars may be more likely to be retrieved as representative of the category or may be more heavily weighted in the computation of similarity (Nosofsky & Palmeri, 1997). The result of this overweighting of recent stimuli is that presentation of any nonfocal hue will cause the category representation to shift in the direction of that hue.…”