Attention, similarity, and the identification–categorization relationship.

Abstract

A unified quantitative approach to modeling subjects' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Sbepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and Categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigrns. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed. Bruner, Goodnow, and Austin marveled at the capacity of people to discriminate stimuli and to identify them as unique items. At the same time they stressed the importance of categorization, the process by which discriminably different things are classified into groups and are thereby rendered equivalent. In one sense the processes of identification and categorization seem diametrically opposed, the former dealing with the particular and the latter with the general. Yet similar principles may underlie subjects' identification and categorization of multidimensional stimuli, and performance in these tasks may be highly related. Indeed, the present research renews the issue explored previously by Shepard, Hovland, and Jenkins (1961) and Shepard and Chang (1963)namely, Do the principles of stimulus generalization underlying identification performance also underlie categorization performance? Furthermore, given knowledge of performance in an identification paradigm, can one predict performance in a categorization paradigm using the same set of stimuli? In their 1956 classic, A Study of Thinking,This article is based on a PhD dissertation submitted to Harvard University.

Limitations of exemplar models of multi-attribute probabilistic inference.

“…We take as a starting point the generalized context model (GCM) of categorization (Nosofsky, 1986). It is straightforward to adapt the model to the present paradigm.…”

“…Nevertheless, it is of interest to consider whether similar cognitive processes may mediate these forms of judgment (cf. Nosofsky, 1986).…”

Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization.

“…In the experimental paradigms under consideration, all of the models are compared in situations in which the stimuli are composed of binary-valued, separable dimensions and in which subjects are classifying the stimuli into one of two categories. For simplicity, we describe the models as they are applied in such a paradigm, although extensions of the models to more general paradigms are straightforward (e.g., Nosofsky, 1986Nosofsky, , 1987Shin & Nosofsky, 1992).…”

“…Likewise, the exemplar model would fit such performances equally well by placing all of its attention weight on this single dimension. (Singledimension rules of this form are a very special case of the types of behavior that the selective-attention exemplar model was designed to explain-see, e.g., Medin & Schaffer, 1978;Nosofsky, 1984Nosofsky, , 1986Nosofsky, , 1991b.) However, the value of ␥ in the exemplar model would need to be set at a sufficiently high value to account for the deterministic pattern of responding assumed in the rule-based process.…”

“…Classification decisions are based on the similarity of an item to the alternative prototypes. By contrast, according to exemplar models (Hintzman, 1986;Medin & Schaffer, 1978;Nosofsky, 1986), people represent categories by storing the individual members or exemplars of a category as separate traces and classify items based on their similarity to these stored exemplars.There is a good deal of converging evidence that prototype models are insufficient as models of categorization, especially in situations in which learners have been repeatedly exposed to the individual exemplars of each category. Memories for the individual exemplars appear to play at least some role in such situations (Homa, Sterling, & Trepel, 1981;Posner & Keele, 1968;Nosofsky, 1992;Smith & Minda, 2000).…”