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 paradigms. 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.

The relationship between subjects ' identification and categorization learning of integral-dimension stimuli was studied within the framework of an exemplar-based generalization model. The model was used to predict subjects ' learning in six different categorization conditions on the basis of data obtained in a single identification learning condition. A crucial assumption in the model is that because of selective attention to component dimensions, similarity relations may change in systematic ways across different experimental contexts. The theoretical analysis provided evidence that, at least under unspeeded conditions, selective attention may play a critical role in determining the identification-categorization relationship for integral stimuli. Evidence was also provided that similarity among exemplars decreased as a function of identification learning. Various alternative classification models, including prototype, multiple-prototype, average distance, and "value-on-dimensions" models, were unable to account for the results. This article seeks to characterize performance relations between the two fundamental classification paradigms of identification and categorization. Whereas in an identification paradigm people identify stimuli as unique items (a one-to-one

Subjects learned to classify perceptual stimuli varying along continuous, separable dimensions into rule-described categories. The categories were designed to contrast the predictions of a selective-attention exemplar model and a simple rule-based model formalizing an economy-ofdescription view. Converging evidence about categorization strategies was obtained by also collecting identification and recognition data and by manipulating strategies via instructions. In free-strategy conditions, the exemplar model generally provided an accurate quantitative account of identification, categorization, and recognition performance, and it allowed for the interrelationship of these paradigms within a unified framework. Analyses of individual subject data also provided some evidence for the use of rules, but in general, the rules seemed to have a great deal in common with exemplar storage processes. Classification and recognition performance for subjects given explicit instructions to use specific rules contrasted dramatically with performance in the free-strategy conditions and could not be predicted by the exemplar model. Markedly different theoretical approaches have been applied to account for the learning and representation of welldefined categories structured according to simple rules and more natural, ill-defined categories (Rosch, 1973; E. E. Smith & Medin, 1981). In the case of well-defined categories, it is generally assumed that people formulate and test hypotheses concerning the "rules " that determine category membership

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Classification experiments were designed to compare the predictions of a linear decision bound model with those of an exemplar-similarity model incorporating an explicit selective attention mechanism. Linear boundaries could account for the data only in tasks involving separable dimension stimuli and where the boundary separating the categories was orthogonal to the psychological dimensions. Linear boundaries provided poor fits to the classification data in situations involving integral dimensions or when the boundary needed to be oriented in oblique directions in the space. The results were consistent with the selection-attention assumptions embodied in the exemplar model. It was argued that similar assumptions about selective attention need to be incorporated within decision bound models. In a seminal investigation concerned with the nature of perceptual categorization, Shepard, Hovland, and Jenkins (1961) studied the relationship between identification and classification learning. In identification, participants are required to learn unique labels for each of a set of n stimuli, whereas, in classification, participants learn to assign stimuli

The goal of avoiding distraction (e.g., ignoring words when naming their print colors in a Stroop task) is opposed intrinsically by the penchant to process conspicuous and correlated characteristics of the environment (e.g., noticing trial-to-trial associations between the colors and the words). To reconcile these opposing forces, the authors propose a tectonic theory of selective attention in which 2 memorybased structures—dimensional imbalance and dimensional uncertainty—drive selection by processing salient, surprising, and/or correlated information contained within and across stimulus dimensions. Each structure modulates the buildup of excitation to targets and the buildup of inhibition to distractors and to memories of previous stimuli. Tectonic theory is implemented to simulate the impact of 4 types of context on the presence, magnitude, and direction of congruity effects and task effects in the Stroop paradigm. The tectonic model is shown to surpass other formal models in explaining the range and diversity of Stroop effects. Humans are prodigious at focusing on selected aspects of their environment. They can attend to a melody played by the string section of a symphonic orchestra apart from another melody played concurrently by the woodwinds. They can concentrate on

s explore this phenomenon, still one of the most intriguing in all of psychology, even after 60 years. Theoretical Background and Research Goals Why is this phenomenon so compelling? Part of the rea- son lies in its size and ease of demonstration. One can lit- erally feel the interference from the incongruent word when trying to name the color in which it is printed (e.g., to say "red;' not "green;' to the word GREEN printed in red ink). But it is not just the empirical power of the Stroop effect that supports its prevalence. This task provides a the- oretical window on how we deal with conflicting stimuli and task demands, and it is a fertile testing ground for ideas about automaticity and the role of learning in the development of that automaticity. These are fundamental ques- tions about how attention works (see, e.g., Shiffrin, 1988). MacLeod's (1991) review article singled out three issues as crucial to understanding the cause(s) of the Stroop This research was supported by Natu

The difference between integral and separable interaction of dimensions is a classic problem in cognitive psychology (Garner, 1970; Shepard, 1964) and remains an essential component of most current experimental and theoretical analyses of category learning (e.g. Ashby & Maddox, 1994; Goldstone, 1994; Kruschke, 1993; Melara, Marks & Potts, 1993; Nosofsky, 1992). So far the problem has been addressed through post-hoc analysis in which empirical evidence of integral and separable processing is used to fit human data, showing how the impact of a pair of dimensions interacting in an integral or a separable manner enters into later learning processes. In this paper, we argue that a mechanistic connectionist explanation for variations in dimensional interactions can provide a new perspective through exploration of how similarities between stimuli are transformed from physical to psychological space when learning to identify, discriminate, and categorize them. We substantiate this claim by demonstrating how even a standard backpropagation network combined with a simple image-processing Gabor filter component provides limited but clear potential to process monochromatic stimuli that are composed of integral pairs of dimensions differently from monochromatic stimuli that are composed of separable pairs of dimensions. Interestingly, the responses from Gabor filters are shown to already capture most of the dimensional interaction, which in turn can be operated upon by the neural network during a given learning task. In addition, we introduce a basic attention mechanism to backpropagation that gives it the ability to selectively attend to relevant dimensions and illustrate how this serves the model in solving a filtration vs. condensation task (Kruschke, 1993). The model may serve a...

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