predictions of exemplar models and that supported prototype models. In the authors ’ view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.’s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. A classic issue in cognitive psychology concerns the manner in which people represent categories in memory. According to prototype models (Homa, 1984; Posner & Keele, 1968; Reed, 1972), people represent categories by forming a summary representation that is a central tendency of all of the experienced members of a

Abstract not found

Nosofsky and Zaki (2002) found that an exemplar similarity model provided better accounts of individual subject classification and generalization performance than did a mixed prototype model proposed by Smith and Minda (1998; Minda & Smith, 2001). However, these previous tests used a nonlinearly separable category structure. In the present work, the authors extend the previous findings by demonstrating a superiority for the exemplar generalization model over the mixed prototype model in a case involving a linearly separable structure. Because this structure has numerous features that Minda and Smith argued should be conducive to prototype-based processing, the results pose a significant challenge to the mixed prototype view. A classic issue in the field of category learning concerns the contrast between exemplar and prototype models. According to exemplar models (Hintzman, 1986; Medin & Schaffer, 1978; Nosofsky, 1986), people represent categories by storing individual exemplars in memory and classify objects on the basis of how similar they are to the stored exemplars. By contrast, according to prototype models (Homa, 1984; Posner & Keele, 1968; Reed, 1972), people form a summary representation of a category, which is usually assumed to be a central tendency computed over the category-training instances. Classification decisions are based on the similarity of objects to the category prototypes. There is a good deal of evidence that prototype models are insufficient as models of classification. Memories for individual exemplars appear to play at least some role in classification learning, especially in situations in which

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. In this article, we address the representation of complex perceptual categories. Contrary to the conventional and widespread assumption that people’s representations are homogeneous and integrated, we show in two experiments that people often master a complex categorization task by forming independent components, or parcels, of knowledge. We also show that once a knowledge

The work upon which this publication was based was supported in part by the

Formal models provide detailed accounts of fundamental aspects of categorization, yet are potentially incomplete in not providing accounts of how new features might be created. Although the notion of feature creation is enticing, how this complex process might operate is not specified. Moreover, arguments favoring feature creation accounts that are founded on the alleged implausibility of feature weighting accounts are not convincing. Schyns, Goldstone, and Thibaut provide some compelling arguments in favor of flexible feature creation as opposed to the fixed feature spaces assumed by many theories of categorization and object recognition. I agree that while assuming fixed feature spaces should not be problematic for categorization and recognition of fairly simple experimental stimuli, such as forms of canonical shapes and colors, flexible features may be necessary for categorization and recognition of more complex stimuli, such as faces or radiological images. While I certainly find the ideas presented in the target article appealing, this commentary will instead focus on some apparent misconceptions, mischaracterizations, and omissions as they pertain to categorization.

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Results from the classic dot pattern distortion paradigm have sometimes yielded prototype enhancement effects that could not be accounted for by exemplar models of categorization. However, in these experiments the status of the prototype was confounded with certain stimulus-specific properties as well as with the frequency of presentation of the prototype during testing. In two mock-subliminal experiments, participants made categorization judgments to patterns that were generated as prototypes, low-level distortions, or high-level distortions. The participants rated the prototypes as being more likely to be members of a category, although no patterns were presented during training, and there was no objective category structure. In two other experiments, greater prototype enhancement effects were observed when the prototype and low-level distortions were presented with greater frequency during transfer. These results suggest that classic prototype enhancement effects may not be due to the abstraction of a prototype at time of original learning, but rather to other factors not formalized in extant models.

Abstract This paper describes InterModeller, a computer program intended to assist children in primary and secondary schools to learn concepts and skills associated with classification. The design of the program consolidates and extends previous work by including support for multiple forms of representation, provision for automatic transformation between representational forms, and the ability to convert an inefficient model into an efficient one. A ‘seven steps ’ methodology for classroom model-building is proposed and justified. Evaluation evidence suggests that the program engages learners effectively in constructive thinking and that its incorporation of a variety of forms of representation enriches the model-building process.