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.

When ratings of judged similarity or frequencies of stimulus identification are averaged across subjects, the psychological structure ofthe data is fundamentally changed. Regardless of the structure of the individual-subject data, the averaged similarity data will likely be well fit by a standard multidimensional scaling model, and the averaged identification data will likely be well fit by the similarity-choice model. In fact, both models often provide excellent fits to averaged data, even if they fail to fit the data of each individual subject. Thus, a good fit of either model to averaged data cannot be taken as evidence that the model describes the psychological structure that characterizes individual subjects. We hypothesize that these effects are due to the increased symmetry that is a mathematical consequence of the averaging operation. It is common practice to average across subjects when analyzing

This paper aims to introduce and discuss a geometrically based model, the relative prominence model, which is inspired by Tversky's (1977) finding that a factor behind asymmetric similarity seems to be "relative prominence ". The model proposes that the experienced directed similarity from I to J is proportional to some symmetric similarity measure between I and J, and the quotient between the "prominences" for J and I. Analysis of empirical data from different areas shows that it is possible for a procedure to estimate the parameters of the model quite well. The paper is concluded with a discussion of the differences between the relative prominence model and related models that handle asymmetry in terms of "stimulus bias".

This paper presents a neural model of similarity perception in identification tasks. It is based on self-organizing maps and population coding and is examined through five different identification experiments. Simulating an identification task, the neural model generates a confusion matrix that can be compared directly with that of human subjects. The model achieves a fairly accurate match with the pertaining experimental data both during training and thereafter. To achieve this fit, we find that the entire activity in the network should decline while learning the identification task, and that the population encoding of the specific stimuli should become sparse as the network organizes. Our results thus suggest that a self-organizing neural model employing population coding can account for identification processing, while suggesting computational constraints on the underlying cortical networks. * To whom correspondence should be addressed.

Luce's choice model and Thurstone's categorical judgment model compared: Kornbrot's data revisited