A single-system interpretation of dissociations between recognition and categorization in a task involving object-like stimuli

In previous research (Reed, Squire, Patalano, Smith, & Jonides, 1999), amnesic patients performed at near normal levels in a categorization task involving stimuli with discrete features, but showed impaired recall of the features. These results were taken as evidence of the existence of separate memory systems for categorization and recall/recognition. The present research addresses a single-system account of this dissociation. In Experiment 1, results closely matching the dissociation pattern were obtained when normal participants' classification and recognition performance was tested either immediately or after a week delay. In addition, formal modeling of the data suggested that participants use only a few of the dimensions in the categorization task, whereas they must use many dimensions in the cued-recall task. In Experiment 2, we found that many participants could perform the categorization task without any exposure to the training sequence. These results suggest that different memory demands across the two tasks may be responsible for the observed dissociation—separate memory systems are not necessarily involved.     

Similarity-scaling studies of dot-pattern classification and recognition.

Classification performance in the dot-pattern, prototype-distortion paradigm (e.g., Posner & Keele, 1968) was modeled within a multidimensional scaling (MDS) framework. MDS solutions were derived for sets of dot patterns that were generated from prototypes. These MDS solutions were then used in conjunction with exemplar, prototype, and combined models to predict classification and recognition performance. Across 3 experiments, an MDS-based exemplar model accounted for the effects of several fundamental learning variables, including level of distortion of the patterns, category size, delay of transfer phase, and item frequency. Most important, the model quantitatively predicted classification probabilities for individual dot patterns in the sets, not simply general trends of performance. There was little evidence for the existence of a prototype-abstraction process that operated above and beyond pure exemplar-based generalization.     

Exemplar representation without generalization? Comment on Smith and Minda's (2000) "Thirty categorization results in search of a model"

J. D. Smith and J. P. Minda (2000) conducted a meta-analysis of 30 data sets reported in the classification literature that involved use of the "5-4" category structure introduced by D. L. Medin and M. M. Schaffer (1978). The meta-analysis was aimed at investigating exemplar and elaborated prototype models of categorization. In this commentary, the author argues that the meta-analysis is misleading because it includes many data sets from experimental designs that are inappropriate for distinguishing the models. Often, the designs involved manipulations in which the actual 5-4 structure was not, in reality, tested, voiding the predictions of the models. The commentary also clarifies various aspects of the workings of the exemplar-based context model. Finally, concerns are raised that the all-or-none exemplar processes that form part of Smith and Minda's (2000) elaborated prototype models are implausible and lacking in generality.     

Exemplar-based accounts of "multiple-system" phenomena in perceptual categorization

We demonstrate that a wide variety of recently reported "rule-described" and "prototype-described" phenomena in perceptual classification, which have led to the development of a number of multiple-system models, can be given an alternative interpretation in terms of a single-system exemplar-similarity model. The phenomena include various rule- and prototype-described patterns of generalization, dissociations between categorization and similarity judgments, and dissociations between categorization and old-new recognition. The alternative exemplar-based interpretation relies on the idea that similarity is not an invariant relation but a context-dependent one. Similarity relations among exemplars change systematically because of selective attention to dimensions and because of changes in the level of sensitivity relating judged similarity to distance in psychological space. Adaptive learning principles may help explain the systematic influence of the selective attention process and of modulation in sensitivity settings on judged similarity.     

An extension of the exemplar-based random-walk model to separable-dimension stimuli

An extension of Nosofsky and Palmeri's (Psychol. Rev. 104 (1997a) 266) exemplar-based random-walk (EBRW) model of categorization is presented as a model of the time course of categorization of separable-dimension stimuli. Nosofsky and Palmeri (1997a) assumed that the perceptual encoding of all stimuli was identical. However, in the current model, we assume as in Lamberts (J. Exp. Psychol.: General 124 (1995) 161) that the inclusion of individual stimulus dimensions into the similarity calculations is a stochastic process with the probability of inclusion based on the perceptual salience of the dimensions. Thus, the exemplars that enter into the random-walk change dynamically during the time course of processing. This model is implemented as a Markov chain. Its predictions are compared with alternative models in a speeded categorization experiment with separable-dimension stimuli.     

Classification response times in probabilistic rule-based category structures: Contrasting exemplar-retrieval and decision-boundary models

Experiments were conducted to contrast the predictions from exemplar models and rule-based decisionboundary models of perceptual classification. Observers classified multidimensional stimuli into categories that could be described in terms of easily verbalized logical rules. The critical manipulation was that some pairs of stimuli received probabilistic feedback, whereas other control pairs received deterministic feedback. Despite the probabilistic feedback, the probabilistic pairs and the deterministic pairs were the same distance from idealobserver, rule-based decision boundaries. Across two experiments with varying category structures, observers classified the probabilistic pairs with slower response times (RTs) and lower accuracies than the comparison deterministic pairs. The effects were relatively long term, extending into test blocks in which all feedback was withheld. The results were as predicted by exemplar models, but challenged models that posit that RT is a function solely of the distance of a stimulus from rule-based boundaries. The studies add considerable generality to previous ones and suggest that, even in domains involving rule-based category structures, exemplar-retrieval processes play a significant role. Supplemental materials related to this article may be downloaded from http:// mc.psychonomic-journals.org/content/supplemental.     

Combining exemplar-based category representations and connectionist learning rules.

Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments.     

Attention, similarity, and the identification-categorization relationship

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 Shepard'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.     

Dissociations Between Categorization and Recognition in Amnesic and Normal Individuals: An Exemplar-Based Interpretation

In recent work, the finding of dissociations between categorization and recognition in amnesic and normal individuals has been taken as evidence of multiple memory systems mediating these tasks. The present research provides support for the alternative idea that these dissociations can be interpreted in terms of a single-system exemplar-memory model that makes allowance for parameter differences across groups. In one experiment, a parameter change in memory sensitivity was induced by testing classification and recognition at varying delays; the results closely matched the ones observed by Knowlton and Squire (1993) for normal and amnesic participants. The exemplar model also yielded good quantitative predictions of the categorization-recognition dissociation. A second analysis demonstrated that dissociations between early versus late probabilistic classification learning and memory sensitivity were also well predicted by the single-system exemplar model. Limitations of the exemplar interpretation and future research directions are also discussed.