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.     

A rule-plus-exception model for classifying objects in continuous-dimension spaces

The authors propose a rule-plus-exception (RULEX) model for how observers classify stimuli residing in continuous-dimension spaces. The model follows in the spirit of the discrete-dimension version of RULEX developed by Nosofsky, Palmeri, and McKinley (1994). According to the model, observers learn categories by forming simple logical rules along single dimensions and by remembering occasional exceptions to those rules. In the continuous-dimension version of RULEX, the rules are formalized in terms of linear decision boundaries that are orthogonal to the coordinate axes of the psychological space. In addition, a similarity-comparison process governs whether stored exceptions are used to classify an object. The model provides excellent quantitative fits both to averaged classification transfer data and to distributions of generalizations observed at the individual-participant level. The modeling analyses suggest that, when multiple rules are available for solving a problem, averaged classification data often represent a probabilistic mixture of idiosyncratic rule-plus-exception strategies.     

Prototype and exemplar accounts of category learning and attentional allocation: a reassessment

In a recent article. J. P. Minda and J. D. Smith (2002; see record 2002-00620-002) argued that an exemplar model provided worse quantitative fits than an alternative prototype model to individual subject data from the classic D. L. Medin and M. M. Schaffer (1978) 5/4 categorization paradigm. In addition, they argued that the exemplar model achieved its fits by making untenable assumptions regarding how observers distribute their attention. In this article, we demonstrate that when the models are equated in terms of their response-rule flexibility, the exemplar model provides a substantially better account of the categorization data than does a prototype or mixed model. In addition, we point to shortcomings in the attention-allocation analyses conducted by J. P. Minda and J. D. Smith (2002). When these shortcomings are corrected, we find no evidence that challenges the attention-allocation assumptions of the exemplar model.     

A hybrid-similarity exemplar model for predicting distinctiveness effects in perceptual old-new recognition

In 2 sets of experiments, the authors investigated the basis for old-item distinctiveness effects in perceptual recognition, whereby distinctive old items are recognized with higher probability than are typical old items. In Experiment 1, distinctive old items were defined as those lying in isolated regions of a continuous-dimension similarity space. In this case, any beneficial effects of distinctiveness were absent or small, regardless of the structure of the test list used to assess recognition memory. In Experiment 2, distinctive items were defined as those objects containing certain discrete, individuating features. In this case, large old-item distinctive effects were observed, with the nature of the effects being modulated by the structure of the test lists. A hybrid-similarity exemplar model, combining elements of continuous-dimension distance and discrete-feature matching, was used to account for these distinctiveness effects in the recognition data.     

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.     

Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization

In three perceptual classification experiments involving ill-defined category structures, extreme prototype enhancement effects were observed in which prototypes were classified more accurately than other category instances. Such empirical findings can prove theoretically challenging to exemplar-based models of categorization if prototypes are psychological central tendencies of category instances. We found instead that category prototypes were sometimes better characterized as psychological extreme points relative to contrast categories. Extending a classic and widely cited study (Posner & Keele, 1968), participants learned categories created from distortions of dot patterns arranged in familiar shapes. Participants then made pairwise similarity judgements of the patterns. Multidimensional scaling (MDS) analyses of the similarity data revealed the prototypes to be psychological extreme points, not central tendencies. Evidence for extreme point representations was also found for novel prototype patterns displaying a symmetry structure and for prototypes of grid patterns used in recent studies by McLaren and colleagues (McLaren, Bennet, Guttman-Nahir, Kim, & M ackintosh, 1995). When used in combination with the derived M DS solutions, an exemplar-based model of categorization, the Generalized Context Model (Nosofsky, 1986), provided good fits to the observed categorization data in all three experiments.