A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Al-though the rational model considers just I level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989). Anderson (1990) presented a rational analysis ot 6 human cog-nition. The term rational derives from similar "rational-man" analyses in economics. Rational analyses in other fields are sometimes called adaptationist analyses. Basically, they are ef-forts to explain the behavior in some domain on the assump-tion that the behavior is optimized with respect to some criteria of adaptive importance. This article begins with a general char-acterization ofhow one develops a rational theory of a particu-lar cognitive phenomenon. Then I present the basic theory of categorization developed in Anderson (1990) and review the applications from that book. Since the writing of the book, the theory has been greatly extended and applied to many new phenomena. Most of this article describes these new develop-ments and applications. A Rational Analysis Several theorists have promoted the idea that psychologists might understand human behavior by assuming it is adapted to the environment (e.g., Brunswik, 1956; Campbell, 1974; Gib-

this article is whether children can incorporate this information into inductive reasoning

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According to the diversity principle, diverse evidence is strong evidence. There has been considerable evidence that people respect this principle in inductive reasoning. However, exceptions may be particularly informative. Medin, Coley, Storms, and Hayes (2003) introduced a relevance theory of inductive reasoning, and used this theory to predict exceptions, including the non-diversity by property reinforcement effect. A new experiment that investigated this phenomenon is reported here. Subjects made inductive strength judgments and similarity judgments for stimuli from Medin et al. The inductive strength judgments showed the same pattern as in Medin et al., however the similarity judgments suggested that the pattern should be interpreted as a diversity effect rather than non-diversity. It is concluded that the evidence regarding the predicted non-diversity by property reinforcement effect does not give distinctive support for relevance theory, although this theory does address other r...

In a recent article, J. P. Minda and J. D. Smith (2002) 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 classic issue in the categorization literature has been whether people represent categories in terms of abstracted prototypes or in terms of specific exemplars. According to prototype models, people represent categories in terms of some central tendency computed over the category training instances and classify objects on the basis of how similar they are to the prototypes of the alternative

Recent research suggests that exemplar models of classification are disconfirmed by the finding of extreme prototype-enhancement effects and steep typicality gradients in a version of the prototype-learning paradigm. We argue that these results are due to learning-during-transfer effects and not to the abstraction of a prototype from the training instances. In the standard version of the paradigm, observers are flooded with multiple presentations of the prototype and its low distortions during transfer. In a modified transfer condition, we instead present multiple instances of an arbitrary high distortion and low distortions of that high distortion. An extreme “high-distortion enhancement effect ” is observed. Also, there is a flattening of the typicality gradient associated with the standard patterns (prototype, low distortions, and standard high distortions). The results provide dramatic evidence of the role of learning during transfer in this task and force a reevaluation of the dominant current interpretation of the steep typicality gradient. One of the central issues in categorization research has been the debate over the nature of category representation. According to prototype models, people abstract the central tendency, or prototype, of a category, and use that abstraction as the basis for classifying new items (Homa &

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.