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Several pairs of correlated features were embedded in descriptions that had a wealth of exemplar-specific (i.e., idiosyncratic) information, and sensitivity to these correlations was examined as a function of intentional and incidental encoding conditions. Participants in incidental conditions were able to access information about several embedded correlations, even when the correlations involved 3 rather than 2 dimensions, and when complex inferences were required to recover correlations. In intentional conditions, however, little access to correlations was observed. The richness of the stimuli made it difficult to detect correlations at encoding, and the representations that resulted from analysis appeared too impoverished to allow covariations to be recovered. The results are discussed in terms of the advantages of storing examples for addressing unanticipated needs and goals.

Four experiments examined sensitivity to feature frequencies and feature correlations as a function of intentional and incidental concept learning. Feature frequencies were encoded equally well across variations in learning strategies, and although classification decisions in both intentional and incidental conditions preserved correlated features, this sensitivity was achieved through different processes. With intentional learning, sensitivity to correlations resulted from explicit rules, whereas incidental encoding preserved correlations through a similarity-based analogical process. In incidental tasks that promoted exemplar storage, classification decisions were mediated by similarity to retrieved examples, and correlated features were indirectly preserved in this process. The results are discussed in terms of the diversity of encoding processes and representations that can occur with incidental category learning.

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The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations.

Although many experiments have investigated factors that constrain perceptual category construction, there have been no investigations of factors that constrain memory-based (MB) category construction. Six experiments examined the extent to which perceptual and MB sorting were influenced by correlated dimensions, family resemblance principles, and conceptual knowledge. Sensitivity to many types of relational information (e.g., correlated features, causal relations, interactive properties of objects, and family resemblance relations) was observed with perceptual sorting, but these properties were rarely used to organize information in MB sorting conditions. Instead, there was a clear preference to organize categories around single dimensions. Even when perfectly correlated features were causally related, Ss in memory conditions did not use correlations to construct categories. The strengths and limitations of MB analyses and categorizations are discussed.