This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

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Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of withincategory information. Accordingly, we predicted that classification learning would produce a deficit in people’s ability to draw novel contrasts—distinctions that were not part of training—compared with feature inference learning. Two experiments confirmed that classification learners were at a disadvantage at making novel distinctions. Eye movement data indicated that this conceptual inflexibility was due to (a) a narrower attention profile that reduces the encoding of many category features and (b) learned inattention that inhibits the reallocation of attention to newly relevant information. Implications of these costs of supervised classification learning for views of conceptual structure are discussed.

Several studies propose that exemplar retrieval contributes to multi-attribute decisions. The authors have proposed a process theory enabling a priori predictions of what cognitive representations people use as input to their judgment process (Sigma, for “summation”; P. Juslin, L. Karlsson, & H. Olsson, 2008). According to Sigma, exemplar retrieval is a back-up system when the task does not allow for additive and linear abstraction and integration of cue-criterion knowledge (e.g., when the task is non-additive). An important question is to what extent such shifts occur spontaneously as part of automatic procedures, such as error-minimization with the Delta rule, or if they are controlled strategy shifts contingent on the ability to identify a sufficiently successful judgment strategy. In this article data are reviewed that demonstrate a shift between exemplar memory and cue abstraction, as well as data where the expected shift does not occur. In contrast to a common assumption of previous models, these results suggest a controlled and contingent strategy shift. Keywords: exemplar memory, cue abstraction, strategy shifts, multi-attribute decisions, Sigma. 1

Previous research suggests that categories learned through classification focus on exemplar information, while categories learned by making predictive inferences focus on summary (i.e., prototype) information. To test this idea further, we demonstrated that it is more difficult to learn nonlinearly separable categories by making inferences than by classifying. This research also supports previous studies by indicating that different processes are likely to mediate inference and classification In this paper, we examine the type of categorical information people assess in the process of obtaining inductive knowledge. Specifically, we investigate the extent to which abstract summary information about a category and specific information about individual exemplars of a category are used to make feature inferences .

The present research explored the role of working memory (WM) in unsupervised category learning, learning without an external tutor or even knowing that categories exist, by investigating its role using a pattern-sequence manipulation. A pattern-sequence manipulation compares learning when items from categories are presented together (blocked) versus when the items are presented in random order (mixed). Experiment 1 extended the pattern-sequence manipulation to assess category knowledge separate from paired-associate learning. Participants performed equally well on new and studied items, supporting the hypothesis that the pattern-sequence manipulation results in the acquisition of category information, not simply memory for item-feature associations. Experiment 2 introduced a WM factor, administering the method used in Experiment 1 to a group of high and low WM span participants. High WM span was predicted to interact with the pattern-sequence effect to produce greater learning when the items were blocked than mixed. There was reliable support for a role of WM span in the discovery and acquisition of category knowledge, but this role was different from the one hypothesized. The high WM span participants exhibited higher overall accuracies than the low WM span participants. This result supports a role for WM in unsupervised category learning, but did not benefit more from the pattern-sequence effect than did the low WM span participants as predicted. Implications for theories of category learning and WM are discussed.

One central function of categories is to allow people to infer the presence of features that cannot be directly observed.

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