An eyetracking version of the classic Shepard, Hovland and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning includes learning how to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. However, we also found that neither associationist accounts of gradual learning nor hypothesis-testing accounts accurately predicted the pattern of eye movements leading up to successful learning. The implication of these results, and the use of eyetracking technology more generally, for categorization theory are discussed.

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.

No abstract

Despite the recent interest in the theoretical knowledge embedded in human representations of categories, little research has systematically manipulated the structure of such knowledge. Across four experiments this study assessed the effects of interattribute causal laws on a number of category-based judgments. The authors found that (a) any attribute occupying a central position in a network of causal relationships comes to dominate category membership, (b) combinations of attribute values are important to category membership to the extent they jointly confirm or violate the causal laws, and (c) the presence of causal knowledge affects the induction of new properties to the category. These effects were a result of the causal laws, rather than the empirical correlations produced by those laws. Implications for the doctrine of psychological essentialism, similarity-based models of categorization, and the representation of causal knowledge are discussed.

An eyetracking study testing D. L. Medin and M. M. Schaffer's (1978) 5-4 category structure was conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5-4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5-4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates' eye movements while the students learned the 5-4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5-4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed.