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.

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. In this article, we address the representation of complex perceptual categories. Contrary to the conventional and widespread assumption that people’s representations are homogeneous and integrated, we show in two experiments that people often master a complex categorization task by forming independent components, or parcels, of knowledge. We also show that once a knowledge

Minda and Ross (2004) described two experiments where subjects simultaneously learned both a category and a function. They showed that when both tasks were performed in parallel on the same stimuli, the inductive bias on the categorization task–to focus on a single attribute–spread to the function learning task. Here, we present a new computational model of this phenomenon, using the ALCOVE model of categorization, a new model of function learning, and a hypothesis for their interaction: shared selective attention. The model parsimoniously succeeds in learning the category and function, then in accounting for human generalization patterns on conflicting transfer stimuli. The novel function-learning component of the model, extending previous work in mixture-of-experts approaches (Kalish, Lewandowsky, & Kruschke, 2004; Harris & Minda, 2005), is also introduced.

This article reports the results of an experiment addressing extrapolation in function learning, in particular the issue of whether participants can extrapolate in a nonmonotonic manner. Existing models of function learning, including the extrapolation association model of function learning (EXAM; E. L

Subtyping as a knowledge preservation