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Abstract
This thesis examined how task demands shape the category representations formed through classification, inference and incidental learning. Experiments 1 to 3 examined the claim that the representations formed through inference learning are based only on the encoding of prototypical features (e.g., Yamauchi & Markman, 1998, 2000). Adults learned artificial categories through exemplar classification or feature inference. Inference learning either did or did not require attention to prototypical features. At test, all participants classified exemplars and inferred the values of missing features. Classification learning resulted in the encoding of both prototypical and atypical features. Inference learning also led to the representation of both prototypical and atypical features when attention to both was required during learning. Experiment 4 extended these results to inferences about novel items varying in similarity to training items. Inference learners required to attend to prototypical and atypical features during training were more sensitive to exemplar similarity when making novel inferences than those who attended only to prototypical features.
Experiment 5 examined developmental change in the impact of noun and feature labels on feature inferences. Adults, 7-year-olds, and 5-yearolds were shown pairs of base and target exemplars. The base was given a noun or feature label. Participants were asked to predict the value of a missing feature of the target, when it was given the same or a different label as the base. Both adults and children were more likely to make inferences based on noun than feature labels. Hence, by five years of age, children grasp the inductive potential of noun labels. Experiments 6 to 9 compared incidental category learning with intentional classification. Adults classified categories of geometric shapes or learned the categories through an incidental task. Incidental recognition learning resulted in a broader allocation of attention than classification learning. Performing recognition before classification resulted in a broader attentional allocation than performing recognition after classification. Together with the results from mathematical modelling, these findings support a view of category learning in which the specific attentional demands of different learning tasks determine the nature of the category representations that are acquired.