Coherent category training enhances generalization in prototype-based categories

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence acquisition of conceptual knowledge: the number of training examples (set size) and the similarity of training examples to the category average (set coherence). Across participants, size and coherence of category training sets were varied in a fully-crossed design. After training, participants demonstrated the breadth of their category knowledge by categorizing novel examples varying in their distance from the category center. Results showed better generalization following more coherent training sets, even when categorizing items furthest from the category center. Training set size had limited effects on performance. We also tested the types of representations underlying categorization decisions by fitting formal prototype and exemplar models. Prototype models posit abstract category representations based on the category’s central tendency, whereas exemplar models posit that categories are represented by individual category members. In Experiment 1, low coherence training led to fewer participants relying on prototype representations, except when training length was extended. In Experiment 2, low coherence training led to chance performance and no clear representational strategy for nearly half of the participants. The results indicate that highlighting commonalities among exemplars during training facilitates learning and generalization and may also affect the types of concept representations that individuals form.