Coherent category training enhances generalization and increases reliance on prototype representations

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. Using category learning as a representative domain, the present study tested 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. There was little effect of set size. 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. We show that more subjects rely on a prototype strategy following high coherence training, suggesting that more coherent training sets facilitate extraction of the category average. Together, these results provide strong evidence for the benefit of training on examples that are similar to one another and to the category center.