Feature frequency in concept learning: What is counted?

The basic feature estimation and whole exemplar tests were designed such that both models make the same predictions, whereas the typicality rating, classification, and confignral feature estimation tests weredesigned to distinguish between the models.

Abstract

Frequency theories of concept learning assume that people count how often features occur among instances of a concept, but different versions make various assumptions about what features they count. According to the basic feature model, only basic features are counted, whereas according to the configural model, basic features and configural features (all combinations of basic features) are counted. Two experiments assessed the predictions of both versions of frequency theory. Subjects viewed schematic human faces, which included both positive and negative instances of the concept to be learned, and then provided typicality ratings, classification responses, and frequency estimates of configural features, basic features, and whole exemplars. Because both models assume that basic features are counted, they make the same predictions in many situations. Here, the basic feature estimation and whole exemplar tests were designed such that both models make the same predictions, whereas the typicality rating, classification, and confignral feature estimation tests were designed to distinguish between the models. The pattern of results clearly supported the basic feature version of frequency theory.