This article is based on the Estes (1986b) article that examined learning processes associated with categorization in relation to new-old recognition. The focus of the commentary is on alternative views of recognition/classification relations as well as the implications of the more detailed analyses of learning for exemplar-based classification models. The argument is made that strategies typically used by experimental participants and exemplar processing have some fundamental properties in common. This implies that a good fit to classification data by an exemplar model does not necessarily mean that performance is based on comparisons with remembered exemplars but suggests that abstract representations may not be different kinds of entities from the memory representation of a specific experience.