[Download a pdf version of this paper.]

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appear necessary for explaining the structure of natural categories. We suggest that this mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members (often without the explicit intention to form a category). Thus, laboratory studies must explore a range of category uses. This paper reviews the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and of combining inference and classification is reviewed. This work suggests that exploring a range of category uses will help close the gap between models based on laboratory research and observations from natural categories.