ON THE INTERACTION OF THEORY AND DATA IN CONCEPT-LEARNING

Standard models of concept learning generally focus on deriving statis tical properties of a category based on data (i.e., category members a nd the features that describe them) but fail to give appropriate weigh t to the contact between people's intuitive theories and these data. T wo experiments explored the role of people's prior knowledge or intuit ive theories on category learning by manipulating the labels associate d with the category. Learning differed dramatically when categories of children's drawings were meaningfully labeled (e.g., ''done by creati ve children'') compared to when they were labeled in a neutral manner. When categories are meaningfully labeled, people bring intuitive theo ries to the learning context. Learning then involves a process in whic h people search for evidence in the data that supports abstract featur es or hypotheses that have been activated by the intuitive theories. I n contrast, when categories are labeled in a neutral manner, people se arch for simple features that distinguish one category from another. I mportantly, the final study suggests that learning involves an interac tion of people's intuitive theories with data, in which theories and d ata mutually influence each other. The results strongly suggest that s traight-forward, relatively modular ways of incorporating prior knowle dge into models of category learning are inadequate. More telling, the results suggest that standard models may have fundamental limitations . We outline a speculative model of learning in which the interaction of theory and data is tightly coupled. The article concludes by compar ing the results to recent artificial intelligence systems that use pri or knowledge during learning.