Comparing Models of Function Learning

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DESCRIPTION (provided by applicant): Human concepts are complex, varied, and serve myriad purposes. One way concepts are used is to categorize people or things and infer properties from category membership. Historically, this view of concepts has dominated the theoretical and empirical literature in cognitive psychology. But this view is too restrictive and another important way concepts are used is to make predictions from strengths of causes to magnitudes of effects on the basis of continuous functional relationships. The general purpose of the proposed research is to provide the foundations for a more formal, systematic, and integrative approach to function learning that parallels the existing progress in category learning. More specifically, we aim to achieve the following three specific goals. First, we plan to rigorously test rule versus associative based models of function learning in a restricted domain that includes only single input - single output functions. Our second line of research addresses the possibility that the rule-abstraction and exemplar-based processes that are observed by individuals in function learning indicate a general learning orientation that produces characteristic learning outcomes for a host of higher-order cognitive tasks. In our third major focus, we examine the interrelations between learning and decision-making by manipulating the type of payoffs provided as feedback during function learning. This last line of research is designed to test the basic learning mechanisms underlying almost all connectionist-learning approaches.