Generalizing Functions in Sparse Domains

We propose that when humans learn sets of relationships they are able to learn the abstract structure or type of a family of relationships, and exploit that knowledge to improve their ability to learn and generalize in the future, especially in the face of sparse or ambiguous data. In two experiments we found that participants choose patterns and extrapolate in ways consistent with sets of previously learned relations, as measured by extrapolation judgments and forced-choice tasks. We take these results to suggest that humans can detect shared abstract relations and apply this learned regularity to perform rapid and flexible generalization.