We propose that when humans learn sets of relationships theyare able to learn the abstract structure or type of a family of re-lationships, and exploit that knowledge to improve their abilityto learn and generalize in the future, especially in the face ofsparse or ambiguous data. In two experiments we found thatparticipants choose patterns and extrapolate in ways consistentwith sets of previously learned relations, as measured by ex-trapolation judgments and forced-choice tasks. We take theseresults to suggest that humans can detect shared abstract re-lations and apply this learned regularity to perform rapid andflexible generalization.
