FEATURE-BASED AND RULE-BASED GENERALIZATION IN HUMAN ASSOCIATIVE LEARNING

Two experiments examined the contributions of feature- and rule-based knowledge in a human associative learning task. Participants were pres ented with concurrent negative (A --> O, B --> O, AB --> no O) and pos itive (C --> no O, D --> no O, CD --> O) patterning problems in which certain combinations of foods were associated with an allergy outcome (O). In the test stage, some participants showed normal feature-based generalization to novel trial types, whereas other participants transf erred the patterning rule (i.e., a compound and its elements signal op posite outcomes). Mastery of the discrimination presented in the train ing phase was strongly linked to rule-based generalization. The result s suggest that models; of human associative learning need to incorpora te mechanisms for rule-based as well as for feature-based generalizati on.