Feature- and Rule-Based Generalization in Human Associative Learning

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Abstract

Two experiments examined the contributions of feature- and rule-based knowledge in a human associative learning task. Participants were presented with concurrent negative (A → O, B → O, AB → no O) and positive (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 transferred the patterning rule (i.e., a compound and its elements signal opposite outcomes). Mastery of the discrimination presented in the training phase was strongly linked to rule-based generalization. The results suggest that models of human associative learning need to incorporate mechanisms for rule-based as well as for feature-based generalization.

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