The effects of category generalizations and instance similarity on schema abstraction.

Three experiments were designed to differentiate two models of schema abstraction. One model, called the generalization model, proposes that category generalizations, defined as feature combinations which occur frequently across study items, are abstracted during learning and used to classify transfer items. According to the other model, called the instance-only model, transfer items are classified according to their similarity to studied items. Study materials were constructed which either yielded category generalizations (generalize condition) or did not (control condition). Transfer items differed on whether they were classifiable by category generalizations and on their similarity to study items. In Experiments I and III, accuracy and confidence on transfer items was better in the generalize condition than in the control condition. Experiment II manipulated the order in which generalizable study items were presented for study: Items were either blocked, so that items contributing to a category generalization occurred close in the study sequence, or randomly ordered. Study items were learned faster and transfer performance was better with blocked presentation than with random presentation. In all three experiments, there was an effect for the similarity of transfer items to study material. There was some evidence suggesting an advantage for partially matching a category generalization. The results support a schema abstraction model in which transfer is a function of similarity to both specific category instances and to higher-order category information. (Author)