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Alternative Approaches to Causal Induction: The Probabilistic Contrast Versus the Rescorla-Wagner Model

Abstract

Rescorla and Wagner's (1972) model of associative learning (RWM ) and Cheng and Novick's (1990, 1991, 1992) Probabilistic Contrast Model (PCM) represent competing approaches to modeling the covariation component of human causal induction. Given certain patterns of environmental inputs to the learner, these models sometimes make contradictory predictions about what will be learned. Some of these situations have been tested in Pavlovian conditioning experiments using animal subjects. W e interpret these results according to PCM, and find that they are consistent with the predictions of the model. The current experiment implements similar experimental designs as a causal inference task involving humans as subjects. Tw o experimental conditions were compared to examine each model's predictions regarding when the extinction of conditioned inhibition will occur. In one condition, the RW M predicts that a previously perceived inhibitory stimulus will be judged as less inhibitory, whereas the PC M predicts that subjects will not change their causal judgments; in the second condition, the two models make the reverse claims. The data provide strong evidence favoring the PCM

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