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

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 PC

Problem Representation in Experts and Novices: Part 2. Underlying Processing Mechanisms

It has been well established that experts and novices focus on different aspects of problems, with novices focusing more on surface features rather than on deep principled features of a problem. What is less clear are the mechanisms that underlie these differences in construal of problem representation. The current study, which uses an `old/new' recognition procedure, examines expert and novice representation of arithmetic equations in which the deep relational properties (i.e., principles of commutativity and associativity) were well known to both groups. Results indicate that both novices and experts encode both surface and principled features in the same serial manner, with surface features preceding principled features for both. At the same time, only for novices and not for experts, surface features compete with deep features, thus requiring additional resources to inhibit this attentional competition

Problem Representation in Experts and Novices: Part 1. Differences in the Content Of Representation

Two experiments examined the content of novice and expert representations for both surface and deep structural elements of arithmetic equations. Experiment 1, which used a forcedchoice categorization task in which surface features of equations (e.g., digits) competed with deep structural principles of mathematics (associativity and commutativity), found that experts were more likely to focus on principles in their judgments than were novices, who focused more often on surface elements. Experiment 2, using a similar task, introduced trials in which only principled elements varied. Novices were able to focus on principled elements in this case, but failed to transfer these representations when surface features were reintroduced