Causal Induction in the Presence of a Perfect Negative Cue: Contrasting Predictions from Associative and Statistical Models

Two experiments on human causal induction with multiple candidate causes are reported. Experiment 1 investigated the influence of a perfect preventive cause on the ratings of a less contingent cause. Whereas the Rescorla-Wagner model (RWM) and Cheng's probabilistic contrast model predict that the less contingent cause should be completely discounted, the Pearce model predicts, in most cases, an enhancement of that cause's perceived importance. Results corresponded more closely tothe predictions of the Pearce model. The predictions of both the RWM and the Pearce model rely on a constant context cue acquiring associative strength, yet no such cue was explicitly identified in the task scenario employed in Experiment 1. Experiment 2 replicated a number of key conditions of Experiment 1 with a task scenario that afforded ratings of the causal importance of the context in which the effectiveness of the discrete candidate causes was evaluated. In addition, the number of trials was increased to test the possibility that the ratings in Experiment 1 were the product of incomplete learning. The results of the first experiment were replicated and the ratings of the effectiveness of the context cue were anticipated by both the RWM and the Pearce model. Overall, the Pearce model offers a more comprehensive account of the causal inferences recorded in this study.