Abstract
Our research focuses on learning about causal relationships between events when a candidate cause is a compound integrated by several individual causes. In two experiments, we compared the predictions of the Associative Models of Rescorla and Wagner (1972) and Pearce (1994), the Inductive Models of Cheng and Novick (1992) and Novick and Cheng (2004). In contrast with previous research about this topic, in these experiments, a causality judgments task was used in which the information about the presence/absence of the causes and the effect was presented through small samples of cases. Our results showed that the learning mechanisms involved in compound cue processing could be associative in origin.
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