Causal Reasoning in Humans and Rats

Patricia Cheng
University of California, Los Angeles (UCLA)

This presentation reviews empirical findings on causal learning and diagnostic inference, primarily in humans but also in rats. Some findings are consistent with Bayes nets; others are not. The findings will be discussed with respect to an associationist and a causal approach. The two approaches differ on whether reasoners make tacit generic assumptions about unobservable causal events. The assumptions enable a differentiation between causation and mere association. They also have implications for statistics and lead to novel predictions that go beyond intuition. Deviations from Bayes nets may be explained by a process that is adaptive in view of attention and memory limitations.

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