Traditional psychological approaches to causality assume that we acquire causal information by extracting it from our experience of events. One possibility is that we can directly perceive causality (or can use a specialized perceptual module) to detect causal interactions. Another possibility is that we infer causality from correlational evidence—the co-occurrence of particular causes and effects. This chapter argues that these purely bottom-up solutions are unlikely to succeed. It considers a new approach to causal cognition that may alleviate these difficulties which comes from research on Bayes nets in computer science.
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