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date: 13 December 2019

Abstract and Keywords

Humans are clearly sensitive to causal structures—we can describe and understand causal mechanisms and make predictions based on them. But this chapter asks: Is causal learning always causal? Or might seemingly causal behavior sometimes be based on associations that merely encode the information that two events “go together,” not that one causes the other? This associative view supposes that people often (mis)interpret associations as supporting the existence of a causal relationship between events; they make the everyday mistake of confusing correlation with causation. To assess the validity of this view, one must move away from considering specific implementations of associative models and instead focus on the general principle embodied by the associative approach—that the rules governing learning are general-purpose, and so do not differentiate between situations involving cause–effect relationships and those involving signaling relationships that are non-causal.

Keywords: learning, causal, association, associative, covariation, correlation

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