Causal Schema-based Inductive Reasoning

Abstract

Inductive reasoning allows us to go beyond the target hypothesis and capitalize on prior knowledge. Past research has shown that both similarity relations and specific causal knowledge affect the inductive plausibility of hypotheses. The present experiment goes one step further by investigating the role of abstract causal schemas about main effects and interactions. We were interested in exploring whether the functional form of a causal schema influences our inductions even when no more specific causal knowledge is available. Our experiment shows that reasoners have different prior beliefs about the likelihood of main-effect versus interactive schemas, and rationally combine these prior beliefs with new evidence in a way that can be modeled as Bayesian belief updating. This finding casts doubt on theories which ignore the important role of priors in inductions involving causal schemas.


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