Abstract

People use information about the covariation between a putative cause and an outcome to determine if a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two studies, we adapted paradigms for studying causal learning to apply signal detection analysis to this phenomenon. We investigated whether the presence of a stronger alternative makes the task more difficult (indexed by differences in d’), or if people change the standard by which they assess causality (measured by β). Our results indicate the effect is due to bias. R579B Causal Discounting 3 Humans can use knowledge of covariation to predict events and to infer their underlying causes (Cheng, 1997). Although research has demonstrated a number of systematic phenomena in covariation and causal judgment, it is unclear whether these effects occur during the learning or decision process. Here we use signal detection theory (SDT) to tease apart these alternatives for one phenomenon: causal discounting. Discounting is a cue interaction effect, in which someone judges a moderately effective

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