People use information about the covariation between a putative cause and an outcome to determine whether a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than is the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two experiments, we adapted paradigms for studying causal learning in order 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 whether people change the standard by which they assess causality (measured by Β). Our results indicate that the effect is due to bias.
(ProQuest: ... denotes formulae omitted.)
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 the decision process. Here, we use signal detection theory (SDT) to tease apart these alternatives for one phenomenon: causal discounting.
Causal discounting is a cue interaction effect, in which someone judges a moderately effective cause as less effective when it is learned about in the presence of a highly effective alternative (e.g., Baker, Mercier, Vallee- Tourangeau, Frank, & Pan, 1993; Goedert & Spellman, 2005). For example, a person taking a steroid and an antihistamine for allergies may believe a 50%-effective antihistamine is less effective, if used with a 90%-effective steroid. Another type of cue interaction arises when the occurrence of two cues is confounded and participants control for the alternative cue when judging the target (Spellman, 1996). Here, we focus on the case in which participants devalue a target cause in the presence of an alternative and the causes are unconfounded-that is, causal discounting proper (Goedert, Harsch, & Spellman, 2005).
Although cue interaction phenomena are reliably observed in both causal judgment and prediction, it is debated whether these phenomena reflect learning processes or decision processes (Stout & Miller, 2007). For instance, in our example above, the perceived effectiveness of the antihistamine may be lower because less is learned about it when the more effective steroid is present. Alternatively, the highly effective steroid may bias the judgment process by "raising the bar" for effectiveness. Signal detection analyses allow one to determine whether changes in performance reflect changes in the participant's sensitivity-that is, the ability to detect contingency between the cause and outcome-or changes in the participant's decision criterion.
SDT (Macmillan & Creelman, 2005) is a data-analytic tool that disentangles a person's sensitivity to detect a stimulus (d') from that person's bias to say "yes" (Β). These latent variables are calculated from two components of participants' responses: The hit rate (h) is the proportion of trials on which participants say "yes" when the candidate is causal. The false alarm rate ( fa) is the proportion of trials on which participants say "yes" when the candidate is noncausal.
SDT has successfully differentiated learning from decision processes in memory research. For example, training in mnemonic techniques affects sensitivity, leaving bias unchanged (McNicol & Ryder, 1971). Conversely, changing the payoff structure for correct responses affects bias, not sensitivity (Healy & Kubovy, 1978). A brief sketch will illustrate these ideas. Imagine a word list in which people typically recognize 62.5% of the old words (h) but also say that they remember 47.5% of the lures ( fa). One group is taught a new study strategy for remembering words. At test, their performance is much improved, relative to the standard: h = 75% and fa = 25% (Figure 1A). …