When is a Cause the “Same”? Incoherent Generalization across Contexts

Abstract

A theory or model of cause such as Cheng's power (p) allows people to predict the effectiveness of a cause in a different causal context from the one in which they observed its actions. Liljeholm and Cheng demonstrated that people could detect differences in the effectiveness of the cause when causal power varied across contexts of different outcome base rates, but that they did not detect similar changes when only the cause-outcome contingency, ∆p, but not power, varied. However, their procedure allowed participants to simplify the causal scenarios and consider only a subsample of observations with a base rate of zero. This confounds p, ∆p, and the probability of an outcome (O) given a cause (C), P(O|C). Furthermore, the contingencies that they used confounded p and P(O|C) in the overall sample. Following the work of Liljeholm and Cheng, we examined whether causal induction in a wider range of situations follows the principles suggested by Cheng. Experiments 1a and 1b compared the procedure used by Liljeholm and Cheng with one that did not allow the sample of observations to be simplified. Experiments 2a and 2b compared the same two procedures using contingencies that controlled for P(O|C). The results indicated that, if the possibility of converting all contexts to a zero base rate situation was avoided, people were sensitive to changes in P(O|C), p, and ∆p when each of these was varied. This is inconsistent with Liljeholm and Cheng's conclusion that people detect only changes in p. These results question the idea that people naturally extract the metric or model of cause from their observation of stochastic events and then, reasonably exclusively, use this theory of a causal mechanism, or for that matter any simple normative theory, to generalize their experience to alternative contexts.

What Is Transferred in Causal Generalization Across Contexts?

“…Most previous studies on causal learning investigated causal induction. Only a handful of studies investigated causal generalization (Barberia, Baetu, Sansa, & Baker, 2014; Lien & Cheng, 2000; Liljeholm & Cheng, 2007).…”

“…To our knowledge, no previous studies have examined such direct transfer questions. There are a few studies on causal generalization (Barberia et al, 2014; Liljeholm & Cheng, 2007). These studies presented participants with complete information of two hypothetical medical intervention studies in different contexts, and then asked them to judge whether a candidate cause interacted with background causes that varied from one context to another or whether the candidate cause influenced the patients in each context in the same way.…”

“…She claimed that the causal power approach is an innate and automatic method of causal induction that happens automatically under normal circumstances with a trial-by-trial presentation format of causal instances, whereby people can automatically base their causal ratings on unobservable causal power. The normalization ensures that p is independent of the Δ P context such that it could be generalized across contexts (Barberia et al, 2014; Cheng, 1997; Liljeholm & Cheng, 2007). Thus, the power PC theory predicts that people will generalize the capacity of a cause in accordance with its causal power.…”

“…In tests of the covariation and causal power approach of causal induction, many previous studies showed that in most situations people make causal ratings in accordance with the Δ P rule rather than causal power (Barberia et al, 2014; Cheng & Holyoak, 1995; Collins & Shanks, 2006; Danks, 2003; Lober & Shanks, 2000; Perales & Shanks, 2008; Rescorla & Wagner, 1972; Spellman, 1996). Moreover, some causal learning studies using a trial-by-trial presentation format found a P ( E | C ) effect meaning that causal ratings increase with P ( E | C ) (Barberia et al, 2014; Collins & Shanks, 2006; Perales & Shanks, 2008). The P ( E | C ) effect implies strategy that some subjects base their causal ratings on P ( E | C ) without considering the effect base rate P ( E |∼ C ).…”

Two Heads Are Better Than One, but How Much?

“…During the last decade, many experiments have tried to find out whether causal inferences spontaneously made by people conform to Equation 2 . Unfortunately, this literature has failed to yield a clear and consistent pattern of results (cf., Allan, 2003 ; Barberia, Baetu, Sansa, & Baker, in press ; Buehner, Cheng, & Clifford, 2003 ; Liljeholm & Cheng, 2007 ; Lober & Shanks, 2000 ; Perales & Shanks, 2003 , 2007 ). Most of these experiments have relied on experimental preparations in which the participants received information about the probability of the effect in the presence of the candidate cause and the probability of the effect in the absence of that cause.…”