Explanations and Causal Judgments Are Differentially Sensitive to Covariation and Mechanism Information

Abstract

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to be more sensitive to mechanism and less sensitive to covariation – even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition.

The Broad Reach of Multivariable Thinking

“…Such explanations seem more satisfying compared to ones that identify factors thought to be associated with the outcome and shown capable of producing it but without explaining the mechanism involved (Zemla et al 2017(Zemla et al 2022. Mechanistic explanations are thought to be preferred because they provide a sense of understanding (Vasilyeva and Lombrozo 2015;Rozenblit and Keil 2002) whether or not the explanation is correct (Ahn et al 1995). 'This is how it could happen' overrides evidence that this is what does happen (Kuhn 1991).…”

“…'This is how it could happen' overrides evidence that this is what does happen (Kuhn 1991). Both empirical evidence and mechanistic explanation have justifiable roles to play in explaining events, and it is less than straightforward to prescriptively judge their relative roles (Ahn et al 1995;Koslowski 1996;Walker et al 2017;Vasilyeva and Lombrozo 2015).…”

“…A second preference identified in both psychological and philosophical study has been the preference for simple explanations over more complex ones that invoke multiple factors (Johnson et al 2020;Lombrozo 2007;Thagard 1989). Parsimony is valued among experts as well as laypeople, as supported by findings going back as far as a large literature on discounting (Kelley 1973) although recent work has suggested that this preference may be less than universal if multiple factors are seen as providing a superior account of mechanism (Vasilyeva and Lombrozo, 2015;Zemla et al 2017Zemla et al , 2022. Additionally, there are task factors that influence this preference, such as prediction vs. attribution, with the latter task being more strongly associated with single-factor reasoning (Johnson et al 2019;Pilditch et al 2019).…”

Not so Simple! Mechanisms Increase Preference for Complex Explanations

“…Though many factors have been shown to affect how we judge explanations, we ask whether preferences change depending on whether or not an explanation describes causal mechanisms. While some studies have directly compared explanations with and without mechanisms (e.g., Ahn & Bailenson, 1996;Vasilyeva & Lombrozo, 2015), most have not. Here, we test whether explanations that describe mechanisms are evaluated differently than explanations that do not contain mechanisms, with a focus on one explanatory virtue: simplicity.…”

“…Johnson and Keil (2017) found this to be especially common when evaluating token claims (e.g., smoking caused John's lung cancer) compared to type claims (e.g., smoking causes cancer). Vasilyeva and Lombrozo (2015) found that explanations with mechanisms are typically judged as better than their counterparts without mechanisms. In the same study, participants rated their subjective understanding of a phenomenon as higher when the explanations contained mechanisms.…”