Evaluating everyday explanations

Abstract

People frequently rely on explanations provided by others to understand complex phenomena. A fair amount of attention has been devoted to the study of scientific explanation, and less on understanding how people evaluate naturalistic, everyday explanations. Using a corpus of diverse explanations from Reddit's "Explain Like I'm Five" and other online sources, we assessed how well a variety of explanatory criteria predict judgments of explanation quality. We find that while some criteria previously identified as explanatory virtues do predict explanation quality in naturalistic settings, other criteria such as simplicity do not. Notably, we find that people have a preference for complex explanations that invoke more causal mechanisms to explain an effect. We propose that this preference for complexity is driven by a desire to identify enough causes to make the effect seem inevitable. EVALUATING EVERYDAY EXPLANATIONS 3 Evaluating Everyday ExplanationsPeople are explanatory creatures. We often seek to generate explanations based on our own knowledge of how the world works. However our ability to generate complete explanations on our own is frequently inadequate. We may not have all of the evidence or the expertise to be able to form accurate models of complex phenomena. So we use the knowledge of experts, friends, and communities to piece together explanations. Our beliefs about science are not limited to intuitive preconceptions, but are also derived from scientists who inform us of how things work. Our beliefs about the economy are affected not only by our own experiences, but also by what economists and politicians tell us about large-scale financial systems. We rely on the explanations of others to form our own beliefs.How, then, do we evaluate the explanations of others? Explanatory CriteriaA common view has emerged that the quality or value of an explanation can be determined by how well it satisfies a set of criteria known as explanatory virtues (Lipton, 2004;Thagard, 1978;Harman, 1965; Mackonis, 2003;Glymour, 2014;Lombrozo, 2011).However, there is disagreement about what counts as an explanatory virtue, how these virtues are defined and measured, and how they are weighted when we evaluate an explanation. Two commonly proposed virtues are simplicity and coherence. For example, a good explanation should be simple, requiring the fewest number of causes to explain a phenomenon (e.g., Lombrozo, 2007). A good explanation should also be coherent; it should be compatible with our existing beliefs and consistent with the evidence and with itself (e.g., Thagard, 1989). EVALUATING EVERYDAY EXPLANATIONS 4We may also evaluate an explanation using other criteria, such as the credibility of the explainer or how well the explanation is articulated, that do not reflect the intrinsic value of an explanation. These criteria are useful in satisfying goals beyond identifying the information inherent to an explanation (Patterson, Operskalski, & Barbey, 2015). For instance, a well-articulated explanation can be useful for ...

The False Promise of Sexiness: Perceived Importance, Interest, and Citation Patterns for Counterintuitive Research Findings

“…Finally, comprehensibility was a significant negative predictor of importance: More comprehensible findings were consistently seen as less important. This last effect is consistent with some prior research: Technical language, such as methods descriptions, is associated with a greater perception of "scientificness" and of trust (Thomm & Bromme, 2011), even irrelevant technical details improve perceived explanatory quality (Weisberg et al, 2008), and people often are willing to embrace more complex explanations (Johnson, Valenti & Keil, 2019;Zemla et al, 2017). However, these results are seemingly in tension with research on fluency, which suggests people associate more complex texts with lower intelligence (Oppenheimer, 2006).…”

Not so simple! Mechanisms increase preference for complex explanations

“…Some research has hinted that a person's explanatory goals can change which explanatory virtues are given priority. Zemla et al (2017) suggest that a preference for simplicity in explanation may shift depending on what the goal of an explanation is. Here, we test whether explanations that describe mechanisms are evaluated differently than explanations that do not contain mechanisms.…”

The Preference for Joint Attributions to Contrast-factor Attributions in Causal Contrast Situations

“…In many cases, causal attributions depend on causal explanations in that a good causal attribution should provide a good explanation. Previous studies on causal explanation suggest that explanatory factors such as explanatory sufficiency and simplicity may affect people's preferences for causal explanations (Glymour, 2001;Johnson & Ahn, 2017;Lombrozo & Vasilyeva, 2017;Zemla, Sloman, Bechlivanidis, & Lagnado, 2017;Zemla, et al, 2017). Thus, we conjecture that the two approaches to causal attributions may be associated with some explanatory factors.…”

“…Moreover, according to the complexity account (Zemla, et al, 2017), the participants would prefer the E option, which was the most complex attribution among the five options.…”

“…For the above issue, we think that the influence of explanatory sufficiency will trump the influence of explanatory simplicity, because a good causal attribution should primarily be sufficient for the effect. This prediction is suggested by the recent finding that for a causal explanation question without contrasts, people prefer complex explanations invoking two independent causes to simple explanations invoking one of the two causes because the former are more sufficient than the Joint Attributions in Causal Contrast Situations 6 latter (Zemla, et al, 2017). They proposed a complexity account that people prefer complex explanations because complex explanations are more sufficient than simple explanations.…”