Motivated to learn: An account of explanatory satisfaction

Are ethical explanations explanatory? Meta-ethical beliefs shape judgments about explanations for social change

Why were women given the right to vote? "Because it is morally wrong to deny women the right to vote." This explanation does not seem to fit the typical pattern for explaining an event: rather than citing a cause, it appeals to an ethical claim. Do people judge ethical claims to be genuinely explanatory? And if so, why? In Studies 1 (N = 220) and 2 (N = 293), we find that many participants accept ethical explanations for social change and that this is predicted by their meta-ethical beliefs in moral progress and moral principles, suggesting that these participants treat morality as a directional feature of the world, somewhat akin to a causal force. In Studies 3 (N = 513) and 4 (N = 328), we find that participants recognize this relationship between ethical explanations and meta-ethical commitments, using the former to make inferences about individuals' beliefs in moral progress and moral principles. Together these studies demonstrate that our beliefs about the nature of morality shape our judgments of explanations and that explanations shape our inferences about others' moral commitments.

Inside Ockham's razor: A mechanism driving preferences for simpler explanations

People often prefer simpler explanations, defined as those that posit the presence of fewer causes (e.g., positing the presence of a single cause, Cause A, rather than two causes, Causes B and C, to explain observed effects). Here, we test one hypothesis about the mechanisms underlying this preference: that people tend to reason as if they are using "agnostic" explanations, which remain neutral about the presence/absence of additional causes (e.g., comparing "A" vs. "B and C," while remaining neutral about the status of B and C when considering "A," or of A when considering "B and C"), even in cases where "atheist" explanations, which specify the absence of additional causes (e.g., "A and not B or C" vs. "B and C and not A"), are more appropriate. Three studies with US-based samples (total N = 982) tested this idea by using scenarios for which agnostic and atheist strategies produce diverging simplicity/complexity preferences, and asking participants to compare explanations provided in atheist form. Results suggest that people tend to ignore absent causes, thus overgeneralizing agnostic strategies, which can produce preferences for simpler explanations even when the complex explanation is objectively more probable. However, these unwarranted preferences were reduced by manipulations that encouraged participants to consider absent causes: making absences necessary to produce the effects (Study 2), or describing absences as causes that produce alternative effects (Study 3). These results shed light on the mechanisms driving preferences for simpler explanations, and on when these mechanisms are likely to lead people astray.

Adults' learning of complex explanations violates their intuitions about optimal explanatory order

Should you first teach about the purpose of a microwave or about how it heats food? Adults strongly prefer explanations to present function before mechanism and information about a whole to precede information about its component parts. Here we replicate those preferences (Study 1). Using the same stimuli, we then ask whether those pedagogical preferences reflect ease of learning of labels, function, or mechanism. Surprisingly, explanations that accord with function-before-mechanism and whole-before-part structure show no learning benefits to participants compared to other participants who see lessons that violate one or both intuitions (Study 2). Even when potential scaffolds are removed (i.e., diagrams) the preferred pedagogical order does not predict better learning (Study 3). Finally, explanatory order has only modest effects on experiential outcomes (e.g., curiosity, frustration; Study 4). In all cases, all orders of presentation support learning in comparison to controls and are not constrained by either ceiling or floor effects. Reasons for the clash between intuitions about learning and actual outcomes are explored.

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

Tautological formal explanations: does prior knowledge affect their satisfiability?

It is known that formal explanations with categorical labels are more satisfying than explicit tautologies. However, would they still be more satisfying if they are implicitly tautological themselves? In two experiments, we compared the degree of satisfaction between tautological formal explanations, explicit tautologies, and proper explanations. Additionally, we examined whether participants knew the correct definitions for the labels used in the formal explanations. Finally, we asked whether cultural and linguistic differences can play a role in the treatment of formal explanations with categorical labels. To this end, the first experiment involved Chilean students (N = 50), and the second experiment involved Russian students (N = 51). It was found that formal explanations, despite their intentional tautology, were still rated as more convincing compared to explicit tautologies (but less convincing than proper explanations). Furthermore, this effect did not depend on participants' previous knowledge (the label's definitions) or linguistic and cultural background. Taking all this into account, we consider this effect as a relatively universal psychological phenomenon and relate our findings to existing theories of formal explanations.

People are curious about immoral and morally ambiguous others

Looking to the popularity of superheroes, true crime stories, and anti-heroic characters like Tony Soprano, we investigated whether moral extremity, especially moral badness, piques curiosity. Across five experiments (N = 2429), we examine moral curiosity, testing under what conditions the moral minds of others spark explanation-seeking behavior. In Experiment 1, we find that among the most widely watched Netflix shows in the US over a five-month period, the more immoral the protagonist, the more hours people spent watching. In Experiments 2a and 2b, we find that when given a choice to learn more about morally good, bad, ambiguous, or average others, people preferred to learn more about morally extreme people, both good and bad. Experiment 3 reveals that people are more curious for explanations about (vs. descriptions of) morally bad and ambiguous people compared to morally good ones. Finally, Experiment 4 tests the uniqueness of curiosity for moral ambiguity. We find that people are more drawn to moral rather than aesthetic ambiguity, suggesting that ambiguity, which is cognitively taxing and sometimes avoided, preferentially engenders information seeking in the moral domain. These findings suggest deviations from moral normativity, especially badness, spur curiosity. People are curious about immorality and agents who differ from the norm.

Explanations in the wild

Why do some explanations strike people as highly satisfying while others, seemingly equally accurate, satisfy them less? We asked laypeople to generate and rate thousands of open-ended explanations in response to 'Why?' questions spanning multiple domains, and analyzed the properties of these explanations to discover (1) what kinds of features are associated with greater explanation quality; (2) whether people can tell how good their explanations are; and (3) which cognitive traits predict the ability to generate good explanations. Our results support a pluralistic view of explanation, where satisfaction is best predicted by either functional or mechanistic content. Respondents were better able to judge how accurate their explanations were than how satisfying they were to others. Insight problem solving ability was the cognitive ability most strongly associated with the generation of satisfying explanations.

Simplicity as a Cue to Probability: Multiple Roles for Simplicity in Evaluating Explanations

People often face the challenge of evaluating competing explanations. One approach is to assess the explanations' relative probabilities-for example, applying Bayesian inference to compute their posterior probabilities. Another approach is to consider an explanation's qualities or "virtues," such as its relative simplicity (i.e., the number of unexplained causes it invokes). The current work investigates how these two approaches are related. Study 1 found that simplicity is used to infer the inputs to Bayesian inference (explanations' priors and likelihoods). Studies 1 and 2 found that simplicity is also used as a direct cue to the outputs of Bayesian inference (the posterior probability of an explanation), such that simplicity affects estimates of posterior probability even after controlling for elicited (Study 1) or provided (Study 2) priors and likelihoods, with simplicity having a larger effect in Study 1, where posteriors are more uncertain and difficult to compute. Comparing Studies 1 and 2 also suggested that simplicity plays additional roles unrelated to approximating probabilities, as reflected in simplicity's effect on how "satisfying" (vs. probable) an explanation is, which remained largely unaffected by the difficulty of computing posteriors. Together, these results suggest that the virtue of simplicity is used in multiple ways to approximate probabilities (i.e., serving as a cue to priors, likelihoods, and posteriors) when these probabilities are otherwise uncertain or difficult to compute, but that the influence of simplicity also goes beyond these roles.