Scientific and religious explanations often coexist in the sense that they are both endorsed by the same individuals, and they are sometimes conjoined such that a single explanation draws upon both scientific and religious components. In this chapter we consider the psychology of such explanations, drawing upon recent research in cognitive and social psychology. We argue that scientific and religious explanations often serve different psychological functions, with scientific explanations seen as better serving epistemic functions (such as supporting accurate models of the world), and religious explanations seen as better serving non-epistemic functions (such as offering emotional comfort or supporting moral behavior). This functional differentiation points to a potential benefit of conjunctive explanations: by fulfilling multiple psychological functions, they will sometimes satisfy a broader range of explanatory goals. Generalizing from the case of science and religion, we suggest that conjunctive explanations may be especially appealing when a given explanatory framework faces tradeoffs between different explanatory goals (such as generality versus precision), resulting in an advantage to explanations that draw upon multiple explanatory frameworks instantiating different tradeoffs.

Humans are avid explainers: we ask “why?” and derive satisfaction from a good answer. But humans are also selective explainers: only some observations prompt us to ask “why?”, and only some answers are satisfying. This article reviews recent work on selectivity in explanation-seeking curiosity and explanatory satisfaction, with a focus on how this selectivity makes us effective learners in a complex world. Research finds that curiosity about the answer to a why-question is stronger when it is expected to yield useful learning, and that explanations are judged more satisfying when they are perceived to support useful learning. While such perceptions are imperfect, there is nonetheless evidence that seeking and evaluating explanations – in the selective way humans do – can play an important role in learning.

What changes people’s judgments on moral issues, such as the ethics of abortion or eating meat? On some views, moral judgments result from deliberation, such that reasons and reasoning should be primary drivers of moral change. On other views, moral judgments reflect intuition, with reasons offered as post-hoc rationalizations. We test predictions of these accounts by investigating whether exposure to a moral philosophy course (vs. control courses) changes moral judgments, and if so, via what mechanism(s). In line with deliberative accounts of morality, we find that exposure to moral philosophy changes moral views. In line with intuitionist accounts, we find that the mechanism of change is reduced reliance on intuition, not increased reliance on deliberation; in fact, deliberation is related to increased confidence in judgments, not change. These findings suggest a new way to reconcile deliberative and intuitionist accounts: Exposure to reasons and evidence can change moral views, but primarily by discounting intuitions.

People often engage in biased reasoning, favoring some beliefs over others even when the result is a departure from impartial or evidence-based reasoning. Psychologists have long assumed that people are unaware of these biases and operate under an “illusion of objectivity.” We identify an important domain of life in which people harbor little illusion about their biases – when they are biased for moral reasons. For instance, people endorse and feel justified believing morally desirable propositions even when they think they lack evidence for them (Study 1a/1b). Moreover, when people engage in morally desirable motivated reasoning, they recognize the influence of moral biases on their judgment, but nevertheless evaluate their reasoning as ideal (Studies 2–4). These findings overturn longstanding assumptions about motivated reasoning and identify a boundary condition on Naïve Realism and the Bias Blind Spot. People’s tendency to be aware and proud of their biases provides both new opportunities, and new challenges, for resolving ideological conflict and improving reasoning.

Curiosity plays a key role in directing learning throughout the lifespan. Prior work finds that violations of expectations can be powerful triggers of curiosity in both children and adults, but it is unclear which expectation-violating events induce the greatest curiosity and how this might vary over development. Some theories have suggested a U-shaped function such that stimuli of moderate extremity pique the greatest curiosity. However, expectation-violations vary not only in degree, but in kind: for example, some things violate an intuitive theory (e.g., an alligator that can talk) and others are merely unlikely (e.g., an alligator hiding under your bed). Combining research on curiosity with distinctions posited in the cognitive science of religion, we test whether minimally counterintuitive (MCI) stimuli, which involve one violation of an intuitive theory, are especially effective at triggering curiosity. We presented adults (N = 77) and 4- and 5-year-olds (N = 36) in the United States with stimuli that were ordinary, unlikely, MCI, and very counterintuitive (VCI) and asked which one they would like to learn more about. Adults and 5-year-olds chose Unlikely over Ordinary and MCI over Unlikely, but not VCI over MCI, more often than chance. Our results suggest that (i) minimally counterintuitive stimuli trigger greater curiosity than merely unlikely stimuli, (ii) surprisingness has diminishing returns, and (iii) sensitivity to surprisingness increases with age, appearing in our task by age 5.

Abstract The notion of belief appears frequently in cognitive science. Yet it has resisted definition of the sort that could clarify inquiry. How then might a cognitive science of belief proceed? Here we propose a form of pluralism about believing. According to this view, there are importantly different ways to believe an idea. These distinct psychological kinds occur within a multi-dimensional property space, with different property clusters within that space constituting distinct varieties of believing. We propose that discovering such property clusters is empirically tractable, and that this approach can help sidestep merely verbal disputes about what constitutes “belief.”

Intuitions play an important role in the debate on the causal status of high-level properties. For instance, Kim has claimed that his “exclusion argument” relies on “a perfectly intuitive ... understanding of the causal relation.” We report the results of three experiments examining whether laypeople really have the relevant intuitions. We find little support for Kim's view and the principles on which it relies. Instead, we find that laypeople are willing to count both a multiply realized property and its realizers as causes, and regard the systematic overdetermination implied by this view as unproblematic.

Identifying abstract relations is essential for commonsense reasoning. Research suggests that even young children can infer relations such as “same” and “different,” but often fail to apply these concepts. Might the process of explaining facilitate the recognition and application of relational concepts? Based on prior work suggesting that explanation can be a powerful tool to promote abstract reasoning, we predicted that children would be more likely to discover and use an abstract relational rule when they were prompted to explain observations instantiating that rule, compared to when they received demonstration alone. Five- and 6-year-olds were given a modified Relational Match to Sample (RMTS) task, with repeated demonstrations of relational (same) matches by an adult. Half of the children were prompted to explain these matches; the other half reported the match they observed. Children who were prompted to explain showed immediate, stable success, while those only asked to report the outcome of the pedagogical demonstration did not. Findings provide evidence that explanation facilitates early abstraction over and above demonstration alone.

How did the universe come to exist? What happens after we die? Answers to existential questions tend to elicit both scientific and religious explanations, offering a unique opportunity to evaluate how these domains differ in their psychological roles. Across 3 studies (N = 1,647), we investigate whether (and by whom) scientific and religious explanations are perceived to have epistemic merits—such as evidential and logical support—versus nonepistemic merits—such as social, emotional, or moral benefits. We find that scientific explanations are attributed more epistemic merits than are religious explanations (Study 1), that an explanation’s perceived epistemic merits are more strongly predicted by endorsement of that explanation for science than for religion (Study 2), and that scientific explanations are more likely to be generated when participants are prompted for an explanation high in epistemic merits (Study 3). By contrast, we find that religious explanations are attributed more nonepistemic merits than are scientific explanations (Study 1), that an explanation’s perceived nonepistemic merits are more strongly predicted by endorsement of that explanation for religion than for science (Study 2), and that religious explanations are more likely to be generated when participants are prompted for an explanation high in nonepistemic merits (Study 3). These findings inform theories of the relationship between religion and science, and they provide insight into accounts of the coexistence of scientific and religious cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

How and why does the moon cause the tides? How and why does God answer prayers? For many, the answer to the former question is unknown; the answer to the latter question is a mystery. Across three studies testing a largely Christian sample within the United States (N = 2,524), we investigate attitudes towards ignorance and inquiry as a window onto scientific versus religious belief. In Experiment 1, we find that science and religion are associated with different forms of ignorance: scientific ignorance is typically expressed as a personal unknown (“it’s unknown to me”), whereas religious ignorance is expressed as a universal mystery (“it’s a mystery”), with scientific unknowns additionally regarded as more viable and valuable targets for inquiry. In Experiment 2, we show that these forms of ignorance are differentially associated with epistemic goals and norms: expressing ignorance in the form of “unknown” (versus “mystery”) more strongly signals epistemic values and achievements. Experiments 2 and 3 additionally show that ignorance is perceived to be a greater threat to science and scientific belief than to religion and religious belief. Together, these studies shed light on the psychological roles of scientific and religious belief in human cognition.

Curiosity is considered essential for learning and sustained engagement, yet stimulating curiosity in educational contexts remains a challenge. Can people’s curiosity about a scientific topic be stimulated by providing evidence that knowledge about the topic has potential value to society? Here, we show that increasing perceptions of ‘social usefulness’ regarding a scientific topic also increases curiosity and subsequent information search. Our results also show that simply presenting interesting facts is not enough to influence curiosity, and that people are more likely to be curious about a scientific topic if they perceive it to be useful personally and socially. Given the link between curiosity and learning, these results have important implications for science communication and education more broadly.

Knowing which features are frequent among a biological kind (e.g., that most zebras have stripes) shapes people’s representations of what category members are like (e.g., that typical zebras have stripes) and normative judgments about what they ought to be like (e.g., that zebras should have stripes). In the current work, we ask if people’s inclination to explain why features are frequent is a key mechanism through which what “is” shapes beliefs about what “ought” to be. Across four studies (N = 591), we find that frequent features are often explained by appeal to feature function (e.g., that stripes are for camouflage), that functional explanations in turn shape judgments of typicality, and that functional explanations and typicality both predict normative judgments that category members ought to have functional features. We also identify the causal assumptions that license inferences from feature frequency and function, as well as the nature of the normative inferences that are drawn: by specifying an instrumental goal (e.g., camouflage), functional explanations establish a basis for normative evaluation. These findings shed light on how and why our representations of how the natural world is shape our judgments of how it ought to be.

Many explanations have a distinctive, positive phenomenology: receiving or generating these explanations feels satisfying. Accordingly, we might expect this feeling of explanatory satisfaction to reinforce and motivate inquiry. Across five studies, we investigate how explanatory satisfaction plays this role: by motivating and reinforcing inquiry quite generally (“brute motivation” account), or by selectively guiding inquiry to support useful learning about the target of explanation (“aligned motivation” account). In Studies 1–2, we find that satisfaction with an explanation is related to several measures of perceived useful learning, and that greater satisfaction in turn predicts stronger curiosity about questions related to the explanation. However, in Studies 2–4, we find only tenuous evidence that satisfaction is related to actual learning, measured objectively through multiple-choice or free recall tests. In Study 4, we additionally show that perceptions of learning fully explain one seemingly specious feature of explanatory preferences studied in prior research: the preference for uninformative “reductive” explanations. Finally, in Study 5, we find that perceived learning is (at least in part) causally responsible for feelings of satisfaction. Together, these results point to what we call the “imperfectly aligned motivation” account: explanatory satisfaction selectively motivates inquiry towards learning explanatory information, but primarily through fallible perceptions of learning. Thus, satisfaction is likely to guide individuals towards lines of inquiry that support perceptions of learning, whether or not individuals actually are learning.

Deliberative analysis enables us to weigh features, simulate futures, and arrive at good, tractable decisions. So why do we so often eschew deliberation, and instead rely on more intuitive, gut responses? We propose that intuition might be prescribed for some decisions because people’s folk theory of decision-making accords a special role to authenticity, which is associated with intuitive choice. Five pre-registered experiments find evidence in favor of this claim. In Experiment 1 (N = 654), we show that participants prescribe intuition and deliberation as a basis for decisions differentially across domains, and that these prescriptions predict reported choice. In Experiment 2 (N = 555), we find that choosing intuitively vs. deliberately leads to different inferences concerning the decision-maker’s commitment and authenticity—with only inferences about the decision-maker’s authenticity showing variation across domains that matches that observed for the prescription of intuition in Experiment 1. In Experiment 3 (N = 631), we replicate our prior results and rule out plausible confounds. Finally, in Experiment 4 (N = 177) and Experiment 5 (N = 526), we find that an experimental manipulation of the importance of authenticity affects the prescribed role for intuition as well as the endorsement of expert human or algorithmic advice. These effects hold beyond previously recognized influences on intuitive vs. deliberative choice, such as computational costs, presumed reliability, objectivity, complexity, and expertise.

Explanations highlight inductively rich relationships that support further generalizations: if a knife is sharp because it is for cutting, we can infer that other things for cutting might also be sharp. Do children see explanations as good guides to generalization? We asked 108 4- to 7-year-old children to evaluate mechanistic, functional, and categorical explanations of object properties, and to generalize those properties to novel objects on the basis of shared mechanisms, functions, or category membership. Children were significantly more likely to generalize when the explanation they had received matched the subsequent basis for generalization (e.g., generalizing on the basis of a shared mechanism after hearing a mechanistic explanation). This effect appeared to be driven by older children. Explanation-to-generalization coordination also appeared to vary across relationships, mirroring the development of corresponding explanatory preferences. These findings fill an important gap in our understanding of how explanations guide young children’s generalization and learning.

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.

People often face the challenge of evaluating competing explanations. One approach is to assess the explanations’ relative probabilities – e.g., 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.

Scientific reasoning is characterized by commitments to evidence and objectivity. New research suggests that under some conditions, people are prone to reject these commitments, and instead sanction motivated reasoning and bias. Moreover, people’s tendency to devalue scientific reasoning likely explains the emergence and persistence of many biased beliefs. However, recent work in epistemology has identified ways in which bias might be legitimately incorporated into belief formation. Researchers can leverage these insights to evaluate when commonsense affirmation of bias is justified and when it is unjustified and therefore a good target for intervention. Making reasoning more scientific may require more than merely teaching people what constitutes scientific reasoning; it may require affirming the value of such reasoning in the first place.

When faced with a dilemma between believing what is supported by an impartial assessment of the evidence (e.g., that one's friend is guilty of a crime) and believing what would better fulfill a moral obligation (e.g., that the friend is innocent), people often believe in line with the latter. But is this how people think beliefs ought to be formed? We addressed this question across three studies and found that, across a diverse set of everyday situations, people treat moral considerations as legitimate grounds for believing propositions that are unsupported by objective, evidence-based reasoning. We further document two ways in which moral considerations affect how people evaluate others' beliefs. First, the moral value of a belief affects the evidential threshold required to believe, such that morally beneficial beliefs demand less evidence than morally risky beliefs. Second, people sometimes treat the moral value of a belief as an independent justification for belief, and on that basis, sometimes prescribe evidentially poor beliefs to others. Together these results show that, in the folk ethics of belief, morality can justify and demand motivated reasoning.

Our actions and decisions are regularly influenced by the social environment around us. Can socialcues be leveraged to induce curiosity and affect subsequent behavior? Across two experiments, we show that curiosity is contagious: the social environment can influence people’s curiosity about the answers to scientific questions. Participants were presented with everyday questions about science from a popular on-line forum, and these were shown with a high or low number of up-votes as a social cue to popularity. Participants indicated their curiosity about the answers, and they were given an opportunity to reveal a subset of those answers. Participants reported greater curiosity about the answers to questions when the questions were presented with a high (vs. low) number of up-votes, and they were also more likely to choose to reveal the answers to questions with a high (vs. low) number of up-votes. These effects were partially mediated by surprise and by the inferredusefulness of knowledge, with a more dramatic effect of low up-votes in reducing curiosity than of high up-votes in boosting curiosity. Taken together, these results highlight the important role social information plays in shaping our curiosity.

Folk-biological concepts are sensitive to both statistical information about feature prevalence (Hampton, 1995; Kim & Murphy, 2011; Rosch & Mervis, 1975) and teleological beliefs about function (Atran, 1995; Keil, 1994; Kelemen, Rottman, & Seston, 2013; Lombrozo & Rehder, 2012), but it is unknown how these two types of information interact to shape concepts. In three studies (N = 438) using novel animal kinds, we found that information about prevalence and teleology inform each other: People assume that common features are functional, and they assume that functional features are common. However, people use teleological information to predict the future distribution of features across the category, despite conflicting information about current prevalence. Thus, both information about prevalence and teleological beliefs serve important conceptual functions: Prevalence information encodes the current state of the category, while teleological functions provides a means of predicting future category change.

People often endorse explanations that appeal to purpose, even when these ‘teleological’ explanations are scientifically unwarranted (e.g., “water exists so that life can survive on Earth”). In the present research, we explore teleological endorsement in a novel domain—human purpose—and its relationship to moral judgments. Across four studies, we address three questions: (1) Do people believe the human species exists for a purpose? (2) Do these beliefs drive moral condemnation of individuals who fail to fulfill this purpose? And if so, (3) what explains the link between teleological beliefs and moral condemnation? Study 1 (N=188) found that many adults endorsed anthropic teleology (e.g., that humans exist in order to procreate), and that these beliefs correlated with moral condemnation of purpose violations (e.g., judging those who do not procreate immoral). Study 2 (N=199) found evidence of a bi-directional causal relationship: teleological claims about a species resulted in moral condemnation of purpose violations, and stipulating that an action is immoral increased judgments that the species exists for that purpose. Study 3 (N=94) replicated a causal effect of species-level purpose on moral condemnation with novel actions and more implicit character judgments. Study 4 (N=52) found that when a species is believed to exist to perform some action, participants infer that the action is good for the species, and that this belief in turn supports moral condemnation of individuals who choose not to perform the action. Together, these findings shed light on how our descriptive understanding can shape our prescriptive judgments.

In this paper, we investigate the informativeness of 4- to 6-year-old (N = 125) children’s questions using a combined qualitative and quantitative approach. Children were presented with a hierarchical version of the 20-questions game, in which they were given an array of objects that could be organized into three category levels based on shared features. We then tested whether it is possible to scaffold children’s question-asking abilities without extensive training. In particular, we supported children’s categorization performance by providing the object-related features needed to ask effective constraint-seeking questions. We found that with both age and scaffolding children asked more effective questions, targeting higher category levels and therefore reaching the solution with fewer questions. We discuss the practical and theoretical implications of these results.

People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, experiential explanations support mental simulation and survive transitions across background theories; as a result, they support learning and help us translate between competing frameworks. Experiential explanations play an irreducible role in human cognition—and perhaps in science.

Mental simulation — such as imagining tilting a glass to figure out the angle at which water would spill — can be a way of coming to know the answer to an internally or externally posed query. Is this form of learning a species of inference or a form of observation? We argue that it is neither: learning through simulation is a genuinely distinct form of learning. On our account, simulation can provide knowledge of the answer to a query even when the basis for that answer is opaque to the learner. Moreover, through repeated simulation, the learner can reduce this opacity, supporting self-training and the acquisition of more accurate models of the world. Simulation is thus an essential part of the story of how creatures like us become effective learners and knowers.

Previous research suggests that preschoolers struggle with understanding abstract relations andwithreasoning by analogy. Four experiments find, in contrast, that 3-and 4-year-olds (N=168) are surprisingly adept at relational and analogical reasoning within a causal context. In earlier studies preschoolers routinely favoredimagesthat share thematic or perceptual commonalities with a target image(object matches) over choices that match the target along abstract relations (relational matches). The present studies embed suchchoice taskswithin a cause-and-effect framework. Withoutcausal framing, preschoolers strongly favor object matches, replicating the results of previous studies. But withcausal framing, preschoolers succeed at analogical transfer (i.e., choose relational matches). These findings suggest that causal framing facilitates early analogical reasoning.

There has been extensive discussion about gender gaps in representation and career advancement in the sciences. However, psychological science itself has yet to be the focus of discussion or systematic review, despite our field’s investment in questions of equity, status, well-being, gender bias, and gender disparities. In the present article, we consider 10 topics relevant for women’s career advancement in psychological science. We focus on issues that have been the subject of empirical study, discuss relevant evidence within and outside of psychological science, and draw on established psychological theory and social-science research to begin to chart a path forward. We hope that better understanding of these issues within the field will shed light on areas of existing gender gaps in the discipline and areas where positive change has happened, and spark conversation within our field about how to create lasting change to mitigate remaining gender differences in psychological science.

Why do some (and only some) observations prompt people to ask “why?” We proposea functional approach to “Explanation-Seeking Curiosity” (ESC): the state that motivates people to seek an explanation. If ESC tends to prompt explanation search when doing so is likely to be beneficial, we can use prior work on the functional consequences of explanation search to derive “forward-looking” candidate triggers of ESC—those that concern expectations about the downstream consequences of pursuing explanation search. Across three studies (N = 877), we test hypotheses derived from this functional approach. In Studies 1-3, we find that ESC is most strongly predicted by expectations about future learningand future utility. We also find thatjudgments of novelty, surprise, and information gap predict ESC,consistent with prior work on curiosity; however, the role for forward-looking considerations is not reducible to these factors. In Studies 2-3, we findthat predictors of ESCform three clusters, expectations about learning(about the target of explanation), expectations aboutexport (to other cases and future contexts), and backward-looking considerations (having to do with the relationship between the target of explanation and prior knowledge). Additionally, these clusters are consistent across stimulus sets that probe ESC,but not fact-seeking curiosity.These findings suggest that explanation-seeking curiosity is aroused in a systematic way, and that people are not only sensitive to the match or mismatch between a given stimulus and their current or former beliefs, but to how they expect an explanation for that stimulus to improve their epistemic state.

Children are known for asking “why?”—a query motivated by their desire for explanations. Research suggests that explanation-seeking curiosity (ESC) is triggered by first person cues (such as novelty or surprise), third-person cues (such as a knowledgeable adults’ surprise or question), and future-oriented cues (such as expectations about information gain or future value). Once triggered, ESC is satisfied by an adequate explanation, typically obtained through causal intervention or question asking, both of which change in efficiency over development. ESC is an important driver of children’s learning because it combines the power of active learning and intrinsic motivation with the value of explanatory content, which can reveal the unobservable and causal structure of the world to support generalizable knowledge.

Some claims (e.g., that the earth goes around the sun) seem to call out for explanation: they make us wonder “why?”. For other claims (e.g., that God exists), one might accept that the explanation is a mystery. In the present research, we investigate “need for explanation” and “mystery acceptability” across the domains of science and religion, as a window onto differences between scientific and religious cognition more broadly. In Study 1, we find that scientific “why” questions are judged to be in greater need of explanation and less adequately answered by appeals to mystery than religious “why” questions. Moreover, this holds for both religious believers and non-believers. In Study 2, we find that these domain differences persist after statistically controlling for confidence in the premises of scientific and religious why questions (e.g., that “the earth goes around the sun” and that “there is a God”). In Study 3, we match levels of confidence within-participants, and we find that domain differences in need for explanation and mystery acceptability are systematically related to domain differences in epistemic commitments (whether an explanation is within human comprehension, whether the same explanation is true for everyone) and explanatory norms (whether an explanation should be pursued), which could signal domain differences in epistemic and social functions, respectively. Together, these studies shed light on the role of explanatory inquiry across domains, and point to different functional roles for scientific and religious cognition.

How do scientific explanations for beliefs affect people's confidence that those beliefs are true? For example, do people think neuroscience-based explanations for belief in God support or challenge God's existence? In five experiments, we find that people tend to think explanations for beliefs corroborate those beliefs if the explanations invoke normally-functioning mechanisms, but not if they invoke abnormal functioning (where “normality” is a matter of proper functioning). This emerges across a variety of kinds of scientific explanations and beliefs (religious, moral, and scientific). We also find evidence that these effects can interact with people's prior beliefs to produce motivated judgments.

Many theories of kind representation suggest that people posit internal, essence-like factors that underlie kind membership and explain properties of category members. Across three studies (N = 281), we document the characteristics of an alternative form of construal according to which the properties of social kinds are seen as products of structural factors: stable, external constraints that obtain due to the kind’s social position. Internalist and structural construals are similar in that both support formal explanations (i.e., “category member has property P due to category membership C”), generic claims (“Cs have P”), and the generalization of category properties to individual category members when kind membership and social position are confounded. Our findings thus challenge these phenomena as signatures of internalist thinking. However, once category membership and structural position are unconfounded, different patterns of generalization emerge across internalist and structural construals, as do different judgments concerning category definitions and the dispensability of properties for category membership. We discuss the broader implications of these findings for accounts of formal explanation, generic language, and kind representation.

Generating explanations can be highly effective in promoting category learning; however, the underlying mechanisms are not fully understood. We propose that engaging in explanation can recruit comparison processes, and that this in turn contributes to the effectiveness of explanation in supporting category learning. Three experiments evaluated the interplay between explanation and various comparison strategies in learning artificial categories. In Experiment 1, as expected, prompting participants to explain items’ category membership led to (a) higher ratings of self-reported comparison processing and (b) increased likelihood of discovering a rule underlying category membership. Indeed, prompts to explain led to more self- reported comparison than did direct prompts to compare pairs of items. Experiment 2 showed that prompts to compare all members of a particular category (“group comparison”) were more effective in supporting rule learning than were pairwise comparison prompts. Experiment 3 found that group comparison (as assessed by self-report) partially mediated the relationship between explanation and category learning. These results suggest that one way in which explanation benefits category learning is by inviting comparisons in the service of identifying broad patterns.