Decisions are often better when pursued after deliberation and careful thought. So why do we so often eschew deliberation, and instead rely on more intuitive, gut responses? We suggest that in addition to well-recognized factors (such as the costs of deliberation), people hold normative commitments concerning how decisions ought to be made. In some cases (e.g., when choosing a romantic partner), relying on deliberation (over intuition) could be seen as inauthentic or send a problematic social signal. In Experiment 1 (N = 654), we show that people in fact hold such domain-sensitive processing commitments, that they are distinct from reported descriptive tendencies, and that they contribute to predicting reported choice. In Experiment 2 (N = 555), we show that choosing intuitively vs. deliberately supports different inferences concerning confidence and authenticity, with the domain variation in inferences in Experiment 2 closely tracking the domain variation in normative commitments observed in Experiment 1. In Experiment 3 (N = 1002), we rule out an alternative explanation. These findings inform theories of judgment and decision-making, as well as efforts towards improving decision-making through critical thinking.

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.

Generic language (e.g., “Women are nurturing”; “Women do not like math”) is prominent in child-directed speech, and has been shown to promote essentialist beliefs about the relevant kind, supporting stereotyping and prejudice. Here we investigate a theoretically-motivated intervention to break the link between generics and essentialist assumptions. In a study with 223 3-8-year-old children who learned about novel social groups from generic language, we demonstrate that a structural construal of generics (attributing the category-property association to stable external constraints) mitigates essentialist assumptions about social categories. We discuss practical applications for reducing stereotype endorsement, and theoretical implications regarding the meaning of generic language and the development of social kind representations.

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.

Curiosity is considered essential for learning and sustained engagement, yet stimulating curiosity in educational contexts remains a challenge. Can people’s curiosity about a topic be stimulated by evidence that the topic has potential value? In two experiments we show that increasing people’s perceptions about the usefulness of a scientific topic also influences their 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 topic if they perceive it to be directly valuable to them. Given the link between curiosity and learning, these results have important implications for science communication and education more broadly.

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.

Scientific norms value skepticism; many religious traditions value faith. We test the hypothesis that these different attitudes towards inquiry and belief result in different inferences from epistemic behavior: Whereas the pursuit of evidence or explanations is taken as a signal of commitment to science, forgoing further evidence and explanation is taken as a signal of commitment to religion. Two studies (N = 401) support these predictions. We also find that deciding to pursue inquiry is judged more moral and trustworthy, with moderating effects of participant religiosity and scientism. These findings suggest that epistemic behavior can be a social signal, and shed light on the epistemic and social functions of scientific vs. religious belief.

Explanations not only increase understanding; they are often deeply satisfying. In the present research, we explore how this phenomenological sense of “explanatory satisfaction” relates to the functional role of explanation within the process of inquiry. In two studies, we address the following questions: 1) Does explanatory satisfaction track the epistemic, learning-directed features of explanation? and 2) How does explanatory satisfaction relate to both antecedent and subsequent curiosity? In answering these questions, we uncover novel determinants of explanatory satisfaction and contribute to the broader literature on explanation and inquiry.

Many natural and artificial entities can be predicted and explained both mechanistically, in term of parts and proximate causal processes, as well as functionally, in terms of functions and goals. Do these distinct “stances” or “modes of construal” support fundamentally different kinds of understanding? Based on recent work in epistemology and philosophy of science, as well as empirical evidence from cognitive and developmental psychology, we argue for what we call the “weak differentiation thesis”: the claim that mechanistic and functional understanding are distinct in that they involve importantly different objects. We also consider more tentative arguments for the “strong differentiation thesis”: the claim that mechanistic and functional understanding involve different epistemic relationships between mind and world.

This chapter introduces “learning by thinking” (LbT) as a form of learning distinct from familiar forms of learning through observation. When learning by thinking, the learner gains genuinely new insight in the absence of novel observations “outside the head.” Scientific thought experiments are canonical examples, but the phenomenon is much more widespread, and includes learning by explaining to oneself, through analogical reasoning, or through mental simulation. The chapter argues that episodes of LbT can be re-expressed as explicit arguments or inferences but are neither psychologically nor epistemically reducible to explicit arguments or inferences, and that this partially explains the novelty of the conclusions reached through LbT. It also introduces a new perspective on the epistemic value of LbT processes as practices with potentially beneficial epistemic consequences, even when the commitments they invoke and the conclusions they immediately deliver are not themselves true.

Evidence is typically consistent with more than one hypothesis. How do we decide which hypothesis to pursue (e.g., to subject to further consideration and testing)? Research has shown that explanatory considerations play an important role in learning and inference: we tend to seek and favor hypotheses that offer good explanations for the evidence we invoke them to explain. Here we report three studies testing the proposal that explanatory considerations similarly inform decisions concerning pursuit. We find that ratings of explanatory goodness predict pursuit (though to a lesser extent than they predict belief), and that these effects hold after adjusting for subjective probability. These findings contribute to a growing body of work suggesting an important role for explanatory considerations in shaping inquiry.

The capacity to search for information effectively by asking informative questions is crucial for self-directed learning and develops throughout the preschool years and beyond. We tested the hypothesis that explaining observations in a given domain prepares children to ask more informative questions in that domain, and that it does so by promoting the identification of features that apply to multiple objects, thus supporting more effective questions. Across two experiments, 4- to 7-year-old children (N  = 168) were prompted to explain observed evidence or to complete a control task prior to a 20-questions game. We found that prior prompts to explain led to a decrease in the number of questions needed to complete the game, but only for older children (ages 6-7). Moreover, we found that effects of explanation manifested as a shift away from questions that targeted single objects. These findings shed light on the development of question asking in childhood and on the role of explanation in learning.

Most theories of kind representation suggest that people posit internal, essence-like factors believed to underlie kind membership and the observable properties of members. Across two studies (N = 234), we show that adults can construe properties of social kinds as products of both internal and structural (stable external) factors. 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 a particular pattern of generalization to individuals when the individuals’ category membership and structural position are preserved. Our findings thus challenge these phenomena as signatures of essentialist 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 property mutability. These findings have important implications for reasoning about social kinds.

Occam's razor-the idea that all else being equal, we should pick the simpler hypothesis-plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor (BOR) is that more complex hypotheses tend to be more flexible-they can accommodate a wider range of possible data-and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people's judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be "tuned" to fit the data better than comparatively simpler hypotheses.

Much recent work on explanation in the interventionist tradition emphasizes the explanatory value of stable causal generalizations—i.e., causal generalizations that remain true in a wide range of background circumstances. We argue that two separate explanatory virtues are lumped together under the heading of `stability’. We call these two virtues breadth and guidancerespectively. In our view, these two virtues are importantly distinct, but this fact is neglected or at least under-appreciated in the literature on stability. We argue that an adequate theory of explanatory goodness should recognize breadth and guidance as distinct virtues, as breadth and guidance track different ideals of explanation, satisfy different cognitive and pragmatic ends, and play different theoretical roles in (for example) helping us understand the explanatory value of mechanisms. Thus keeping track of the distinction between these two forms of stability yields a more accurate and perspicuous picture of the role that stability considerations play in explanation.

An actor's mental states-whether she acted knowingly and with bad intentions-typically play an important role in evaluating the extent to which an action is wrong and in determining appropriate levels of punishment. In four experiments, we find that this role for knowledge and intent is significantly weaker when evaluating transgressions of conventional rules as opposed to moral rules. We also find that this attenuated role for knowledge and intent is partly due to the fact that conventional rules are judged to be more arbitrary than moral rules; whereas moral transgressions are associated with actions that are intrinsically wrong (e.g., hitting another person), conventional transgressions are associated with actions that are only contingently wrong (e.g., wearing pajamas to school, which is only wrong if it violates a dress code that could have been otherwise). Finally, we find that it is the perpetrator's belief about the arbitrary or non-arbitrary basis of the rule-not the reality-that drives this differential effect of knowledge and intent across types of transgressions.

Can science explain romantic love, morality, and religious belief? We documented intuitive beliefs about the limits of science in explaining the human mind. We considered both epistemic evaluations (concerning whether science could possibly fully explain a given psychological phenomenon) and nonepistemic judgments (concerning whether scientific explanations for a given phenomenon would generate discomfort), and we identified factors that characterize phenomena judged to fall beyond the scope of science. Across six studies, we found that participants were more likely to judge scientific explanations for psychological phenomena to be impossible and uncomfortable when, among other factors, they support first-person, introspective access (e.g., feeling empathetic as opposed to reaching for objects), contribute to making humans exceptional (e.g., appreciating music as opposed to forgetfulness), and involve conscious will (e.g., acting immorally as opposed to having headaches). These judgments about the scope of science have implications for science education, policy, and the public reception of psychological science.

Awe has traditionally been considered a religious or spiritual emotion, yet scientists often report that awe motivates them to answer questions about the natural world, and to do so in naturalistic terms. Indeed, awe may be closely related to scientific discovery and theoretical advance. Awe is typically triggered by something vast (either literally or metaphorically) and initiates processes of accommodation, in which existing mental schemas are revised to make sense of the awe‐inspiring stimuli. This process of accommodation is essential for the kind of belief revision that characterizes scientific reasoning and theory change. Across six studies, we find that the tendency to experience awe is positively associated with scientific thinking, and that this association is not shared by other positive emotions. Specifically, we show that the disposition to experience awe predicts a more accurate understanding of how science works, rejection of creationism, and rejection of unwarranted teleological explanations more broadly.

Is morality intuitive or deliberative? This distinction can obscure the role of folk moral theories in moral judgment; judgments may arise “intuitively” yet result from abstract theoretical and philosophical commitments that participate in “deliberative” reasoning.

Research has found that when children or adults attempt to explain novel observations in the course of learning, they are more likely to discover patterns that support ideal explanations: explanations that are maximally simple and broad. However, not all learning contexts support such explanations. Can explaining facilitate discovery nonetheless? We present a study in which participants were tasked with discovering a rule governing the classification of items, where the items were consistent two non-ideal rules: one correctly classified 66% of cases, the other 83%. We find that when there is no ideal rule to be discovered (i.e., no 100% rule), participants prompted to explain are better than control participants at discovering the best available rule (i.e., the 83% rule). This supports the idea that seeking ideal explanations can be beneficial in a non-ideal world because the pursuit of an ideal explanation can facilitate the discovery of imperfect patterns along the way.

Teleological explanations, which appeal to a function or purpose (e.g., “kangaroos have long tails for balance”), seem to play a special role within the biological domain. We propose that such explanations are compelling because they are evaluated on the basis of a salient cue: structure-function fit, or the correspondence between a biological feature’s form (e.g., tail length) and its function (e.g., balance). Across five studies with 843 participants in total, we find support for three predictions that follow from this proposal. First, we find that function information decreases reliance on mechanistic considerations when evaluating explanations (Experiments 1- 3), indicating the presence of a salient, function-based cue. Second, we demonstrate that structure-function fit is the best candidate for this cue (Experiments 3-4). Third, we show that scientifically-unwarranted teleological explanations are more likely to be accepted under speeded and unspeeded conditions when they are high in structure-function fit (Experiment 5). Experiment 5 also finds that structure-function fit extends beyond biology to teleological explanations in other domains. Jointly, these studies provide a new account of how teleological explanations are evaluated and why they are often (but not universally) compelling.

Much of human learning throughout the lifespan is achieved through seeking and generating explanations. However, very little is known about what triggers a learner to seek an explanation. In two studies, we investigate what makes a given event or phenomenon stand in need of explanation. In Study 1, we show that a learner's judgment of "need for explanation" for a given question predicts that learner's likelihood of seeking an answer to this question. In Study 2, we explore several potential predictors of need for explanation. We find that the need for explanation is greater for questions expected to have useful answers that require expert understanding, and that "need for explanation" can be differentiated from general curiosity. 

Both science and religion offer explanations for everyday events, but they differ with respect to their tolerance for mysteries. In the present research, we investigate laypeople's perceptions about the extent to which religious and scientific questions demand an explanation and the extent to which an appeal to mystery can satisfy that demand. In Study 1, we document a large domain difference between science and religion: scientific questions are judged to be more in need of explanation and less appropriately answered by appeal to mystery than religious questions. In Study 2, we demonstrate that these differences are not driven by differing levels of belief in the content of these domains. While the source of these domain differences remains unclear, we propose several hypotheses in the General Discussion.

Young children often endorse explanations of the natural world that appeal to functions or purpose—for example, that rocks are pointy so animals can scratch on them. By contrast, most Western-educated adults reject such explanations. What accounts for this change? We investigated 4- to 5-year-old children’s ability to generalize the form of an explanation from examples by presenting them with novel teleological explanations, novel mechanistic explanations, or no explanations for 5 nonliving natural objects. We then asked children to explain novel instances of the same objects and novel kinds of objects. We found that children were able to learn and generalize explanations of both types, suggesting an ability to draw generalizations over the form of an explanation. We also found that teleological and mechanistic explanations were learned and generalized equally well, suggesting that if a domain-general teleological bias exists, it does not manifest as a bias in learning or generalization.

Our actions and decisions are regularly influenced by the social environment around us. Can social environment be leveraged to induce curiosity and facilitate subsequent learning? Across two experiments, we show that curiosity is contagious: social environment can influence people's curiosity about the answers to scientific questions. Our findings show that people are more likely to become curious about the answers to more popular questions, which in turn influences the information they choose to reveal. Given that curiosity has been linked to better learning, these findings have important implications for education.

We report three experiments investigating whether people's judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization's actual scope of application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people's causal reasoning: the stability of the causal relationship.

Explanations often highlight inductively rich relationships that support further generalizations: learning that the knife is sharp because it is for cutting, we correspondingly infer that other things for cutting might also be sharp. When do children appreciate that explanations are good guides to generalization? We report a study in which 108 4- to 7-year-old children evaluated mechanistic, functional, and categorical explanations for the properties of objects, and subsequently generalized those properties to novel objects on the basis of shared mechanisms, functions, or category membership. Older children, but not younger 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). These findings shed light on how explanation and generalization become coordinated in development, as well as the role of explanations in young children’s learning.

Representations of social categories help us make sense of the social world, supporting predictions and explanations about groups and individuals. In an experiment with 156 participants, we explore whether children and adults are able to understand category-property associations (such as the association between “girls” and “pink”) in structural terms, locating an object of explanation within a larger structure and identifying structural constraints that act on elements of the structure. We show that children as young as 3-4 years old show signs of structural thinking, and that 5-6 year olds show additional differentiation between structural and nonstructural thinking, yet still fall short of adult performance. These findings introduce structural connections as a new type of non-accidental relationship between a property and a category, and present a viable alternative to internalist accounts of social categories, such as psychological essentialism.

Our goal in this paper is to experimentally investigate whether folk conceptions of explanation are psychologistic. In particular, are people more likely to classify speech acts as explanations when they cause understanding in their recipient? The empirical evidence that we present suggests this is so. Using the side-effect effect as a marker of mental state ascriptions, we argue that lay judgments of explanatory status are mediated by judgments of a speaker’s and/or audience’s mental states. First, we show that attributions of both understanding and explanation exhibit a side-effect effect. Next, we show that when the speaker’s and audience’s level of understanding is stipulated, the explanation side-effect effect goes away entirely. These results not only extend the side-effect effect to attributions of understanding, they also suggest that attributions of explanation exhibit a side-effect effect because they depend upon attributions of understanding, supporting the idea that folk conceptions of explanation are psychologistic.

As a strategy for exploring the relationship between understanding and knowledge, we consider whether epistemic luck – which is typically thought to undermine knowledge – undermines understanding. Questions about the etiology of understanding have also been at the heart of recent theoretical debates within epistemology. Kvanvig (2003) put forward the argument that there could be lucky understanding and produced an example that he deemed persuasive. Grimm (2006) responded with a case that, he argued, demonstrated that there could not be lucky understanding. In this paper, we empirically examine how participants' patterns of understanding attributions line up with the predictions of Kvanvig and Grimm. We argue that the data challenge Kvanvig's position. People do not differentiate between knowing-why and understanding-why on the basis of proper etiology: attributions of knowledge and understanding involve comparable (and minimal) roles for epistemic luck. We thus posit that folk knowledge and understanding are etiologically symmetrical.

Can opium's tendency to induce sleep be explained by appeal to a "dormitive virtue"? If the label merely references the tendency being explained, the explanation seems vacuous. Yet the presence of a label could signal genuinely explanatory content concerning the (causal) basis for the property being explained. In Experiments 1 and 2, we find that explanations for a person's behavior that appeal to a named tendency or condition are indeed judged to be more satisfying than equivalent explanations that differ only in omitting the name. In Experiment 3, we find support for one proposal concerning what it is about a name that drives a boost in explanatory satisfaction: named categories lead people to draw an inference to the existence of a cause underlying the category, a cause that is responsible for the behavior being explained. Our findings have implications for theories of explanation and point to the central role of causation in explaining behavior.

When learners explain to themselves as they encounter new information, they recruit a suite of processes that influence subsequent learning. One consequence is that learners are more likely to discover exceptionless rules that underlie what they are trying to explain. Here we investigate what it is about exceptionless rules that satisfies the demands of explanation. Are exceptions unwelcome because they lower predictive accuracy, or because they challenge some other explanatory ideal, such as simplicity and breadth? To compare these alternatives, we introduce a causally rich property explanation task in which exceptions to a general rule are either arbitrary or predictable. If predictive accuracy is sufficient to satisfy the demands of explanation, the introduction of a rule plus exception that supports perfect prediction should block the discovery of a more subtle but exceptionless rule. Across two experiments, we find that effects of explanation go beyond attaining perfect prediction.

How does actively seeking explanations for one’s observations affect information search over the course of learning? Generating explanations could plausibly lead learners to take advantage of the information they have already obtained, resulting in less exploration. Alternatively, explaining could lead learners to explore more, especially after encountering evidence that suggests their current beliefs are incorrect. In two experiments using a modified observe or bet task, we investigate these possibilities and find support for the latter: participants who are prompted to explain their observations in the course of learning tend to explore more, especially after encountering evidence that challenges a current belief.

Explanation and causation are intimately related. Explanations often appeal to causes, and causal claims are often answers to implicit or explicit questions about why or how something occurred. In this chapter we consider what research on explanation can tell us about causal reasoning. In particular, we review an emerging body of work suggesting that explanatory considerations – such as the simplicity or scope of a causal hypothesis – can systematically influence causal inference and learning. We also discuss proposed distinctions among types of explanations and review their differential effects on causal reasoning and representation. Finally, we consider the relationship between explanations and causal mechanisms and raise important questions for future research.

If someone brings about an outcome without intending to, is she causally and morally responsible for it? What if she acts intentionally, but as the result of manipulation by another agent? Previous research has shown that an agent's mental states can affect attributions of causal and moral responsibility to that agent, but little is known about what effect one agent's mental states can have on attributions to another agent. In Experiment 1, we replicate findings that manipulation lowers attributions of responsibility to manipulated agents. Experiments 2-7 isolate which features of manipulation drive this effect, a crucial issue for both philosophical debates about free will and attributions of responsibility in situations involving social influence more generally. Our results suggest that "bypassing" a manipulated agent's mental states generates the greatest reduction in responsibility, and we explain our results in terms of the effects that one agent's mental states can have on the counterfactual relations between another agent and an outcome.

When evaluating causal explanations, simpler explanations are widely regarded as better explanations. However, little is known about how people assess simplicity in causal explanations or what the consequences of such a preference are. We contrast 2 candidate metrics for simplicity in causal explanations: node simplicity (the number of causes invoked in an explanation) and root simplicity (the number of unexplained causes invoked in an explanation). Across 4 experiments, we find that explanatory preferences track root simplicity, not node simplicity; that a preference for root simplicity is tempered (but not eliminated) by probabilistic evidence favoring a more complex explanation; that committing to a less likely but simpler explanation distorts memory for past observations; and that a preference for root simplicity is greater when the root cause is strongly linked to its effects. We suggest that a preference for root-simpler explanations follows from the role of explanations in highlighting and efficiently representing and communicating information that supports future predictions and interventions. (PsycINFO Database Record

Representations of social categories help us make sense of the social world, supporting predictions and explanations about groups and individuals. Here we explore whether children and adults are able to understanding category-property associations in structural terms, locating an object of explanation within a larger structure and identifying structural constraints that act on elements of the structure. We show that children as young 3-4 years of age show signs of structural thinking, but that this capacity does not fully develop until after 7 years of age. These findings introduce a viable alternative to internalist accounts of social categories, such as psychological essentialism.
 

Are explanations of different kinds (formal, mechanistic, teleological) judged differently depending on their contextual utility, defined as the extent to which they support the kinds of inferences required for a given task? We report three studies demonstrating that the perceived "goodness" of an explanation depends on the evaluator's current task: Explanations receive a relative boost when they support task-relevant inferences, even when all three explanation types are warranted. For example, mechanistic explanations receive higher ratings when participants anticipate making further inferences on the basis of proximate causes than when they anticipate making further inferences on the basis of category membership or functions. These findings shed light on the functions of explanation and support pragmatic and pluralist approaches to explanation.

Research suggests that the process of explaining influences causal reasoning by prompting learners to favor hypotheses that offer "good" explanations. One feature of a good explanation is its simplicity. Here, we investigate whether prompting children to generate explanations for observed effects increases the extent to which they favor causal hypotheses that offer simpler explanations, and whether this changes over the course of development. Children aged 4, 5, and 6 years observed several outcomes that could be explained by appeal to a common cause (the simple hypothesis) or two independent causes (the complex hypothesis). We varied whether children were prompted to explain each observation or, in a control condition, to report it. Children were then asked to make additional inferences for which the competing hypotheses generated different predictions. The results revealed developmental differences in the extent to which children favored simpler hypotheses as a basis for further inference in this task: 4-year-olds did not favor the simpler hypothesis in either condition; 5-year-olds favored the simpler hypothesis only when prompted to explain; and 6-year-olds favored the simpler hypothesis whether or not they explained.

Although storybooks are often used as pedagogical tools for conveying moral lessons to children, the ability to spontaneously extract "the moral" of a story develops relatively late. Instead, children tend to represent stories at a concrete level - one that highlights surface features and understates more abstract themes. Here we examine the role of explanation in 5- and 6-year-old children's developing ability to learn the moral of a story. Two experiments demonstrate that, relative to a control condition, prompts to explain aspects of a story facilitate children's ability to override salient surface features, abstract the underlying moral, and generalize that moral to novel contexts. In some cases, generating an explanation is more effective than being explicitly told the moral of the story, as in a more traditional pedagogical exchange. These findings have implications for moral comprehension, the role of explanation in learning, and the development of abstract reasoning in early childhood.

Three experiments investigate how self-generated explanation influences children's causal learning. Five-year-olds (N = 114) observed data consistent with two hypotheses and were prompted to explain or to report each observation. In Study 1, when making novel generalizations, explainers were more likely to favor the hypothesis that accounted for more observations. In Study 2, explainers favored a hypothesis that was consistent with prior knowledge. Study 3 pitted a hypothesis that accounted for more observations against a hypothesis consistent with prior knowledge. Explainers were more likely to base generalizations on prior knowledge. Findings suggest that attempts to explain drive children to evaluate hypotheses using features of "good" explanations, or those supporting generalizations with broad scope, as informed by children's prior knowledge and observations.

Most crimes in America require that the defendant have mens rea, Latin for "guilty mind." However, mens rea is not legally required for strict liability crimes, such as speeding, for which someone is guilty even if ignorant or deceived about her speed. In 3 experiments involving participants responding to descriptive vignettes, we investigated whether the division of strict liability crimes in the law reflects an aspect of laypeople's intuitive moral cognition. Experiment 1 (N = 396; 236 male, 159 female, 1 other; M = 30) found evidence that it does: ignorance and deception were less mitigating for strict liability crimes than for "mens rea" crimes. Experiments 2 (N = 413; 257 male, 154 female, 2 other; M = 31) and 3 (N = 404; 183 male, 221 female, M = 35) revealed that strict liability crimes are not treated as pure moral violations, but additionally as violations of convention. We found that for strict liability crimes, ratings of moral wrongness and punishment were influenced to a greater extent by the fact that a rule had been violated, even when harm was kept constant, mirroring the legal distinction of malum prohibitum (wrong as prohibited) versus malum in se (wrong in itself). Further, we found that rules prohibiting strict liability crimes were judged more arbitrary than corresponding rules for "mens rea" crimes, and that this judgment was related to the role of mental states. Jointly, the findings suggest a surprising correspondence between the law and laypeople's intuitive judgments. (PsycINFO Database Record

Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference.

Explanation has been an important topic of study in philosophy of science, in epistemology, and in other areas of philosophy. In parallel, psychologists have been studying children’s and adults’ explanations, including their role in inference and in learning. This entry reviews recent work that begins to bridge the philosophy and psychology of explanation, with sections introducing recent empirical work on explanation by philosophers, formal and functional accounts of explanation, inference to the best explanation, the role of explanation in discovery, and the implications of empirical work on explanation for the “negative program” in experimental philosophy.

Children are active learners: they learn not only from the information people offer and the evidence they happen to observe, but by actively seeking information. However, children's information search strategies are typically less efficient than those of adults. In two studies, we isolate potential sources of developmental change in how children (7- and 10-year-olds) and adults search for information. To do so, we develop a hierarchical version of the 20-questions game, in which participants either ask questions (Study 1) or test individual objects (Study 2) to discover which category of objects within a nested structure (e.g., animals, birds, or owls) has a novel property. We also develop a computational model of the task, which allows us to evaluate performance in quantitative terms. As expected, we find developmental improvement in the efficiency of information search. In addition, we show that participants' performance exceeds random search, but falls short of optimal performance. We find mixed support for the idea that children's inefficiency stems from difficulty thinking beyond the level of individual objects or hypotheses. Instead, we reveal a previously undocumented source of developmental change: Children are significantly more likely than adults to continue their search for information beyond the point at which a single hypothesis remains, and thus to ask questions and select objects associated with zero information gain. This suggests that one crucial source of developmental change in information search efficiency lies in children's "stopping rules." (PsycINFO Database Record

Natural phenomena, such as illness or adaptation, can be explained in many ways. Typically, this many-to-one mapping between explanations and the phenomena they explain is construed as a source of tension between scientific and religious explanations (e.g., creationism vs. evolution) or between different forms of scientific explanation (e.g., Lamarck’s vs. Darwin’s theory of evolution). However, recent research suggests that competing explanations exist not only across individuals within the same community, but also within individuals themselves, who maintain competing explanations. Here, we explore this phenomenon of “explanatory coexistence” and analyze its implications for conceptual change, or knowledge restructuring at the level of individual concepts. We argue that conceptual change is often better construed as a process of augmentation, in which early-developing concepts coexist with later-developing concepts because both types of concepts remain useful for predicting and explaining the natural world, albeit in different circumstances or for different purposes.

We report two experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a wide range of background circumstances. We demonstrate that people prefer stable causal relationships even when overall causal strength is held constant, and show that this effect is unlikely to be driven by a causal generalization’s actual scope of application. This documents a previously unacknowledged factor that shapes people’s causal reasoning.