Publications

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.

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.

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.

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.

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.

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.

A great deal of work argues that people demand impartial, evidence-based reasoning from others. However, recent findings show that moral values occupy a cardinal position in people’s evaluation of others, raising the possibility that people sometimes prescribe morally-good but evidentially-poor beliefs. We report two studies investigating how people evaluate beliefs when these two ideals conflict and find that people regularly endorse motivated reasoning when it can be morally justified. Furthermore, we document two ways that moral considerations result in prescribed motivated reasoning. First, morality can provide an alternative justification for belief, leading people to prescribe evidentially unsupported beliefs to others. And, second, morality can affect how people evaluate the way evidence is weighed by lowering or raising the threshold of required evidence for morally good and bad beliefs, respectively. These results illuminate longstanding questions about the nature of motivated reasoning and the social regulation of belief.

Curiosity motivates exploration and is beneficial for learning, but curiosity is not always experienced when facing the unknown. In the present research, we address this selectivity: what causes curiosity to be experienced under some circumstances but not others? Using a Bayesian reinforcement learning model, we disentangle four possible influences on curiosity that have typically been confounded in previous research: surprise, local uncertainty/expected information gain, global uncertainty, and global expected information gain. In two experiments, we find that backward-looking influences (concerning beliefs based on prior experience) and forward-looking influences (concerning expectations about future learning) independently predict reported curiosity, and that forward-looking influences explain the most variance. These findings begin to disentangle the complex environmental features that drive curiosity.

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.

Questions about the origins of life and the universe seem to call out for explanation, with science and religion offering candidate answers. These answers clearly differ in content, but do they also differ in psychological function? In Study 1 (N=501) participants on Amazon Mechanical Turk rated scientific and religious answers to existential questions on dimensions related to epistemic functions (e.g., “This explanation is based on evidence”) as well as moral/social/emotional functions (e.g., “If everyone believed this, the world would be a more moral place”; “This explanation is comforting”). For non-religious participants, only scientific explanations were assigned high values along epistemic dimensions; For religious participants, only religious explanations were assigned high values along non-epistemic dimensions. In Study 2 (N=130), priming a non-epistemic need boosted religious participants’ evaluation of the quality of religious (vs. scientific) explanations. These findings shed light on the functions of scientific and religious cognition and raise new questions about explanatory co-existence and the origins of religious belief.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

 

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.

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.

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.

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.

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.

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.

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.

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. 

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.

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.

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.

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

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.

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.

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.
 

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.

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

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.

Three experiments investigate whether and why people accept explanations for symptoms that appeal to mental disorders, such as: “She experiences delusions because she has schizophrenia.” Such explanations are potentially puzzling, as mental disorder diagnoses are made on the basis of symptoms rather than causes. Do laypeople nonetheless conceptualize mental disorder classifications in causal terms? Or is this an instance of non-causal explanation? Experiment 1 shows that such explanations are found explanatory. Experiment 2 presents participants with novel disorders that are stipulated to involve or not involve an underlying cause across symptoms and people. Disorder classifications are found more explanatory when a causal basis is stipulated, or when participants infer that one is present (even after it’s denied in the text). Finally, Experiment 3 finds that merely having a principled, but non-causal, basis for defining symptom clusters is insufficient to reach the explanatory potential of categories with a stipulated common cause.

One way to learn about the world is by asking questions. We investigate how younger children (7- to 8-year-olds), older children (9- to 11-year-olds), and young adults (17- to 18-year-olds) ask questions to identify the cause of an event. We find a developmental shift in children's reliance on hypothesis-scanning questions (which test hypotheses directly) versus constraint-seeking questions (which reduce the space of hypotheses), but also that all age groups ask more constraint-seeking questions when hypothesis-scanning questions are least likely to pay off: When the solution is one among equally likely alternatives (Study 1) or when the problem is difficult (Studies 1 and 2). These findings are the first to demonstrate that even young children dynamically adapt their strategies for inquiry to increase the efficiency of information search.

A perpetrator’s mental state – whether she had mens rea or a “guilty mind” – typically plays an important role in evaluating wrongness and assigning punishment. In two experiments, we find that this role for mental states is weaker in evaluating conventional violations relative to moral violations. We also find that this diminished role for mental states may be associated with the fact that conventional violations are wrong by virtue of having violated a (potentially arbitrary) rule, whereas moral violations are also wrong inherently.

Like scientists, children and adults learn by asking questions and making interventions. How does this ability develop? We investigate how children (7- and 10-year-olds) and adults search for information to learn which kinds of objects share a novel causal property. In particular, we consider whether children ask questions and select interventions that are as informative as those of adults, and whether they recognize when to stop searching for information to provide a solution. We find an anticipated developmental improvement in information search efficiency. We also present a formal analysis that allows us to identify the basis for children’s inefficiency. In our 20-questions-style task, children initially ask questions and make interventions no less efficiently than adults do, but continue to search for information past the point at which they have narrowed their hypothesis space to a single option. In other words, the performance change from age seven to adulthood is due largely to a change in implementing a “stopping rule”; when considering only the minimum number of queries participants would have needed to identify the correct hypothesis, age differences disappear.

We report four experiments demonstrating that judgments of explanatory goodness are sensitive both to covariation evidence and to mechanism information. Compared to judgments of causal strength, explanatory judgments tend to be more sensitive to mechanism and less sensitive to covariation. Judgments of understanding tracked covariation least closely. We discuss implications of our findings for theories of explanation, understanding and causal attribution.

Do people evaluate the quality of explanations differently depending on their goals? In particular, are explanations of different kinds (formal, mechanistic, teleological) judged differently depending on the future judgments the evaluator anticipates making? We report two studies demonstrating that the perceived “goodness” of explanations depends on the evaluator’s current goals, with explanations receiving a relative boost when they are based on relationships that support anticipated judgments. These findings shed light on the functions of explanation and support pragmatic and pluralist approaches to explanation.
 

Two studies examined the specificity of effects of explanation on learning by prompting 3- to 6-year-old children to explain a mechanical toy and comparing what they learned about the toy's causal and non-causal properties with children who only observed the toy, both with and without accompanying verbalization. In Study 1, children were experimentally assigned to either explain or observe the mechanical toy. In Study 2, children were classified according to whether the content of their response to an undirected prompt involved explanation. Dependent measures included whether children understood the toy's functional-mechanical relationships, remembered perceptual features of the toy, effectively reconstructed the toy, and (for Study 2) generalized the function of the toy when constructing a new one. Results demonstrate that across age groups, explanation promotes causal learning and generalization but does not improve (and in younger children can even impair) memory for causally irrelevant perceptual details.

The ability to generalize from the known to the unknown is central to learning and inference. Two experiments explore the relationship between how a property is explained and how that property is generalized to novel species and artifacts. The experiments contrast the consequences of explaining a property mechanistically, by appeal to parts and processes, with the consequences of explaining the property functionally, by appeal to functions and goals. The findings suggest that properties that are explained functionally are more likely to be generalized on the basis of shared functions, with a weaker relationship between mechanistic explanations and generalization on the basis of shared parts and processes. The influence of explanation type on generalization holds even though all participants are provided with the same mechanistic and functional information, and whether an explanation type is freely generated (Experiment 1), experimentally provided (Experiment 2), or experimentally induced (Experiment 2). The experiments also demonstrate that explanations and generalizations of a particular type (mechanistic or functional) can be experimentally induced by providing sample explanations of that type, with a comparable effect when the sample explanations come from the same domain or from a different domains. These results suggest that explanations serve as a guide to generalization, and contribute to a growing body of work supporting the value of distinguishing mechanistic and functional explanations.

Four experiments with preschool-aged children test the hypothesis that engaging in explanation promotes inductive reasoning on the basis of shared causal properties as opposed to salient (but superficial) perceptual properties. In Experiments 1a and 1b, 3- to 5-year-old children prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. Experiment 2 replicated this effect of explanation in a case of label extension (i.e., categorization). Experiment 3 demonstrated that explanation improves memory for clusters of causally relevant (non-perceptual) features, but impairs memory for superficial (perceptual) features, providing evidence that effects of explanation are selective in scope and apply to memory as well as inference. In sum, our data support the proposal that engaging in explanation influences children's reasoning by privileging inductively rich, causal properties.

Although comparison and explanation have typically been studied independently, recent work suggests connections between these processes. Three experiments investigated effects of comparison and explanation on analogical problem solving. In Experiment 1, explaining the solutions to two analogous stories increased spontaneous transfer to an analogical problem. In Experiment 2, explaining a single story promoted analogical transfer, but only after receiving a hint that may have facilitated comparison. In Experiment 3, irrelevant stories were interspersed among the two story analogs to block unprompted comparison; prompts to compare were effective, but prompts to explain were not. This pattern suggests that effects of explanation on analogical transfer may be greatest when combined with comparison.

Three experiments investigate whether neuroscientific explanations for belief in some proposition (e.g., that God exists) are judged to reinforce, undermine, or have no effect on confidence that the corresponding proposition is true. Participants learned that an individual’s religious, moral, or scientific belief activated a (fictional) brain region and indicated how this information would and should influence the individual’s confidence. When the region was associated with true or false beliefs (Experiment 1), the predicted and endorsed responses were an increase or decrease in confidence, respectively. However, we found that epistemically-neutral but “normal” neural function was taken to reinforce belief, and “abnormal” function to have no effect or to undermine it, whether the (ab)normality was explicitly stated (Experiment 2) or implied (Experiment 3), suggesting that proper functioning is treated as a proxy for epistemic reliability. These findings have implications for science communication, philosophy, and our understanding of belief revision and folk epistemology.

One way to learn about the world is by asking questions. We investigate how children (n= 287, 7- to 11-year olds) and young adults (n=160 17- to 18-year olds) ask questions to identify the cause of an event. We find a developmental shift in children’s reliance on hypothesis-scanning questions (which test hypotheses directly) versus constraint-seeking questions (which reduce the space of hypotheses), but also that all age groups ask more constraint-seeking questions when hypothesis-scanning questions are unlikely to pay off: when the problem is difficult (Studies 1 and 2) or the solution is one among equally likely alternatives (Study 2). These findings are the first to demonstrate that even young children adapt their strategies for inquiry to increase the efficiency of information search.

Prompting learners to explain their beliefs can help them correct misconceptions upon encountering anomalies — facts and observations that conflict with learners’ current understanding. We have developed a way to augment online interfaces for learning by adding prompts for users to explain a fact or observation. We conducted two experiments testing the effects of these explanation prompts, finding that they increase learners’ self-correction of misconceptions, though these benefits of explaining depend on: (1) How many anomalies the prompts require people to explain, and (2) Whether anomalies are distributed so that individual observations guide learners to correct ideas by conflicting with multiple misconceptions at once.

The present study investigates 'folk theories' about the causes of insomnia. Participants with insomnia ( = 69) completed a qualitative and quantitative assessment of their folk theories. The qualitative assessment was to speak aloud for 1 minute in response to: 'What do you think causes your insomnia?'. The quantitative assessment involved completing the 'Causal Attributions of My Insomnia Questionnaire' (CAM-I), developed for this study. The three most common folk theories for both the causes of one's own insomnia as well as insomnia in others were 'emotions', 'thinking patterns' and 'sleep-related emotions'. Interventions targeting these factors were also perceived as most likely to be viable treatments. Seventy-five percent of the folk theories of insomnia investigated with the CAM-I were rated as more likely to be alleviated by a psychological versus a biological treatment. The results are consistent with research highlighting that folk theories are generally coherent and inform a range of judgments. Future research should focus on congruence of 'folk theories' between treatment providers and patients, as well as the role of folk theories in treatment choice, engagement, compliance and outcome.

Seeking explanations is central to science, education, and everyday thinking, and prompting learners to explain is often beneficial. Nonetheless, in 2 category learning experiments across artifact and social domains, we demonstrate that the very properties of explanation that support learning can impair learning by fostering overgeneralizations. We find that explaining encourages learners to seek broad patterns, hindering learning when patterns involve exceptions. By revealing how effects of explanation depend on the structure of what is being learned, these experiments simultaneously demonstrate the hazards of explaining and provide evidence for why explaining is so often beneficial. For better or for worse, explaining recruits the remarkable human capacity to seek underlying patterns that go beyond individual observations.

Generating explanations and making comparisons have both been shown to improve learning. While each process has been studied individually, the relationship between explanation and comparison is not well understood. Three experiments evaluated the effectiveness of explanation and comparison prompts in learning novel categories. In Experiment 1, participants explained items’ category membership, performed pairwise comparisons between items (listed similarities and differences), did both, or did a control task. The explanation task increased the discovery of rules underlying category membership; however, the comparison task decreased rule discovery. Experiments 2 and 3 showed that (1) comparing all four category exemplars was more effective than either within-category or between-category pairwise comparisons, and that (2) “explain” participants reported higher levels of both spontaneous explanation and comparison than “compare” participants. This work provides insights into when explanation and comparison are most effective, and how these processes can work together to maximize learning.

We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments. In two experiments, we present human participants with causal structures for which the models make divergent predictions and either solicit the best explanation for an observed event (Experiment 1) or have participants rate provided explanations for an observed event (Experiment 2). Across two versions of two causal structures and across both experiments we find that the Causal Explanation Tree and Most Relevant Explanation models provide better fits to human data than either Most Probable Explanation or Explanation Tree models. We identify strengths and shortcomings of these models and what they can reveal about human explanation. We conclude by suggesting the value of pursuing computational and psychological investigations of explanation in parallel.

Three experiments test the hypothesis that engaging in explanation prompts children to favor inductively rich properties when generalizing to novel cases. In Experiment 1, preschoolers prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. In Experiment 2, we replicated this effect of explanation in a case of label extension. Experiment 3 demonstrated that explanation improves memory for internal features and labels, but impairs memory for superficial features. We conclude that explaining can influence learning by prompting children to favor inductively rich properties over surface similarity.

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in allowing participants to relate prior knowledge to patterns underlying category membership. The experiments revealed a superadditive interaction between explanation and informative labels, with explainers who received informative labels most likely to discover (Experiments 1 and 2) and generalize (Experiments 3 and 4) a pattern consistent with prior knowledge. However, explainers were no more likely than controls to discover multiple patterns (Experiments 1 and 2), indicating that effects of explanation are relatively targeted. We suggest that explanation recruits prior knowledge to assess whether candidate patterns are likely to have broad scope (i.e., to generalize within and beyond study observations). This interpretation is supported by the finding that effects of explanation on prior knowledge were attenuated when learners believed prior knowledge was irrelevant to generalizing category membership (Experiment 4). This research provides evidence that explanation can serve as a mechanism for deploying prior knowledge to assess the scope of observed patterns.

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and inference, but little is known about how children generate and select competing explanations. This study investigates whether young children prefer explanations that are simple, where simplicity is quantified as the number of causes invoked in an explanation, and how this preference is reconciled with probability information. Both preschool-aged children and adults were asked to explain an event that could be generated by 1 or 2 causes, where the probabilities of the causes varied across conditions. In 2 experiments, it was found that children preferred explanations involving 1 cause over 2 but were also sensitive to the probability of competing explanations. Adults, in contrast, responded on the basis of probability alone. These data suggest that children employ a principle of parsimony like Occam's razor as an inductive constraint and that this constraint is employed when more reliable bases for inference are unavailable.

Contemporary debates about the nature of semantic reference have tended to focus on two competing approaches: theories which emphasize the importance of descriptive information associated with a referring term, and those which emphasize causal facts about the conditions under which the use of the term originated and was passed on. Recent empirical work by Machery and colleagues suggests that both causal and descriptive information can play a role in judgments about the reference of proper names, with findings of cross-cultural variation in judgments that imply differences between individuals with respect to whether they favor causal or descriptive information in making reference judgments. We extend this theoretical and empirical line of inquiry to views of the reference of natural and nominal kind concepts, which face similar challenges to those concerning the reference of proper names. In two experiments, we find evidence that both descriptive and causal factors contribute to judgments of concept reference, with no reliable differences between natural and nominal kinds. Moreover, we find evidence that the same individuals’ judgments can rely on both descriptive and causal information, such that variation between individuals cannot be explained by appeal to a mixed population of “pure descriptive theorists” and “pure causal theorists.” These findings suggest that the contrast between descriptive and causal theories of reference may be inappropriate; intuitions may instead support a hybrid theory of reference that includes both causal and descriptive factors. We propose that future research should focus on the relationship between these factors, and describe several possible frameworks for pursuing these issues. Our findings have implications for theories of semantic reference, as well as for theories of conceptual structure.

Biological traits that serve functions, such as a zebra's coloration (for camouflage) or a kangaroo's tail (for balance), seem to have a special role in conceptual representations for biological kinds. In five experiments, we investigate whether and why functional features are privileged in biological kind classification. Experiment 1 experimentally manipulates whether a feature serves a function and finds that functional features are judged more diagnostic of category membership as well as more likely to have a deep evolutionary history, be frequent in the current population, and persist in future populations. Experiments 2-5 reveal that these inferences about history, frequency, and persistence account for nearly all the effect of function on classification. We conclude that functional features are privileged because their relationship with the kind is viewed as stable over time and thus as especially well suited for establishing category membership, with implications for theories of classification and folk biological understanding.

How does explaining novel observations influence the extent to which learners revise beliefs in the face of anomalies — observations inconsistent with their beliefs? On one hand, explaining could recruit prior beliefs and reduce belief revision if learners “explain away” or discount anomalies. On the other hand, explaining could promote belief revision by encouraging learners to modify beliefs to better accommodate anomalies. We explore these possibilities in a statistical judgment task in which participants learned to rank students’ performance across courses by observing sample rankings. We manipulated whether participants were prompted to explain the rankings or to share their thoughts about them during study, and also the proportion of observations that were anomalous with respect to intuitive statistical misconceptions. Explaining promoted greater belief revision when anomalies were common, but had no effect when rare. In contrast, increasing the number of anomalies had no effect on belief revision without prompts to explain.
 

Scientific and ‘intuitive’ or ‘folk’ theories are typically characterized as serving three critical functions: prediction, explanation, and control. While prediction and control have clear instrumental value, the value of explanation is less transparent. This paper reviews an emerging body of research from the cognitive sciences suggesting that the process of seeking, generating, and evaluating explanations in fact contributes to future prediction and control, albeit indirectly by facilitating the discovery and confirmation of instrumentally valuable theories. Theoretical and empirical considerations also suggest why explanations may nonetheless feel intrinsically valuable. The paper concludes by considering some implications of the psychology of explanation for a naturalized philosophy of explanation.
 

Children’s and adults’ attempts to explain the world around them plays a key role in promoting learning and understanding, but little is known about how and why explaining has this effect. An experiment investigated explaining in the social context of learning to predict and explain individuals’ behavior, examining if explaining observations exerts a selective constraint to seek patterns or regularities underlying the observations, regardless of whether such patterns are harmful or helpful for learning. When there were reliable patterns- such as personality types that predict charitable behavior- explaining promoted learning. But when these patterns were misleading, explaining produced an impairment whereby participants exhibited less accurate learning and prediction of individuals’ behavior. This novel approach of contrasting explanation’s positive and negative effects suggests that explanation’s benefits are not merely due to increased motivation, attention or time, and that explaining may undermine learning in domains where regularities are absent, spurious, or unreliable.

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions.

Machery emphasizes the centrality of explanation for theory-based approaches to concepts. I endorse Machery's emphasis on explanation and consider recent advances in psychology that point to the "heterogeneity" of explanation, with consequences for Machery's heterogeneity hypothesis about concepts.

Theory of mind, the capacity to understand and ascribe mental states, has traditionally been conceptualized as analogous to a scientific theory. However, recent work in philosophy and psychology has documented a "side-effect effect" suggesting that moral evaluations influence mental state ascriptions, and in particular whether a behavior is described as having been performed 'intentionally.' This evidence challenges the idea that theory of mind is analogous to scientific psychology in serving the function of predicting and explaining, rather than evaluating, behavior. In three experiments, we demonstrate that moral evaluations do inform ascriptions of intentional action, but that this relationship arises because behavior that conforms to norms (moral or otherwise) is less informative about underlying mental states than is behavior that violates norms. This analysis preserves the traditional understanding of theory of mind as a tool for predicting and explaining behavior, but also suggests the importance of normative considerations in social cognition.

Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations-even to themselves-they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three experiments provide evidence for the subsumptive constraints account: prompting participants to explain while learning artificial categories promotes the induction of a broad generalization underlying category membership, relative to describing items (Exp. 1), thinking aloud (Exp. 2), or free study (Exp. 3). Although explaining facilitates discovery, Experiment 1 finds that description is more beneficial for learning item details. Experiment 2 additionally suggests that explaining anomalous observations may play a special role in belief revision. The findings provide insight into explanation's role in discovery and generalization.