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.

Four experiments investigate the folk concept of ‘‘understanding,’’ in particular when and why it is deployed differently from the concept of knowledge. We argue for the positions that (1) people have higher demands with respect to explanatory depth when it comes to attributing understanding, and (2) that this is true, in part, because understanding attributions play a functional role in identifying experts who should be heeded with respect to the general field in question. These claims are supported by our findings that people differentially withhold attributions of understanding (rather than knowledge) when the object of attribution has minimal explanatory information. We also show that this tendency significantly correlates with people’s willingness to defer to others as potential experts. This work bears on a pressing issue in epistemology concerning the place and value of understanding. Our results also provide reason against positing a simple equation of knowledge(- why) and understanding(-why). We contend that, because deference plays a crucial role in many aspects of everyday reasoning, the fact that we use understanding attributions to demarcate experts reveals a potential mechanism for achieving our epistemic aims in many domains.

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.

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.

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.
 

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.

In pedagogical contexts and in everyday life, we often come to believe something because it would best explain the data. What is it about the explanatory endeavor that makes it essential to everyday learning and to scientific progress? There are at least two plausible answers. On one view, there is something special about having true explanations. This view is highly intuitive: it’s clear why true explanations might improve one’s epistemic position. However, there is another possibility—it could be that the process of seeking, generating, or evaluating explanations itself puts one in a better epistemic position, even when the outcome of the process is not a true explanation. In other words, it could be that accurate explanations are beneficial, or it could be that high-quality explaining is beneficial, where there is something about the activity of looking for an explanation that improves our epistemic standing. The main goal of this paper is to tease apart these two possibilities, both theoretically and empirically, which we align with ‘‘Inference to the Best Explanation’’ (IBE) and ‘‘Explaining for the Best Inference’’ (EBI), respectively. We also provide some initial support for EBI and identify promising directions for future research.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Everyday cognition reveals a sophisticated capacity to seek, generate, and evaluate explanations for the social and physical worlds around us. Why are we so driven to explain, and what accounts for our systematic explanatory preferences? This chapter reviews evidence from cognitive psychology and cognitive development concerning the structure and function of explanations, with a focus on the role of explanations in learning and inference. The findings highlight the value of understanding explanation and abductive inference both as phenomena in their own right and for the insights they provide concerning foundational aspects of human cognition, such as representation, learning, and inference.

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.

In two studies, we examine how prompting 5- and 6-year-olds to explain observed outcomes influences causal learning. In Study 1, children were presented with data consistent with two causal regularities. Explainers outperformed controls in generalizing the regularity that accounted for more observations. In Study 2, this regularity was pitted against an alternative that accounted for fewer observations but was consistent with prior knowledge. Explainers were less likely than controls to generalize the regularity that accounted for more observations. These findings suggest that explaining drives children to favor causal regularities that they expect to generalize, where current observations and prior knowledge both provide cues.

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.
 

In his book Doing Without Concepts, Edouard Machery argues that cognitive scientists should reject the concept of “concept” as a natural, psychological kind. I review and critique several of Machery’s arguments, focusing on his definition of “concept” and on claims against the possibility and utility of a unified account of concepts. In particular, I suggest ways in which prototype, exemplar, and theory-theory approaches to concepts might be integrated.

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.

Teleological explanations – explanations in terms of functions, purposes, or goals – are pervasive in religion and feature prominently in intuitive theories about the world, such as theory of mind and folk biology. Previous findings suggest that such explanations reflect a deep, explanatory preference. Here we explore the mechanisms underlying the prevalence and persistence of such explanations, following a method developed by Boyer and Ramble (2001) to examine which religious concepts are likely to survive processes of cultural transmission. Specifically, we test the prediction that novel teleological explanations are remembered better than mechanistic explanations, even when effects of an explanation’s quality are taken into account. Two experiments support this prediction for artifact and biological trait explanations, but find the opposite pattern for explanations of non-living natural entities.

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.

Knobe considers two explanations for the influence of moral considerations on “non-moral” cognitive systems: the “person as moralist” position, and the “person as [biased] scientist” position. We suggest that this dichotomy conflates questions at computational and algorithmic levels, and suggest that distinguishing the issues at these levels reveals a third, viable option, which we call the “rational scientist” position.

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.

A great deal of research has demonstrated that learning is influenced by the learner’s prior background knowledge (e.g. Murphy, 2002; Keil, 1990), but little is known about the processes by which prior knowledge is deployed. We explore the role of explanation in deploying prior knowledge by examining the joint effects of eliciting explanations and providing prior knowledge in a task where each should aid learning. Three hypotheses are considered: that explanation and prior knowledge have independent and additive effects on learning, that their joint effects on learning are subadditive, and that their effects are superadditive. A category learning experiment finds evidence for a superadditive effect: explaining drives the discovery of regularities, while prior knowledge constrains which regularities learners discover. This is consistent with an account of explanation’s effects on learning proposed in Williams & Lombrozo (in press).

Engaging in explanation, even to oneself, can enhance learning. What underlies this effect? Williams & Lombrozo (in press) propose that explanation exerts subsumptive constraints on processing, driving learners to discover underlying patterns. A category-learning experiment demonstrates that explanation can enhance or impair learning depending on whether these constraints match the structure of the material being learned. Explaining can help learning when reliable patterns are present, but actually impairs learning when patterns are misleading. This explanation impairment effect is predicted by the subsumptive constraints account, but challenges alternative hypotheses according to which explaining helps learning by increasing task engagement through motivation, attention, or processing time. The findings have both theoretical and practical implications for learning and education.

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.

Traditional approaches to moral psychology assumed that moral judgments resulted from the application of explicit commitments, such as those embodied in consequentialist or deontological philosophies. In contrast, recent work suggests that moral judgments often result from unconscious or emotional processes, with explicit commitments generated post hoc. This paper explores the intermediate position that moral commitments mediate moral judgments, but not through their explicit and consistent application in the course of judgment. An experiment with 336 participants finds that individuals vary in the extent to which their moral commitments are consequentialist or deontological, and that this variation is systematically but imperfectly related to the moral judgments elicited by trolley car problems. Consequentialist participants find action in trolley car scenarios more permissible than do deontologists, and only consequentialists moderate their judgments when scenarios that typically elicit different intuitions are presented side by side. The findings emphasize the need for a theory of moral reasoning that can accommodate both the associations and dissociations between moral commitments and moral judgments.

Recent theoretical and empirical work suggests that explanation and categorization are intimately related. This paper explores the hypothesis that explanations can help structure conceptual representations, and thereby influence the relative importance of features in categorization decisions. In particular, features may be differentially important depending on the role they play in explaining other features or aspects of category membership. Two experiments manipulate whether a feature is explained mechanistically, by appeal to proximate causes, or functionally, by appeal to a function or goal. Explanation type has a significant impact on the relative importance of features in subsequent categorization judgments, with functional explanations reversing previously documented effects of 'causal status'. The findings suggest that a feature's explanatory importance can impact categorization, and that explanatory relationships, in addition to causal relationships, are critical to understanding conceptual representation.

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. to accomplish the function of predicting and explaining behavior.

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 explores a potential mechanism underlying this effect, motivated by philosophical accounts of the structure of explanations: that explaining guides learners to interpret observations in terms of unifying patterns or regularities, which in turn promotes the discovery of broad generalizations. Experiment 1 finds that prompting participants to explain while learning artificial categories promotes the induction of a broad generalization underlying category membership. Experiment 2 suggests that explanation most readily prompts discovery in the presence of anomalies: observations inconsistent with current beliefs. Experiment 1 additionally suggests that explaining might result in reduced memory for details. These findings provide evidence for the proposed mechanism and insight into the potential role of explanation in discovery and generalization.

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and development, but little is known about how children evaluate explanations, especially in the absence of probability information or robust prior beliefs. Previous findings demonstrate that adults balance several explanatory virtues in evaluating competing explanations, including simplicity and probability. Specifically, adults treat simplicity as a probabilistic cue that trades-off with frequency information. However , no work has investigated whether children are similarly sensitive to simplicity and probability. We report an experiment investigating how preschoolers evaluate causal explanations, and in particular whether they employ a principle of parsimony like Ockham’s razor as an inductive constraint. Results suggest that even preschoolers are sensitive to the simplicity of explanations, and require disproportionate probabilistic evidence before a complex explanation will be favored over a simpler alternative.