Tania Lombrozo

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.

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

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.

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 guidance respectively. 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 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.

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.

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

Consider the following two (hypothetical) generic causal claims: “Living in a neighborhood with many families with children increases purchases of bicycles” and “living in an affluent neighborhood with many families with children increases purchases of bicycles.” These claims not only differ in what they suggest about how bicycle ownership is distributed across different neighborhoods (i.e., “the data”), but also have the potential to communicate something about the speakers’ values: namely, the prominence they accord to affluence in representing and making decisions about the social world. Here, we examine the relationship between the level of granularity with which a cause is described in a generic causal claim (e.g., neighborhood vs. affluent neighborhood) and the value of the information contained in the causal model that generates that claim. We argue that listeners who know any two of the following can make reliable inferences about the third: 1) the level of granularity at which a speaker makes a generic causal claim, 2) the speaker’s values, and 3) the data available to the speaker. We present results of four experiments (N=1,323) in the domain of social categories that provide evidence in keeping with these predictions.

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 people have higher demands with respect to explanatory depth when it comes to attributing understanding, and 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 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 and understanding. 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

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.

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.

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.

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.

Editorial: Dimensions of Experimental Philosophy Content Type Journal Article Pages 315-318 DOI 10.1007/s13164-010-0037-9 Authors Joshua Knobe, Program in Cognitive Science and Department of Philosophy, Yale University, New Haven, CT USA Tania Lombrozo, Department of Psychology, UC Berkeley, 3210 Tolman Hall, Berkeley, CA 94720, USA Edouard Machery, Department of History and Philosophy of Science, University of Pittsburgh, 1017 CL, Pittsburgh, PA 15260, USA Journal Review of Philosophy and Psychology Online ISSN 1878-5166 Print ISSN 1878-5158 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.

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.

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.

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.

The new field of experimental philosophy has emerged as the methods of psychological science have been brought to bear on traditional philosophical issues. Oxford Studies in Experimental Philosophy will be the place to go to see outstanding new work in the field. It will feature papers by philosophers, papers by psychologists, and papers co-authored by people in both disciplines. The series heralds the emergence of a truly interdisciplinary field in which people from different disciplines are working together to address a shared set of questions.The inaugural volume is roughly structured into four sections. The first three papers focus on recent developments in moral psychology, a topic that has seen lively debate and a great deal of progress over the last decade. The second section highlights three contributions that bring new methods to moral psychology: formal modeling and special populations. The third section brings together four papers that adopt an experimental philosophy approach to novel topics, including intuitive dualism, generics, joint action, and happiness. And the last two papers provide critical and historical context to the development of experimental philosophy.

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.

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

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 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.

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

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted-for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.

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.

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.

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” and “Explaining for the Best Inference”, respectively. We also provide some initial support for EBI and identify promising directions for future research

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.

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.

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 to 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.