Getting Emotional About Explanations: Social Explanations and Social Explanatory Styles as Bases of Prosocial Emotions and Intergroup Attitudes

We are interested in the bases of social emotions such as compassion and hostility. Our analysis centers on social explanations, or people’s answers to the question: Why does the target behave that way or experience those outcomes? Below, we review classic approaches to social explanation and then review work linking explanations to emotions. Finally, we focus on work from our lab that connects explanations to prosocial emotions and intergroup attitudes, including compassion for the disadvantaged and reduced vengefulness toward the violent. A crucial contribution of our work is to illuminate complex connections between explanations and emotions: A given explanation has different socio‐emotional implications depending on the explainer’s motives. Finally, we review our work suggesting that individuals have social explanatory styles, and that particular styles are predictive of dispositional compassion. A key implication of our work is that social explanations are another basis of prosociality, in addition to factors such as empathy and moral principles.     

How chance explains

What explains the outcomes of chance processes? We claim that their setups do. Chances, we think, mediate these explanations of outcome by setup but do not feature in them. Facts about chances do feature in explanations of a different kind: higher-order explanations, which explain how and why setups explain their outcomes. In this paper, we elucidate this 'mediator view' of chancy explanation and defend it from a series of objections. We then show how it changes the playing field in four metaphysical disputes concerning chance. First, it makes it more plausible that even low chances can have explanatory power. Second, it undercuts a circularity objection against reductionist theories of chance. Third, it redirects the debate about a prominent argument against epistemic theories of chance. Finally, it sheds light on potential chancy explanations of the Universe's origin.     

Judgments of voluntary and physical causes in causal chains: probabilistic and social functionalist criteria for attributions

Four experiments investigated judgments about voluntary human actions and physical causes that were embedded in causal chains ending in negative outcomes (e.g., a forest fire). Causes were judged for their explanatory quality, their effect on the probability of the outcome, and the extent to which they could be socially controlled. Results supported legal theorists' claim that voluntary actions are judged better explanations than physical causes. Indices derived from theories of probability change generally failed to predict the preference for voluntary actions. In contrast, this preference was mediated by the perceived extent to which voluntary versus physical causes may be brought under social control. These results suggest that causal explanation, at least within causal chains, is not driven solely by changes in the probability of an outcome when a cause is added, and that observers recognize the potential social function of explanations in drawing attention to socially controllable causes. Copyright © 2006 John Wiley & Sons, Ltd.     

Normative Explanation And Justification*

Normative explanations of why things are wrong, good, or unfair are ubiquitous in ordinary practice and normative theory. This paper argues that normative explanation is subject to a justification condition: a correct complete explanation of why a normative fact holds must identify features that would go at least some way towards justifying certain actions or attitudes. I first explain and motivate the condition I propose. I then support it by arguing that it fits well with various theories of normative reasons, makes good sense of certain legitimate moves in ordinary normative explanatory discourse, and helps to make sense of our judgments about explanatory priority in certain cases of normative explanation. This last argument also helps to highlight respects in which normative explanation won't be worryingly discontinuous with explanations in other domains even though these other explanations aren't subject to the justification condition. Thus the paper aims not only to do some constructive theorizing about the relatively neglected topic of normative explanation but also to cast light on the broader question of how normative explanation may be similar to and different from explanations in other domains.     

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself.     

Little Bayesians or little Einsteins? Probability and explanatory virtue in children's inferences

Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists – such as Einstein – do? Four experiments support the latter possibility. In particular, we demonstrate in four experiments that 4‐ to 8‐year‐old children, like adults, have a robust latent scope bias that leads to inferences that do not maximize posterior probability. When faced with two explanations equally consistent with observed data, where one explanation makes an unverified prediction, children consistently preferred the explanation that does not make this prediction (Experiment 1), even if the prior probabilities are identical (Experiment 3). Additional evidence suggests that this latent scope bias may result from the same explanatory strategies used by adults (Experiments 1 and 2), and can be attenuated by strong prior odds (Experiment 4). We argue that children, like adults, rely on ‘explanatory virtues’ in inference – a strategy that often leads to normative responses, but can also lead to systematic error.     

Proving quadratic reciprocity: explanation,disagreement, transparency and depth

Gauss’s quadratic reciprocity theorem is among the most important results in the history of number theory. It’s also among the most mysterious: since its discovery in the late eighteenth century, mathematicians have regarded reciprocity as a deeply surprising fact in need of explanation. Intriguingly, though, there’s little agreement on how the theorem is best explained. Two quite different kinds of proof are most often praised as explanatory: an elementary argument that gives the theorem an intuitive geometric interpretation, due to Gauss and Eisenstein, and a sophisticated proof using algebraic number theory, due to Hilbert. Philosophers have yet to look carefully at such explanatory disagreements in mathematics. I do so here. According to the view I defend, there are two important explanatory virtues—depth and transparency—which different proofs (and other potential explanations) possess to different degrees. Although not mutually exclusive in principle, the packages of features associated with the two stand in some tension with one another, so that very deep explanations are rarely transparent, and vice versa. After developing the theory of depth and transparency and applying it to the case of quadratic reciprocity, I draw some morals about the nature of mathematical explanation.

     

Causal Relations and Explanatory Strategies in Physics

Many philosophers now regard causal approaches to explanation as highly promising, even in physics. This is due in large part to James Woodward's influential argument that a wide variety of scientific explanations are causal, based on his interventionist approach to causation. This article argues that some derivations describing causal relations and satisfying Woodward's criteria for causal explanation fail to be explanatory. Further, causal relations are unnecessary for a range of explanations, widespread in physics, involving highly idealized models. These constitute significant limitations on the scope of causal explanation. We have good reason to doubt that causal explanation is as widespread or important in physics as Woodward and other proponents maintain.     

Why inference to the best explanation doesn’t secure empirical grounds for mathematical platonism

Proponents of the explanatory indispensability argument for mathematical platonism maintain that claims about mathematical entities play an essential explanatory role in some of our best scientific explanations. They infer that the existence of mathematical entities is supported by way of inference to the best explanation from empirical phenomena and therefore that there are the same sort of empirical grounds for believing in mathematical entities as there are for believing in concrete unobservables such as quarks. I object that this inference depends on a false view of how abductive considerations mediate the transfer of empirical support. More specifically, I argue that even if inference to the best explanation is cogent, and claims about mathematical entities play an essential explanatory role in some of our best scientific explanations, it doesn’t follow that the empirical phenomena that license those explanations also provide empirical support for the claim that mathematical entities exist.

     

Exploring the Effects of the Naturalistic Fallacy: Evidence That Genetic Explanations Increase the Acceptability of Killing and Male Promiscuity

The naturalistic fallacy is the erroneous belief that what is natural is morally acceptable. Two studies assessed whether people commit the naturalistic fallacy by testing whether genetic explanations for killing and male promiscuity, as compared to experiential explanations (i.e., learning/“nurture” explanations) increase acceptance of these behaviors. In Study 1, participants who read a genetic explanation for why people kill bugs viewed bug killing as more morally acceptable than participants who read an experiential explanation, although they did not reliably kill more bugs. In Study 2, men who read a genetic explanation for why men are more promiscuous than women reported decreased interest in long‐term romantic commitment compared with men who read experiential explanations and women who read either explanation.     

Narratives,mechanisms and progress in historical science

Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical science. I argue that at least sometimes which strategy is appropriate is not a pragmatic issue, but turns on the nature of the target. Moreover, the distinction reveals a counterintuitive pattern of progress in some historical explanation: shifting from simple to complex. Sometimes, historical scientists rightly abandon simple, unified explanations in favour of disunified, complex narratives. Finally I compare narrative and mechanistic explanation, arguing that mechanistic approaches are inappropriate for complex narrative explanations.     

Explanatory pluralism in the medical sciences: Theory and practice

Explanatory pluralism is the view that the best form and level of explanation depends on the kind of question one seeks to answer by the explanation, and that in order to answer all questions in the best way possible, we need more than one form and level of explanation. In the first part of this article, we argue that explanatory pluralism holds for the medical sciences, at least in theory. However, in the second part of the article we show that medical research and practice is actually not fully and truly explanatory pluralist yet. Although the literature demonstrates a slowly growing interest in non-reductive explanations in medicine, the dominant approach in medicine is still methodologically reductionist. This implies that non-reductive explanations often do not get the attention they deserve. We argue that the field of medicine could benefit greatly by reconsidering its reductive tendencies and becoming fully and truly explanatory pluralist. Nonetheless, trying to achieve the right balance in the search for and application of reductive and non-reductive explanations will in any case be a difficult exercise.     

Reasoning Through Instructional Analogies

This article aims to account for students’ assessments of the plausibility and applicability of analogical explanations, and individual differences in these assessments, by analyzing properties of students’ underlying knowledge systems. We developed a model of explanation and change in explanation focusing on knowledge elements that provide a sense of satisfaction to those judging the explanation. We call these elements “explanatory primitives.” In this model, explanations are accepted or rejected on the basis of (a) the individual's convictions concerning particular explanatory primitives and (b) the fit of these primitives to current circumstances. Data are drawn from clinical interviews with three high school students who worked through a bridging analogies tutoring sequence on the existence of the normal force in mechanics. Methodologically, our work involves fine-grain analysis of process data and explicit principles of empirical accountability; we believe it marks a methodological advance over most previously reported empirical studies of analogical reasoning.     

LOT 2: The Language of Thought Revisited

A main thread of the debate over mathematical realism has come down to whether mathematics does explanatory work of its own in some of our best scientific explanations of empirical facts. Realists argue that it does; anti-realists argue that it doesn't. Part of this debate depends on how mathematics might be able to do explanatory work in an explanation. Everyone agrees that it's not enough that there merely be some mathematics in the explanation. Anti-realists claim there is nothing mathematics can do to make an explanation mathematical; realists think something can be done, but they are not clear about what that something is.

I argue that many of the examples of mathematical explanations of empirical facts in the literature can be accounted for in terms of Jackson and Pettit's [1990] notion of program explanation, and that mathematical realists can use the notion of program explanation to support their realism. This is exactly what has happened in a recent thread of the debate over moral realism (in this journal). I explain how the two debates are analogous and how moves that have been made in the moral realism debate can be made in the mathematical realism debate. However, I conclude that one can be a mathematical realist without having to be a moral realist.     

The effect of explanation in simple binary decision tasks

Many studies of explanation have focused on higher level tasks and on how explanations draw upon relevant prior knowledge, which then helps in understanding some event or observation. However, explanations may also affect performance in simple tasks even when they include no task-relevant information. In three experiments, we show that explanations adding no task-relevant information alter performance in a sequential binary decision task. Whereas people with no explanation for why two events occurred at different rates tended to predict each outcome in proportion to their probability of occurrence (to "probability match"), people with an explanation tended to predict the more likely event more often (to "overmatch," a better strategy). These results suggest a broader view of explanation, which includes a role in shaping simple tasks outside of higher level reasoning.     

Evidence,explanation and enhanced indispensability

In this paper I shall adopt a possible reading of the notions of ‘explanatory indispensability’ and ‘genuine mathematical explanation in science’ on which the Enhanced Indispensability Argument (EIA) proposed by Alan Baker is based. Furthermore, I shall propose two examples of mathematical explanation in science and I shall show that, whether the EIA-partisans accept the reading I suggest, they are easily caught in a dilemma. To escape this dilemma they need to adopt some account of explanation and offer a plausible answer to the following ‘question of evidence’: What is a genuine mathematical explanation in empirical science and on what basis do we consider it as such? Finally, I shall suggest how a possible answer to the question of evidence might be given through a specific account of mathematical explanation in science. Nevertheless, the price of adopting this standpoint is that the genuineness of mathematical explanations of scientific facts turns out to be dependent on pragmatic constraints and therefore cannot be plugged in EIA and used to establish existential claims about mathematical objects.     

Learning the Relevance of Relevance and the Trouble with Truth: Evaluating Explanatory Relevance across Childhood

ABSTRACT

In four experiments, we investigate how the ability to detect irrelevant explanations develops. In Experiments 1 and 2, 4- to 8-year-olds and adults rated different types of explanations about “what makes cars go” individually, in the absence of a direct contrast. Each explanation was true and relevant (e.g., “Cars have engines that turn gasoline into power”), true and irrelevant (e.g., “Cars have radios that play music”), or a false statement that would be relevant if it were true (e.g., “Cars have rockets that speed them up”). Participants of all ages spontaneously indicated that false explanations were less helpful than relevant explanations. However, there was a developmental shift for irrelevant explanations: 4-year-olds only detected irrelevant explanations that did not involve internal features of cars (e.g., “Cars have parking lots that they park in”). Crucially, this shift between age 4 and 5 cannot be explained by 4-year-olds’ lack of knowledge since 4-year-olds correctly indicated that relevant explanations were more helpful than irrelevant feature explanations when given a direct contrast in Experiment 3. These results are further clarified in Experiment 4, in which we provided a different explanatory goal (“where to find cars”) and found that even young children have a nuanced understanding of explanatory relevance that is sensitive to differing explanatory goals. Together, these four experiments suggest an early-emerging ability to understand relevance, but a shift between age 4 and 5 in the ability to spontaneously use this understanding when evaluating individual explanations in isolation.     

IV. Understanding Peter Winch

Peter Winch's The Idea of a Social Science has been the subject of repeated misunderstanding. This discussion takes one recent example and shows how Winch's argument is gravely distorted. What is at issue is not, as is usually supposed, whether we can accept or endorse another society's explanations of its activities, but whether we have to look for an explanatory connection between concepts and action. Winch's argument is that before we can try to explain actions, we have to identify them correctly. This can only be done by seeing how they, and the concepts they are associated with, fit within a way of life. Grasping its rule‐following character is understanding action. Once the difficulties in making such identifications are appreciated, we will be less inclined to accept facile explanations why people in other societies do the things they do.     

Dreaming in the multilevel framework

Biological realism ( [Revonsuo, 2001] and [Revonsuo, 2006] ) states that dreaming is a biological phenomenon and therefore explainable in naturalistic terms, similar to the explanation of other biological phenomena. In the biological sciences, the structure of explanations can be described with the help of a framework called ‘multilevel explanation’. The multilevel model provides a context that assists to clarify what needs to be explained and how, and how to place different theories into the same model. Here, I will argue that the multilevel framework would be useful when we try to construct scientific explanations of dreaming.