In this dissertation, I argue that understanding possesses unique epistemic value. I propose and defend a novel account of understanding that I call the management account of understanding, which is the view that an agent A understands a subject matter S just in case A has the ability to extract the relevant information and exploit it with the relevant cognitive capacities to answer questions in S. Since inquiry is the process of raising and answering questions, I argue that without understanding, (...) it would be impossible to engage in successful inquiry. I argue that understanding is indispensable for effective cognition and that it is irreducible to other epistemic categories, such as knowledge, justification, or rationality. Understanding is an irreducible component of epistemic excellence. In arguing for this account, I focus on the nature and requirements of cognition and pay less attention to intuitions about hypothetical cases, in contrast to previous approaches to understanding. I draw upon psychology and cognitive science, especially work on the frame problem, to demonstrate the importance (and difficulty) of finding relevant information and exploiting it in relevant ways to answer questions. One’s cognitive system must be organized in such a way that one has the ability to think the relevant thoughts in the relevant ways to answer the relevant questions. This reveals well-defined directions in which future research on understanding should move. In addition to proposing and defending the management account of understanding, I explore several features of understanding, including some interesting consequences of the management account. I argue that the management account and commonly used methods of measuring degrees of understanding imply that understanding can be based on false beliefs. I argue that the management account (and any account of understanding that implies understanding is an ability) implies that understanding can be acquired by luck. I also argue that understanding is ontologically prior to explanation: a good explanation is essentially an act that produces understanding in the relevant cognitive background. (shrink)

Defenders of pragmatic encroachment in epistemology (or what I call practicalism) need to address two main problems. First, the view seems to imply, absurdly, that knowledge can come and go quite easily—in particular, that it might come and go along with our variable practical interests. We can call this the stability problem. Second, there seems to be no fully satisfying way of explaining whose practical interests matter. We can call this the “whose stakes?” problem. I argue that both problems can (...) be addressed in roughly the same terms. More exactly, I argue that by first clarifying the whose stakes? problem an answer to the stability problem naturally falls out. (shrink)

This chapter examines the status of inference to the best explanation in naturalistic metaphysics. The methodology of inference to the best explanation in metaphysics is studied from the perspective of contemporary views on scientific explanation and explanatory inferences in the history and philosophy of science. This reveals serious shortcomings in prevalent attempts to vindicate metaphysical "explanationism" by reference to similarities between science and naturalistic metaphysics. This critique is brought out by considering a common gambit of methodological unity: (1) Both metaphysics (...) and science employ inference to the best explanation. (2) One has no reason to think that if explanationism is truth-conducive in science, it is not so in metaphysics. (3) One has a positive reason to think that if explanationism is truth-conducive in science, it is also so in metaphysics. (shrink)

This paper examines the relevance of philosophical work on consciousness to its scientific study. Of particular concern is the debate over whether consciousness can be naturalized, which is typically taken to have consequences for the prospects for its scientific investigation. It is not at all clear that philosophers of consciousness have properly identified and evaluated the assumptions about scientific activity made by both naturalization and anti- naturalization projects. I argue that there is good reason to think that some of the (...) assumptions about physicalism and explanation made by the parties to the debate are open to serious doubt. Thus this paper is an invitation for those inquiring into whether consciousness can be naturalized to more carefully consider the expected payoff of such efforts. (shrink)

Many philosophers of science follow Hempel in embracing both substantive and methodological anti-psychologism regarding the study of explanation. The former thesis denies that explanations are constituted by psychological events, and the latter denies that psychological research can contribute much to the philosophical investigation of the nature of explanation. Substantive anti-psychologism is commonly defended by citing cases, such as hyper-complex descriptions or vast computer simulations, which are reputedly generally agreed to constitute explanations but which defy human comprehension and, as a result, (...) fail to engender any relevant psychological events. It is commonly held that the truth of the substantive thesis would lend support to the methodological thesis. However, the standard argument for the substantive thesis presumes that philosophers’ own judgments about the aforementioned cases issues from mastery of the lay or scientific norms regarding the use of ‘explanation.’ Here we challenge this presumption with a series of experiments indicating that both lay and scientific populations require of explanations that they actually render their targets intelligible. This research not only undermines a standard line of argument for substantive anti-psychologism, it demonstrates the utility of psychological research methods for answering meta-questions about the norms regarding the use of ‘explanation.’. (shrink)

One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...) basis of theoretical models that provide how-possibly explanations. These models, while they do not explain in the strict sense, afford understanding. In response, I propose an alternative epistemology of understanding, broad KAU, that takes cases of theoretical modelling into account. (shrink)

How can we use models to understand real phenomena if models misrepresent the very phenomena we seek to understand? Some accounts suggest that models may afford understanding by providing causal knowledge about phenomena via how-possibly explanations. However, general equilibrium models, for example, pose a challenge to this solution since their contribution appears to be purely mathematical results. Despite this, practitioners widely acknowledge that it improves our understanding of the world. I argue that the Arrow–Debreu model provides a mathematical how-possibly explanation (...) which establishes claims of mathematical dependence. The account developed reveals how mathematical knowledge can inform claims about the world, allow ‘what-if-things-had-been-different’ inferences, and thus improve our understanding. (shrink)

In debates about the nature of scientific explanation, one theme repeatedly arises: that explanation is about providing understanding. However, the concept of understanding has only recently been explored in any depth, and this paper attempts to introduce a useful concept of understanding to that literature and explore it. Understanding is a higher level cognition, the recognition of connections between various pieces of knowledge. This conception can be brought to bear on the conceptual issues that have thus far been unclear in (...) the literature. Though this notion of understanding is broad, explaining various concepts of explanation, it is robust enough to underwrite an objective and useful notion of explanation. (shrink)

Recent work in epistemology and philosophy of science has argued that understanding is an important cognitive state that philosophers should seek to analyse. This paper offers a new perspective on understanding by looking to work in philosophy of mind and cognitive science. Understanding is normally taken to be inside the head. I argue that this view is mistaken. Often, understanding is a state that criss-crosses brain, body and world. To support this claim, I draw on extended cognition, a burgeoning framework (...) in cognitive science that stresses the crucial role played by tools, material representations and the wider environment in our cognitive processes. I defend an extended view of understanding against likely objections and argue that it has important consequences for questions concerning the nature of understanding and its relationship to explanation. (shrink)

Here, I take alethic views of understanding to be all views that hold that whether an explanation is true or false matters for whether that explanation provides understanding. I then argue that there is (as yet) no naturalistic defence of alethic views of understanding in cognitive science, because there is no agreement about the correct descriptions of the content of cognitive scientific explanations. I use this claim to argue for the provisional acceptance of afactivism in cognitive science, which is the (...) view that the truth or falsity of an explanation of cognition is irrelevant to whether that explanation provides understanding. I conclude by discussing the relation between understanding in cognitive science and understanding in other domains. (shrink)

Scientific understanding, this paper argues, can be analyzed entirely in terms of a mental act of “grasping” and a notion of explanation. To understand why a phenomenon occurs is to grasp a correct explanation of the phenomenon. To understand a scientific theory is to be able to construct, or at least to grasp, a range of potential explanations in which that theory accounts for other phenomena. There is no route to scientific understanding, then, that does not go by way of (...) scientific explanation. (shrink)

There is a recent interest within both philosophy of science as well as within epistemology to provide a defensible account of understanding. In the present article I build on insights from previous work in attempt to provide an account of two related forms of understanding in terms of the ability to form rational intentions when using specific types of mental representations. I propose first that “understanding that X” requires that one form a representation of X and, further, that one must (...) be capable of forming rational intentions using this representation across a range of conceivable conditions. I then propose that “understanding why X” requires that one possess a representation of a successful explanation for why X, and that one must be similarly capable of forming rational intentions using this representation across a range of conceivable conditions. I conclude the manuscript by reviewing objections and considering the way this account relates to other literature on explanation and understanding. (shrink)

Starting with the slogan that understanding is a ‘knowledge of causes’, Stephen Grimm and John Greco have argued that understanding comes from a knowledge of dependence relations. Grounding is the trendiest dependence relation on the market, and if Grimm and Greco are correct, then instances of grounding should also give rise to understanding. In this paper, I will show that this prediction is correct – grounding does indeed generate understanding in just the way that Grimm and Greco anticipate. However, grounding (...) examples of understanding also show that Grimm and Greco are not telling the full story when it comes to understanding. Understanding can only be generated by a particular subset of dependence relations – those dependence relations that are also explanatory. Grimm and Greco should thus appeal to a privileged class of dependence relations, relations like grounding that can also give rise to explanation. (shrink)

This paper argues that the semantic facts about ‘because’ are best explained via a metaphorical treatment of metaphysical explanation that treats causal explanation as explanation par excellence. Along the way, it defends a commitment to a unified causal sense of ‘because’ and offers a proprietary explanation of grounding skepticism. With the causal metaphor account of metaphysical explanation on the table, an extended discussion of the relationship between conceptual structure and metaphysics ends with a suggestion that the semantic facts about ‘because’ (...) tell against grounding-causation unity. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

Catherine Elgin has recently argued that a nonfactive conception of understanding is required to accommodate the epistemic successes of science that make essential use of idealizations and models. In this paper, I argue that the fact that our best scientific models and theories are pervasively inaccurate representations can be made compatible with a more nuanced form of scientific realism that I call Understanding Realism. According to this view, science aims at (and often achieves) factive scientific understanding of natural phenomena. I (...) contend that this factive scientific understanding is provided by grasping a set of true modal information about the phenomenon of interest. Furthermore, contrary to Elgin’s view, I argue that the facticity of this kind of scientific understanding can be separated from the inaccuracy of the models and theories used to produce it. (shrink)

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.

Explaining phenomena is a primary goal of science. Consequently, it is unsurprising that gaining a proper understanding of the nature of explanation is an important goal of science education. In order to properly understand explanation, however, it is not enough to simply consider theories of the nature of explanation. Properly understanding explanation requires grasping the relation between explanation and understanding, as well as how explanations can lead to scientific knowledge. This article examines the nature of explanation, its relation to understanding, (...) and how explanations are used to generate scientific knowledge via inferences to the best explanation. Studying these features and applications of explanation not only provides insight into a concept that is important for science education in its own right, but also sheds light on an aspect of recent debates concerning the so-called consensus view of nature of science (NOS). Once the relation between explanation, understanding, and knowledge is clear, it becomes apparent that science is unified in important ways. Seeing this unification provides some support for thinking that there are general features of NOS of the sort proposed by the consensus view and that teaching about these general features of NOS should be a goal of science education. (shrink)

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. (shrink)

In the last couple of years a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what they are explanatory. These questions raise further issues about (...) counterfactuals, modality, and explanatory asymmetries: i.e., do mathematical and non-causal explanations support... (shrink)

In the last couple of years a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the question what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e. explanations that don’t cite causes in the explanans) sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what (...) they are explanatory. These questions raise further issues about counterfactuals, modality and explanatory asymmetries, i.e. do mathematical and non-causal explanations support counterfactuals, and how to understand explanatory asymmetries in non-causal explanations. Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology, where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is whether in network models in biology, ecology, neuroscience and computer science the mathematical or more precisely topological properties can be explanatory of physical phenomena, or they are just different ways to represent causal structures. The aim of the special issue is to unify all these debates around several overlapping questions. (shrink)

In this paper, I outline a heuristic for thinking about the relation between explanation and understanding that can be used to capture various levels of “intimacy”, between them. I argue that the level of complexity in the structure of explanation is inversely proportional to the level of intimacy between explanation and understanding, i.e. the more complexity the less intimacy. I further argue that the level of complexity in the structure of explanation also affects the explanatory depth in a similar way (...) to intimacy between explanation and understanding, i.e. the less complexity the greater explanatory depth and vice versa. (shrink)

Jonathan Kvanvig has argued that “objectual” understanding, i.e. the understanding we have of a large body of information, cannot be reduced to explanatory concepts. In this paper, I show that Kvanvig fails to establish this point, and then propose a framework for reducing objectual understanding to explanatory understanding.

Recently, several authors have argued that scientific understanding should be a new topic of philosophical research. In this article, I argue that the three most developed accounts of understanding--Grimm's, de Regt's, and de Regt and Dieks's--can be replaced by earlier accounts of scientific explanation without loss. Indeed, in some cases, such replacements have clear benefits.

A sensible epistemologist may not see how she could know that she is not a brain in a vat ; but she doesn’t panic. She sticks with her empirical beliefs, and as that requires, believes that she is not a BIV. (She does not inferentially base her belief that she is not a BIV on her empirical knowledge—she rejects that ‘Moorean’ response to skepticism.) Drawing on the psychological literature on metacognition, I describe a mechanism that’s plausibly responsible for a sensible (...) epistemologist coming to believe she is not a BIV. I propose she thereby knows that she is not a BIV. The particular belief-forming mechanism employed explains why she overlooks this account of how she knows she is not a BIV, making it seem that there is no way for her to know it. I argue this proposal satisfactorily resolves the skeptical puzzle. (shrink)

Science frequently gives us multiple, compatible ways of solving the same problem or formulating the same theory. These compatible formulations change our understanding of the world, despite providing the same explanations. According to what I call "conceptualism," reformulations change our understanding by clarifying the epistemic structure of theories. I illustrate conceptualism by analyzing a typical example of symmetry-based reformulation in chemical physics. This case study poses a problem for "explanationism," the rival thesis that differences in understanding require ontic explanatory differences. (...) To defend conceptualism, I consider how prominent accounts of explanation might accommodate this case study. I argue that either they do not succeed, or they generate a skeptical challenge. (shrink)

Accounts of scientific explanation disagree about what’s required for a cause, law, or other fact to be a reason why an event occurs. In short, they disagree about the conditions for explanatory relevance. Nonetheless, most accounts presuppose that claims about explanatory relevance play a descriptive role in tracking reality. By rejecting the need for this descriptivist assumption, I develop an expressivist account of explanatory relevance and explanation: to judge that an answer is explanatory is to express an attitude ofbeing for (...) being satisfied by that answer. I show how expressivism vindicates ordinary scientific discourse about explanation, including claims about the objectivity and mind-independence of explanations. By avoiding commitment to ontic relevance relations, I rehabilitate an irrealist conception of explanation. (shrink)

Many philosophers would, in theory, agree that the methods and tools of philosophy ought to be supplemented by those of other academic disciplines. In practice, however, the sociological data suggest that most philosophers fail to engage or collaborate with other academics, and this article argues that this is problematic for philosophy as a discipline. In relation to the value of interdisciplinary collaboration, the article highlights how experimental philosophers can benefit the field, but only insofar as they draw from the distinctive (...) methods of philosophy and overcome the charge of “amateur psychology” by more consistently collaborating with the scientists they seek to emulate. It concludes that philosophers ought to collaborate with other academics in order to gain an experience-based understanding of the methods of other disciplines in addition to an understanding of the content of these disciplines. (shrink)

What is it that makes someone wise, or one person wiser than another? I argue that wisdom consists in knowledge of how to live well, and that this knowledge of how to live well is constituted by various further kinds of knowledge. One concern for this view is that knowledge is not needed for wisdom but rather some state short of knowledge, such as having rational or justified beliefs about various topics. Another concern is that the emphasis on knowing how (...) to live well fails to do justice to the ancient tradition of ‘theoretical wisdom’. I address both of these concerns in filling out the account. (shrink)

Over the last several years a number of leading philosophers – including Catherine Elgin, Linda Zagzebski, Jonathan Kvanvig, and Duncan Pritchard – have grown increasingly dissatisfied with the contemporary focus on knowledge in epistemology and have attempted to “recover” the notion of understanding. According to some of these philosophers, in fact, understanding deserves not just to be recovered, but to supplant knowledge as the focus of epistemological inquiry. This entry considers some of the main reasons why philosophers have taken understanding (...) to be more valuable than knowledge, focusing on claims that it is more transparent, that it better reflects or mirrors the world, and that it is a greater intellectual achievement. (shrink)

I defend the claim that understanding is the goal of explanation against various persistent criticisms, especially the criticism that understanding is not truth-connected in the appropriate way, and hence is a merely psychological state. Part of the reason why understanding has been dismissed as the goal of explanation, I suggest, is because the psychological dimension of the goal of explanation has itself been almost entirely neglected. In turn, the psychological dimension of understanding—the Aha! experience, the sense that a certain explanation (...) “feels right”, and so on—has been conspicuously overemphasized. I try to correct for both of these exaggerations. Just as the goal of explanation includes a richer psychological—including phenomenological—dimension than is generally acknowledged, so too understanding has a stronger truth connection than is generally acknowledged.Keywords: Understanding; Explanation; Philosophy of science; Epistemology. (shrink)

In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents (...) of mechanistic explanations that such models do not really explain cognitive phenomena . I will argue against this view. Although I agree that the three arguments usually offered to vindicate the explanatory power of non-mechanistic dynamical models are not enough, I consider a fourth argument, namely that such models provide understanding. The Voss strong anticipation model is used to illustrate this. (shrink)

This article analyzes the epistemic value of understanding and offers an account of the role of understanding in science. First, I discuss the objectivist view of the relation between explanation and understanding, defended by Carl Hempel and J. D. Trout. I challenge this view by arguing that pragmatic aspects of explanation are crucial for achieving the epistemic aims of science. Subsequently, I present an analysis of these pragmatic aspects in terms of ‘intelligibility’ and a contextual account of scientific understanding based (...) on this notion. †To contact the author, please write to: Faculty of Philosophy, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands; e‐mail: [email protected]. (shrink)

Strategic Reliabilism is a framework that yields relative epistemic evaluations of belief-producing cognitive processes. It is a theory of cognitive excellence, or more colloquially, a theory of reasoning excellence (where 'reasoning' is understood very broadly as any sort of cognitive process for coming to judgments or beliefs). First introduced in our book, Epistemology and the Psychology of Human Judgment (henceforth EPHJ), the basic idea behind SR is that epistemically excellent reasoning is efficient reasoning that leads in a robustly reliable fashion (...) to significant, true beliefs. It differs from most contemporary epistemological theories in two ways. First, it is not a theory of justification or knowledge – a theory of epistemically worthy belief. Strategic Reliabilism is a theory of epistemically worthy ways of forming beliefs. And second, Strategic Reliabilism does not attempt to account for an epistemological property that is assumed to be faithfully reflected in the epistemic judgments and intuitions of philosophers. If SR makes recommendations that accord with our reflective epistemic judgments and intuitions, great. If not, then so much the worse for our reflective epistemic judgments and intuitions. (shrink)

Bainbridge’s well known “Ironies of Automation” Analysis, design and evaluation of man–machine systems. Elsevier, Amsterdam, pp 129–135, 1983. https://doi.org/10.1016/B978-0-08-029348-6.50026-9) laid out a set of fundamental criticisms surrounding the promises of automation that, even 30 years later, remain both relevant and, in many cases, intractable. Similarly, a set of ironies in technologies for sensor driven self-quantification is laid out here, spanning from instrumental problems in human factors design to much broader social problems. As with automation, these ironies stand in the way (...) of many of the promised benefits of these wearable technologies. It is argued here that without addressing these ironies now, the promises of wearables may not come to fruition, and instead users may experience outcomes that are opposite to those which the designers seek to afford, or, at the very least, those which consumers believe they are being offered. This paper describes four key ironies of sensor driven self-quantification: know more, know better versus no more, no better; greater self-control versus greater social control; well-being versus never being well enough; more choice versus erosion of choice. (shrink)

Many philosophers would, in theory, agree that the methods and tools of philosophy ought to be supplemented by those of other academic disciplines. In practice, however, the sociological data suggest that most philosophers fail to engage or collaborate with other academics, and this article argues that this is problematic for philosophy as a discipline. In relation to the value of interdisciplinary collaboration, the article highlights how experimental philosophers can benefit the field, but only insofar as they draw from the distinctive (...) methods of philosophy and overcome the charge of “amateur psychology” by more consistently collaborating with the scientists they seek to emulate. It concludes that philosophers ought to collaborate with other academics in order to gain an experience‐based understanding of the methods of other disciplines in addition to an understanding of the content of these disciplines. (shrink)

Philosophers of mind and epistemologists are increasingly making room in their theories for epistemic emotions (E-emotions) and, drawing on metacognition research in psychology, epistemic – or noetic or metacognitive – feelings (E-feelings). Since philoso- phers have only recently begun to draw on empirical research on E-feelings, in particular, we begin by providing a general characterization of E-feelings (section 1) and reviewing some highlights of relevant research (section 2). We then turn to philosophical work on E-feelings and E-emotions, situating the contributions (...) to the focus section (two articles devoted to E-feelings and two devoted to E-emotions) with respect to both the existing literature and each other (section 3). We conclude by brie y describing some promising avenues for further philosophical research on E-feelings and E-emotions (section 4). (shrink)

Scientific explanation is an important goal of scientific practise. Philosophers have proposed a striking diversity of seemingly incompatible accounts of explanation, from deductive-nomological to statistical relevance, unification, pragmatic, causal-mechanical, mechanistic, causal intervention, asymptotic, and model-based accounts. In this dissertation I apply two novel methods to reexamine our evidence about scientific explanation in practise and thereby address the fragmentation of philosophical accounts. I start by collecting a data set of 781 articles from one year of the journal Science. Using automated text (...) mining techniques I measure the frequency and distribution of several groups of philosophically interesting words, such as "explain", "cause", "evidence", "theory", "law", "mechanism", and "model". I show that "explain" words are much more common in scientific writing than in other genres, occurring in roughly half of all articles, and that their use is very often qualified or negated. These results about the use of words complement traditional conceptual analysis. Next I use random samples from the data set to develop a large number of small case studies across a wide range of scientific disciplines. I use a sample of "explain" sentences to develop and defend a new general philosophical account of scientific explanation, and then test my account against a larger set of randomly sampled sentences and abstracts. Five coarse categories can classify the explanans and explananda of my cases: data, entities, kinds, models, and theories. The pair of the categories of the explanans and explanandum indicates the "form" of an explanation. The explain-relation supports counterfactual reasoning about the dependence of qualities of the explanandum on qualities of the explanans. But for each form there is a different "core relation" between explanans and explanandum that supports the explain-relation. Causation, modelling, and argument are the core relations for different forms of scientific explanation between different categories of explanans and explananda. This flexibility allows me to resolve some of the fragmentation in the philosophical literature. I provide empirical evidence to show that my general philosophical account successfully describes a wide range of scientific practise across a large number of scientific disciplines. (shrink)

Many philosophers of science believe that empirical psychology can contribute little to the philosophical investigation of explanations. They take this to be shown by the fact that certain explanations fail to elicit any relevant psychological events (e.g., familiarity, insight, intelligibility, etc.). We report results from a study suggesting that, at least among those with extensive science training, a capacity to render an event intelligible is considered a requirement for explanation. We also investigate for whom explanations must be capable of rendering (...) events intelligible and whether or not accuracy is also viewed as a requirement. (shrink)

In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...) (1958) thesis that the predictive power of a scientific theory is directly proportional to its explanatory potential, and vice versa. (shrink)

A new trend in the philosophical literature on scientific explanation is that of starting from a case that has been somehow identified as an explanation and then proceed to bringing to light its characteristic features and to constructing an account for the type of explanation it exemplifies. A type of this approach to thinking about explanation – the piecemeal approach, as I will call it – is used, among others, by Lange (2013) and Pincock (2015) in the context of their (...) treatment of the problem of mathematical explanations of physical phenomena. This problem is of central importance in two different recent philosophical disputes: the dispute about the existence on non-causal scientific explanations and the dispute between realists and antirealists in the philosophy of mathematics. My aim in this paper is twofold. I will first argue that Lange (2013) and Pincock (2015) fail to make a significant contribution to these disputes. They fail to contribute to the dispute in the philosophy of mathematics because, in this context, their approach can be seen as question begging. They also fail to contribute to the dispute in the general philosophy of science because, as I will argue, there are important problems with the cases discussed by Lange and Pincock. I will then argue that the source of the problems with these two papers has to do with the fact that the piecemeal approach used to account for mathematical explanation is problematic. (shrink)