Carl F. Craver investigates what we are doing when we use neuroscience to explain what's going on in the brain. When does an explanation succeed and when does it fail? Craver offers explicit standards for successful explanation of the workings of the brain, on the basis of a systematic view about what neuroscientific explanations are.

By contrasting three general conceptions of scientific explanation, this paper seeks to clarify the explanandum and to exhibit the fundamental philosophical issues involved in the project of explicating scientific explanation. The three conceptions--epistemic, modal, and ontic--have both historical and contemporary importance. In the context of Laplacian determinism, they do not seem importantly distinct, but in the context of irreducibly statistical explanations, the three are seen to diverge sharply. The paper argues for a causal/mechanical version of the ontic conception, and concludes (...) by exhibiting some striking consequences of this approach. (shrink)

Michael Strevens offers an account of causal explanation according to which explanatory practice is shaped by counterbalanced commitments to representing causal influence and abstracting away from overly specific details. In this paper, I challenge a key feature of that account. I argue that what Strevens calls explanatory frameworks figure prominently in explanatory practice because they actually improve explanations. This suggestion is simple but has far-reaching implications. It affects the status of explanations that cite multiply realizable properties; changes the explanatory role (...) of causal factors with small effect; and undermines Strevens’ titular explanatory virtue, depth. This results in greater coherence with explanatory practice and accords with the emphasis that Strevens places on explanatory patterns. Ultimately, my suggestion preserves a tight connection between explanation and the creation of understanding by taking into account explanations’ role in communication. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their (...) styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.

Throughout the history of the Western world, science has possessed an extraordinary amount of authority and prestige. And while its pedestal has been jostled by numerous evolutions and revolutions, science has always managed to maintain its stronghold as the knowing enterprise that explains how the natural world works: we treat such legendary scientists as Galileo, Newton, Darwin, and Einstein with admiration and reverence because they offer profound and sustaining insight into the meaning of the universe. In The Intelligibility of Nature (...) , Peter Dear considers how science as such has evolved and how it has marshaled itself to make sense of the world. His intellectual journey begins with a crucial observation: that the enterprise of science is, and has been, directed toward two distinct but frequently conflated ends—doing and knowing. The ancient Greeks developed this distinction of value between craft on the one hand and understanding on the other, and according to Dear, that distinction has survived to shape attitudes toward science ever since. Teasing out this tension between doing and knowing during key episodes in the history of science—mechanical philosophy and Newtonian gravitation, elective affinities and the chemical revolution, enlightened natural history and taxonomy, evolutionary biology, the dynamical theory of electromagnetism, and quantum theory—Dear reveals how the two principles became formalized into a single enterprise, science, that would be carried out by a new kind of person, the scientist. Finely nuanced and elegantly conceived, The Intelligibility of Nature will be essential reading for aficionados and historians of science alike. (shrink)

Discovery proceeds in stages of construction, evaluation, and revision. Each of these stages is constrained by what is known or conjectured about what is being discovered. A new characterization of mechanism aids in specifying what is to be discovered when a mechanism is sought. Guidance in discovering mechanisms may be provided by the reasoning strategies of schema instantiation, modular subassembly, and forward/backward chaining. Examples are found in mechanisms in molecular biology, biochemistry, immunology, and evolutionary biology.

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

Understanding is a central aim of science and highly important in present-day society. But what precisely is scientific understanding and how can it be achieved? This book answers these questions, through philosophical analysis and historical case studies, and presents a philosophical theory of scientific understanding that highlights its contextual nature.

This volume argues for a new image of science that understands both natural and social phenomena to be the product of mechanisms, casting the work of science as an effort to understand those mechanisms. Glennan offers an account of the nature of mechanisms and of the models used to represent them in physical, life, and social sciences.

Achieving understanding of nature is one of the aims of science. In this paper we offer an analysis of the nature of scientific understanding that accords with actual scientific practice and accommodates the historical diversity of conceptions of understanding. Its core idea is a general criterion for the intelligibility of scientific theories that is essentially contextual: which theories conform to this criterion depends on contextual factors, and can change in the course of time. Our analysis provides a general account of (...) how understanding is provided by scientific explanations of diverse types. In this way, it reconciles conflicting views of explanatory understanding, such as the causal-mechanical and the unificationist conceptions. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...) explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

It is often claimed—especially by scientific realists—that science provides understanding of the world only if its theories are (at least approximately) true descriptions of reality, in its observable as well as unobservable aspects. This paper critically examines this ‘realist thesis’ concerning understanding. A crucial problem for the realist thesis is that (as study of the history and practice of science reveals) understanding is frequently obtained via theories and models that appear to be highly unrealistic or even completely fictional. So we (...) face the dilemma of either giving up the realist thesis that understanding requires truth, or allowing for the possibility that in many if not all practical cases we do not have scientific understanding. I will argue that the first horn is preferable: the link between understanding and truth can be severed. This becomes a live option if we abandon the traditional view that scientific understanding is a special type of knowledge. While this view implies that understanding must be factive, I avoid this implication by identifying understanding with a skill rather than with knowledge. I will develop the idea that understanding phenomena consists in the ability to use a theory to generate predictions of the target system’s behavior. This implies that the crucial condition for understanding is not truth but intelligibility of the theory, where intelligibility is defined as the value that scientists attribute to the theoretical virtues that facilitate the construction of models of the phenomena. I will show, first, that my account accords with the way practicing scientists conceive of understanding, and second, that it allows for the use of idealized or fictional models and theories in achieving understanding. (shrink)

The act of understanding is at the heart of all scientific activity; without it any ostensibly scientific activity is as sterile as that of a high school student substituting numbers into a formula. Ordinary language often uses visual metaphors in connection with understanding. When we finally understand what someone is trying to point out to us, we exclaim: “I see!” When someone really understands a subject matter, we say that she has “insight”. There appears to be a link between visualization (...) and understanding, and between visualizability and intelligibility. This applies in science no less than in daily life: visualization is regarded as a useful means of achieving scientific understanding, even in the .. (shrink)

The Explanatory Model of Scientific Understanding is a deflationary thesis recently advocated by Kareem Khalifa. EMU is committed to two key ideas: all understanding-relevant knowledge is propositional in nature; and the abilities we use to generate understanding are merely our usual logical reasoning skills. In this paper I provide an argument against both ideas, suggesting that scientific understanding requires a significant amount of non-propositional knowledge not captured by logical relations. I use the Inferential Model of Scientific Understanding to reveal how (...) we can better represent what constitutes understanding a scientific event. In particular, this model accounts for not only logical and probabilistic inferences, but also those conceptual associations and categorizations we must make to comprehend an explanation. (shrink)

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.

After a decade of intense debate about mechanisms, there is still no consensus characterization. In this paper we argue for a characterization that applies widely to mechanisms across the sciences. We examine and defend our disagreements with the major current contenders for characterizations of mechanisms. Ultimately, we indicate that the major contenders can all sign up to our characterization.

The chapters in this book highlight the multifaceted nature of the process of scientific research.

Scientists and laypeople alike use the sense of understanding that an explanation conveys as a cue to good or correct explanation. Although the occurrence of this sense or feeling of understanding is neither necessary nor sufficient for good explanation, it does drive judgments of the plausibility and, ultimately, the acceptability, of an explanation. This paper presents evidence that the sense of understanding is in part the routine consequence of two well-documented biases in cognitive psychology: overconfidence and hindsight. In light of (...) the prevalence of counterfeit understanding in the history of science, I argue that many forms of cognitive achievement do not involve a sense of understanding, and that only the truth or accuracy of an explanation make the sense of understanding a valid cue to genuine understanding. (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)

The concept of mechanism in biology has three distinct meanings. It may refer to a philosophical thesis about the nature of life and biology (‘mechanicism’), to the internal workings of a machine-like structure (‘machine mechanism’), or to the causal explanation of a particular phenomenon (‘causal mechanism’). In this paper I trace the conceptual evolution of ‘mechanism’ in the history of biology, and I examine how the three meanings of this term have come to be featured in the philosophy of biology, (...) situating the new ‘mechanismic program’ in this context. I argue that the leading advocates of the mechanismic program (i.e., Craver, Darden, Bechtel, etc.) inadvertently conflate the different senses of ‘mechanism’. Specifically, they all inappropriately endow causal mechanisms with the ontic status of machine mechanisms, and this invariably results in problematic accounts of the role played by mechanism-talk in scientific practice. I suggest that for effective analyses of the concept of mechanism, causal mechanisms need to be distinguished from machine mechanisms, and the new mechanismic program in the philosophy of biology needs to be demarcated from the traditional concerns of mechanistic biology. (shrink)

This paper suggests and discusses an answer to the following question: What distinguishes causal from non-causal or coincidental co-occurrences? The answer derives from Elizabeth Anscombe’s idea that causality is a highly abstract concept whose meaning derives from our understanding of specific causally productive activities, and from her rejection of the assumption that causality can be informatively understood in terms of actual or counterfactual regularities.Keywords: Elizabeth Anscombe; Causality; Explanation; Inhibition.

Craver claims that mechanistic explanation is ontic, while Bechtel claims that it is epistemic. While this distinction between ontic and epistemic explanation originates with Salmon, the ideas have changed in the modern debate on mechanistic explanation, where the frame of the debate is changing. I will explore what Bechtel and Craver’s claims mean, and argue that good mechanistic explanations must satisfy both ontic and epistemic normative constraints on what is a good explanation. I will argue for ontic constraints by drawing (...) on Craver’s work in Sect. 2.1, and argue for epistemic constraints by drawing on Bechtel’s work in Sect. 2.2. Along the way, I will argue that Bechtel and Craver actually agree with this claim. I argue that we should not take either kind of constraints to be fundamental, in Sect. 3, and close in Sect. 4 by considering what remains at stake in making a distinction between ontic and epistemic constraints on mechanistic explanation. I suggest that we should not concentrate on either kind of constraint, to the neglect of the other, arguing for the importance of seeing the relationship as one of integration. (shrink)

Discovery proceeds in stages of construction, evaluation, and revision. Each of these stages is constrained by what is known or conjectured about what is being discovered. A new characterization of mechanism aids in specifying what is to be discovered when a mechanism is sought. Guidance in discovering mechanisms may be provided by the reasoning strategies of schema instantiation, modular subassembly, and forward/backward chaining. Examples are found in mechanisms in molecular biology, biochemistry, immunology, and evolutionary biology.

Science has more to offer than just knowledge of nature; it can give us understanding of nature as well. Epistemology of science is usually focused on knowledge and the criteria of justification, while paying little attention to understanding. In a reversal of this emphasis, this article is more about scientific understanding. I argue that the hallmarks of understanding are similar to an aesthetic feature associated with literature, music, and the visual arts. It is the feature described as coherence, harmony, and (...) inevitability of fit. Aesthetics thus plays an epistemic role in science as an indication of understanding. (shrink)

This article discusses agent-based simulation (ABS) as a tool of sociological understanding. I argue that agent-based simulations can play an important role in the expansion of explanatory understanding in the social sciences. The argument is based on an inferential account of understanding (Ylikoski 2009, Ylikoski & Kuorikoski 2010), according to which computer simulations increase our explanatory understanding by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. The inferential account also suggests a (...) number of ways in which the use of simulation methodology might give rise to illusory understanding. The structure of the article is as .. (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)

This paper examines creative strategies employed inscientific modelling. It is argued that being creativepresents not a discrete event, but rather an ongoingeffort consisting of many individual `creative acts''.These take place over extended periods of time andcan be carried out by different people, working ondifferent aspects of the same project. The example ofextended extragalactic radio sources shows that, inorder to model a complicated phenomenon in itsentirety, the modelling task is split up into smallerproblems that result in several sub-models. This is away (...) of using cognitive resources efficiently and in away which overcomes their limitations. Another aspectof modelling that requires creativity is theemployment of visualisation in order to reassemble,i.e. recreate the unity of, the various sub-models bymeans of visualisation. This illustrates how thecreative effort required to deal with the complexityof the complicated phenomenon of radio sources ischannelled in order to use cognitive resourcesefficiently and to stay within their capacity. (shrink)

I argue that Descartes explains physiology in terms of whole systems, and not in terms of the size, shape and motion of tiny corpuscles (corpuscular mechanics). It is a standard, entrenched view that Descartes’ proper means of explanation in the natural world is through strict reduction to corpuscular mechanics. This view is bolstered by a handful of corpuscular–mechanical explanations in Descartes’ physics, which have been taken to be representative of his treatment of all natural phenomena. However, Descartes’ explanations of the (...) ‘principal parts’ of physiology do not follow the corpuscular–mechanical pattern. Des Chene has identified systems in Descartes’ account of physiology, but takes them ultimately to reduce down to the corpuscle level. I argue that they do not. Rather, Descartes maintains entire systems, with components selected from multiple levels of organization, in order to construct more complete explanations than corpuscular mechanics alone would allow. (shrink)

Contains a brief history of the developments in the philosophy of scientific explanation in the 40 years following the publication in 1948 of Carl G. Hempel and Paul Oppenheim's seminal article, “Studies in the Logic of Explanation” ). Their article, which presents the Deductive–Nomological Model, is the fountainhead from which almost all philosophical work on scientific explanation in the second half of the twentieth century flowed. This essay provides historical insight into philosophical controversies about scientific explanation at the end of (...) the century. These same historical themes are treated more thoroughly in the author's Four Decades of Scientific Explanation. (shrink)

In his “EMU and Inference,” Mark Newman European Journal for Philosophy of Science, 4:55–74, 2014 provides several interesting challenges to my explanatory model of understanding :15–37, 2012). I offer three replies to Newman’s paper. First, Newman incorrectly attributes to EMU an overly restrictive view about the role of abilities in understanding. Second, his main argument against EMU rests on this incorrect attribution, and would still face difficulties even if this attribution were correct. Third, contrary to his stated ambitions, his own, (...) inferential model of understanding does not have any distinctive advantages over EMU. These three points defend EMU against Newman’s objections. (shrink)