The ontic conception of scientific explanation has been constructed and motivated on the basis of a putative lexical ambiguity in the term explanation. I raise a puzzle for this ambiguity claim, and then give a deflationary solution under which all ontically-rendered talk of explanation is merely elliptical; what it is elliptical for is a view of scientific explanation that altogether avoids the ontic conception. This result has revisionary consequences for New Mechanists and other philosophers of science, (...) many of whom have assimilated their conception of explanation to the ontic conception. (shrink)
Jon Elster worries about the explanatory power of the social sciences. His main concern is that they have so few well-established laws. Elster develops an interesting substitute: a special kind of mechanism designed to fill the explanatory gap between laws and mere description. However, his mechanisms suffer from a characteristic problem that I will explore in this article. As our causal knowledge of a specific problem grows we might come to know too much to make use of an Elsterian mechanism (...) but still lack a law. We might then find ourselves in the paradoxical position of knowing more relevant causal truths about the phenomenon we are interested in than we did before, but being able to explain less. If this possibility is realized in social science settings, as I argue it might well be, Elster?s mechanistic account is threatened. Moreover, even if the possibility is rarely realized in that way, it raises, simply as a possibility, a conceptual problem with Elster?s mechanistic framework. (shrink)
We provide an account of mechanistic representation and explanation that has several advantages over previous proposals. In our view, explaining mechanistically is not simply giving an explanation of a mechanism. Rather, an explanation is mechanistic because of particular relations that hold between a mechanical representation, or model, and the target of explanation. Under this interpretation, mechanisticexplanation is possible even when the explanatory target is not a mechanism. We argue that taking this (...) view is not only coherent and plausible, it gives a more sophisticated view of the relationship between mechanical models and their targets. This allows us to address some ambiguities within the mechanist framework, and delivers a more intuitive way to interpret scientists' use of the term "mechanism". (shrink)
Instances of negative causation—preventions, omissions, and the like—have long created philosophical worries. In this paper, I argue that concerns about negative causation can be addressed in the context of causal explanation generally, and mechanisticexplanation specifically. The gravest concern about negative causation is that it exacerbates the problem of causal promiscuity—that is, the problem that arises when a particular account of causation identifies too many causes for a particular effect. In the explanatory context, the problem of promiscuity (...) can be solved by characterizing the phenomenon to be explained as a contrast between two or more events or non-events. This contrastive strategy also can solve other problems that negative causation presents for the leading accounts of mechanisticexplanation. Along the way, I argue that to be effective, accounts of causal explanation must incorporate negative causation. I also develop a taxonomy of negative causation and incorporate each variety of negative causation into the leading accounts of mechanisticexplanation. (shrink)
Evolutionary developmental biology (evo-devo) is considered a ‘mechanistic science,’ in that it causally explains morphological evolution in terms of changes in developmental mechanisms. Evo-devo is also an interdisciplinary and integrative approach, as its explanations use contributions from many fields and pertain to different levels of organismal organization. Philosophical accounts of mechanisticexplanation are currently highly prominent, and have been particularly able to capture the integrative nature of multifield and multilevel explanations. However, I argue that evo-devo demonstrates the (...) need for a broadened philosophical conception of mechanisms and mechanisticexplanation. Mechanisticexplanation (in terms of the qualitative interactions of the structural parts of a whole) has been developed as an alternative to the traditional idea of explanation as derivation from laws or quantitative principles. Against the picture promoted by Carl Craver, that mathematical models describe but do not explain, my discussion of cases from the strand of evo-devo which is concerned with developmental processes points to qualitative phenomena where quantitative mathematical models are an indispensable part of the explanation. While philosophical accounts have focused on the actual organization and operation of mechanisms, properties of developmental mechanisms that are about how a mechanism reacts to modifications are of major evolutionary significance, including robustness, phenotypic plasticity, and modularity. A philosophical conception of mechanisms is needed that takes into account quantitative changes, transient entities and the generation of novel types of entities, feedback loops and complex interaction networks, emergent properties, and, in particular, functional-dynamical aspects of mechanisms, including functional (as opposed to structural) organization and distributed, system-wide phenomena. I conclude with general remarks on philosophical accounts of explanation. (shrink)
This paper discusses the important paper by Paul Thagard on the pathway version of mechanisticexplanation that is currently used in chemical explanation. The author claims that this method of explanation has a respectable pedigree and can be traced back to the Chemical Revolution in the arguments used by the Lavoisier School in their theoretical duels with Richard Kirwan, the proponent of a revised phlogistonian theory. Kirwan believed that complex chemical reactions could be explained by recourse (...) to affinity tables that catalogued the attraction that various simple bodies possessed towards each other. To explain was in effect to make a delayed prediction, it is not enough just to show how a phenomenon fits into the discernible patterns of the world. Lavoisier, Fourcroy and their colleagues used pathway reasoning, although disguising this fact by suggesting that affinities varied when subjected to n-body situations. (shrink)
Accounts of mechanisticexplanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, (...) they frequently find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective. (shrink)
The biological sciences study (bio)complex living systems. Research directed at the mechanisticexplanation of the "live" state truly requires a pluralist research program, i.e. BioComplexity research. The program should apply multiple intra-level and inter-level theories and methodologies. We substantiate this thesis with analysis of BioComplexity: metabolic and modular control analysis of metabolic pathways, emergence of oscillations, and the analysis of the functioning of glycolysis.
The first part of this paper deals with the relations between mechanisticexplanation and reduction. It is argued that there is no insuperable conflict between the two, but that the mechanistic framework adds requirements that are not acknowledged in the model of property reduction. The second part concerns the relations between organization and environmental factors. Internal organization may be so tightly linked to external context that both have to be considered together.
Resurgent interest in both mechanistic and counterfactual theories of explanation has led to a fair amount of discussion regarding the relative merits of these two approaches. James Woodward is currently the pre-eminent counterfactual theorist, and he criticizes the mechanists on the following grounds: Unless mechanists about explanation invoke counterfactuals, they cannot make sense of claims about causal interactions between mechanism parts or of causal explanations put forward absent knowledge of productive mechanisms. He claims that these shortfalls can (...) be offset if mechanists will just borrow key tenets of his counterfactual theory of causal claims. What mechanists must bear in mind, however, is that by pursuing this course they risk both the assimilation of the mechanistic theories of explanation into Woodward’s own favored counterfactual theory, and they risk the marginalization of mechanistic explanations to a proper subset of all explanations. An outcome more favorable to mechanists might be had by pursuing an actualist-mechanist theory of the contents of causal claims. While it may not seem obvious at first blush that such an approach is workable, even in principle, recent empirical research into causal perception, causal belief, and mechanical reasoning provides some grounds for optimism. (shrink)
Both in biology and psychology there has been a tendency on the part of many investigators to focus solely on the mature organism and ignore development. There are many reasons for this, but an important one is that the explanatory framework often invoked in the life sciences for understanding a given phenomenon, according to which explanation consists in identifying the mechanism that produces that phenomenon, both makes it possible to side-step the development issue and to provide inadequate resources for (...) actually explaining development. When biologists and psychologists do take up the question of development, they find themselves confronted with two polarizing positions of nativism and empiricism. However, the mechanistic framework, insofar as it emphasizes organization and recognizes the potential for self-organization, does in fact provide the resources for an account of development which avoids the nativism-empiricism dichotomy. (shrink)
One thing about technical artefacts that needs to be explained is how their physical make-up, or structure, enables them to fulfil the behaviour associated with their function, or, more colloquially, how they work. In this paper I develop an account of such explanations based on the familiar notion of mechanisticexplanation. To accomplish this, I (1) outline two explanatory strategies that provide two different types of insight into an artefact’s functioning, and (2) show how human action inevi- tably (...) plays a role in artefact explanation. I then use my own account to criticize other recent work on mechanisticexplanation and conclude with some general implications for the philosophy of explanation. (shrink)
In a recent book and an article, Carl Craver construes the relations between different levels of a mechanism, which he also refers to as constitutive relations, in terms of mutual manipulability (MM). Interpreted metaphysically, MM implies that inter-level relations are symmetrical. MM thus violates one of the main desiderata of scientific explanation, namely explanatory asymmetry. Parts of Craver’s writings suggest a metaphysical interpretation of MM, and Craver explicitly commits to constitutive relationships being symmetrical. The paper furthermore explores the option (...) of interpreting MM epistemologically, as a means for individuating mechanisms. It is argued that MM then is redundant. MM should therefore better be abandoned. (shrink)
As much as assumptions about mechanisms and mechanisticexplanation 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 mechanisticexplanation. Contrary to some treatments of mechanisticexplanation, 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 mechanisticexplanation has provided the main source of scientific understanding. (shrink)
The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, (...) contemporary dynamicist research reveals the need for a more sophisticated account of mechanisticexplanation. (shrink)
Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation and development (...) of mechanistic models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)
Wegner, Giuliano, and Hertel (1985) defined the notion of a transactive memory system (TMS) as a group level memory system that “involves the operation of the memory systems of the individuals and the processes of communication that occur within the group (p. 191). Those processes are the collaborative procedures (“transactions”) by which groups encode, store, and retrieve information that is distributed among their members. Over the past 25+ years, the conception of a TMS has progressively garnered an increased interest among (...) social and organizational psychologists, communication scholars, and management theorists (Ren & Argote 2011). But there remains considerable disagreement about how exactly Wegner’s appeal to group memory should be understood. My goal in this paper is contribute to this debate, by articulating more clearly the value of conceptualizing groups as TMSs. This value, I argue, consists in providing us with a blueprint for how to explain group memory in terms of collective information-processing mechanisms. Collective information-processing mechanisms are dependent on, and interact with, the brain-bound information-processing of individuals, but cannot be reduced to the latter. In my analysis, I lean on extant accounts of mechanisticexplanation in the philosophy of science (Bechtel & Richardson 1993; Machamer, Darden, & Craver 2000; Wimsatt 2007) that have been used to analyze the explanatory practices of psychology and cognitive neuroscience (Bechtel 2008, 2009). Based on my reconstruction of Wegner’s conceptualization of a TMS, I argue that the reality of emergent group cognition is compatible with its mechanisticexplanation. More generally, my analysis shows that group cognition cannot be reduced to individual cognition, while avoiding the false dilemma between “wholism” and “nothing but-ism” which has hampered traditional construals of the “group mind” thesis (Allport 1968). (shrink)
Researchers in the enactivist tradition have recently argued that social interaction can constitute social cognition, rather than simply serve as the context for social cognition. They contend that a focus on social interaction corrects the overemphasis on mechanisms inside the individual in the explanation of social cognition. I critically assess enactivism’s claims about the explanatory role of social interaction in social cognition. After sketching the enactivist approach to cognition in general and social cognition in particular, I identify problems with (...) an enactivist taxonomy of roles for social interaction in the explanation of social cognition (contextual, enabling, and constitutive). In particular, I show that this enactivist taxonomy does not clearly distinguish between enabling conditions and constitutive elements, which would make them in danger of committing the coupling-constitution fallacy found in some attempts to extend cognition. I explore resources enactivism has to more clearly demarcate constitutive parts of a cognitive system, but identify problems in applying them to some of the main cases of social cognition enactivists characterize as being constituted by social interaction. I offer the mechanistic approach to explanation as an alternative that captures much of what enactivists want to say about the relations between social and individual levels, but views social interactions from the perspective of embedded cognition rather than as being constitutive of social cognition. (shrink)
How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular (...) operation within a mechanism, and highlights the role that various kinds of regularity play in scientific explanation. I defend this regularity requirement in terms of regularity’s role in individuating mechanisms against a background of other causal processes, and by prioritizing mechanisms’ ability to serve as a model of scientific explanation, rather than as a metaphysical account of causation. It is because mechanisms are regular, in the expanded sense described here, that they are capable of supporting the kinds of generalizations that figure prominently in scientific explanations. (shrink)
According to the computational theory of mind (CTM), mental capacities are explained by inner computations, which in biological organisms are realized in the brain. Computational explanation is so popular and entrenched that it’s common for scientists and philosophers to assume CTM without argument.
Hodgkin and Huxley’s model of the action potential is an apparent dream case of covering‐law explanation in biology. The model includes laws of physics and chemistry that, coupled with details about antecedent and background conditions, can be used to derive features of the action potential. Hodgkin and Huxley insist that their model is not an explanation. This suggests either that subsuming a phenomenon under physical laws is insufficient to explain it or that Hodgkin and Huxley were wrong. I (...) defend Hodgkin and Huxley against Weber’s heteronomy thesis and argue that explanations are descriptions of mechanisms. †To contact the author, please write to: Department of Philosophy, Philosophy‐Neuroscience‐Psychology Program, Washington University in St. Louis, One Brookings Drive, Wilson Hall, St. Louis, MO 63130; e‐mail: firstname.lastname@example.org. (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)
Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanisticexplanation plays in cognitive science, while the (...) third focuses on the importance of mental representations in specifically cognitive explanations. The fourth section considers the interdisciplinary nature of cognitive science and explores how multiple disciplines can contribute to explanations that exceed what any single discipline might accomplish. The conclusion sketches some recent developments in cognitive science and their implications for philosophers. (shrink)
This paper is concerned with reasonings that purport to explain why certain organisms have certain traits by showing that their actual design is better than contrasting designs. Biologists call such reasonings ‘functional explanations’. To avoid confusion with other uses of that phrase, I call them ‘design explanations’. This paper discusses the structure of design explanations and how they contribute to scientific understanding. Design explanations are contrastive and often compare real organisms to hypothetical organisms that cannot possibly exist. They are not (...) causal but appeal to functional dependencies between an organism’s different traits. These explanations point out that because an organism has certain traits (e.g., it lives on land), it cannot be alive if the trait to be explained (e.g., having lungs) were replaced by a specified alternative (e.g., having gills). They can be understood from a mechanistic point of view as revealing the constraints on what mechanisms can be alive. (shrink)
Accounts of ontic explanation have often been devised so as to provide an understanding of mechanism and of causation. Ontic accounts differ quite radically in their ontologies, and one of the latest additions to this tradition proposed by Peter Machamer, Lindley Darden and Carl Craver reintroduces the concept of activity. In this paper I ask whether this influential and activity-based account of mechanisms is viable as an ontic account. I focus on polygenic scenarios—scenarios in which the causal truths depend (...) on more than one cause. The importance of polygenic causation was noticed early on by Mill (1893). It has since been shown to be a problem for both causal-law approaches to causation (Cartwright 1983) and accounts of causation cast in terms of capacities (Dupré 1993; Glennan 1997, pp. 605-626). However, whereas mechanistic accounts seem to be attractive precisely because they promise to handle complicated causal scenarios, polygenic causation needs to be examined more thoroughly in the emerging literature on activity-based mechanisms. The activity-based account proposed in Machamer et al. (2000, pp. 1-25) is problematic as an ontic account, I will argue. It seems necessary to ask, of any ontic account, how well it performs in causal situations where—at the explanandum level of mechanism—no activity occurs. In addition, it should be asked how well the activity-based account performs in situations where there are too few activities around to match the polygenic causal origin of the explanandum. The first situation presents an explanandum-problem and the second situation presents an explanans-problem—I will argue—both of which threaten activity-based frameworks. (shrink)
In an instant classic paper (Lazebnik, in Cancer Cell 2(3); 2002 : 179–182) biologist Yuri Lazebnik deplores the poor effectiveness of the approach adopted by biologists to understand and “fix” biological systems. Lazebnik suggests that to remedy this state of things biologist should take inspiration from the approach used by engineers to design, understand, and troubleshoot technological systems. In the present paper I substantiate Lazebnik’s analysis by concretely showing how to apply the engineering approach to biological problems. I use an (...) actual example of electronic circuit troubleshooting to ground the thesis that, in engineering, the crucial phases of any non-trivial troubleshooting process are aimed at generating a mechanisticexplanation of the functioning of the system, which makes extensive recourse to problem-driven qualitative reasoning possibly based on cognitive artifacts applied to systems that are known to have been designed for function . To show how to translate these findings into biological practice I consider a concrete example of biological model building and “troubleshooting”, aimed at the identification of a “fix” for the human immune system in presence of progressing cancer, autoimmune disease, and transplant rejection. The result is a novel immune system model—the danger model with regulatory cells— and new, original hypotheses concerning the development, prophylaxis, and therapy of these unwanted biological processes. Based on the manifest efficacy of the proposed approach, I suggest a refocusing of the activity of theoretical biologists along the engineering-inspired lines illustrated in the paper. (shrink)
Due to the wide array of phenomena that are of interest to them, psychologists offer highly diverse and heterogeneous types of explanations. Initially, this suggests that the question "What is psychological explanation?" has no single answer. To provide appreciation of this diversity, we begin by noting some of the more common types of explanations that psychologists provide, with particular focus on classical examples of explanations advanced in three different areas of psychology: psychophysics, physiological psychology, and information-processing psychology. To analyze (...) what is involved in these types of explanations, we consider the ways in which law-like representations of regularities and representations of mechanisms factor in psychological explanations. This consideration directs us to certain fundamental questions, e.g., "To what extent are laws necessary for psychological explanations?" and "What do psychologists have in mind when they appeal to mechanisms in explanation?" In answering such questions, it appears that laws do play important roles in psychological explanations, although most explanations in psychology appeal to accounts of mechanisms. Consequently, we provide a unifying account of what psychological explanation is. (shrink)
Psychoneural reductionists sometimes claim that sufficient amounts of lower-level explanatory achievement preclude further contributions from higher-level psychological research. Ostensibly, with nothing left to do, the effect of such preclusion on psychological explanation is extinction. Reductionist arguments for preclusion have recently involved a reorientation within the philosophical foundations of neuroscience---namely, away from the philosophical foundations and toward the neuroscience. In this chapter, I review a successful reductive explanation of an aspect of reward function in terms of dopaminergic operations of (...) the mesocorticolimbic system in order to demonstrate why preclusion/extinction claims are dubious. (shrink)
According to some philosophers, computational explanation is proprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline (...) some promising answers that are being developed by a number of authors. (shrink)
This volume brings together a number of perspectives on the nature of realization explanation and experimentation in the ‘special’ and biological sciences as well as the related issues of psychoneural reduction and cognitive extension. The first two papers in the volume may be regarded as offering direct responses to the questions: (1) What model of realization is appropriate for understanding the metaphysics of science? and (2) What kind of philosophical work is such a model ultimately supposed to do?
This paper provides an account of the experimental conditions required for establishing whether correlating or causally relevant factors are constitutive components of a mechanism connecting input (start) and output (finish) conditions. I argue that two-variable experiments, where both the initial conditions and a component postulated by the mechanism are simultaneously manipulated on an independent basis, are usually required in order to differentiate between correlating or causally relevant factors and constitutively relevant ones. Based on a typical research project molecular biology, a (...) flowchart model detailing typical stages in the formulation and testing of hypotheses about mechanistic components is also developed. (shrink)
What is it to explain a psychological phenomenon (e.g., a person remembering a nanie, navigating through campus, untlerstanding huntor) In philoÂ»ophy, a traditional answer is that to explain a phenomenon is toÂ»how it to be the expectecl result of prior circumstances given a scientific law. Influenced by thiÂ» perspective. behaviorists directed psychology toward the search for the laws of learning that explained all behavior as the consequence of particular conditioning regiinens. Although discussion of laws remains comiiionplace in philosophical accounts of..
Recently, some mechanists have embraced reductionism and some reductionists have endorsed mechanism. However, the two camps disagree sharply about the extent to which mechanisticexplanation is a reductionistic enterprise. Reductionists maintain that cellular and molecular mechanisms can explain mental phenomena without necessary appeal to higher-level mechanisms. Mechanists deny this claim. I argue that this dispute turns on whether reduction is a transitive relation. I show that it is. Therefore, mechanistic explanations at the cellular and molecular level explain (...) mental phenomena. I make my case in part by noting that mechanisms at higher levels are composed of mechanisms at lower levels. Compositional relations are transitive. In addition, they are explanatory. I conclude that there are explanatory linkages from cellular and molecular mechanisms to mental phenomena within a hierarchy of nested mechanisms. (shrink)
The aim of this paper is to examine the usefulness of the Machamer, Darden, and Craver (2000) mechanism approach to gaining an understanding of explanation in cognitive neuroscience. We argue that although the mechanism approach can capture many aspects of explanation in cognitive neuroscience, it cannot capture everything. In particular, it cannot completely capture all aspects of the content and significance of mental representations or the evaluative features constitutive of psychopathology.
In this thesis I examine a number of topics that bear on explanation and understanding in molecular and cell biology, in order to shed new light on explanatory practice in those areas and to find novel angles from which to approach relevant philosophical debates. The topics I look at include mechanism, emergence, cellular complexity, and the informational role of the genome. I develop a perspective that stresses the intimacy of the relations between ontology and epistemology. Whether a phenomenon looks (...)mechanistic, or complex, or indeed emergent, is largely an epistemic matter, yet has an objective basis in features of the world. After reviewing several concepts of mechanism I consider the influential recent account of Machamer, Darden and Craver (MDC). That account makes interesting proposals concerning the relationship between mechanisticexplanation and intelligibility, which are consistent with the results of the investigation I undertake into the science surrounding protein folding. In relation to a number of other issues pertaining to biological systems I conclude that the MDC account is insufficiently nuanced, however, leading me to outline an alternative approach to mechanism. This emphasizes the importance of structure—function relations and addresses issues raised by reflection on the nature of cellular complexity. These include the distinction between structure and process and the different possible bases on which system organization may be maintained. The account I give of emergence construes the phenomenon in terms of psychological deficit: phenomena are emergent when we lack the capacity to trace through and model their causal structures using our cognitive schemas. I conclude by developing these ideas into a preliminary and partial account of explanation and understanding. This aspires to cover the significant fraction of work in molecular and cell biology that correlates biological structures, processes and functions by visualizing phenomena and making them imaginable. (shrink)
In this chapter, I argue that some aspects of cognitive phenomena cannot be explained computationally. In the first part, I sketch a mechanistic account of computational explanation that spans multiple levels of organization of cognitive systems. In the second part, I turn my attention to what cannot be explained about cognitive systems in this way. I argue that information-processing mechanisms are indispensable in explanations of cognitive phenomena, and this vindicates the computational explanation of cognition. At the same (...) time, it has to be supplemented with other explanations to make the mechanisticexplanation complete, and that naturally leads to explanatory pluralism in cognitive science. The price to pay for pluralism, however, is the abandonment of the traditional autonomy thesis asserting that cognition is independent of implementation details. (shrink)
How are scientific explanations possible in ecology, given that there do not appear to be many—if any—ecological laws? To answer this question, I present and defend an account of scientific causal explanation in which ecological generalizations are explanatory if they are invariant rather than lawlike. An invariant generalization continues to hold or be valid under a special change—called an intervention—that changes the value of its variables. According to this account, causes are difference-makers that can be intervened upon to manipulate (...) or control their effects. I apply the account to ecological generalizations to show that invariance under interventions as a criterion of explanatory relevance provides interesting interpretations for the explanatory status of many ecological generalizations. Thus, I argue that there could be causal explanations in ecology by generalizations that are not, in a strict sense, laws. I also address the issue of mechanistic explanations in ecology by arguing that invariance and modularity constitute such explanations. (shrink)
This paper is an attempt to further our understanding of mechanisms conceived of as ontologically separable from laws. What opportunities are there for a mechanistic perspective to be independent of, or even more fundamental than, a law perspective? Advocates of the mechanistic view often play with the possibility of internal and external reliability, or with the paralleling possibilities of enforcing, counteracting, redirecting, etc., the mechanisms’ power to produce To further this discussion I adopt a trope ontology. It is (...) independent of the notion of law, and can easily be adapted to account for such characteristics of mechanisms. The idea of tropes as mechanisms is worked out in some detail. According to the resulting picture, there is still an opportunity to link mechanisms and laws. But while the predominant law view conceives of mechanistic approaches as special kinds of law accounts, this study indicates that the converse may be true. Law accounts are special cases of mechanistic accounts, and they work only in those worlds where the mechanisms are of the right kind. (shrink)
This chapter examines the core explanatory strategies of cognitive science and their application to the study of psychopathology. In addition to providing a taxonomy of different strategies, we illustrate their application, with special attention to Autism Spectrum Disorder and Major Depressive Disorder. We conclude by considering two challenges to the prospects of a developed cognitive science of psychopathology.
Mechanisticexplanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of (...) techniques for abstracting the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)
This paper deals with the question: how is computation best individuated? -/- 1. The semantic view of computation: computation is best individuated by its semantic properties. 2. The causal view of computation: computation is best individuated by its causal properties. 3. The functional view of computation: computation is best individuated by its functional properties. -/- Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for that is usually that computation is individuated (...) semantically. I criticize the reasons in support of this view and its presupposition of representation and semantics. Furthermore, I argue that the only justified appeal to a representational individuation of computation might be that it is partly individuated by implicit intrinsic representations. (shrink)
James Woodward offers a conception of explanation and mechanism in terms of interventionist counterfactuals. Based on a case from ecology, I show that ecologists’ approach to that case satisfies Woodward’s conditions for explanation and mechanism, but his conception does not fully capture what ecologists view as explanatory. The new mechanistic philosophy likewise aims to describe central aspects of mechanisms, but I show that it is not sufficient to account for ecological mechanisms. I argue that in ecology (...) class='Hi'>explanation involves identification of invariant and insensitive causal relationships and descriptions of the mechanistic characteristics that make these relations possible. †To contact the author, please write to: Department of Philosophy, University of Dayton, 300 College Park, Dayton, OH 45469‐1546; e‐mail: email@example.com. (shrink)
One way that scientifically recognized properties are multiply realized is by “compensatory differences” among realizing properties. If a property G is jointly realized by two properties F1 and F2, then G can be multiply realized by having changes in the property F1 offset changes in the property F2. In some cases, there are scientific laws that articulate how distinct combinations of physical quantities can determine one and the same value of some other physical quantity. One moral to draw is that (...) in such cases we have the multiple realization of a single determinate, “fine grained” property instance that is exactly similar to another instance. As simple as this moral is, it has ramifications for a number of recent discussions of multiple realization in science. Taken collectively, these ramifications indicate that multiple realization by compensatory adjustments merits greater attention in the philosophy of science literature than it has hitherto received. (shrink)
Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the (...) generation of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)
This paper tracks the commitments of mechanistic explanations focusing on the relation between activities at different levels. It is pointed out that the mechanistic approach is inherently committed to identifying causal connections at higher levels with causal connections at lower levels. For the mechanistic approach to succeed a mechanism as a whole must do the very same thing what its parts organised in a particular way do. The mechanistic approach must also utilise bridge principles connecting different (...) causal terms of different theoretical vocabularies in order to make the identities of causal connections transparent. These general commitments get confronted with two claims made by certain proponents of the mechanistic approach: William Bechtel often argues that within the mechanistic framework it is possible to balance between reducing higher levels and maintaining their autonomy at the same time, whereas, in a recent paper, Craver and Bechtel argue that the mechanistic approach is able to make downward causation intelligible. The paper concludes that the mechanistic approach imbued with identity statements is no better candidate for anchoring higher levels to lower ones while maintaining their autonomy at the same time than standard reductive accounts are, and that what mechanistic explanations are able to do at best is showing that downward causation does not exist. (shrink)
Arguments for the autonomy of psychology or other higher-level sciences have often taken the form of denying the possibility of reduction. The form of reduction most proponents and critics of the autonomy of psychology have in mind is theory reduction. Mechanistic explanations provide a different perspective. Mechanistic explanations are reductionist insofar as they appeal to lower-level entities—the component parts of a mechanism and their operations— to explain a phenomenon. However, unlike theory reductions, mechanistic explanations also recognize the (...) fundamental role of organization in enabling mechanisms to engage their environments as units (as well as the role of yet higher-level structures in constraining such engagement). Especially when organization is non-linear, it can enable mechanisms to generate phenomena that are quite surprising given the operations of the components taken in isolation. Such organization must be discovered—it cannot simply be derived from knowledge of lower-level parts and their operations. Moreover, the organized environments in which mechanisms operate must also be discovered. It is typically the higher-level disciplines that have the tools for discovering the organization within and between mechanisms. Although these inquiries are constrained by the knowledge of the parts and operations constituting the mechanism, they make their own autonomous contribution to understanding how a mechanism actually behaves. Thus, mechanistic explanations provide a strong sense of autonomy for higher levels of organization and the inquiries addressing them even while recognizing the distinctive contributions of reductionistic research investigating the operations of the lower level components. (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)
I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by grounded representational content, (...) that is, content evaluated by appeal to its truth, falsity, accuracy, inaccuracy and other relational properties. (shrink)
The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanisticexplanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of (...) processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level. (shrink)
I begin by tracing some of the confusions regarding levels and reduction to a failure to distinguish two different principles according to which theories can be viewed as hierarchically arranged — epistemic authority and ontological constitution. I then argue that the notion of levels relevant to the debate between symbolic and connectionist paradigms of mental activity answers to neither of these models, but is rather correlative to the hierarchy of functional decompositions of cognitive tasks characteristic of homuncular functionalism. Finally, I (...) suggest that the incommensurability of the intentional and extensional vocabularies constitutes a strongprima facie reason to conclude that there is little likelihood of filling in the story of Bechtel''s missing level in such a way as to bridge the gap between such homuncular functionalism and his own model of mechanisticexplanation. (shrink)
Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ dia- grams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting (...) class='Hi'>mechanistic explanations are considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mech- anistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. Ó 2005 Elsevier Ltd. All rights reserved. (shrink)
The aim of this article is to elucidate the notions of explanation and mechanism, in particular of the social kind. A mechanism is defined as what makes a concrete system tick, and it is argued that to propose an explanation proper is to exhibit a lawful mechanism. The so-called covering law model is shown to exhibit only the logical aspect of explanation: it just subsumes particulars under universals. A full or mechanismic explanation involves mechanismic law statements, (...) not purely descriptive ones such as functional relations and rate equations. Many examples from the natural, biosocial, and social sciences are examined. In particular, macro-micro-micro-macro social relations are shown to explain other wise puzzling macro-macro links. The last part of the article relates the author's progress, over half a century, toward understanding mechanism and explanation. (shrink)
While much of the recent literature on mechanisms has emphasized the superiority of mechanisms and mechanisticexplanation over laws and nomological explanation, paradigmatic mechanisms—e.g., clocks or synapses—actually exhibit a great deal of stability in their behavior. And while mechanisms of this kind are certainly of great importance, there are many events that do not occur as a consequence of the operation of stable mechanisms. Events of natural and human history are often the consequence of causal processes that (...) are ephemeral and capricious. In this paper I shall argue that, notwithstanding their ephemeral nature, these processes deserve to be called mechanisms. Ephemeral mechanisms share important characteristics with their more stable cousins, and these shared characteristics will help us to understand connections between scientific and historical explanation. (shrink)
This paper is about mechanisms and models, and how they interact. In part, it is a response to recent discussion in philosophy of biology regarding whether natural selection is a mechanism. We suggest that this debate is indicative of a more general problem that occurs when scientists produce mechanistic models of populations and their behaviour. We can make sense of claims that there are mechanisms that drive population-level phenomena such as macroeconomics, natural selection, ecology, and epidemiology. But talk of (...) mechanisms and mechanisticexplanation evokes objects with well-defined and localisable parts which interact in discrete ways, while models of populations include parts and interactions that are neither local nor discrete in any actual populations. This apparent tension can be resolved by carefully distinguishing between the properties of a model and those of the system it represents. To this end, we provide an analysis that recognises the flexible relationship between a mechanistic model and its target system. In turn, this reveals a surprising feature of mechanistic representation and explanation: it can occur even when there is a mismatch between the mechanism of the model and that of its target. Our analysis reframes the debate, providing an alternative way to interpret scientists’ mechanism-talk , which initially motivated the issue. We suggest that the relevant question is not whether any population-level phenomenon such as natural selection is a mechanism, but whether it can be usefully modelled as though it were a particular type of mechanism. (shrink)
The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanisticexplanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions (...) or predictions of phenomena. It also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)
Mechanisms are a way of explaining how biological phenomena work rather than why single elements of biological systems are there. However, mechanisms are usually described as physiological entities, and little or no attention is paid to malfunction as an independent theoretical concept. On the other hand, malfunction is the main focus of interest of applied sciences such as medicine. In this paper I argue that malfunctions are parts of pathological mechanisms, which should be considered separate theoretical entities, conceptually having a (...) priority over physiological sequences. While pathological mechanisms can be described in terms of a Cummins-like mechanisticexplanation, they show some unnoticed peculiarities when compared to physiological ones. Some features of pathological mechanisms are considered, such as outcome variability, ambivalence and dependence on a range. (shrink)
Treating consciousness as awareness or attention greatly underestimates it, ignoring the temporary levels of organization associated with higher intellectual function (syntax, planning, logic, music). The tasks that require consciousness tend to be the ones that demand a lot of resources. Routine tasks can be handled on the back burner but dealing with ambiguity, groping around offline, generating creative choices, and performing precision movements may temporarily require substantial allocations of neocortex. Here I will attempt to clarify the appropriate levels of (...) class='Hi'>explanation (ranging from quantum aspects to association cortex dynamics) and then propose a specific mechanism (consciousness as the current winner of Darwinian copying competitions in cerebral cortex) that seems capable of encompassing the higher intellectual function aspects of consciousness as well as some of the attentional aspects. It includes features such as a coding space appropriate for analogies and a supervisory Darwinian process that can bias the operation of other Darwinian processes. (shrink)
Reductionist inquiry, which involves decomposing a mechanism into its parts and operations, is only one of the tasks of mechanistic research. A second task (which may be undertaken largely simultaneously) is recomposing it—conceptually reassembling the parts and operations into an organized arrangement that constitutes the mechanism. Other tasks include determining how multiple operations are orchestrated in real time, and investigating how the mechanism interacts with the environment in which it is situated.
In the context of mechanisticexplanation, reductionistic research pursues a decomposition of complex systems into their component parts and operations. Using research on the mechanisms responsible for circadian rhythms, I consider both the gains that have been made by discovering genes and proteins that figure in these intracellular oscillators and also highlight the increasingly recognized need to understand higher-level integration, both between cells in the central oscillator and between the central and peripheral oscillators. This history illustrates a common (...) need to complement reductionistic inquiry with investigations at higher-levels. Unlike most other accounts of reduction, the mechanistic framework accommodates this complementary relationship between reductionistic and systems approaches. (shrink)
In this work a mechanisticexplanation of the classical algae growth model built by M. R. Droop in the late sixties is proposed. We first recall the history of the construction of the “predictive” variable yield Droop model as well as the meaning of the introduced cell quota. We then introduce some theoretical hypotheses on the biological phenomena involved in nutrient storage by the algae that lead us to a “conceptual” model. Though more complex than Droop’s one, our (...) model remains accessible to a complete mathematical study: its confrontation to the Droop model shows both have the same asymptotic behavior. However, while Droop’s cell quota comes from experimental bio-chemical measurements not related to intra-cellular biological phenomena, its analogous in our model directly follows our theoretical hypotheses. This new model should then be looked at as a re-interpretation of Droop’s work from a theoretical biologist’s point of view. (shrink)
I explore a type of computational social simulation known as artificial societies. Artificial society simulations are dynamic models of real-world social phenomena. I explore the role that these simulations play in social explanation, by situating these simulations within contemporary philosophical work on explanation and on models. Many contemporary philosophers have argued that models provide causal explanations in science, and that models are necessary mediators between theory and data. I argue that artificial society simulations provide causal mechanistic explanations. (...) I conclude that in their current form, these simulations are based on methodologically individualist assumptions that could limit their potential scope of social explanation. (shrink)
Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to (...) show what is required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)
This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and causal-mechanistic), a joint study of discovery and explanation, and an abandonment of models of theory reduction in favor of accounts of explanatory reduction. Of particular current interest are philosophical accounts (...) of complex explanations that appeal to different levels of organismal organization and use contributions from different biological disciplines. The essay lays out one model that views explanatory integration across different disciplines as being structured by scientific problems. I emphasize the philosophical need to take the explanatory aims pursued by different groups of scientists into account, as explanatory aims determine whether different explanations are competing or complementary and govern the dynamics of scientific practice, including interdisciplinary research. I distinguish different kinds of pluralism that philosophers have endorsed in the context of explanation in biology, and draw several implications for science education, especially the need to teach science as an interdisciplinary and dynamic practice guided by scientific problems and explanatory aims. (shrink)
This paper sketches a concept of higher-level objective probability (“short-run mechanistic probability”, SRMP) inspired partly by a style of explanation of relative frequencies known as the “method of arbitrary functions”. SRMP has the potential to fill the need for a theory of objective probability which has wide application at higher levels and which gives probability causal connections to observed relative frequency (without making it equivalent to relative frequency). Though this approach provides probabilities on a space of event types, (...) it does not provide probabilities for outcomes on particular trials. This allows SRMP to coexist with lower-level probabilities which do govern individual trials. (shrink)
Explanatory problems in the philosophy of neuroscience are not well captured by the division between the radical and the trivial neuron doctrines. The actual problem is, instead, whether mechanistic biological explanations across different levels of description can be extended to account for psychological phenomena. According to cognitive neuroscience, some neural levels of description at least are essential for the explanation of psychological phenomena, whereas, in traditional cognitive science, psychological explanations are completely independent of the neural levels of description. (...) The challenge for cognitive neuroscience is to discover the levels of description appropriate for the neural explanation of psychological phenomena. (shrink)
There is more than one explanation for the evolution of sexual reproduction. This paper investigates the possibility that this pluralism exists because these different explanations rely on intuitions provided by different philosophical theories of explanation, namely unifying views and causal mechanical views. I conclude that this is not the case.
The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show (...) in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell’s mechanism for apoptosis. (shrink)
this paper we argue that the formalism can also be applied to modelling the hierarchical structure of physical mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations are vital for prediction, explanation and control respectively, a recursive Bayesian net can be applied to all these tasks. We show how a Recursive Bayesian Net can be used to model mechanisms in (...) cancer science. The highest level of the proposed model will contain variables at the clinical level, while a middle level will map the structure of the DNA damage response mechanism and the lowest level will contain information about gene expression. (shrink)
The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show (...) in particular how a simple two-level RBN can be used tomodel a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell’s mechanism for apoptosis. (shrink)
How can reasons explain actions? What is the force of "because" in "He did this because..." followed by a statement of the agent's intentions? The answer involves some concept of what can count as explanation, and the history of science indicates that the acceptability of explanations depends, in part, on a scientific community which has decided to pursue its inquiries in one direction rather than another. The first part of this paper examines this pragmatic aspect of explanations; the second (...) part draws on this examination in the hope of elucidating the way reasons explain actions. The possibility of eliminating our ordinary, "purposive," explanations of actions in favor of some "mechanistic," neuro-physiological, account is then considered. (shrink)
The neobehaviorist Clark L. Hull and his disciple Kenneth Spence shared in common many views on the nature of science and the role of theories in psychology. However, a telling exchange in their correspondence of the early 1940s reveals a disagreement over the nature of intervening variables in behavior theory. Spence urged Hull to abandon his interpretations of intervening variables in terms of physiological models in favor of positivistic, purely mathematical interpretations that conflicted with Hull's mechanistic explanatory aims (...) and ontological commitment to materialism. This dispute is set against the background of similar disputes in physics, and the origins of Hull's and Spence's divergent views on theoretical explanation are described. (shrink)
Three major ways in which temporal asymmetries enter into scientific induction are discussed as follows: 1. An account is given of the physical basis for the temporal asymmetry of recordability, which obtains in the following sense: except for humanly recorded predictions and one other class of advance indicators to be discussed, interacting systems can contain reliable indicators of only their past and not of their future interactions. To deal with the exceptional cases of non-spontaneous "pre-records," a clarification is offered of (...) the essential differences in the conditions requisite to the production of an indicator having retrodictive significance ("post-record"), on the one hand, and of one having predictive significance ("prerecord" or recorded prediction), on the other. Purported counter-examples to the asymmetry of spontaneous recordability are refuted. 2. It is shown how in cases of asymmetric recordability, the associated retrodiction-prediction asymmetry makes for an asymmetry of assertibility as between an explanandum (or an explanans) referring to a future event and one referring to a past one. But it is argued that this epistemological asymmetry in the assertibility per se must be clearly distinguished from a logical asymmetry between the past and the future in regard to the inferability (deductive or inductive) of the explanandum from the explanans. And it is then contended that the failure to distinguish between an epistemological and a logical asymmetry vitiates the critiques that recent writers have offered of the Popper-Hempel thesis, which affirms symmetry of inferability as between predictive and post-explanatory arguments. In reply to Scriven, it is maintained that predictions based on mere indicators (rather than causes) do not establish an asymmetry in scientific understanding as between predictive arguments and post-explanatory ones. 3. As a further philosophical ramification of the retrodiction-prediction asymmetry, a set of sufficient conditions are stated for the correctness of philosophical mechanism as opposed to teleology. (shrink)
Is conceptual analysis required for reductive explanation? If there is no a priori entailment from microphysical truths to phenomenal truths, does reductive explanation of the phenomenal fail? We say yes (Chalmers 1996; Jackson 1994, 1998). Ned Block and Robert Stalnaker say no (Block and Stalnaker 1999).
Moral philosophers are, among other things, in the business of constructing moral theories. And moral theories are, among other things, supposed to explain moral phenomena. Consequently, one’s views about the nature of moral explanation will influence the kinds of moral theories one is willing to countenance. Many moral philosophers are (explicitly or implicitly) committed to a deductive model of explanation. As I see it, this commitment lies at the heart of the current debate between moral particularists and moral (...) generalists. In this paper I argue that we have good reasons to give up this commitment. In fact, I show that an examination of the literature on scientific explanation reveals that we are used to, and comfortable with, non-deductive explanations in almost all areas of inquiry. As a result, I argue that we have reason to believe that moral explanations need not be grounded in exceptionless moral principles. (shrink)
We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms , in which some structural aspects of a mechanisticexplanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanisticexplanation. By this process, (...) functional analyses are seamlessly integrated with multilevel mechanistic explanations. (shrink)
I argue that there are Leibnizian-style cosmological arguments for the existence of God which start from very mild premises which affirm the mere possibility of a principle of sufficient reason. The utilization of such premises gives a great deal of plausibility to such types of argumentation. I spend the majority of the paper defending three major objections to such mild premises viz., a reductio argument from Peter van Inwagen and William Rowe, which proffers and defends the idea that a necessary (...) proposition cannot explain a contingent one. I, then, turn to an amelioration of the Rowe/van Inwagen argument which attempts to appeal to an entailment relation between explanans and explanandum that is fettered out in terms of relevance logic. Subsequent to dispelling with that worry, I tackle objections to the utilization of weak principles of sufficient reason that depend essentially upon agglomerative accounts of explanation. (shrink)
How do we go about weighing evidence, testing hypotheses, and making inferences? The model of "inference to the best explanation" (IBE) -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, and (...) features a new introduction and two new chapters. Inference to the Best Explanation is an unrivaled exposition of a theory of particular interest in the fields both of epistemology and the philosophy of science. (shrink)
This paper examines a paradigm case of allegedly successful reductive explanation, viz. the explanation of the fact that water boils at 100°C based on facts about H2O. The case figures prominently in Joseph Levine’s explanatory gap argument against physicalism. The paper studies the way the argument evolved in the writings of Levine, focusing especially on the question how the reductive explanation of boiling water figures in the argument. It will turn out that there are two versions of (...) the explanatory gap argument to be found in Levine’s writings. The earlier version relies heavily on conceptual analysis and construes reductive explanation as a process of deduction. The later version makes do without conceptual analysis and understands reductive explanations as based on theoretic reductions that are justified by explanatory power. Along the way will be shown that the bridge principles — which are being neglected in the explanatory gap literature — play a crucial role in the explanatory gap argument. (shrink)
This paper argues that the form of explanation at issue in the hard problem of consciousness is scientifically irrelevant, despite appearances to the contrary. In particular, it is argued that the 'sense of understanding' that plays a critical role in the form of explanation implicated in the hard problem provides neither a necessary nor a sufficient condition on satisfactory scientific explanation. Considerations of the actual tools and methods available to scientists are used to make the case against (...) it being a necessary condition, and work by J.D. Trout that exploits psychological research on the hindsight and overconfidence biases is used to show that it is not a sufficient condition. It is argued, however, that certain intellectual and moral concerns give us good reason to still try to meet the hard problem's explanatory challenge, despite its extrascientific nature. (shrink)
Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes (...) a section of accessible introductory pieces, as well as more advanced and technical pieces, and will make essential work in the philosophy of science readily available to both scholars and students. (shrink)
This paper argues that the increasingly dominant new mechanistic approach to scientific explanation, as developed to date, does not shed new light on explanatory practice. First, I systematize the explanatory account, one according to which explanations are mechanistic models that satisfy three desiderata: 1) they must represent causal relations, 2) describe the proper parts, and 3) depict the system at the right ‘level.’ Then I argue that even the most promising attempts to flesh out these constraints have (...) fallen far short. Finally, I offer a diagnosis of the mechanistic project, locating a common source of both its virtues and shortcomings. (shrink)
Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.
Cognitive neuropsychiatry (CN) is the explanation of psychiatric disorder by the methods of cognitive neuropsychology. Within CN there are, broadly speaking, two approaches to delusion. The first uses a one-stage model, in which delusions are explained as rationalizations of anomalous experiences via reasoning strategies that are not, in themselves, abnormal. Two-stage models invoke additional hypotheses about abnormalities of reasoning. In this paper, I examine what appears to be a very strong argument, developed within CN, in favor of a twostage (...)explanation of the difference in content between the Capgras and Cotard delusions. That explanation treats them as alternative rationalizations of essentially the same phenomenology. I show, however, that once we distinguish the phenomenology (and the neuroetiology), a one-stage model is adequate. In the final section I make some more general remarks on the oneand two-stage models. (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.
Arguing for mathematical realism on the basis of Field’s explanationist version of the Quine–Putnam Indispensability argument, Alan Baker has recently claimed to have found an instance of a genuine mathematical explanation of a physical phenomenon. While I agree that Baker presents a very interesting example in which mathematics plays an essential explanatory role, I show that this example, and the argument built upon it, begs the question against the mathematical nominalist.
It has often been argued that Humean accounts of natural law cannot account for the role played by laws in scientific explanations. Loewer (Philosophical Studies 2012) has offered a new reply to this argument on behalf of Humean accounts—a reply that distinguishes between grounding (which Loewer portrays as underwriting a kind of metaphysical explanation) and scientific explanation. I will argue that Loewer’s reply fails because it cannot accommodate the relation between metaphysical and scientific explanation. This relation also (...) resolves a puzzle about scientific explanation that Hempel and Oppenheim (Philosophy of Science 15:135–75, 1948) encountered. (shrink)
Although in the past three decades interest in mathematical explanation revived, recent literature on the subject seems to neglect the strict connection between explanation and discovery. In this paper I sketch an alternative approach that takes such connection into account. My approach is a revised version of one originally considered by Descartes. The main difference is that my approach is in terms of the analytic method, which is a method of discovery prior to axiomatized mathematics, whereas Descartes’s approach (...) is in terms of the analytic-synthetic method, which is a heuristic pattern in already axiomatized mathematics. (shrink)
The situated cognition movement has emerged in recent decades (although it has roots in psychologists working earlier in the 20th century including Vygotsky, Bartlett, and Dewey) largely in reaction to an approach to explaining cognition that tended to ignore the context in which cognitive activities typically occur. Fodor’s (1980) account of the research strategy of methodological solipsism, according to which only representational states within the mind are viewed as playing causal roles in producing cognitive activity, is an extreme characterization of (...) this approach. (As Keith Gunderson memorably commented when Fodor first presented this characterization, it amounts to reversing behaviorism by construing the mind as a white box in a black world). Critics as far back as the 1970s and 1980s objected to many experimental paradigms in cognitive psychology as not being ecologically valid; that is, they maintained that the findings only applied to the artificial circumstances created in the laboratory and did not generalize to real world settings (Neisser, 1976; 1987). The situated cognition movement, however, goes much further than demanding ecologically valid experiments—it insists that an agent’s cognitive activities are inherently embedded and supported by dynamic interactions with the agent’s body and features of its environment. (shrink)