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  1. (1 other version)Computer Science as Empirical Inquiry: Symbols and Search.Allen Newell & H. A. Simon - 1976 - Communications of the Acm 19:113-126.
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  • Explanation: a mechanist alternative.William Bechtel & Adele Abrahamsen - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):421-441.
    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 diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...)
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  • Confirmation and explaining how possible.Patrick Forber - 2008 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 41 (1):32-40.
    Confirmation in evolutionary biology depends on what biologists take to be the genuine rivals. Investigating what constrains the scope of biological possibility provides part of the story: explaining how possible helps determine what counts as a genuine rival and thus informs confirmation. To clarify the criteria for genuine rivalry I distinguish between global and local constraints on biological possibility, and offer an account of how-possibly explanation. To sharpen the connection between confirmation and explaining how possible I discuss the view that (...)
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  • The Mind as Neural Software? Understanding Functionalism, Computationalism, and Computational Functionalism.Gualtiero Piccinini - 2010 - Philosophy and Phenomenological Research 81 (2):269-311.
    Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and (...)
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  • Multiple Realizability Revisited: Linking Cognitive and Neural States.William Bechtel - 1999 - Philosophy of Science 66 (2):175-207.
    The claim of the multiple realizability of mental states by brain states has been a major feature of the dominant philosophy of mind of the late 20th century. The claim is usually motivated by evidence that mental states are multiply realized, both within humans and between humans and other species. We challenge this contention by focusing on how neuroscientists differentiate brain areas. The fact that they rely centrally on psychological measures in mapping the brain and do so in a comparative (...)
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  • (1 other version)Psychoneural Reduction: The New Wave.John Bickle - 1998 - Bradford.
    One of the central problems in the philosophy of psychology is an updated version of the old mind-body problem: how levels of theories in the behavioral and brain sciences relate to one another. Many contemporary philosophers of mind believe that cognitive-psychological theories are not reducible to neurological theories. However, this antireductionism has not spawned a revival of dualism. Instead, most nonreductive physicalists prefer the idea of a one-way dependence of the mental on the physical.In Psychoneural Reduction, John Bickle presents a (...)
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  • The Importance of Models in Theorizing: A Deflationary Semantic View.Stephen M. Downes - 1992 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:142 - 153.
    I critically examine the semantic view of theories to reveal the following results. First, models in science are not the same as models in mathematics, as holders of the semantic view claim. Second, when several examples of the semantic approach are examined in detail no common thread is found between them, except their close attention to the details of model building in each particular science. These results lead me to propose a deflationary semantic view, which is simply that model construction (...)
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  • Three Kinds of Idealization.Michael Weisberg - 2007 - Journal of Philosophy 104 (12):639-659.
    Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.
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  • Aspects of Scientific Explanation and Other Essays in the Philosophy of Science.Carl Gustav Hempel - 1965 - New York: The Free Press.
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  • (1 other version)Scientific Explanation and the Causal Structure of the World.Wesley C. Salmon - 1984 - Princeton University Press.
    The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...)
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  • Vision.David Marr - 1982 - W. H. Freeman.
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  • Physical law and mechanistic explanation in the Hodgkin and Huxley model of the action potential.Carl F. Craver - 2008 - Philosophy of Science 75 (5):1022-1033.
    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 (...)
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  • Making things happen: a theory of causal explanation.James F. Woodward - 2003 - New York: Oxford University Press.
    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.
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  • (1 other version)Explaining the brain: mechanisms and the mosaic unity of neuroscience.Carl F. Craver - 2007 - New York : Oxford University Press,: Oxford University Press, Clarendon Press.
    Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.
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  • (2 other versions)Special sciences (or: The disunity of science as a working hypothesis).Jerry Fodor - 1974 - Synthese 28 (2):97-115.
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  • Role functions, mechanisms, and hierarchy.Carl F. Craver - 2001 - Philosophy of Science 68 (1):53-74.
    Many areas of science develop by discovering mechanisms and role functions. Cummins' (1975) analysis of role functions-according to which an item's role function is a capacity of that item that appears in an analytic explanation of the capacity of some containing system-captures one important sense of "function" in the biological sciences and elsewhere. Here I synthesize Cummins' account with recent work on mechanisms and causal/mechanical explanation. The synthesis produces an analysis of specifically mechanistic role functions, one that uses the characteristic (...)
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  • After the Philosophy of Mind: Replacing Scholasticism with Science.Tony Chemero & Michael Silberstein - 2008 - Philosophy of Science 75 (1):1-27.
    We provide a taxonomy of the two most important debates in the philosophy of the cognitive and neural sciences. The first debate is over methodological individualism: is the object of the cognitive and neural sciences the brain, the whole animal, or the animal--environment system? The second is over explanatory style: should explanation in cognitive and neural science be reductionist-mechanistic, inter-level mechanistic, or dynamical? After setting out the debates, we discuss the ways in which they are interconnected. Finally, we make some (...)
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  • "How does it work" versus "what are the laws?": Two conceptions of psychological explanation.Robert C. Cummins - 2000 - In Robert A. Wilson & Frank C. Keil (eds.), The Shadows and Shallows of Explanation. MIT Press.
    In the beginning, there was the DN (Deductive Nomological) model of explanation, articulated by Hempel and Oppenheim (1948). According to DN, scientific explanation is subsumption under natural law. Individual events are explained by deducing them from laws together with initial conditions (or boundary conditions), and laws are explained by deriving them from other more fundamental laws, as, for example, the simple pendulum law is derived from Newton's laws of motion.
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  • Thinking about mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
    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.
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  • Computational explanation in neuroscience.Gualtiero Piccinini - 2006 - Synthese 153 (3):343-353.
    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 (...)
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  • Computing mechanisms.Gualtiero Piccinini - 2007 - Philosophy of Science 74 (4):501-526.
    This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing (...)
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  • Computation and Cognition: Toward a Foundation for Cognitive Science.Zenon W. Pylyshyn - 1984 - Cambridge: MIT Press.
    This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 The Psychological Reality (...)
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  • Multiple realizations.Lawrence A. Shapiro - 2000 - Journal of Philosophy 97 (12):635-654.
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  • Functional analysis.Robert E. Cummins - 1975 - Journal of Philosophy 72 (November):741-64.
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  • 精神状态的性质.Hilary Putnam - 1967 - In William H. Capitan & Daniel Davy Merrill (eds.), Art, mind, and religion. [Pittsburgh]: University of Pittsburgh Press. pp. 1--223.
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  • The Language of Thought.Jerry Fodor - 1975 - Harvard University Press.
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  • Philosophy of History.William H. Dray - 1966 - Philosophy 41 (156):183-185.
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  • Regularities and causality; generalizations and causal explanations.Jim Bogen - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):397-420.
    Machamer, Darden, and Craver argue that causal explanations explain effects by describing the operations of the mechanisms which produce them. One of this paper’s aims is to take advantage of neglected resources of Mechanism to rethink the traditional idea that actual or counterfactual natural regularities are essential to the distinction between causal and non-causal co-occurrences, and that generalizations describing natural regularities are essential components of causal explanations. I think that causal productivity and regularity are by no means the same thing, (...)
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  • The Language of Thought.J. A. Fodor - 1978 - Critica 10 (28):140-143.
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  • Idealization and modeling.Robert W. Batterman - 2009 - Synthese 169 (3):427-446.
    This paper examines the role of mathematical idealization in describing and explaining various features of the world. It examines two cases: first, briefly, the modeling of shock formation using the idealization of the continuum. Second, and in more detail, the breaking of droplets from the points of view of both analytic fluid mechanics and molecular dynamical simulations at the nano-level. It argues that the continuum idealizations are explanatorily ineliminable and that a full understanding of certain physical phenomena cannot be obtained (...)
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  • Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research.William Bechtel & Robert C. Richardson - 2010 - Princeton.
    An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...)
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  • (1 other version)Four Decades of Scientific Explanation.Wesley C. Salmon & Anne Fagot-Largeault - 1989 - History and Philosophy of the Life Sciences 16 (2):355.
    As Aristotle stated, scientific explanation is based on deductive argument--yet, Wesley C. Salmon points out, not all deductive arguments are qualified explanations. The validity of the explanation must itself be examined. _Four Decades of Scientific Explanation_ provides a comprehensive account of the developments in scientific explanation that transpired in the last four decades of the twentieth century. It continues to stand as the most comprehensive treatment of the writings on the subject during these years. Building on the historic 1948 essay (...)
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  • Integrating psychology and neuroscience: functional analyses as mechanism sketches.Gualtiero Piccinini & Carl Craver - 2011 - Synthese 183 (3):283-311.
    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 mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated (...)
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  • Philosophy of history.William H. Dray - 1964 - Englewood Cliffs, N.J.,: Prentice-Hall.
    This update of the original version focuses on six central problems in the critical philosophy of history and explores the connections among them. Starting with the fundamentals of each philosophical topic in history and then delving into the specifics of each to better understand the surrounding issues, the reference first offers a comprehensive introduction into these topics then covers explanation and understanding ... objectivity and value judgment .. causes in history ... the nature and role of narrative ... and historical (...)
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  • Theories, models, and equations in biology: The heuristic search for emergent simplifications in neurobiology.Kenneth F. Schaffner - 2008 - Philosophy of Science 75 (5):1008-1021.
    This article considers claims that biology should seek general theories similar to those found in physics but argues for an alternative framework for biological theories as collections of prototypical interlevel models that can be extrapolated by analogy to different organisms. This position is exemplified in the development of the Hodgkin‐Huxley giant squid model for action potentials, which uses equations in specialized ways. This model is viewed as an “emergent unifier.” Such unifiers, which require various simplifications, involve the types of heuristics (...)
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  • Asymptotics and the role of minimal models.Robert W. Batterman - 2002 - British Journal for the Philosophy of Science 53 (1):21-38.
    A traditional view of mathematical modeling holds, roughly, that the more details of the phenomenon being modeled that are represented in the model, the better the model is. This paper argues that often times this ‘details is better’ approach is misguided. One ought, in certain circumstances, to search for an exactly solvable minimal model—one which is, essentially, a caricature of the physics of the phenomenon in question.
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  • The nature and plausibility of cognitivism.John Haugeland - 1978 - Behavioral and Brain Sciences 1 (2):215-26.
    Cognitivism in psychology and philosophy is roughly the position that intelligent behavior can (only) be explained by appeal to internal that is, rational thought in a very broad sense. Sections 1 to 5 attempt to explicate in detail the nature of the scientific enterprise that this intuition has inspired. That enterprise is distinctive in at least three ways: It relies on a style of explanation which is different from that of mathematical physics, in such a way that it is not (...)
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  • The Nature of Psychological Explanation.Robert Cummins - 1983 - MIT Press.
    In exploring the nature of psychological explanation, this book looks at how psychologists theorize about the human ability to calculate, to speak a language and the like. It shows how good theorizing explains or tries to explain such abilities as perception and cognition. It recasts the familiar explanations of "intelligence" and "cognitive capacity" as put forward by philosophers such as Fodor, Dennett, and others in terms of a theory of explanation that makes established doctrine more intelligible to professionals and their (...)
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  • Neurophilosophy: Toward A Unified Science of the Mind-Brain.Patricia Smith Churchland - 1986 - MIT Press.
    This is a unique book. It is excellently written, crammed with information, wise and a pleasure to read.' ---Daniel C. Dennett, Tufts University.
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  • Decomposing the brain: A long term pursuit. [REVIEW]William P. Bechtel - 2002 - Brain and Mind 3 (1):229-242.
    This paper defends cognitive neuroscience’s project of developing mechanistic explan- ations of cognitive processes through decomposition and localization against objections raised by William Uttal in The New Phrenology. The key issue between Uttal and researchers pursuing cognitive neuroscience is that Uttal bets against the possibility of decomposing mental operations into component elementary operations which are localized in distinct brain regions. The paper argues that it is through advancing and revising what are likely to be overly simplistic and incorrect decompositions that (...)
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  • On the proper treatment of connectionism.Paul Smolensky - 1988 - Behavioral and Brain Sciences 11 (1):1-23.
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  • Theoretical neuroscience: computational and mathematical modeling of neural systems.Peter Dayan & L. Abbott - 2001 - Philosophical Psychology 15 (4):563-577.
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  • Mechanism and explanation in cognitive neuroscience.Jeffrey S. Poland & Barbara Von Eckardt - 2004 - Philosophy of Science 71 (5):972-984.
    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.
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  • Computational neuroscience.Terrence J. Sejnowski - 1986 - Behavioral and Brain Sciences 9 (1):104-105.
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  • (1 other version)Explaining the Brain.Carl F. Craver - 2007 - Oxford, GB: Oxford University Press.
    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.
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  • Functionalism at Forty: A Critical Retrospective.Paul M. Churchland - 2005 - Journal of Philosophy 102 (1):33 - 50.
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  • Causes without mechanisms: Experimental regularities, physical laws, and neuroscientific explanation.Marcel Weber - 2008 - Philosophy of Science 75 (5):995-1007.
    This article examines the role of experimental generalizations and physical laws in neuroscientific explanations, using Hodgkin and Huxley’s electrophysiological model from 1952 as a test case. I show that the fact that the model was partly fitted to experimental data did not affect its explanatory status, nor did the false mechanistic assumptions made by Hodgkin and Huxley. The model satisfies two important criteria of explanatory status: it contains invariant generalizations and it is modular (both in James Woodward’s sense). Further, I (...)
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  • On the nature of explanation in the neurosciences.Antti Revonsuo - 2001 - In Peter McLaughlin, Peter Machamer & Rick Grush (eds.), Theory and Method in the Neurosciences. Pittsburgh University Press. pp. 45--69.
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  • The hodgkin‐huxley equations and the concrete model: Comments on Craver, Schaffner, and Weber.Jim Bogen - 2008 - Philosophy of Science 75 (5):1034-1046.
    I claim that the Hodgkin‐Huxley (HH) current equations owe a great deal of their importance to their role in bringing results from experiments on squid giant action preparations to bear on the study of the action potential in other neurons in other in vitro and in vivo environments. I consider ideas from Weber and Craver about the role of Coulomb’s and other fundamental equations in explaining the action potential and in HH’s development of their equations. Also, I offer an embellishment (...)
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  • The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective.David Michael Kaplan & Carl F. Craver - 2011 - Philosophy of Science 78 (4):601-627.
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...)
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