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  1. Is There A Monist Theory of Causal and Non-Causal Explanations? The Counterfactual Theory of Scientific Explanation.Alexander Reutlinger - 2016 - Philosophy of Science 83 (5):733-745.
    The goal of this paper is to develop a counterfactual theory of explanation. The CTE provides a monist framework for causal and non-causal explanations, according to which both causal and non-causal explanations are explanatory by virtue of revealing counterfactual dependencies between the explanandum and the explanans. I argue that the CTE is applicable to two paradigmatic examples of non-causal explanations: Euler’s explanation and renormalization group explanations of universality.
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  • Optimality Modeling and Explanatory Generality.Angela Potochnik - 2007 - Philosophy of Science 74 (5):680-691.
    The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, optimality (...)
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  • Models and Mechanisms in Psychological Explanation.Daniel A. Weiskopf - 2011 - Synthese 183 (3):313-338.
    Mechanistic explanation 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 (...)
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  • Explanation in Computational Neuroscience: Causal and Non-Causal.M. Chirimuuta - 2018 - British Journal for the Philosophy of Science 69 (3):849-880.
    This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...)
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  • Abstraction and the Organization of Mechanisms.Arnon Levy & William Bechtel - 2013 - Philosophy of Science 80 (2):241-261.
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  • The Dappled World: A Study of the Boundaries of Science.Nancy Cartwright - 1999 - Noûs 36 (4):699-725.
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  • The Dappled World: A Study of the Boundaries of Science.Nancy Cartwright - 1999 - Philosophy 75 (294):613-616.
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  • Science Without Laws.Ronald N. Giere - 1999 - Mind 111 (441):111-114.
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  • Thinking About Evolutionary Mechanisms: Natural Selection.Robert Skipper & Roberta Millstein - 2004 - Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):327-347.
    This paper explores whether natural selection, a putative evolutionary mechanism, and a main one at that, can be characterized on either of the two dominant conceptions of mechanism, due to Glennan and the team of Machamer, Darden, and Craver, that constitute the “new mechanistic philosophy.” The results of the analysis are that neither of the dominant conceptions of mechanism adequately captures natural selection. Nevertheless, the new mechanistic philosophy possesses the resources for an understanding of natural selection under the rubric.
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  • Reassessing Woodward’s Account of Explanation: Regularities, Counterfactuals, and Noncausal Explanations.Juha Saatsi & Mark Pexton - 2013 - Philosophy of Science 80 (5):613-624.
    We reassess Woodward’s counterfactual account of explanation in relation to regularity explananda. Woodward presents an account of causal explanation. We argue, by using an explanation of Kleiber’s law to illustrate, that the account can also cover some noncausal explanations. This leads to a tension between the two key aspects of Woodward’s account: the counterfactual aspect and the causal aspect. We explore this tension and make a case for jettisoning the causal aspect as constitutive of explanatory power in connection with regularity (...)
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  • Explanatory Fictions—for Real?Samuel Schindler - 2014 - Synthese 191 (8):1741-1755.
    In this article I assess Alisa Bokulich’s idea that explanatory model fictions can be genuinely explanatory. I draw attention to a tension in her account between the claim that model fictions are explanatorily autonomous, and the demand that model fictions be justified in order for them to be genuinely explanatory. I also explore the consequences that arise from Bokulich’s use of Woodward’s account of counterfactual explanation and her abandonment of Woodward’s notion of an intervention. As it stands, Bokulich’s account must (...)
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  • Explanation and Description in Computational Neuroscience.David Michael Kaplan - 2011 - Synthese 183 (3):339-373.
    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 mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...)
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  • Science Without Laws.Ronald N. Giere - 1999 - University of Chicago Press.
    Debate over the nature of science has recently moved from the halls of academia into the public sphere, where it has taken shape as the "science wars." At issue is the question of whether scientific knowledge is objective and universal or socially mediated, whether scientific truths are independent of human values and beliefs. Ronald Giere is a philosopher of science who has been at the forefront of this debate from its inception, and Science without Laws offers a much-needed mediating perspective (...)
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  • Design Explanation: Determining the Constraints on What Can Be Alive.Arno G. Wouters - 2007 - Erkenntnis 67 (1):65-80.
    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 (...)
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  • Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience.M. Chirimuuta - 2014 - Synthese 191 (2):127-153.
    In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...)
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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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  • The Cognitive Neuroscience Revolution.Worth Boone & Gualtiero Piccinini - 2016 - Synthese 193 (5):1509-1534.
    We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...)
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  • Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience.Carl F. Craver - 2007 - 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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  • Making Things Happen. A Theory of Causal Explanation.James Woodward - 2007 - Philosophy and Phenomenological Research 74 (1):233-249.
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  • ``Two'' Many Optimalities.Oscar Vilarroya - 2002 - Biology and Philosophy 17 (2):251-270.
    In evolutionary biology, a trait is said to be optimal if it maximizes the fitness of the organism, that is, if the trait allows the organism to survive and reproduce better than any other competing trait would. In engineering, a design is said to be optimal if it complies with its functional requirements as well as possible. Cognitive science is both a biological and engineering discipline and hence it uses both notions of optimality. Unfortunately, the lack of a clear methodological (...)
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  • How Mathematics Can Make a Difference.Sam Baron, Mark Colyvan & David Ripley - 2017 - Philosophers' Imprint 17.
    Standard approaches to counterfactuals in the philosophy of explanation are geared toward causal explanation. We show how to extend the counterfactual theory of explanation to non-causal cases, involving extra-mathematical explanation: the explanation of physical facts by mathematical facts. Using a structural equation framework, we model impossible perturbations to mathematics and the resulting differences made to physical explananda in two important cases of extra-mathematical explanation. We address some objections to our approach.
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  • Towards a Mechanistic Philosophy of Neuroscience.Carl F. Craver & David M. Kaplan - 2011 - In Steven French & Juha Saatsi (eds.), Continuum Companion to the Philosophy of Science. London: Continuum. pp. 268.
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  • Design Sans Adaptation.Sara Green, Arnon Levy & William Bechtel - 2015 - European Journal for Philosophy of Science 5 (1):15-29.
    Design thinking in general, and optimality modeling in particular, have traditionally been associated with adaptationism—a research agenda that gives pride of place to natural selection in shaping biological characters. Our goal is to evaluate the role of design thinking in non-evolutionary analyses. Specifically, we focus on research into abstract design principles that underpin the functional organization of extant organisms. Drawing on case studies from engineering-inspired approaches in biology we show how optimality analysis, and other design-related methods, play a specific methodological (...)
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  • Equilibrium Explanation.Elliott Sober - 1983 - Philosophical Studies 43 (2):201 - 210.
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  • The Brain From 25000 Feet: High Level Explorations of Brain Complexity, Perception, Innateness and Vagueness.Mark A. Changizi - 2003 - Kluwer Academic Publishers.
    This book is a must-read for researchers interested in taking a high-level, non-mechanistic approach to answering age-old fundamental questions in the brain ...
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  • The Functional Perspective of Organismal Biology.Arno Wouters - 2005 - In Thomas Reydon & Lia Hemerik (eds.), Current Themes in Theoretical Biology. Springer. pp. 33--69.
    Following Mayr (1961) evolutionary biologists often maintain that the hallmark of biology is its evolutionary perspective. In this view, biologists distinguish themselves from other natural scientists by their emphasis on why-questions. Why-questions are legitimate in biology but not in other natural sciences because of the selective character of the process by means of which living objects acquire their characteristics. For that reason, why-questions should be answered in terms of natural selection. Functional biology is seen as a reductionist science that applies (...)
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  • Optimality Explanations: A Plea for an Alternative Approach.Collin Rice - 2012 - Biology and Philosophy 27 (5):685-703.
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