Search results for 'Computational Explanation' (try it on Scholar)

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  1. Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.score: 240.0
    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 (...) explanation and outline some promising answers that are being developed by a number of authors. (shrink)
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  2. Marcin Miłkowski (2012). Limits of Computational Explanation of Cognition. In Vincent Muller (ed.), Philosophy and Theory of Artificial Intelligence. Springer.score: 240.0
    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 (...)
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  3. M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.score: 202.0
    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 (...)
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  4. David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.score: 198.0
    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 (...)
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  5. Gualtiero Piccinini (2007). Computational Explanation and Mechanistic Explanation of Mind. In Francesco Ferretti, Massimo Marraffa & Mario De Caro (eds.), Cartographies of the Mind: The Interface Between Philosophy and Cognitive Science. Springer. 343-353.score: 180.0
    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.
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  6. Gualtiero Piccinini (2007). Computational Modeling Vs. Computational Explanation: Is Everything a Turing Machine, and Does It Matter to the Philosophy of Mind? Australasian Journal of Philosophy 85 (1):93 – 115.score: 180.0
    According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.
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  7. P. Morton (1993). Supervenience and Computational Explanation in Vision Theory. Philosophy of Science 60 (1):86-99.score: 164.0
    According to Marr's theory of vision, computational processes of early vision rely for their success on certain "natural constraints" in the physical environment. I examine the implications of this feature of Marr's theory for the question whether psychological states supervene on neural states. It is reasonable to hold that Marr's theory is nonindividualistic in that, given the role of natural constraints, distinct computational theories of the same neural processes may be justified in different environments. But to avoid trivializing (...)
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  8. Michael E. Cuffaro (2013). On the Physical Explanation for Quantum Computational Speedup. Dissertation, The University of Western Ontarioscore: 156.0
    The aim of this dissertation is to clarify the debate over the explanation of quantum speedup and to submit, for the reader's consideration, a tentative resolution to it. In particular, I argue, in this dissertation, that the physical explanation for quantum speedup is precisely the fact that the phenomenon of quantum entanglement enables a quantum computer to fully exploit the representational capacity of Hilbert space. This is impossible for classical systems, joint states of which must always be representable (...)
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  9. Mike Oaksford & Nick Chater (1995). Theories of Reasoning and the Computational Explanation of Everyday Inference. Thinking and Reasoning 1 (2):121 – 152.score: 150.0
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  10. Paul R. Thagard (1991). Philosophical and Computational Models of Explanation. Philosophical Studies 64 (October):87-104.score: 144.0
  11. Keith R. Sawyer (2004). Social Explanation and Computational Simulation. Philosophical Explorations 7 (3):219 – 231.score: 144.0
    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. (...)
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  12. Marcin Zajenkowski, Rafał Styła & Jakub Szymanik (2011). A Computational Approach to Quantifiers as an Explanation for Some Language Impairments in Schizophrenia. Journal of Communication Disorder 44:2011.score: 138.0
    We compared the processing of natural language quantifiers in a group of patients with schizophrenia and a healthy control group. In both groups, the difficulty of the quantifiers was consistent with computational predictions, and patients with schizophrenia took more time to solve the problems. However, they were significantly less accurate only with proportional quantifiers, like more than half. This can be explained by noting that, according to the complexity perspective, only proportional quantifiers require working memory engagement.
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  13. Christopher Peacocke (1986). Explanation in Computational Psychology: Language, Perception and Level. Mind and Language 1 (2):101-23.score: 132.0
  14. James T. Higginbotham (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:358-361.score: 132.0
  15. Christopher Peacocke (1986). Reply to Humphreys, Quinlan, Higginbotham, Schiffer and Soames's Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:388-402.score: 132.0
  16. Stephen R. Schiffer (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:362-371.score: 132.0
  17. Scott Soames (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:372-387.score: 132.0
  18. William Bechtel & Adele Abrahamsen (2010). Dynamic Mechanistic Explanation: Computational Modeling of Circadian Rhythms as an Exemplar for Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):321-333.score: 126.0
    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 (...)
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  19. Daniel Gilman (1996). Optimization and Simplicity: Computational Vision and Biological Explanation. Synthese 107 (3):293 - 323.score: 120.0
    David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for cognitive (...)
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  20. Glyn W. Humphreys & Philip T. Quinlan (1986). Comments on ?Explanation in Computational Psychology? By C. Peacocke (Mind and Language, Vol. 1, No. 2). Mind and Language 1 (4):355-357.score: 120.0
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  21. R. Keith Sawyer (2004). Social Explanation and Computational Simulation. Philosophical Explorations 7 (3):219-231.score: 120.0
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  22. Glyn W. Humphreys & Philip T. Quinlan (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:355-357.score: 120.0
  23. Frances Egan (forthcoming). Function-Theoretic Explanation and Neural Mechanisms. In David M. Kaplan (ed.), Integrating Mind and Brain Science: Mechanistic Perspectives and Beyond.score: 90.0
    A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it (...)
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  24. Daniel J. Gilman (1993). Optimization and Simplicity: Marr's Theory of Vision and Biological Explanation. Synthese 107 (3):293-323.score: 90.0
     
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  25. John Symons (2008). Computational Models of Emergent Properties. Minds and Machines 18 (4):475-491.score: 84.0
    Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to (...)
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  26. Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds And Machines 13 (2):269-284.score: 84.0
    My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation in which a computing (...)
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  27. Marcin Miłkowski (2013). Wyjaśnianie w kognitywistyce. Przeglad Filozoficzny - Nowa Seria 22 (2):151-166.score: 84.0
    The paper defends the claim that the mechanistic explanation of information processing is the fundamental kind of explanation in cognitive science. These mechanisms are complex organized systems whose functioning depends on the orchestrated interaction of their component parts and processes. A constitutive explanation of every mechanism must include both appeal to its environment and to the role it plays in it. This role has been traditionally dubbed competence. To fully explain how this role is played it is (...)
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  28. Marcin Miłkowski (2013). Explaining the Computational Mind. MIT Press.score: 84.0
    In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.
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  29. Jeffrey Hershfield (2005). Is There Life After the Death of the Computational Theory of Mind? Minds and Machines 15 (2):183-194.score: 82.0
    In this paper, I explore the implications of Fodor’s attacks on the Computational Theory of Mind (CTM), which get their most recent airing in The Mind Doesn’t Work That Way. I argue that if Fodor is right that the CTM founders on the global nature of abductive inference, then several of the philosophical views about the mind that he has championed over the years founder as well. I focus on Fodor’s accounts of mental causation, psychological explanation, and intentionality.
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  30. Marcin Miłkowski (2011). Beyond Formal Structure: A Mechanistic Perspective on Computation and Implementation. Journal of Cognitive Science 12 (4):359-379.score: 76.0
    In this article, after presenting the basic idea of causal accounts of implementation and the problems they are supposed to solve, I sketch the model of computation preferred by Chalmers and argue that it is too limited to do full justice to computational theories in cognitive science. I also argue that it does not suffice to replace Chalmers’ favorite model with a better abstract model of computation; it is necessary to acknowledge the causal structure of physical computers that is (...)
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  31. William Bechtel (2012). Understanding Endogenously Active Mechanisms: A Scientific and Philosophical Challenge. [REVIEW] European Journal for Philosophy of Science 2 (2):233-248.score: 72.0
    Abstract Although noting the importance of organization in mechanisms, the new mechanistic philosophers of science have followed most biologists in focusing primarily on only the simplest mode of organization in which operations are envisaged as occurring sequentially. Increasingly, though, biologists are recognizing that the mechanisms they confront are non-sequential and the operations nonlinear. To understand how such mechanisms function through time, they are turning to computational models and tools of dynamical systems theory. Recent research on circadian rhythms addressing both (...)
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  32. Iris Rooij, Cory D. Wright & Todd Wareham (2012). Intractability and the Use of Heuristics in Psychological Explanations. Synthese 187 (2):471-487.score: 70.0
    Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.
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  33. Carlos Zednik (2011). The Nature of Dynamical Explanation. Philosophy of Science 78 (2):238-263.score: 66.0
    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, (...)
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  34. John Symons (2001). Explanation, Representation and the Dynamical Hypothesis. Minds and Machines 11 (4):521-541.score: 66.0
    This paper challenges arguments that systematic patterns of intelligent behavior license the claim that representations must play a role in the cognitive system analogous to that played by syntactical structures in a computer program. In place of traditional computational models, I argue that research inspired by Dynamical Systems theory can support an alternative view of representations. My suggestion is that we treat linguistic and representational structures as providing complex multi-dimensional targets for the development of individual brains. This approach acknowledges (...)
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  35. Sara Bernal (2005). Object Lessons: Spelke Principles and Psychological Explanation. Philosophical Psychology 18 (3):289-312.score: 66.0
    There is general agreement that from the first few months of life, our apprehension of physical objects accords, in some sense, with certain principles. In one philosopher's locution, we are 'perceptually sensitive' to physical principles describing the behavior of objects. But in what does this accordance or sensitivity consist? Are these principles explicitly represented or merely 'implemented'? And what sort of explanation do we accomplish in claiming that our object perception accords with these principles? My main goal here is (...)
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  36. Lorenzo Magnani (2004). Conjectures and Manipulations. Computational Modeling and the Extra- Theoretical Dimension of Scientific Discovery. Minds and Machines 14 (4):507-538.score: 66.0
    Computational philosophy (CP) aims at investigating many important concepts and problems of the philosophical and epistemological tradition in a new way by taking advantage of information-theoretic, cognitive, and artificial intelligence methodologies. I maintain that the results of computational philosophy meet the classical requirements of some Peircian pragmatic ambitions. Indeed, more than a 100 years ago, the American philosopher C.S. Peirce, when working on logical and philosophical problems, suggested the concept of pragmatism(pragmaticism, in his own words) as a logical (...)
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  37. Elizabeth Irvine (forthcoming). Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology. Synthese:1-17.score: 66.0
    This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and (...)
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  38. Rick Grush (2001). The Semantic Challenge to Computational Neuroscience. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press. 155--172.score: 64.0
    I examine one of the conceptual cornerstones of the field known as computational neuroscience, especially as articulated in Churchland et al. (1990), an article that is arguably the locus classicus of this term and its meaning. The authors of that article try, but I claim ultimately fail, to mark off the enterprise of computational neuroscience as an interdisciplinary approach to understanding the cognitive, information-processing functions of the brain. The failure is a result of the fact that the authors (...)
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  39. Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.score: 64.0
    Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The (...)
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  40. Michael E. Cuffaro (forthcoming). How-Possibly Explanations in Quantum Computer Science. Philosophy of Science.score: 64.0
    A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds, and in so doing to describe the possibility spaces associated with these processes. By doing this we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is gained (...)
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  41. David J. Chalmers (2011). A Computational Foundation for the Study of Cognition. Journal of Cognitive Science 12 (4):323-357.score: 60.0
    Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of (...)
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  42. William Bechtel & Adele A. Abrahamsen (2013). Thinking Dynamically About Biological Mechanisms: Networks of Coupled Oscillators. [REVIEW] Foundations of Science 18 (4):707-723.score: 60.0
    Explaining the complex dynamics exhibited in many biological mechanisms requires extending the recent philosophical treatment of mechanisms that emphasizes sequences of operations. To understand how nonsequentially organized mechanisms will behave, scientists often advance what we call dynamic mechanistic explanations. These begin with a decomposition of the mechanism into component parts and operations, using a variety of laboratory-based strategies. Crucially, the mechanism is then recomposed by means of computational models in which variables or terms in differential equations correspond to properties (...)
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  43. Marco Mazzone (2013). Mental States as Generalizations From Experience: A Neuro-Computational Hypothesis. Philosophical Explorations 17 (2):1-18.score: 60.0
    The opposition between behaviour- and mind-reading accounts of data on infants and non-human primates could be less dramatic than has been thought up to now. In this paper, I argue for this thesis by analysing a possible neuro-computational explanation of early mind-reading, based on a mechanism of associative generalization which is apt to implement the notion of mental states as intervening variables proposed by Andrew Whiten. This account allows capturing important continuities between behaviour-reading and mind-reading, insofar as both (...)
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  44. Michael E. Cuffaro, On the Necessity of Entanglement for the Explanation of Quantum Speedup.score: 58.0
    Of the many and varied applications of quantum information theory, perhaps the most fascinating is the sub-field of quantum computation. In this sub-field, computational algorithms are designed which utilise the resources available in quantum systems in order to compute solutions to computational problems with, in some cases, exponentially fewer resources than any known classical algorithm. While the fact of quantum computational speedup is almost beyond doubt, the source of quantum speedup is still a matter of debate. In (...)
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  45. Steven Horst (1999). Symbols and Computation: A Critique of the Computational Theory of Mind. Minds and Machines 9 (3):347-381.score: 58.0
    Over the past several decades, the philosophical community has witnessed the emergence of an important new paradigm for understanding the mind.1 The paradigm is that of machine computation, and its influence has been felt not only in philosophy, but also in all of the empirical disciplines devoted to the study of cognition. Of the several strategies for applying the resources provided by computer and cognitive science to the philosophy of mind, the one that has gained the most attention from philosophers (...)
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  46. Davide Rizza (2011). Magicicada, Mathematical Explanation and Mathematical Realism. Erkenntnis 74 (1):101-114.score: 54.0
    Baker (2005) claims to provide an example of mathematical explanation of an empirical phenomenon which leads to ontological commitment to mathematical objects. This is meant to show that the positing of mathematical entities is necessary for satisfactory scientific explanations and thus that the application of mathematics to science can be used, at least in some cases, to support mathematical realism. In this paper I show that the example of explanation Baker considers can actually be given without postulating mathematical (...)
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  47. Frances Egan (forthcoming). Function-Theoretic Explanation and the Search for Neural Mechanisms. In David M. Kaplan (ed.), Integrating Mind and Brain Science: Mechanistic Perspectives and Beyond. Oxford University Press.score: 54.0
    A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function contributes to the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals the causal structure (...)
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  48. Christopher D. Green & John Vervaeke, What Kind of Explanation, If Any, is a Connectionist Net?score: 54.0
    Connectionist models of cognition are all the rage these days. They are said to provide better explanations than traditional symbolic computational models in a wide array of cognitive areas, from perception to memory to language to reasoning to motor action. But what does it actually mean to say that they "explain" cognition at all? In what sense do the dozens of nodes and hundreds of connections in a typical connectionist network explain anything? It is the purpose of this paper (...)
     
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  49. Oron Shagrir (2010). Marr on Computational-Level Theories. Philosophy of Science 77 (4):477-500.score: 54.0
    According to Marr, a computational-level theory consists of two elements, the what and the why . This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: ( a ) that the Why element plays an explanatory role in computational-level theories, ( b ) that its goal is to explain why the computed function (specified by the What element) is appropriate for a given visual task, and ( c (...)
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  50. Zoltan Jakab (2000). Ineffability of Qualia: A Straightforward Naturalistic Explanation. Consciousness and Cognition 9 (3):329-351.score: 54.0
    In this paper I offer an explanation of the ineffability (linguistic inexpressibility) of sensory experiences. My explanation is put in terms of computational functionalism and standard externalist theories of representational content. As I will argue, many or most sensory experiences are representational states without constituent structure. This property determines both the representational function these states can serve and the information that can be extracted from them when they are processed. Sensory experiences can indicate the presence of certain (...)
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