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.
    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.  23
    Marcin Miłkowski (2013). Limits of Computational Explanation of Cognition. In Vincent C. Müller (ed.), Philosophy and Theory of Artificial Intelligence. Springer 69-84.
    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.  87
    David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. 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 (...)
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  4.  21
    Maria Serban (2015). The Scope and Limits of a Mechanistic View of Computational Explanation. Synthese 192 (10):3371-3396.
    An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive neuroscience as (...)
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  5.  44
    M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. 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 (...)
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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.
    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. Gualtiero Piccinini (2007). Computational Explanation and Mechanistic Explanation of Mind. In Francesco Ferretti, Massimo Marraffa & Mario De Caro (eds.), Synthese. Springer 343-353.
    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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  8.  10
    S. Delarivière & J. Frans (2015). Computational Explanation in Cognitive Sciences: The Mechanist Turn. Constructivist Foundations 10 (3):426-429.
    Upshot: The computational theory of mind has been elaborated in many different ways throughout the last decades. In Explaining the Computational Mind, Milkowski defends his view that the mind can be explained as computational through his defense of mechanistic explanation. At no point in this book is there explicit mention of constructivist approaches to this topic. We will, nevertheless, argue that it is interesting for constructivist readers.
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  9.  20
    P. Morton (1993). Supervenience and Computational Explanation in Vision Theory. Philosophy of Science 60 (1):86-99.
    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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  10.  10
    Mike Oaksford & Nick Chater (1995). Theories of Reasoning and the Computational Explanation of Everyday Inference. Thinking and Reasoning 1 (2):121 – 152.
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  11.  18
    Keith R. Sawyer (2004). Social Explanation and Computational Simulation. Philosophical Explorations 7 (3):219 – 231.
    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. 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.
    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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  13.  30
    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.
    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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  14.  82
    Michael E. Cuffaro (2013). On the Physical Explanation for Quantum Computational Speedup. Dissertation, The University of Western Ontario
    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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  15.  56
    Christopher Peacocke (1986). Explanation in Computational Psychology: Language, Perception and Level. Mind and Language 1 (2):101-23.
  16.  40
    Paul R. Thagard (1991). Philosophical and Computational Models of Explanation. Philosophical Studies 64 (October):87-104.
  17. James T. Higginbotham (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:358-361.
     
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  18. 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.
     
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  19. Stephen R. Schiffer (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:362-371.
     
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  20. Scott Soames (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:372-387.
     
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  21.  5
    Johannes Lenhard (2014). Autonomy and Automation: Computational Modeling, Reduction, and Explanation in Quantum Chemistry. The Monist 97 (3):339-358.
    This paper discusses how computational modeling combines the autonomy of models with the automation of computational procedures. In particular, the case of ab-initio methods in quantum chemistry will be investigated to draw two lessons from the analysis of computational modeling. The first belongs to general philosophy of science: Computational modeling faces a trade-off and enlarges predictive force at the cost of explanatory force. The other lesson is about the philosophy of chemistry: The methodology of computational (...)
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  22.  25
    Daniel Gilman (1996). Optimization and Simplicity: Computational Vision and Biological Explanation. Synthese 107 (3):293 - 323.
    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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  23.  12
    R. Keith Sawyer (2004). Social Explanation and Computational Simulation. Philosophical Explorations 7 (3):219-231.
  24.  2
    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.
  25. Glyn W. Humphreys & Philip T. Quinlan (1986). Comments on Peacocke's Explanation in Computational Psychology. Mind and Language 1:355-357.
     
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  26. Daniel J. Gilman (1993). Optimization and Simplicity: Marr's Theory of Vision and Biological Explanation. Synthese 107 (3):293-323.
  27.  23
    Eric Hochstein (2016). One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation. Synthese 193 (5):1387-1407.
    There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...)
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  28. Marcin Miłkowski (2013). Wyjaśnianie w kognitywistyce. Przeglad Filozoficzny - Nowa Seria 22 (2):151-166.
    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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  29.  63
    John Symons (2008). Computational Models of Emergent Properties. Minds and Machines 18 (4):475-491.
    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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  30.  26
    Marcin Miłkowski (2013). Explaining the Computational Mind. MIT Press.
    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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  31.  21
    William Bechtel (2012). Understanding Endogenously Active Mechanisms: A Scientific and Philosophical Challenge. [REVIEW] European Journal for Philosophy of Science 2 (2):233-248.
    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. Carlos Zednik (2011). The Nature of Dynamical Explanation. Philosophy of Science 78 (2):238-263.
    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. (...)
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  33. Marcin Miłkowski (2011). Beyond Formal Structure: A Mechanistic Perspective on Computation and Implementation. Journal of Cognitive Science 12 (4):359-379.
    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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  34.  14
    Richard P. Cooper & David Peebles (2015). Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation. Topics in Cognitive Science 7 (2):243-258.
    We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system (...)
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  35.  5
    A. Nicolás Venturelli (forthcoming). A Cautionary Contribution to the Philosophy of Explanation in the Cognitive Neurosciences. Minds and Machines:1-27.
    I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these arguments, I (...)
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  36.  43
    Elizabeth Irvine (2015). Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology. Synthese 192 (12):3943-3959.
    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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  37.  55
    Sara Bernal (2005). Object Lessons: Spelke Principles and Psychological Explanation. Philosophical Psychology 18 (3):289-312.
    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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  38.  52
    John Symons (2001). Explanation, Representation and the Dynamical Hypothesis. Minds and Machines 11 (4):521-541.
    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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  39.  87
    Jeffrey Hershfield (2005). Is There Life After the Death of the Computational Theory of Mind? Minds and Machines 15 (2):183-194.
    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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  40.  10
    Bernardo Aguilera (2015). Behavioural Explanation in the Realm of Non-Mental Computing Agents. Minds and Machines 25 (1):37-56.
    Recently, many philosophers have been inclined to ascribe mentality to animals on the main grounds that they possess certain complex computational abilities. In this paper I contend that this view is misleading, since it wrongly assumes that those computational abilities demand a psychological explanation. On the contrary, they can be just characterised from a computational level of explanation, which picks up a domain of computation and information processing that is common to many computing systems but (...)
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  41.  26
    Lorenzo Magnani (2004). Conjectures and Manipulations. Computational Modeling and the Extra- Theoretical Dimension of Scientific Discovery. Minds and Machines 14 (4):507-538.
    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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  42. David J. Chalmers (2011). A Computational Foundation for the Study of Cognition. Journal of Cognitive Science 12 (4):323-357.
    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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  43. Marco Mazzone (2013). Mental States as Generalizations From Experience: A Neuro-Computational Hypothesis. Philosophical Explorations 17 (2):1-18.
    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. 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.
    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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  45.  8
    J. Brendan Ritchie, Chalmers on Implementation and Computational Sufficiency.
    Chalmers argues for the following two principles: computational sufficiency and computational explanation. In this commentary I present two criticisms of Chalmers’ argument for the principle of computational sufficiency, which states that implementing the appropriate kind of computational structure suffices for possessing mentality. First, Chalmers only establishes that a system has its mental properties in virtue of the computations it performs in the trivial sense that any physical system can be described computationally to some arbitrary level (...)
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  46.  36
    William Bechtel & Oron Shagrir (2015). The Non‐Redundant Contributions of Marr's Three Levels of Analysis for Explaining Information‐Processing Mechanisms. Topics in Cognitive Science 7 (2):312-322.
    Are all three of Marr's levels needed? Should they be kept distinct? We argue for the distinct contributions and methodologies of each level of analysis. It is important to maintain them because they provide three different perspectives required to understand mechanisms, especially information-processing mechanisms. The computational perspective provides an understanding of how a mechanism functions in broader environments that determines the computations it needs to perform. The representation and algorithmic perspective offers an understanding of how information about (...)
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  47. William Bechtel & Adele A. Abrahamsen (2013). Thinking Dynamically About Biological Mechanisms: Networks of Coupled Oscillators. [REVIEW] Foundations of Science 18 (4):707-723.
    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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  48.  76
    Davide Rizza (2011). Magicicada, Mathematical Explanation and Mathematical Realism. Erkenntnis 74 (1):101-114.
    Baker 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 objects (...)
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  49.  10
    Richard L. Lewis, Andrew Howes & Satinder Singh (2014). Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization. Topics in Cognitive Science 6 (2):279-311.
    We propose a framework for including information-processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into (...)
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  50.  49
    Oron Shagrir (2010). Marr on Computational-Level Theories. Philosophy of Science 77 (4):477-500.
    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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