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  1. Thinking with Things: An Embodied Enactive Account of Mind–Technology Interaction.Anco Peeters - 2019 - Dissertation, University of Wollongong
    Technological artefacts have, in recent years, invited increasingly intimate ways of interaction. But surprisingly little attention has been devoted to how such interactions, like with wearable devices or household robots, shape our minds, cognitive capacities, and moral character. In this thesis, I develop an embodied, enactive account of mind--technology interaction that takes the reciprocal influence of artefacts on minds seriously. First, I examine how recent developments in philosophy of technology can inform the phenomenology of mind--technology interaction as seen through an (...)
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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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  • The Functional Sense of Mechanism.Justin Garson - 2013 - Philosophy of Science 80 (3):317-333.
    This article presents a distinct sense of ‘mechanism’, which I call the functional sense of mechanism. According to this sense, mechanisms serve functions, and this fact places substantive restrictions on the kinds of system activities ‘for which’ there can be a mechanism. On this view, there are no mechanisms for pathology; pathologies result from disrupting mechanisms for functions. Second, on this sense, natural selection is probably not a mechanism for evolution because it does not serve a function. After distinguishing this (...)
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  • Classical Computational Models.Richard Samuels - 2018 - In Mark Sprevak & Matteo Colombo (ed.), The Routledge Handbook of the Computational Mind. Oxford, UK: pp. 103-119.
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  • Remembering Without Storing: Beyond Archival Models in the Science and Philosophy of Human Memory.Ian O'Loughlin - 2014 - Dissertation,
    Models of memory in cognitive science and philosophy have traditionally explained human remembering in terms of storage and retrieval. This tendency has been entrenched by reliance on computationalist explanations over the course of the twentieth century; even research programs that eschew computationalism in name, or attempt the revision of traditional models, demonstrate tacit commitment to computationalist assumptions. It is assumed that memory must be stored by means of an isomorphic trace, that memory processes must divide into conceptually distinct systems and (...)
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  • Causal Explanation in Psychiatry.Tuomas K. Pernu - 2019 - In Şerife Tekin & Robyn Bluhm (eds.), The Bloomsbury Companion to Philosophy of Psychiatry. London: Bloomsbury Academic.
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  • Cognitive Ontology in Flux: The Possibility of Protean Brains.Daniel D. Hutto, Anco Peeters & Miguel Segundo-Ortin - 2017 - Philosophical Explorations 20 (2):209-223.
    This paper motivates taking seriously the possibility that brains are basically protean: that they make use of neural structures in inventive, on-the-fly improvisations to suit circumstance and context. Accordingly, we should not always expect cognition to divide into functionally stable neural parts and pieces. We begin by reviewing recent work in cognitive ontology that highlights the inadequacy of traditional neuroscientific approaches when it comes to divining the function and structure of cognition. Cathy J. Price and Karl J. Friston, and Colin (...)
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  • Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing.William J. Rapaport - 2012 - International Journal of Signs and Semiotic Systems 2 (1):32-71.
    In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...)
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  • Developing the Explanatory Dimensions of Part–Whole Realization.Ronald Endicott - 2016 - Philosophical Studies 173 (12):3347-3368.
    I use Carl Gillett’s much heralded dimensioned theory of realization as a platform to develop a plausible part–whole theory. I begin with some basic desiderata for a theory of realization that its key terms should be defined and that it should be explanatory. I then argue that Gillett’s original theory violates these conditions because its explanatory force rests upon an unspecified “in virtue of” relation. I then examine Gillett’s later version that appeals instead to theoretical terms tied to “mechanisms.” Yet (...)
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  • The Functional Unity of Special Science Kinds.D. A. Weiskopf - 2011 - British Journal for the Philosophy of Science 62 (2):233-258.
    The view that special science properties are multiply realizable has been attacked in recent years by Shapiro, Bechtel and Mundale, Polger, and others. Focusing on psychological and neuroscientific properties, I argue that these attacks are unsuccessful. By drawing on interspecies physiological comparisons I show that diverse physical mechanisms can converge on common functional properties at multiple levels. This is illustrated with examples from the psychophysics and neuroscience of early vision. This convergence is compatible with the existence of general constraints on (...)
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  • Information Processing, Computation, and Cognition.Gualtiero Piccinini & Andrea Scarantino - 2011 - Journal of Biological Physics 37 (1):1-38.
    Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...)
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  • Morphological Computation: Nothing but Physical Computation.Marcin Miłkowski - 2018 - Entropy 10 (20):942.
    The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, (...)
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  • Objections to Computationalism: A Survey.Marcin Miłkowski - 2018 - Roczniki Filozoficzne 66 (3):57-75.
    In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.
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  • Functionalism and the Problem of Occurrent States.Gary Bartlett - 2018 - Philosophical Quarterly 68 (270):1-20.
    In 1956 U. T. Place proposed that consciousness is a brain process. More attention should be paid to his word ‘process’. There is near-universal agreement that experiences are processive—as witnessed in the platitude that experiences are occurrent states. The abandonment of talk of brain processes has benefited functionalism, because a functional state, as it is usually conceived, cannot be a process. This point is dimly recognized in a well-known but little-discussed argument that conscious experiences cannot be functional states because the (...)
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  • What is a Computer? A Survey.William Rapaport - 2018 - Minds and Machines 28 (3):385-426.
    A critical survey of some attempts to define ‘computer’, beginning with some informal ones, then critically evaluating those of three philosophers, and concluding with an examination of whether the brain and the universe are computers.
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  • Absent Qualia and Categorical Properties.Brendan O’Sullivan - 2012 - Erkenntnis 76 (3):353-371.
    Qualia have proved difficult to integrate into a broadly physicalistic worldview. In this paper, I argue that despite popular wisdom in the philosophy of mind, qualia’s intrinsicality is not sufficient for their non-reducibility. Second, I diagnose why philosophers mistakenly focused on intrinsicality. I then proceed to argue that qualia are categorical and end with some reflections on how the conceptual territory looks when we keep our focus on categoricity.
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  • Intractability and the Use of Heuristics in Psychological Explanations.Iris Rooij, Cory Wright & Todd Wareham - 2012 - Synthese 187 (2):471-487.
    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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  • Misplacing Memories? An Enactive Approach to the Virtual Memory Palace.Anco Peeters & Miguel Segundo-Ortin - 2019 - Consciousness and Cognition 76:102834.
    In this paper, we evaluate the pragmatic turn towards embodied, enactive thinking in cognitive science, in the context of recent empirical research on the memory palace technique. The memory palace is a powerful method for remembering yet it faces two problems. First, cognitive scientists are currently unable to clarify its efficacy. Second, the technique faces significant practical challenges to its users. Virtual reality devices are sometimes presented as a way to solve these practical challenges, but currently fall short of delivering (...)
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  • The Computational Stance is Unfit for Consciousness.Riccardo Manzotti - 2012 - International Journal of Machine Consciousness 4 (2):401-420.
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  • Computationalism in the Philosophy of Mind.Gualtiero Piccinini - 2009 - Philosophy Compass 4 (3):515-532.
    Computationalism has been the mainstream view of cognition for decades. There are periodic reports of its demise, but they are greatly exaggerated. This essay surveys some recent literature on computationalism. It concludes that computationalism is a family of theories about the mechanisms of cognition. The main relevant evidence for testing it comes from neuroscience, though psychology and AI are relevant too. Computationalism comes in many versions, which continue to guide competing research programs in philosophy of mind as well as psychology (...)
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  • Get the Latest Upgrade: Functionalism 6.3.1.Corey Maley & Gualtiero Piccinini - 2013 - Philosophia Scientae 17 (2):135-149.
    Functionalism is a popular solution to the mind–body problem. It has a number of versions. We outline some of the major releases of functionalism, listing some of their important features as well as some of the bugs that plagued these releases. We outline how different versions are related. Many have been pessimistic about functionalism’s prospects, but most criticisms have missed the latest upgrades. We end by suggesting a version of functionalism that provides a complete account of the mind.
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  • Computational Functionalism for the Deep Learning Era.Ezequiel López-Rubio - 2018 - Minds and Machines 28 (4):667-688.
    Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...)
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  • Computation in Physical Systems.Gualtiero Piccinini - 2010 - Stanford Encyclopedia of Philosophy.
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