Search results for 'Computationalism' (try it on Scholar)

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  1. Gualtiero Piccinini (2004). Functionalism, Computationalism, and Mental Contents. Canadian Journal of Philosophy 34 (3):375-410.score: 18.0
    Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.
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  2. Roberto Cordeschi & Marcello Frixione (2007). Computationalism Under Attack. In M. Marraffa, M. De Caro & F. Ferretti (eds.), Cartographies of the Mind: Philosophy and Psychology in Intersection. Springer.score: 18.0
    Since the early eighties, computationalism in the study of the mind has been “under attack” by several critics of the so-called “classic” or “symbolic” approaches in AI and cognitive science. Computationalism was generically identified with such approaches. For example, it was identified with both Allen Newell and Herbert Simon’s Physical Symbol System Hypothesis and Jerry Fodor’s theory of Language of Thought, usually without taking into account the fact ,that such approaches are very different as to their methods and (...)
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  3. Marcin Miłkowski (2007). Is Computationalism Trivial? In Gordana Dodig Crnkovic & Susan Stuart (eds.), Computation, Information, Cognition: The Nexus and the Liminal. Cambridge Scholars Press.score: 18.0
    In this paper, I want to deal with the triviality threat to computationalism. On one hand, the controversial and vague claim that cognition involves computation is still denied. On the other, contemporary physicists and philosophers alike claim that all physical processes are indeed computational or algorithmic. This claim would justify the computationalism claim by making it utterly trivial. I will show that even if these two claims were true, computationalism would not have to be trivial.
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  4. David Davenport (2012). Computationalism: Still the Only Game in Town. [REVIEW] Minds and Machines 22 (3):183-190.score: 18.0
    Abstract Mental representations, Swiatczak (Minds Mach 21:19–32, 2011) argues, are fundamentally biochemical and their operations depend on consciousness; hence the computational theory of mind, based as it is on multiple realisability and purely syntactic operations, must be wrong. Swiatczak, however, is mistaken. Computation, properly understood, can afford descriptions/explanations of any physical process, and since Swiatczak accepts that consciousness has a physical basis, his argument against computationalism must fail. Of course, we may not have much idea how consciousness (itself a (...)
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  5. Gualtiero Piccinini (2004). Functionalism, Computationalism, & Mental States. Studies in the History and Philosophy of Science 35 (4):811-833.score: 18.0
    Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions.
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  6. David Longinotti (2009). Computationalism and the Locality Principle. Minds and Machines 19 (4):495-506.score: 18.0
    Computationalism, a specie of functionalism, posits that a mental state like pain is realized by a ‘core’ computational state within a particular causal network of such states. This entails that what is realized by the core state is contingent on events remote in space and time, which puts computationalism at odds with the locality principle of physics. If computationalism is amended to respect locality, then it posits that a type of phenomenal experience is determined by a single (...)
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  7. Matthias Scheutz (2002). Computationalism: The Next Generation. In , Computationalism: New Directions. MIT Press. 517-524.score: 18.0
  8. Gordana Dodig Crnkovic (2010). The Cybersemiotics and Info-Computationalist Research Programmes. Entropy 12 (4):878-901.score: 18.0
    Both Cybersemiotics and Info-computationalist research programmes represent attempts to unify understanding of information, knowledge and communication. The first one takes into account phenomenological aspects of signification which are insisting on the human experience "from within". The second adopts solely the view "from the outside" based on scientific practice, with an observing agent generating inter-subjective knowledge in a research community. The process of knowledge production, embodied into networks of cognizing agents interacting with the environment and developing through evolution is studied on (...)
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  9. Robert A. Wilson (1994). Wide Computationalism. Mind 103 (411):351-72.score: 15.0
  10. Georges Rey (2003). Why Wittgenstein Ought to Have Been a Computationalist (and What a Computationalist Can Gain From Wittgenstein). Croatian Journal of Philosophy 3 (9):231-264.score: 15.0
    Wittgenstein’s views invite a modest, functionalist account of mental states and regularities, or more specifically a causal/computational, representational theory of the mind (CRTT). It is only by understandingWittgenstein’s remarks in the context of a theory like CRTT that his insights have any real force; and it is only by recognizing those insights that CRTT can begin to account for sensations and our thoughts about them. For instance, Wittgenstein’s (in)famous remark that “an inner process stands in need of outward criteria” (PI:§580), (...)
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  11. Oron Shagrir (1997). Two Dogmas of Computationalism. Minds and Machines 7 (3):321-44.score: 15.0
    This paper challenges two orthodox theses: (a) that computational processes must be algorithmic; and (b) that all computed functions must be Turing-computable. Section 2 advances the claim that the works in computability theory, including Turing's analysis of the effective computable functions, do not substantiate the two theses. It is then shown (Section 3) that we can describe a system that computes a number-theoretic function which is not Turing-computable. The argument against the first thesis proceeds in two stages. It is first (...)
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  12. Matthias Scheutz (2002). New Computationalism. Conceptus Studien 14.score: 15.0
  13. Eric Dietrich (1990). Computationalism. Social Epistemology 4 (2):135-154.score: 15.0
  14. Stuart S. Glennan (1995). Computationalism and the Problem of Other Minds. Philosophical Psychology 8 (4):375-88.score: 15.0
    In this paper I discuss Searle's claim that the computational properties of a system could never cause a system to be conscious. In the first section of the paper I argue that Searle is correct that, even if a system both behaves in a way that is characteristic of conscious agents (like ourselves) and has a computational structure similar to those agents, one cannot be certain that that system is conscious. On the other hand, I suggest that Searle's intuition that (...)
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  15. Nenad Miscevic (1996). Computationalism and the Kripke-Wittgenstein Paradox. Proceedings of the Aristotelian Society 96:215-29.score: 15.0
  16. Gualtiero Piccinini (2009). Computationalism in the Philosophy of Mind. Philosophy Compass 4 (3):515-532.score: 12.0
    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 (...)
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  17. Gualtiero Piccinini (2010). The Mind as Neural Software? Understanding Functionalism, Computationalism, and Computational Functionalism. Philosophy and Phenomenological Research 81 (2):269-311.score: 12.0
    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 (...)
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  18. Gualtiero Piccinini (2010). The Resilience of Computationalism. Philosophy of Science 77 (5):852-861.score: 12.0
    Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might (...)
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  19. Daniel D. Hutto (1995). The Mindlessness of Computationalism: The Neglected Aspects of Cognition. In P. Pyllkkänen & P. Pyllkkö (eds.), New Directions in Cognitive Science. Finnish Society for Artificial Intelligence.score: 12.0
    The emergence of cognitive science as a multi-disciplinary investigation into the nature of mind has historically revolved around the core assumption that the central ‘cognitive’ aspects of mind are computational in character. Although there is some disagreement and philosophical speculation concerning the precise formulation of this ‘core assumption’ it is generally agreed that computationalism in some form lies at the heart of cognitive science as it is currently conceived. Von Eckardt’s recent work on this topic is useful in enabling (...)
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  20. Gualtiero Piccinini (2007). Computationalism, the Church–Turing Thesis, and the Church–Turing Fallacy. Synthese 154 (1):97-120.score: 12.0
    The Church–Turing Thesis (CTT) is often employed in arguments for computationalism. I scrutinize the most prominent of such arguments in light of recent work on CTT and argue that they are unsound. Although CTT does nothing to support computationalism, it is not irrelevant to it. By eliminating misunderstandings about the relationship between CTT and computationalism, we deepen our appreciation of computationalism as an empirical hypothesis.
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  21. Stuart C. Shapiro (1995). Computationalism. Minds and Machines 5 (4):467-87.score: 12.0
    Computationalism, the notion that cognition is computation, is a working hypothesis of many AI researchers and Cognitive Scientists. Although it has not been proved, neither has it been disproved. In this paper, I give some refutations to some well-known alleged refutations of computationalism. My arguments have two themes: people are more limited than is often recognized in these debates; computer systems are more complicated than is often recognized in these debates. To underline the latter point, I sketch the (...)
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  22. William S. Robinson (1995). Brain Symbols and Computationalist Explanation. Minds and Machines 5 (1):25-44.score: 12.0
    Computationalist theories of mind require brain symbols, that is, neural events that represent kinds or instances of kinds. Standard models of computation require multiple inscriptions of symbols with the same representational content. The satisfaction of two conditions makes it easy to see how this requirement is met in computers, but we have no reason to think that these conditions are satisfied in the brain. Thus, if we wish to give computationalist explanations of human cognition, without committing ourselvesa priori to a (...)
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  23. William Demopoulos (1987). On Some Fundamental Distinctions of Computationalism. Synthese 70 (January):79-96.score: 12.0
    The following paper presents a characterization of three distinctions fundamental to computationalism, viz., the distinction between analog and digital machines, representation and nonrepresentation-using systems, and direct and indirect perceptual processes. Each distinction is shown to rest on nothing more than the methodological principles which justify the explanatory framework of the special sciences.
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  24. Michael G. Dyer & Boelter Hall, Computationalism, Neural Networks and Minds, Analog or Otherwise.score: 12.0
    A working hypothesis of computationalism is that Mind arises, not from the intrinsic nature of the causal properties of particular forms of matter, but from the organization of matter. If this hypothesis is correct, then a wide range of physical systems (e.g. optical, chemical, various hybrids, etc.) should support Mind, especially computers, since they have the capability to create/manipulate organizations of bits of arbitrarily complexity and dynamics. In any particular computer, these bit patterns are quite physical, but their particular (...)
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  25. Matthias Scheutz (ed.) (2002). Computationalism: New Directions. MIT Press.score: 12.0
    A new computationalist view of the mind that takes into account real-world issues of embodiment, interaction, physical implementation, and semantics.
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  26. Ronald L. Chrisley, Transparent Computationalism.score: 12.0
    Summary. A distinction is made between two senses of the claim “cognition is computation”. One sense, the opaque reading, takes computation to be whatever is described by our current computational theory and claims that cognition is best understood in terms of that theory. The transparent reading, which has its primary allegiance to the phenomenon of computation, rather than to any particular theory of it, is the claim that the best account of cognition will be given by whatever theory turns out (...)
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  27. Laureano Luna & Christopher Small (2009). Intentionality and Computationalism. A Diagonal Argument. Mind and Matter 7 (1):81-90.score: 12.0
    Computationalism is the claim that all possible thoughts are computations, i.e. executions of algorithms. The aim of the paper is to show that if intentionality is semantically clear, in a way defined in the paper, then computationalism must be false. Using a convenient version of the phenomenological relation of intentionality and a diagonalization device inspired by Thomson's theorem of 1962, we show there exists a thought that canno be a computation.
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  28. C. Gershenson (2014). Info-Computationalism or Materialism? Neither and Both. Constructivist Foundations 9 (2):241-242.score: 12.0
    Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic. Upshot: The limitations of materialism for studying cognition have motivated alternative epistemologies based on information and computation. I argue that these alternatives are also inherently limited and that these limits can only be overcome by considering materialism, info-computationalism, and cognition at the same time.
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  29. William J. Rapaport (2012). Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing. International Journal of Signs and Semiotic Systems 2 (1):32-71.score: 9.0
    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 (...)
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  30. John R. Searle (2001). The Failures of Computationalism. Http.score: 9.0
    Harnad and I agree that the Chinese Room Argument deals a knockout blow to Strong AI, but beyond that point we do not agree on much at all. So let's begin by pondering the implications of the Chinese Room. The Chinese Room shows that a system, me for example, could pass the Turing Test for understanding Chinese, for example, and could implement any program you like and still not understand a word of Chinese. Now, why? What does the genuine Chinese (...)
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  31. John Mark Bishop (2009). Why Computers Can't Feel Pain. Minds and Machines 19 (4):507-516.score: 9.0
    The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, (...)
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  32. Carrie Figdor (2009). Semantic Externalism and the Mechanics of Thought. Minds and Machines 19 (1):1-24.score: 9.0
    I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this (...)
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  33. Gualtiero Piccinini & Andrea Scarantino (2010). Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):237-246.score: 9.0
    Since the cognitive revolution, it’s become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theoristError: Illegal entry in bfrange block in ToUnicode (...)
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  34. Eric Dietrich & Chris Fields (1996). Role of the Frame Problem in Fodor's Modularity Thesis. In Ken Ford & Zenon Pylyshyn (eds.), The Robot's Dilemma Revisited.score: 9.0
    It is shown that the Fodor's interpretation of the frame problem is the central indication that his version of the Modularity Thesis is incompatible with computationalism. Since computationalism is far more plausible than this thesis, the latter should be rejected.
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  35. Erich Rast (2012). De Se Puzzles, the Knowledge Argument, and the Formation of Internal Knowledge. Analysis and Metaphysics 11 (December):106-132.score: 9.0
    ABSTRACT. Thought experiments about de se attitudes and Jackson’s original Knowledge Argument are compared with each other and discussed from the perspective of a computational theory of mind. It is argued that internal knowledge, i.e. knowledge formed on the basis of signals that encode aspects of their own processing rather than being intentionally directed towards external objects, suffices for explaining the seminal puzzles without resorting to acquaintance or phenomenal character as primitive notions. Since computationalism is ontologically neutral, the account (...)
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  36. Nigel J. T. Thomas, Coding Dualism: Conscious Thought Without Cartesianism or Computationalism.score: 9.0
    The principal temptation toward substance dualisms, or otherwise incorporating a question begging homunculus into our psychologies, arises not from the problem of consciousness in general, nor from the problem of intentionality, but from the question of our awareness and understanding of our own mental contents, and the control of the deliberate, conscious thinking in which we employ them. Dennett has called this "Hume's problem". Cognitivist philosophers have generally either denied the experiential reality of thought, as did the Behaviorists, or have (...)
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  37. Gordana Dodig-Crnkovic, Semantics of Information as Interactive Computation. Proceedings of the Fifth International Workshop on Philosophy and Informatics 2008.score: 9.0
    Computers today are not only the calculation tools - they are directly (inter)acting in the physical world which itself may be conceived of as the universal computer (Zuse, Fredkin, Wolfram, Chaitin, Lloyd). In expanding its domains from abstract logical symbol manipulation to physical embedded and networked devices, computing goes beyond Church-Turing limit (Copeland, Siegelman, Burgin, Schachter). Computational processes are distributed, reactive, interactive, agent-based and concurrent. The main criterion of success of computation is not its termination, but the adequacy of its (...)
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  38. Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.score: 9.0
    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 extreme (...)
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  39. Selmer Bringsjord, Computationalism is Dead; Now What?score: 9.0
    In this paper I place Jim Fetzer's esemplastic burial of the computational conceptionof mind within the context of both my own burial and the theory of mind I would put in place of this dead doctrine. My view..
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  40. Geoffrey Laforte, Pat Hayes & Kenneth M. Ford (1998). Why Godel's Theorem Cannot Refute Computationalism: A Reply to Penrose. Artificial Intelligence 104.score: 9.0
  41. Mark H. Bickhard (1996). Troubles with Computationalism. In W. O'Donahue & Richard F. Kitchener (eds.), The Philosophy of Psychology. Sage Publications. 173--183.score: 9.0
  42. Michael G. Dyer (1990). Intentionality and Computationalism: Minds, Machines, Searle and Harnad. Journal of Experimental and Theoretical Artificial Intelligence 2:303-19.score: 9.0
  43. Vincent C. Müller (2009). Symbol Grounding in Computational Systems: A Paradox of Intentions. [REVIEW] Minds and Machines 19 (4):529-541.score: 9.0
    The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to (...)
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  44. Albert E. Lyngzeidetson (1990). Massively Parallel Distributed Processing and a Computationalist Foundation for Cognitive Science. British Journal for the Philosophy of Science 41 (March):121-127.score: 9.0
    My purpose in this brief paper is to consider the implications of a radically different computer architecure to some fundamental problems in the foundations of Cognitive Science. More exactly, I wish to consider the ramifications of the 'Gödel-Minds-Machines' controversy of the late 1960s on a dynamically changing computer architecture which, I venture to suggest, is going to revolutionize which 'functions' of the human mind can and cannot be modelled by (non-human) computational automata. I will proceed on the presupposition that the (...)
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  45. Richard Stöckle-Schobel (2012). Perceptual Learning and Feature-Based Approaches to Concepts - a Critical Discussion. Frontiers in Psychology 3:1-10.score: 9.0
    A central challenge for any theory of concept learning comes from Fodor’s argument against the learning of concepts, which lies at the basis of contemporary computationalist accounts of the mind. Robert Goldstone and his colleagues propose a theory of perceptual learning that attempts to overcome Fodor’s challenge. Its main component is the addition of a cognitive device at the interface of perception and conception, which slowly builds “cognitivesymbols” out of perceptual stimuli. Two main mechanisms of concept creation are unitization and (...)
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  46. Richard Samuels (2010). Classical Computationalism and the Many Problems of Cognitive Relevance. Studies in History and Philosophy of Science Part A 41 (3):280-293.score: 9.0
  47. Valerie Gray Hardcastle (1995). Computationalism. Synthese 105 (3):303-17.score: 9.0
    What counts as a computation and how it relates to cognitive function are important questions for scientists interested in understanding how the mind thinks. This paper argues that pragmatic aspects of explanation ultimately determine how we answer those questions by examining what is needed to make rigorous the notion of computation used in the (cognitive) sciences. It (1) outlines the connection between the Church-Turing Thesis and computational theories of physical systems, (2) differentiates merely satisfying a computational function from true computation, (...)
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  48. Colin Hales (2011). On the Status of Computationalism as a Law of Nature. International Journal of Machine Consciousness 3 (01):55-89.score: 9.0
  49. Stevan Harnad (1992). Virtual Symposium on Virtual Mind. Minds and Machines 2 (3):217-238.score: 9.0
    When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaninguful conversation). These higher levels of interpretability are called ‘virtual’ systems. If such a virtual system is interpretable as if it had a mind, is such a ‘virtual (...)
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  50. J. R. Kazez (1994). Computationalism and the Causal Role of Content. Philosophical Studies 75 (3):231-60.score: 9.0
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