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  1. Robert P. Abelson (1980). Searle's Argument is Just a Set of Chinese Symbols. Behavioral and Brain Sciences 3 (3):424.
  2. Joana Acha & Manuel Perea (2008). The Effect of Neighborhood Frequency in Reading: Evidence with Transposed-Letter Neighbors. Cognition 108 (1):290-300.
  3. A. David Redish Adam Johnson, André A. Fenton, Cliff Kentros (2009). Looking for Cognition in the Structure Within the Noise. Trends in Cognitive Sciences 13 (2):55.
  4. Philip E. Agre (2002). The Practical Logic of Computer Work. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press
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  5. Kenneth Aizawa (2010). Computation in Cognitive Science: It is Not All About Turing-Equivalent Computation. Studies in History and Philosophy of Science Part A 41 (3):227-236.
    One account of the history of computation might begin in the 1930's with some of the work of Alonzo Church, Alan Turing, and Emil Post. One might say that this is where something like the core concept of computation was first formally articulated. Here were the first attempts to formalize an informal notion of an algorithm or effective procedure by which a mathematician might decide one or another logico-mathematical question. As each of these formalisms was shown to compute the same (...)
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  6. P. B. Andersen (1999). Does Mind Matter?('Symbols, Computation, and Intentionality'by Steven W. Horst). Semiotica 123 (3-4):327-342.
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  7. James A. Anderson (2003). Arithmetic on a Parallel Computer: Perception Versus Logic. [REVIEW] Brain and Mind 4 (2):169-188.
    This article discusses the properties of a controllable, flexible, hybrid parallel computing architecture that potentially merges pattern recognition and arithmetic. Humans perform integer arithmetic in a fundamentally different way than logic-based computers. Even though the human approach to arithmetic is both slow and inaccurate it can have substantial advantages when useful approximations ( intuition ) are more valuable than high precision. Such a computational strategy may be particularly useful when computers based on nanocomponents become feasible because it offers a way (...)
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  8. John R. Anderson & Christian Lebiere (2003). The Newell Test for a Theory of Cognition. Behavioral and Brain Sciences 26 (5):587-601.
    Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories (...)
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  9. John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder (1998). ACT-R: A Higher-Level Account of Processing Capacity. Behavioral and Brain Sciences 21 (6):831-832.
    We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.
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  10. Michael L. Anderson (2003). Embodied Cognition: A Field Guide. Artificial Intelligence 149 (1):91-130.
    The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus.
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  11. Johannes Andres & Rainer Mausfeld (2008). Structural Description and Qualitative Content in Perception Theory. Consciousness & Cognition 17 (1):307-311.
  12. Radical Answers (1991). Of Mental Representations. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer 9--355.
  13. Louise M. Antony (1997). Feeling Fine About the Mind. Philosophy and Phenomenological Research 57 (2):381-87.
    The article presents a critique of John Searle's attack on computationalist theories of mind in his recent book, The Rediscovery of the Mind. Searle is guilty of caricaturing his opponents, and of ignoring their arguments. Moreover, his own positive theory of mind, which he claims "takes account of" subjectivity, turns out to offer no discernible advantages over the views he rejects.
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  14. Eoghan Mac Aogáin (1999). Information and Appearance. Behavioral and Brain Sciences 22 (1):159-160.
    O'Brien & Opie's connectionist interpretation of “vehicle,” “process,” and “explicit representation” depends heavily on the notions of “information” and “information processing” that underlie the classic account. When the “cognitivist” assumptions, shared by both accounts, are removed, the connectionist versus classic contrast appears to be between behavioral and linguistic accounts.
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  15. Peter M. Asaro (2001). Hans Moravec, Robot. Mere Machine to Transcendent Mind, New York, NY: Oxford University Press, Inc., 1999, IX + 227 Pp., $25.00 (Cloth), ISBN 0-19-511630-. [REVIEW] Minds and Machines 11 (1):143-147.
  16. Iep Author, Computational Theory of Mind.
    The Computational Theory of Mind The Computational Theory of Mind (CTM) claims that the mind is a computer, so the theory is also known as computationalism. It is generally assumed that CTM is the main working hypothesis of cognitive science. CTM is often understood as a specific variant of the Representational Theory of Mind (RTM), […].
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  17. Murat Aydede (2005). Computation and Functionalism: Syntactic Theory of Mind Revisited. In Gurol Irzik & Guven Guzeldere (eds.), Boston Studies in the History and Philosophy of Science. Springer
    I argue that Stich's Syntactic Theory of Mind (STM) and a naturalistic narrow content functionalism run on a Language of Though story have the same exact structure. I elaborate on the argument that narrow content functionalism is either irremediably holistic in a rather destructive sense, or else doesn't have the resources for individuating contents interpersonally. So I show that, contrary to his own advertisement, Stich's STM has exactly the same problems (like holism, vagueness, observer-relativity, etc.) that he claims plague content-based (...)
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  18. Murat Aydede (2005). Computation and Functionalism: Syntactic Theory of Mind Revisited. In G. Irzik & G. Guezeldere (eds.), Turkish Studies in the History and Philosophy of Science. Springer
    There is a thesis often aired by some philosophers of psychology that syntax is all we need and there is no need to advert to intentional/semantic properties of symbols for purposes of psychological explanation. Indeed, the worry has been present since the first explicit articulation of so-called Computational Theory of Mind (CTM). Even Fodor, who has been the most ardent defender of the Language of Thought Hypoth- esis (LOTH) (which requires the CTM), has raised worries about its apparent consequences. The (...)
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  19. Murat Aydede (2000). Computation and Intentional Psychology. Dialogue 39 (2):365-379.
    The relation between computational and intentional psychology has always been a vexing issue. The worry is that if mental processes are computational, then these processes, which are defined over symbols, are sensitive solely to the non-semantic properties of symbols. If so, perhaps psychology could dispense with adverting in its laws to intentional/semantic properties of symbols. Stich, as is well-known, has made a great deal out of this tension and argued for a purely "syntactic" psychology by driving a wedge between a (...)
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  20. Katalin Balog (2009). Jerry Fodor on Non-Conceptual Content. Synthese 167 (3):311 - 320.
    Proponents of non-conceptual content have recruited it for various philosophical jobs. Some epistemologists have suggested that it may play the role of “the given” that Sellars is supposed to have exorcised from philosophy. Some philosophers of mind (e.g., Dretske) have suggested that it plays an important role in the project of naturalizing semantics as a kind of halfway between merely information bearing and possessing conceptual content. Here I will focus on a recent proposal by Jerry Fodor. In a recent paper (...)
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  21. 1Imre Balogh, Brian Beakley, Paul Churchland, Michael Gorman, Stevan Harnad, David Mertz, H. H. Pattee, William Ramsey, John Ringen, Georg Schwarz, Brian Slator, Alan Strudler & Charles Wallis (1990). Responses to 'Computationalism'. Social Epistemology 4 (2):155 – 199.
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  22. Eleanor Olds Batchelder (1998). Can a Computer Really Model Cognition? A Case Study of Six Computational Models of Infant Word Discovery. In M. A. Gernsbacher & S. J. Derry (eds.), Proceedings of the 20th Annual Conference of the Cognitive Science Society. Lawrence Erlbaum Associates
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  23. Bert Baumgaertner (2012). Vagueness Intuitions and the Mobility of Cognitive Sortals. Minds and Machines 22 (3):213-234.
    One feature of vague predicates is that, as far as appearances go, they lack sharp application boundaries. I argue that we would not be able to locate boundaries even if vague predicates had sharp boundaries. I do so by developing an idealized cognitive model of a categorization faculty which has mobile and dynamic sortals (`classes', `concepts' or `categories') and formally prove that the degree of precision with which boundaries of such sortals can be located is inversely constrained by their flexibility. (...)
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  24. William Bechtel (1998). Dynamicists Versus Computationalists: Whither Mechanists? Behavioral and Brain Sciences 21 (5):629-629.
    Van Gelder's characterization of the differences between the dynamical and computational hypotheses, in terms of the contrast between change versus state and geometry versus structure, suggests that the dynamical approach is also at odds with classical mechanism. Dynamical and mechanistic approaches are in fact allies: mechanism can identify components whose properties define the variables that are related in dynamical analyses.
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  25. Randall D. Beer (1998). Framing the Debate Between Computational and Dynamical Approaches to Cognitive Science. Behavioral and Brain Sciences 21 (5):630-630.
    van Gelder argues that computational and dynamical systems are mathematically distinct kinds of systems. Although there are real experimental and theoretical differences between adopting a computational or dynamical perspective on cognition, and the dynamical approach has much to recommend it, the debate cannot be framed this rigorously. Instead, what is needed is careful study of concrete models to improve our intuitions.
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  26. Francesco Berto & Jacopo Tagliabue (2012). Cellular Automata. Stanford Encyclopedia of Philosophy.
    Cellular automata (henceforth: CA) are discrete, abstract computational systems that have proved useful both as general models of complexity and as more specific representations of non-linear dynamics in a variety of scientific fields. Firstly, CA are (typically) spatially and temporally discrete: they are composed of a finite or denumerable set of homogeneous, simple units, the atoms or cells. At each time unit, the cells instantiate one of a finite set of states. They evolve in parallel at discrete time steps, following (...)
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  27. Mark H. Bickhard (1996). Troubles with Computationalism. In W. O'Donahue & Richard F. Kitchener (eds.), The Philosophy of Psychology. Sage Publications 173--183.
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  28. Lucio Biggiero, Organizations as Cognitive Systems is Knowledge an Emergent Property of Information Networks?
    The substitution of knowledge to information as the entity that organizations process and deliver raises a number of questions concerning the nature of knowledge. The dispute on the codifiability of tacit knowledge and that juxtaposing the epistemology of practice vs. the epistemology of possession can be better faced by revisiting two crucial debates. One concerns the nature of cognition and the other the famous mind-body problem. Cognition can be associated with the capability of manipulating symbols, like in the traditional computational (...)
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  29. Dorrit Billman & Justin Peterson (1989). Critique of Structural Analysis in Modeling Cognition: A Case Study of Jackendoff's Theory. Philosophical Psychology 2 (3):283 – 296.
    Modeling cognition by structural analysis of representation leads to systematic difficulties which are not resolvable. We analyse the merits and limits of a representation-based methodology to modeling cognition by treating Jackendoff's Consciousness and the Computational Mind as a good case study. We note the effects this choice of methodology has on the view of consciousness he proposes, as well as a more detailed consideration of the computational mind. The fundamental difficulty we identify is the conflict between the desire for modular (...)
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  30. Horst Bischof (1997). Locality, Modularity, and Computational Neural Networks. Behavioral and Brain Sciences 20 (3):516-517.
    There is a distinction between locality and modularity. These two terms have often been used interchangeably in the target article and commentary. Using this distinction we argue in favor of a modularity. In addition we also argue that both PDP-type networks and box-and-arrow models have their own strengths and pitfalls.
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  31. James Blackmon (2013). Searle's Wall. Erkenntnis 78 (1):109-117.
    In addition to his famous Chinese Room argument, John Searle has posed a more radical problem for views on which minds can be understood as programs. Even his wall, he claims, implements the WordStar program according to the standard definition of implementation because there is some ‘‘pattern of molecule movements’’ that is isomorphic to the formal structure of WordStar. Program implementation, Searle charges, is merely observer-relative and thus not an intrinsic feature of the world. I argue, first, that analogous charges (...)
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  32. Ned Block (1995). The Mind as the Software of the Brain. In Daniel N. Osherson, Lila Gleitman, Stephen M. Kosslyn, S. Smith & Saadya Sternberg (eds.), An Invitation to Cognitive Science. MIT Press 170--185.
    In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological.
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  33. Ned Block (1990). The Computer Model of Mind. In Daniel N. Osherson & Edward E. Smith (eds.), An Invitation to Cognitive Science. MIT Press
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  34. Giuseppe Boccignone & Roberto Cordeschi, Bayesian Models and Simulations in Cognitive Science. Workshop Models and Simulations 2, Tillburg, NL.
    Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.
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  35. Margaret Boden (2008). Mind as Machine: A History of Cognitive Science. OUP Oxford.
    The development of cognitive science is one of the most remarkable and fascinating intellectual achievements of the modern era. The quest to understand the mind is as old as recorded human thought; but the progress of modern science has offered new methods and techniques which have revolutionized this enquiry. Oxford University Press now presents a masterful history of cognitive science, told by one of its most eminent practitioners. -/- Cognitive science is the project of understanding the mind by modelling its (...)
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  36. Margaret A. Boden (1988). Computer Models On Mind: Computational Approaches In Theoretical Psychology. Cambridge University Press.
    What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For instance, should the mind (...)
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  37. Margaret A. Boden (1984). What is Computational Psychology? Proceedings of the Aristotelian Society 58:17-35.
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  38. Margaret A. Boden (1984). What is Computational Psychology, Part I. Proceedings of the Aristotelian Society 17:17-36.
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  39. Margaret A. Boden (1981). Minds And Mechanisms: Philosophical Psychology And Computational Models. Ithaca: Cornell University Press.
  40. Margaret A. Boden (1979). The Computational Metaphor in Psychology. In Philosophical Problems In Psychology. London: Methuen
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  41. C. F. Boyle (1994). Computation as an Intrinsic Property. Minds and Machines 4 (4):451-67.
    In an effort to uncover fundamental differences between computers and brains, this paper identifies computation with a particular kind of physical process, in contrast to interpreting the behaviors of physical systems as one or more abstract computations. That is, whether or not a system is computing depends on how those aspects of the system we consider to be informational physically cause change rather than on our capacity to describe its behaviors in computational terms. A physical framework based on the notion (...)
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  42. Pauli Brattico (2010). Recursion Hypothesis Considered as a Research Program for Cognitive Science. Minds and Machines 20 (2):213-241.
    Humans grasp discrete infinities within several cognitive domains, such as in language, thought, social cognition and tool-making. It is sometimes suggested that any such generative ability is based on a computational system processing hierarchical and recursive mental representations. One view concerning such generativity has been that each of the mind’s modules defining a cognitive domain implements its own recursive computational system. In this paper recent evidence to the contrary is reviewed and it is proposed that there is only one supramodal (...)
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  43. Manuel Bremer (2012). How Are Metarepresentations Built and Processed. Kriterion: Journal of Philosophy 26 (1):22-38.
  44. Selmer Bringsjord, Computationalism is Dead; Now What?
    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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  45. Selmer Bringsjord, The Impact of Computing on Epistemology: Knowing Gödel's Mind Through Computation.
    I know that those of you who know my mind know that I think I know that we can't know Gödel's mind through computation: ``The Impact : Failing to Know " If computationalism is false, observant philosophers willing to get their hands dirty should be able to find tell-tale signs today: automated theorem proving tomorrow (Eastern APA): robots as zombanimals But let's start with little 'ol me, and literary, not mathematical, creativity: Selmer (samples) vs. Brutus1 (samples again).
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  46. Selmer Bringsjord (2004). The Modal Argument for Hypercomputing Minds. Theoretical Computer Science 317.
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  47. Selmer Bringsjord (2001). In Computation, Parallel is Nothing, Physical Everything. Minds and Machines 11 (1):95-99.
    Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply (...)
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  48. Selmer Bringsjord (2000). Clarifying the Logic of Anti-Computationalism: Reply to Hauser. [REVIEW] Minds and Machines 10 (1):111-113.
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  49. Selmer Bringsjord (1998). Cognition is Not Computation: The Argument From Irreversibility. Synthese 113 (2):285-320.
    The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.
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  50. Selmer Bringsjord (1994). Computation, Among Other Things, is Beneath Us. Minds and Machines 4 (4):469-88.
    What''s computation? The received answer is that computation is a computer at work, and a computer at work is that which can be modelled as a Turing machine at work. Unfortunately, as John Searle has recently argued, and as others have agreed, the received answer appears to imply that AI and Cog Sci are a royal waste of time. The argument here is alarmingly simple: AI and Cog Sci (of the Strong sort, anyway) are committed to the view that cognition (...)
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