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  1. Kenneth Aizawa, It is Not All About Turing-Equivalent Computation.
    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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  2. 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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  3. 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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  4. Ian O. Angell (2010). Science's First Mistake: Delusions in Pursuit of Theory. Bloomsbury Academic.
    because whenever an observer observes, he creates a contingent distinction between what is observed and what is by necessity left unobserved. ...
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  5. 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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  6. 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.
  7. 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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  8. Jeffrey A. Barrett & Wayne Aitken, On the Physical Possibility of Ordinal Computation (Draft).
    α-recursion lifts classical recursion theory from the first transfinite ordinal ω to an arbitrary admissible ordinal α [10]. Idealized computational models for α-recursion analogous to Turing machine models for classical recursion have been proposed and studied [4] and [5] and are applicable in computational approaches to the foundations of logic and mathematics [8]. They also provide a natural setting for modeling extensions of the algorithmic logic described in [1] and [2]. On such models, an α-Turing machine can complete a θ-step (...)
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  9. 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 with their (...)
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  10. 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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  11. 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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  12. L. Birnbaum (1991). Rigor Mortis: A Response to Nilsson's 'Logic and Artificial Intelligence'. Artificial Intelligence 47:57-78.
  13. 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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  14. 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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  15. Margaret Boden (2006). Of Islands and Interactions. Journal of Consciousness Studies 13 (5):53-63.
    John Ziman-- the much-missed-- reminds us that 'no man is an island', and takes us to task for working from an individualistic theoretical base. That 'us' includes nearly all social scientists, and most Anglo-American philosophers too. For sure, it includes cognitive scientists, who theorize people in terms of concepts drawn from cybernetics and/or artificial intelligence. (I'll use the term 'computational concepts' broadly, to cover both types.) Indeed, it's a common complaint that cognitive science is overly individualistic.
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  16. 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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  17. 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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  18. 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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  19. Joanna J. Bryson (2002). Language Isn't Quite That Special. Behavioral and Brain Sciences 25 (6):679-680.
    Language isn't the only way to cross modules, nor is it the only module with access to both input and output. Minds don't generally work across modules because this leads to combinatorial explosion in search and planning. Language is special in being a good vector for mimetics, so it becomes associated with useful cross-module concepts we acquire culturally. Further, language is indexical, so it facilitates computationally expensive operations.
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  20. David J. Buller (1993). Confirmation and the Computational Paradigm (Or: Why Do You Think They Call Itartificial Intelligence?). [REVIEW] Minds and Machines 3 (2):155-181.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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  21. Stephen Andrew Butterfill (2007). What Are Modules and What is Their Role in Development? Mind and Language 22 (4):450–473.
    Modules are widely held to play a central role in explaining mental development and in accounts of the mind generally. But there is much disagreement about what modules are, which shows that we do not adequately understand modularity. This paper outlines a Fodoresque approach to understanding one type of modularity. It suggests that we can distinguish modular from nonmodular cognition by reference to the kinds of process involved, and that modular cognition differs from nonmodular forms of cognition in being a (...)
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  22. H. C. (2003). Notes on Landauer's Principle, Reversible Computation, and Maxwell's Demon. Studies in History and Philosophy of Science Part B 34 (3):501-510.
    Landauer's principle, often regarded as the basic principle of the thermodynamics of information processing, holds that any logically irreversible manipulation of information, such as the erasure of a bit or the merging of two computation paths, must be accompanied by a corresponding entropy increase in non-information-bearing degrees of freedom of the information-processing apparatus or its environment. Conversely, it is generally accepted that any logically reversible transformation of information can in principle be accomplished by an appropriate physical mechanism operating in a (...)
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  23. Nick Chater (2009). Rational Models of Conditioning. Behavioral and Brain Sciences 32 (2):204-205.
    Mitchell et al. argue that conditioning phenomena may be better explained by high-level, rational processes, rather than by non-cognitive associative mechanisms. This commentary argues that this viewpoint is compatible with neuroscientific data, may extend to nonhuman animals, and casts computational models of reinforcement learning in a new light.
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  24. Nick Chater & Martin Pickering (1997). Two Projects for Understanding the Mind: A Response to Morris and Richardson. [REVIEW] Minds and Machines 7 (4):553-569.
    We respond to Morris and Richardson's (1995) claim that Pickering and Chater's (1995) arguments about the lack of a relation between cognitive science and folk psychology are flawed. We note that possible controversies about the appropriate uses for the two terms do not affect our arguments. We then address their claim that computational explanation of knowledge-rich processes has proved possible in the domains of problem solving, scientific discovery, and reasoning. We argue that, in all cases, computational explanation is only (...)
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  25. Tony Chemero, Representation and “Reliable Presence”.
    Summary. The “New Computationalism” that is the subject of this special issue requires an appropriate notion of representation. The purpose of this essay is to recommend such a notion. In cognitive science generally, there have been two primary candidates for spelling out what it is to be a representation: teleological accounts and accounts based on “decoupling.” I argue that the latter sort of account has two serious problems. First, it is multiply ambiguous; second, it is revisionist and alienating to many (...)
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  26. Glyn Humphreys Christian Olivers, Dietmar Heinke, Hermann M.Ü & Ller (1999). Close Interactions Between “When” and “Where” in Saccade Target Selection: Multiple Saliency and Distractor Effects. Behavioral and Brain Sciences 22 (4):693-694.
    A model of when and where saccades are made necessarily incorporates a model of the “When” and “Where” of target selection. We suggest that the framework proposed by Findlay & Walker does not specify sufficiently how (and by what means) selection processes contribute to the spatial and temporal determinants of saccade generation. Examples from across-trial priming in visual search and from the inhibition of temporally segmented distractors show linkage between the processes involved in computing when and where selection operates, so (...)
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  27. W. J. Clancey (forthcoming). The Biology of Consciousness: Comparative Review of Israel Rosenfield, the Strange, Familiar, and Forgotten: An Anatomy of Consciousness and Gerald M. Edelman, Bright Air, Brilliant Fire: On the Matter of the Mind. .
    For many years, most AI researchers and cognitive scientists have reserved the topic of consciousness for after dinner conversation. Like "intuition," the idea of consciousness appeared to be too vague or general to be a good starting place for understanding cognition. Work on narrowly-defined problems in specialized domains such as medicine and manufacturing focused our concerns on the nature of representation, memory, strategies for problem-solving, and learning. Some writers, notably Ornstein(1972) and Hofstadter (1979), continued to explore the ideas, but implications (...)
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  28. Alex Clark & Shalom Lappin, Unsupervised Learning and Grammar Induction.
    In this chapter we consider unsupervised learning from two perspectives. First, we briefly look at its advantages and disadvantages as an engineering technique applied to large corpora in natural language processing. While supervised learning generally achieves greater accuracy with less data, unsupervised learning offers significant savings in the intensive labour required for annotating text. Second, we discuss the possible relevance of unsupervised learning to debates on the cognitive basis of human language acquisition. In this context we explore the implications of (...)
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  29. Andy Clark (2008). The Frozen Cyborg: A Reply to Selinger and Engström. [REVIEW] Phenomenology and the Cognitive Sciences 7 (3):343-346.
    Selinger and Engstrom, A moratorium on cyborgs: Computation, cognition and commerce, 2008 (this issue) urge upon us a moratorium on ‘cyborg discourse’. But the argument underestimates the richness and complexity of our ongoing communal explorations. It leans on a somewhat outdated version of the machine metaphor (exemplified perhaps by a frozen 1970’s Cyborg). The modern cyborg, informed by an evolving computational model of mind, can play a positive role in the critical discussions that Selinger and Engstrom seek.
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  30. Axel Cleeremans, Please Visit the NEW Wiki Website: Http://Grey.Colorado.Edu/CompCogNeuro/Index.Php/CECN.
    The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprised of networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons and the neural networks (...)
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  31. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  32. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  33. Timothy Colburn & Gary Shute (2011). Decoupling as a Fundamental Value of Computer Science. Minds and Machines 21 (2):241-259.
    Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm shifts in both natural and computer science (...)
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  34. John Collier, Information, Causation and Computation.
    Causation can be understood as a computational process once we understand causation in informational terms. I argue that if we see processes as information channels, then causal processes are most readily interpreted as the transfer of information from one state to another. This directly implies that the later state is a computation from the earlier state, given causal laws, which can also be interpreted computationally. This approach unifies the ideas of causation and computation.
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  35. John Collier, Some Limitations of Behaviorist and Computational Models of Mind.
    The purpose of this paper is to describe some limitations on scientific behaviorist and computational models of the mind. These limitations stem from the inability of either model to account for the integration of experience and behavior. Behaviorism fails to give an adequate account of felt experience, whereas the computational model cannot account for the integration of our behavior with the world. Both approaches attempt to deal with their limitations by denying that the domain outside their limits is a part (...)
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  36. Richard Cooper & Bradley Franks (1993). Interruptibility as a Constraint on Hybrid Systems. Minds and Machines 3 (1):73-96.
    It is widely mooted that a plausible computational cognitive model should involve both symbolic and connectionist components. However, sound principles for combining these components within a hybrid system are currently lacking; the design of such systems is oftenad hoc. In an attempt to ameliorate this we provide a framework of types of hybrid systems and constraints therein, within which to explore the issues. In particular, we suggest the use of system independent constraints, whose source lies in general considerations about cognitive (...)
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  37. 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.
    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 aims. Zenon (...)
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  38. Erica Cosentino & Francesco Ferretti (2014). Communication as Navigation: A New Role for Consciousness in Language. Topoi 33 (1):263-274.
    Classical cognitive science has been characterized by an association with the computational theory of mind. Although this association has produced highly significant results, it has also limited the scope of scientific psychology. In this paper, we analyse the limits of the specific kind of computational model represented by the Chomskian-Fodorian tradition in the study of mind and language. In our opinion, the adhesion to the principle of formality imposed by this specific computational model has motivated the exclusion of consciousness in (...)
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  39. Rick Dale (2012). Integrating and Extending the Distributed Approach in Cognitive Science. Interaction Studies 13 (1):125-138.
    This special issue is a refreshing contrast to the intuitively influential notion of language as an internal system. This internal approach to language is going strong in some segments of the cognitive sciences. As an assumption, internalism drives much empirical work on language, and it is the basis of prominent theories of language – its nature (e.g. an internalised computational system), its evolution (e.g. a single still-unknown mutation), and its function (e.g. thinking, not communication). -/- Radical fundamentalist versions of these (...)
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  40. Frédéric Dandurand & Thomas R. Shultz (2002). Modeling Consciousness. Behavioral and Brain Sciences 25 (3):334-334.
    Perruchet & Vinter do not fully resolve issues about the role of consciousness and the unconscious in cognition and learning, and it is doubtful that consciousness has been computationally implemented. The cascade-correlation (CC) connectionist model develops high-order feature detectors as it learns a problem. We describe an extension, knowledge-based cascade-correlation (KBCC), that uses knowledge to learn in a hierarchical fashion.
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  41. Lindley Darden (2002). Strategies for Discovering Mechanisms: Schema Instantiation, Modular Subassembly, Forward/Backward Chaining. Proceedings of the Philosophy of Science Association 2002 (3):S354-S365.
  42. Lindley Darden (1998). Anomaly-Driven Theory Redesign: Computational Philosophy of Science Experiments. In T. W. Bynum & J. Moor (eds.), The Digital Phoenix. Cambridge: Blackwell. 62--78.
  43. Jerry DeJohn & Eric Dietrich, Subvert the Dominant Paradigm!
    We again press the case for computationalism by considering the latest in illconceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anticomputationalist collection.
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  44. Eric Dietrich (2002). Subvert the Dominant Paradigm! J. Of Experimental and Theoretical AI.
    We again press the case for computationalism by considering the latest in ill- conceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anti- computationalist collection.
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  45. Eric Dietrich (2002). Subvert the Dominant Paradigm! [REVIEW] J. Of Experimental and Theoretical AI.
    We again press the case for computationalism by considering the latest in illconceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anticomputationalist collection.
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  46. Eric Dietrich (1996). AI, Situatedness, Creativity, and Intelligence; or the Evolution of the Little Hearing Bones. J. Of Experimental and Theoretical AI 8 (1):1-6.
    Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like to propose a new metaphor to help us understand intelligence. Of course, whether the metaphor is any good or not depends on whether it actually does help us. (What I am going to propose is not something opposed to computationalism -- the hypothesis that cognition is computation. Noncomputational metaphors are in vogue these days, (...)
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  47. Eric Dietrich (1993). The Ubiquity of Computation. Think (Defunct) 2 (June):27-29.
    For many years now, Harnad has argued that transduction is special among cognitive capacities -- special enough to block Searle's Chinese Room Argument. His arguments (as well as Searle's) have been important and useful, but not correct, it seems to me. Their arguments have provided the modern impetus for getting clear about computationalism and the nature of computing. This task has proven to be quite difficult. Which is simply to say that dealing with Harnad's arguments (as well as Searle's) has (...)
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  48. Gordana Dodig Crnkovic (2010). Constructivist Research and Info-Computational Knowledge Generation. In Lorenzo Magnani, Walter Carnielli & Claudio Pizzi (eds.), MODEL-BASED REASONING IN SCIENCE AND TECHNOLOGY. Springer.
    It is usual when writing on research methodology in dissertations and thesis work within Software Engineering to refer to Empirical Methods, Grounded Theory and Action Research. Analysis of Constructive Research Methods which are fundamental for all knowledge production and especially for concept formation, modeling and the use of artifacts is seldom given, so the relevant first-hand knowledge is missing. This article argues for introducing of the analysis of Constructive Research Methods, as crucial for understanding of research process and knowledge production. (...)
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  49. Georg Dorffner (1998). Flexible Features, Connectionism, and Computational Learning Theory. Behavioral and Brain Sciences 21 (1):24-25.
    This commentary is an elaboration on Schyns, Goldstone & Thibaut's proposal for flexible features in categorization in the light of three areas not explicitly discussed by the authors: connectionist models of categorization, computational learning theory, and constructivist theories of the mind. In general, the authors' proposal is strongly supported, paving the way for model extensions and for interesting novel cognitive research. Nor is the authors' proposal incompatible with theories positing some fixed set of features.
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  50. Bart D’Hooghe & Jaroslaw Pykacz (2004). Quantum Mechanics and Computation. Foundations of Science 9 (4):387-404.
    In quantum computation non classical features such as superposition states and entanglement are used to solve problems in new ways, impossible on classical digital computers.We illustrate by Deutsch algorithm how a quantum computer can use superposition states to outperform any classical computer. We comment on the view of a quantum computer as a massive parallel computer and recall Amdahls law for a classical parallel computer. We argue that the view on quantum computation as a massive parallel computation disregards the presence (...)
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