Results for 'connectionism'

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  1.  67
    Connectionism and the Philosophy of Psychology.Terence Horgan & John Tienson - 1996 - MIT Press.
    In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition.
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  2.  2
    Connectionism and the Mind.William Bechtel & Adele Abrahamsen - 1991 - Wiley-Blackwell.
    Something remarkable is happening in the cognitive sciences. After a quarter of a century of cognitive models that were inspired by the metaphor of the digital computer, the newest cognitive models are inspired by the properties of the brain itself. Variously referred to as connectionist, parallel distributed processing, or neutral network models, they explore the idea that complex intellectual operations can be carried out by large networks of simple, neuron-like units. The units themselves are identical, very low-level and 'stupid'. Intelligent (...)
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  3. Connectionism and Cognitive Architecture: A Critical Analysis.Jerry A. Fodor & Zenon W. Pylyshyn - 1988 - Cognition 28 (1-2):3-71.
    This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...)
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  4. Connectionism: Debates on Psychological Explanation.Cynthia MacDonald & Graham MacDonald (eds.) - 1991 - Blackwell.
    This volume provides an introduction to and review of key contemporary debates concerning connectionism, and the nature of explanation and methodology in cognitive psychology. The first debate centers on the question of whether human cognition is best modeled by classical or by connectionist architectures. The second centres on the question of the compatibility between folk, or commonsense, psychological explanation and explanations based on connectionist models of cognition. Each of the two sections includes a classic reading along with important responses, (...)
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  5. Connectionist Networks Do Not Model Brain Function.Roy Eagleson & David P. Carey - 1992 - Behavioral and Brain Sciences 15 (4):734-735.
     
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  6.  38
    Connectionism and the Philosophy of Mind.Terence E. Horgan & John L. Tienson (eds.) - 1991 - Kluwer Academic Publishers.
    "A third of the papers in this volume originated at the 1987 Spindel Conference ... at Memphis State University"--Pref.
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  7.  22
    Connectionism and the Mind: An Introduction to Parallel Processing in Networks.David Pickles, William Bechtel & Adele Abrahamson - 1992 - Philosophical Quarterly 42 (166):101.
  8. Connectionism and the Problem of Systematicity: Why Smolensky's Solution Doesn't Work.Jerry Fodor & Brian P. McLaughlin - 1990 - Cognition 35 (2):183-205.
  9. A Connectionist Theory of Phenomenal Experience.Jonathan Opie & Gerard O'Brien - 1999 - Behavioral and Brain Sciences 22 (1):127-148.
    When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches _vehicle_ and _process_ theories of consciousness, respectively. However, while there may be space (...)
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  10. Concepts, Connectionism, and the Language of Thought.Martin Davies - 1991 - In W Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 485-503.
    The aim of this paper is to demonstrate a _prima facie_ tension between our commonsense conception of ourselves as thinkers and the connectionist programme for modelling cognitive processes. The language of thought hypothesis plays a pivotal role. The connectionist paradigm is opposed to the language of thought; and there is an argument for the language of thought that draws on features of the commonsense scheme of thoughts, concepts, and inference. Most of the paper (Sections 3-7) is taken up with the (...)
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  11.  32
    What Connectionist Models Learn: Learning and Representation in Connectionist Networks.Stephen José Hanson & David J. Burr - 1990 - Behavioral and Brain Sciences 13 (3):471-489.
    Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...)
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  12. Connectionist Modelling in Psychology: A Localist Manifesto.Mike Page - 2000 - Behavioral and Brain Sciences 23 (4):443-467.
    Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult for fully (...)
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  13.  35
    Connectionist Models and Their Properties.J. A. Feldman & D. H. Ballard - 1982 - Cognitive Science 6 (3):205-254.
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  14.  25
    A Connectionist Model of English Past Tense and Plural Morphology.Kim Plunkett & Patrick Juola - 1999 - Cognitive Science 23 (4):463-490.
    The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns and (...)
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  15.  97
    Philosophy and Connectionist Theory.William Ramsey, Stephen P. Stich & D. M. Rumelhart (eds.) - 1991 - Lawrence Erlbaum.
    The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and higher visual processing -- as rule-governed symbol manipulation. However, this situation has changed dramatically over the last half dozen years. In that period there has been an enormous shift of attention toward connectionist models of cognition that are (...)
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  16. Connectionism and Compositionality: Why Fodor and Pylyshyn Were Wrong.David J. Chalmers - 1993 - Philosophical Psychology 6 (3):305-319.
    This paper offers both a theoretical and an experimental perspective on the relationship between connectionist and Classical (symbol-processing) models. Firstly, a serious flaw in Fodor and Pylyshyn’s argument against connectionism is pointed out: if, in fact, a part of their argument is valid, then it establishes a conclusion quite different from that which they intend, a conclusion which is demonstrably false. The source of this flaw is traced to an underestimation of the differences between localist and distributed representation. It (...)
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  17. The Connectionist Construction of Concepts.Adrian Cussins - 1990 - In Margaret A. Boden (ed.), The Philosophy of Artificial Intelligence. Oxford University Press.
    The character of computational modelling of cognition depends on an underlying theory of representation. Classical cognitive science has exploited the syntax/semantics theory of representation that derives from logic. But this has had the consequence that the kind of psychological explanation supported by classical cognitive science is " _conceptualist_: " psychological phenomena are modelled in terms of relations that hold between concepts, and between the sensors/effectors and concepts. This kind of explanation is inappropriate for the Proper Treatment of Connectionism.
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  18. Connectionism, Eliminativism, and the Future of Folk Psychology.William Ramsey, Stephen P. Stich & J. Garon - 1991 - In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophical Perspectives. Lawrence Erlbaum. pp. 499-533.
  19.  5
    Connectionism and the Mind: Parallel Processing, Dynamics, and Evolution in Networks.William Bechtel & Adele Abrahamsen - 2002 - Wiley-Blackwell.
    Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdf.
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  20. Connectionism, Modularity, and Tacit Knowledge.Martin Davies - 1989 - British Journal for the Philosophy of Science 40 (December):541-55.
    In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the processing (...)
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  21.  9
    Connectionism and Human Learning: Critique of Gluck and Bower.David R. Shanks - 1990 - Journal of Experimental Psychology: General 119 (1):101-104.
  22.  17
    A Connectionist Approach to Word Reading and Acquired Dyslexia: Extension to Sequential Processing.David C. Plaut - 1999 - Cognitive Science 23 (4):543-568.
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  23. Associative Engines: Connectionism, Concepts, and Representational Change.Andy Clark - 1993 - MIT Press.
    As Ruben notes, the macrostrategy can allow that the distinction may also be drawn at some micro level, but it insists that descent to the micro level is ...
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  24. Connectionism, Constituency and the Language of Thought.Paul Smolensky - 1991 - In Barry M. Loewer & Georges Rey (eds.), Meaning in Mind: Fodor and His Critics. Blackwell.
  25.  38
    Connectionist Models and Linguistic Theory: Investigations of Stress Systems in Language.Prahlad Gupta & David S. Touretzky - 1994 - Cognitive Science 18 (1):1-50.
  26. Connectionism, Analogicity and Mental Content.Gerard O'Brien - 1998 - Acta Analytica 13:111-31.
    In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed (...)
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  27. Philosophy and Memory Traces: Descartes to Connectionism.John Sutton - 1998 - Cambridge University Press.
    Philosophy and Memory Traces defends two theories of autobiographical memory. One is a bewildering historical view of memories as dynamic patterns in fleeting animal spirits, nervous fluids which rummaged through the pores of brain and body. The other is new connectionism, in which memories are 'stored' only superpositionally, and reconstructed rather than reproduced. Both models, argues John Sutton, depart from static archival metaphors by employing distributed representation, which brings interference and confusion between memory traces. Both raise urgent issues about (...)
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  28. Connectionism, Generalization, and Propositional Attitudes: A Catalogue of Challenging Issues.John A. Barnden - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 149--178.
     
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  29.  46
    Connectionist Natural Language Processing: The State of the Art.Morten H. Christiansen & Nick Chater - 1999 - Cognitive Science 23 (4):417-437.
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  30.  83
    Connectionism and Epistemology: Goldman on Winner-Take-All Networks.Paul Thagard - 1989 - Philosophia 19 (2-3):189-196.
    This paper examines Alvin Goldman's discussion of acceptance and uncertainty in chapter 15 of his book, Epistemology and Cognition. Goldman discusses how acceptance and rejection of beliefs might be understood in terms of "winner-take-all" connectionist networks. The paper answers some of the questions he raises in his epistemic evaluation of connectionist programs. The major tool for doing this is a connectionist model of explanatory coherence judgments (Thagard, Behavioral and Brain Sciences, 1989). Finally, there is a discussion of problems for Goldman's (...)
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  31. Language of Thought: The Connectionist Contribution.Murat Aydede - 1997 - Minds and Machines 7 (1):57-101.
    Fodor and Pylyshyn's critique of connectionism has posed a challenge to connectionists: Adequately explain such nomological regularities as systematicity and productivity without postulating a "language of thought" (LOT). Some connectionists like Smolensky took the challenge very seriously, and attempted to meet it by developing models that were supposed to be non-classical. At the core of these attempts lies the claim that connectionist models can provide a representational system with a combinatorial syntax and processes sensitive to syntactic structure. They are (...)
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  32.  53
    Connectionism and Rules and Representation Systems: Are They Compatible?William Bechtel - 1988 - Philosophical Psychology 1 (1):5-16.
    The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not totally (...)
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  33. Connectionism, Systematicity, and the Frame Problem.W. F. G. Haselager & J. F. H. Van Rappard - 1998 - Minds and Machines 8 (2):161-179.
    This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this (...)
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  34.  35
    Connectionism and Cognition: Why Fodor and Pylyshyn Are Wrong.James H. Fetzer - 1992 - In A. Clark & Ronald Lutz (eds.), Connectionism in Context. Springer Verlag. pp. 305-319.
  35.  7
    Does Connectionism Suffice?Steven W. Zucker - 1985 - Behavioral and Brain Sciences 8 (2):301-302.
  36. Connectionism, Competence and Explanation.Andy Clark - 1990 - British Journal for the Philosophy of Science 41 (June):195-222.
    A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some (...)
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  37.  96
    Connectionism and the Philosophical Foundations of Cognitive Science.Terence Horgan - 1997 - Metaphilosophy 28 (1-2):1-30.
    This is an overview of recent philosophical discussion about connectionism and the foundations of cognitive science. Connectionist modeling in cognitive science is described. Three broad conceptions of the mind are characterized, and their comparative strengths and weaknesses are discussed: the classical computation conception in cognitive science; a popular foundational interpretation of connectionism that John Tienson and I call “non‐sentential computationalism”; and an alternative interpretation of connectionism we call “dynamical cognition.” Also discussed are two recent philosophical attempts to (...)
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  38. Connectionism and the Fate of Folk Psychology: A Reply to Ramsey, Stich and Garon.Malcolm Forster & Eric Saidel - 1994 - Philosophical Psychology 7 (4):437 – 452.
    Ramsey, Stick and Garon (1991) argue that if the correct theory of mind is some parallel distributed processing theory, then folk psychology must be false. Their idea is that if the nodes and connections that encode one representation are causally active then all representations encoded by the same set of nodes and connections are also causally active. We present a clear, and concrete, counterexample to RSG's argument. In conclusion, we suggest that folk psychology and connectionism are best understood as (...)
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  39.  21
    Are Connectionist Models Cognitive?Benny Shanon - 1992 - Philosophical Psychology 5 (3):235-255.
    In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed (...)
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  40.  27
    Connectionist and Memory‐Array Models of Artificial Grammar Learning.Zoltan Dienes - 1992 - Cognitive Science 16 (1):41-79.
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  41.  24
    A Connectionist Approach to Knowledge Representation and Limited Inference.Lokendra Shastri - 1988 - Cognitive Science 12 (3):331-392.
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  42.  11
    Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions.Roger Ratcliff - 1990 - Psychological Review 97 (2):285-308.
  43.  40
    Natural Ethical Facts: Evolution, Connectionism, and Moral Cognition.William D. Casebeer - 2003 - Bradford.
    In Natural Ethical Facts William Casebeer argues that we can articulate a fully naturalized ethical theory using concepts from evolutionary biology and cognitive science, and that we can study moral cognition just as we study other forms of cognition. His goal is to show that we have "softly fixed" human natures, that these natures are evolved, and that our lives go well or badly depending on how we satisfy the functional demands of these natures. Natural Ethical Facts is a comprehensive (...)
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  44.  29
    CAB: Connectionist Analogy Builder.Levi B. Larkey & Bradley C. Love - 2003 - Cognitive Science 27 (5):781-794.
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  45. Connectionism.Kim Sterelny - 1990 - In The Representational Theory of Mind. Blackwell.
     
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  46. Connectionist Learning Procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
  47.  83
    Connectionism, Classical Cognitive Science and Experimental Psychology.Mike Oaksford, Nick Chater & Keith Stenning - 1990 - AI and Society 4 (1):73-90.
    Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model (...)
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  48.  58
    Connectionism, Explicit Rules, and Symbolic Manipulation.Robert F. Hadley - 1993 - Minds and Machines 3 (2):183-200.
    At present, the prevailing Connectionist methodology forrepresenting rules is toimplicitly embody rules in neurally-wired networks. That is, the methodology adopts the stance that rules must either be hard-wired or trained into neural structures, rather than represented via explicit symbolic structures. Even recent attempts to implementproduction systems within connectionist networks have assumed that condition-action rules (or rule schema) are to be embodied in thestructure of individual networks. Such networks must be grown or trained over a significant span of time. However, arguments (...)
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  49. Connectionism and the Problem of Systematicity (Continued): Why Smolensky's Solution Still Doesn't Work.Jerry A. Fodor - 1997 - Cognition 62 (1):109-19.
  50. Do Connectionist Representations Earn Their Explanatory Keep?William Ramsey - 1997 - Mind and Language 12 (1):34-66.
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