Results for 'Connectionism'

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  1.  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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  2. 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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  3.  71
    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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  4. Connectionism and the Problem of Systematicity: Why Smolensky's Solution Doesn't Work.Jerry Fodor & Brian P. McLaughlin - 1990 - Cognition 35 (2):183-205.
  5.  4
    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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  6. 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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  7.  37
    Connectionist Models and Their Properties.J. A. Feldman & D. H. Ballard - 1982 - Cognitive Science 6 (3):205-254.
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  8. 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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  9.  33
    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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  10. 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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  11. 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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  12. 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.
  13. 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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  14. 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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  15.  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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  16. 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.
  17. 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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  18.  22
    Connectionism and the Mind: An Introduction to Parallel Processing in Networks.David Pickles, William Bechtel & Adele Abrahamson - 1992 - Philosophical Quarterly 42 (166):101.
  19.  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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  20.  84
    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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  21. 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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  22.  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.
  23. 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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  24. 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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  25.  22
    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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  26.  7
    Does Connectionism Suffice?Steven W. Zucker - 1985 - Behavioral and Brain Sciences 8 (2):301-302.
  27. 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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  28.  24
    A Connectionist Approach to Knowledge Representation and Limited Inference.Lokendra Shastri - 1988 - Cognitive Science 12 (3):331-392.
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  29.  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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  30.  29
    CAB: Connectionist Analogy Builder.Levi B. Larkey & Bradley C. Love - 2003 - Cognitive Science 27 (5):781-794.
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  31. Connectionism and the Problem of Systematicity (Continued): Why Smolensky's Solution Still Doesn't Work.Jerry A. Fodor - 1997 - Cognition 62 (1):109-19.
  32.  6
    The Algebraic Mind: Integrating Connectionism and Cognitive Science.Gary F. Marcus - 2001 - MIT Press.
    1 Cognitive Architectures 2 Multilayer Perceptrons 3 Relations between Variables 4 Structured Representations 5 Individuals 6 Where does the Machinery of Symbol Manipulation Come From? 7 Conclusions.
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  33. Do Connectionist Representations Earn Their Explanatory Keep?William Ramsey - 1997 - Mind and Language 12 (1):34-66.
  34.  4
    Philosophy and Connectionist Theory.William Ramsey, Stephen P. Stich & David E. Rumelhart - 1991 - Psychology Press.
    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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  35. The Connectionism/Classicism Battle to Win Souls.Brian P. McLaughlin - 1993 - Philosophical Studies 71 (2):163-190.
  36.  27
    Connectionist and Memory‐Array Models of Artificial Grammar Learning.Zoltan Dienes - 1992 - Cognitive Science 16 (1):41-79.
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  37. Connectionism.Kim Sterelny - 1990 - In The Representational Theory of Mind. Blackwell.
     
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  38. Connectionist Learning Procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
  39. Connectionism, Eliminativism, and the Semantic View of Theories.John Bickle - 1993 - Erkenntnis 39 (3):359-382.
    Recently some philosophers have urged that connectionist artificial intelligence is (potentially) eliminative for the propositional attitudes of folk psychology. At the same time, however, these philosophers have also insisted that since philosophy of science has failed to provide criteria distinguishing ontologically retentive from eliminative theory changes, the resulting eliminativism is not principled. Application of some resources developed within the semantic view of scientific theories, particularly recent formal work on the theory reduction relation, reveals these philosophers to be wrong in this (...)
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  40. Integrating Connectionism and Symbol Manipulation: The Importance of Implementation.F. Van der Velde - forthcoming - Behavioral and Brain Sciences.
     
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  41.  19
    Connectionist Models and Their Applications: Introduction.Jerome A. Feldman - 1985 - Cognitive Science 9 (1):1-2.
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  42.  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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  43.  35
    Connectionism, Classical Cognitivism and the Relation Between Cognitive and Implementational Levels of Analysis.Keith Butler - 1993 - Philosophical Psychology 6 (3):321-33.
    This paper discusses the relation between cognitive and implementational levels of analysis. Chalmers (1990, 1993) argues that a connectionist implementation of a classical cognitive architecture possesses a compositional semantics, and therefore undercuts Fodor and Pylyshyn's (1988) argument that connectionist networks cannot possess a compositional semantics. I argue that Chalmers argument misconstrues the relation between cognitive and implementational levels of analysis. This paper clarifies the distinction, and shows that while Fodor and Pylyshyn's argument survives Chalmers' critique, it cannot be used to (...)
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  44.  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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  45. Connectionism and Three Levels of Nativism.William Ramsey & Stephen P. Stich - 1990 - Synthese 82 (2):177-205.
    Along with the increasing popularity of connectionist language models has come a number of provocative suggestions about the challenge these models present to Chomsky's arguments for nativism. The aim of this paper is to assess these claims. We begin by reconstructing Chomsky's argument from the poverty of the stimulus and arguing that it is best understood as three related arguments, with increasingly strong conclusions. Next, we provide a brief introduction to connectionism and give a quick survey of recent efforts (...)
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  46.  89
    Connectionist Vehicles, Structural Resemblance, and the Phenomenal Mind.Gerard O'Brien & Jonathan Opie - 2001 - Communication and Cognition: An Interdisciplinary Quarterly Journal 34 (1-2):13-38.
    We think the best prospect for a naturalistic explanation of phenomenal consciousness is to be found at the confluence of two influential ideas about the mind. The first is the _computational _ _theory of mind_: the theory that treats human cognitive processes as disciplined operations over neurally realised representing vehicles.1 The second is the _representationalist theory of _ _consciousness_: the theory that takes the phenomenal character of conscious experiences (the “what-it-is-likeness”) to be constituted by their representational content.2 Together these two (...)
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  47.  71
    Is Connectionism Commonsense?Gerard J. O'Brien - 1991 - Philosophical Psychology 4 (2):165-78.
  48. Symbolic Connectionism in Natural Language Disambiguation.James Franklin & S. W. K. Chan - 1998 - IEEE Transactions on Neural Networks 9:739-755.
    Uses connectionism (neural networks) to extract the "gist" of a story in order to represent a context going forward for the disambiguation of incoming words as a text is processed.
     
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  49.  12
    Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions.Roger Ratcliff - 1990 - Psychological Review 97 (2):285-308.
  50. 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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