Results for 'connectionism'

954 found
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  1.  4
    George Graham.Connectionism in Pavlovtan Harness - 1991 - In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer Academic Publishers. pp. 143.
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  2.  7
    Jamd w, oarson.What Connectionists Cannot Do - 1991 - In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer Academic Publishers. pp. 113.
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  3.  51
    CAB: Connectionist Analogy Builder.Levi B. Larkey & Bradley C. Love - 2003 - Cognitive Science 27 (5):781-794.
    The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance (...)
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  4. Connectionism and the Intentionality of the Programmer.Mark Ressler - 2003 - Dissertation, San Diego State University
    Connectionism seems to avoid many of the problems of classical artificial intelligence, but has it avoided all of them? In this thesis I examine the problem that Intentionality, the directedness of thought to an object, raises for connectionism. As a preliminary approach, I consider the role of Intentionality in classical artificial intelligence from the programmer’s point of view. In this investigation, one problem I identify with classical artificial intelligence is that the Intentionality of the programmer seems to be (...)
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  5. 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.
  6.  72
    Connectionist Models and Their Properties.J. A. Feldman & D. H. Ballard - 1982 - Cognitive Science 6 (3):205-254.
    Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...)
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  7.  25
    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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  8.  36
    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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  9. Connectionism, generalization, and propositional attitudes: A catalogue of challenging issues.John A. Barnden - 1992 - In John Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 149--178.
    [Edited from Conclusion section:] We have looked at various challenging issues to do with getting connectionism to cope with high-level cognitive activities such a reasoning and natural language understanding. The issues are to do with various facets of generalization that are not commonly noted. We have been concerned in particular with the special forms these issues take in the arena of propositional attitude processing. The main problems we have looked at are: (1) The need to construct explicit representations of (...)
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  10. Can Connectionists Explain Systematicity?Robert J. Matthews - 1997 - Mind and Language 12 (2):154-177.
    Classicists and connectionists alike claim to be able to explain systematicity. The proposed classicist explanation, I argue, is little more than a promissory note, one that classicists have no idea how to redeem. Smolensky's (1995) proposed connectionist explanation fares little better: it is not vulnerable to recent classicist objections, but it nonetheless fails, particularly if one requires, as some classicists do, that explanations of systematicity take the form of a‘functional analysis’. Nonetheless, there are, I argue, reasons for cautious optimism about (...)
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  11. 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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  12.  53
    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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  13.  20
    Explanation and connectionist models.Catherine Stinson - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge. pp. 120-133.
    This chapter explores the epistemic roles played by connectionist models of cognition, and offers a formal analysis of how connectionist models explain. It looks at how other types of computational models explain. Classical artificial intelligence (AI) programs explain using abductive reasoning, or inference to the best explanation; they begin with the phenomena to be explained, and devise rules that can produce the right outcome. The chapter also looks at several examples of connectionist models of cognition, observing what sorts of constraints (...)
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  14. 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: (1) the classical computation conception in cognitive science; (2) a popular foundational interpretation of connectionism that John Tienson and I call “non‐sentential computationalism”; and (3) an alternative interpretation of connectionism we call “dynamical cognition.” Also discussed are two recent (...)
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  15. 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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  16. Do Connectionist Representations Earn Their Explanatory Keep?William Ramsey - 1997 - Mind and Language 12 (1):34-66.
    In this paper I assess the explanatory role of internal representations in connectionist models of cognition. Focusing on both the internal‘hidden’units and the connection weights between units, I argue that the standard reasons for viewing these components as representations are inadequate to bestow an explanatorily useful notion of representation. Hence, nothing would be lost from connectionist accounts of cognitive processes if we were to stop viewing the weights and hidden units as internal representations.
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  17.  16
    Computationalism, Connectionism, and the Philosophy of Mind.Brian P. McLaughlin - 2003 - In Luciano Floridi (ed.), The Blackwell guide to the philosophy of computing and information. Blackwell. pp. 135–151.
    The prelims comprise: Introduction The Computational Theory of Mind The Symbol‐system Paradigm The Connectionist Computational Paradigm How are Paradigms Related?
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  18. Connectionism and the philosophy of mind.William P. Bechtel - 1987 - Southern Journal of Philosophy Supplement 26:17-41.
  19.  76
    Connectionism, reduction, and multiple realizability.John Bickle - 1995 - Behavior and Philosophy 23 (2):29-39.
    I sketch a theory of cognitive representation from recent "connectionist" cognitive science. I then argue that (i) this theory is reducible to neuroscientific theories, yet (ii) its kinds are multiply realized at a neurobiological level. This argument demonstrates that multiple realizability alone is no barrier to the reducibility of psychological theories. I conclude that the multiple realizability argument, the most influential argument against psychophysical reductionism, should be abandoned.
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  20. Natural deduction in connectionist systems.William Bechtel - 1994 - Synthese 101 (3):433-463.
    The relation between logic and thought has long been controversial, but has recently influenced theorizing about the nature of mental processes in cognitive science. One prominent tradition argues that to explain the systematicity of thought we must posit syntactically structured representations inside the cognitive system which can be operated upon by structure sensitive rules similar to those employed in systems of natural deduction. I have argued elsewhere that the systematicity of human thought might better be explained as resulting from the (...)
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  21.  58
    The Curious Case of Connectionism.Istvan S. N. Berkeley - 2019 - Open Philosophy 2 (1):190-205.
    Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, (...)
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  22. Concepts, connectionism, and the language of thought.Martin Davies - 1991 - In William Ramsey, Stephen P. Stich & D. M. Rumelhart (eds.), Philosophy and Connectionist Theory. Hillsdale, N.J.: Lawrence Erlbaum. 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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  23.  19
    Aristotle, Connectionism, and the Morally Excellent Brain.David DeMoss - 1998 - The Paideia Archive: Twentieth World Congress of Philosophy 19:13-20.
    Can a mass of networked neurons produce moral human agents? I shall argue that it can; a brain can be morally excellent. A connectionist account of how the brain works can explain how a person might be morally excellent in Aristotle's sense of the term. According to connectionism, the brain is a maze of interconnections trained to recognize and respond to patterns of stimulation. According to Aristotle, a morally excellent human is a practically wise person trained in good habits. (...)
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  24. Connectionist Minds.Andy Clark - 1990 - Proceedings of the Aristotelian Society 90 (1):83-102.
    Andy Clark; VI*—Connectionist Minds, Proceedings of the Aristotelian Society, Volume 90, Issue 1, 1 June 1990, Pages 83–102, https://doi.org/10.1093/aristotelia.
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  25. Connectionist Synthetic Epistemology: Requirements for the Development of Objectivity.Ron Chrisley & Andy Holland - unknown
    A connectionist system that is capable of learning about the spatial structure of a simple world is used for the purposes of synthetic epistemology: the creation and analysis of artificial systems in order to clarify philosophical issues that arise in the explanation of how agents, both natural and artificial, represent the world. In this case, the issues to be clarified focus on the content of representational states that exist prior to a fully objective understanding of a spatial domain. In particular, (...)
     
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  26.  23
    Connectionist learning procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
  27.  43
    A Connectionist Approach to Knowledge Representation and Limited Inference.Lokendra Shastri - 1988 - Cognitive Science 12 (3):331-392.
    Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with problems in knowledge representation and inference has often been questioned. This paper partly answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach. The paper presents a connectionist realization of semantic networks, that is, it describes how knowledge about concepts, their (...)
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  28.  47
    The connectionist self in action.David DeMoss - 2007 - Mind and Society 6 (1):19-33.
    ObjectiveTo demonstrate that the human brain, as a connectionist system, has the capacity to become a free, rational, moral, agent—that is, the capacity to become a self—and that the brain becomes a self by engaging second-order reflection in the hermeneutical task of constructing narratives that rationalise action. StructureSection 2 explains the connectionist brain and its relevant capacities: to categorise, to develop goal-directed dispositions, to problem-solve what it should do, and to second-order reflect. Section 3 argues that the connectionist brain constitutes (...)
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  29. 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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  30.  33
    A Connectionist Solution to Problems Posed by Plato and Aristotle.Arthur Falk - 1995 - Behavior and Philosophy 23 (3-1):1 - 12.
    Intentionality occurs in connectionist nets among those traits of the nets that scientists call flaws. This label has obscured for philosophers the fact that the naturalistic basis of intentionality has been discovered. I show this while staying on our profession's common ground of discourse about ancient philosophy. In the "Theaetetus", Plato invokes a homunculus to explain perceptual misrecognition, and in "On Memory and Recollection", Aristotle invokes a mental operation of disregarding in order to overcome the extraneous determinateness of mental images. (...)
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  31.  41
    (1 other version)Wittgenstein and connectionism: A significant complementarity?Stephen L. Mills - 1993 - Philosophy 34:137-157.
    Between the later views of Wittgenstein and those of connectionism 1 on the subject of the mastery of language there is an impressively large number of similarities. The task of establishing this claim is carried out in the second section of this paper.
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  32.  93
    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, 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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  34.  91
    Connectionism isn't magic.Hugh Clapin - 1991 - Minds and Machines 1 (2):167-84.
    Ramsey, Stich and Garon's recent paper Connectionism, Eliminativism, and the Future of Folk Psychology claims a certain style of connectionism to be the final nail in the coffin of folk psychology. I argue that their paper fails to show this, and that the style of connectionism they illustrate can in fact supplement, rather than compete with, the claims of a theory of cognition based in folk psychology's ontology. Ramsey, Stich and Garon's argument relies on the lack of (...)
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  35.  78
    A Connectionist Defence of the Inscrutability Thesis.Francisco Calvo Garzón - 2000 - Mind and Language 15 (5):465-480.
    This paper consists of four parts. In section 1, I shall offer a strategy to bypass a counter‐example which Gareth Evans (1975) offers against Quine’s Thesis of the Inscrutability of Reference. In section 2, I shall introduce a criterion recently pro‐duced by Crispin Wright (1997) in terms of ‘psychological simplicity’ which threatens the perverse route offered in section 1. In section 3, I shall argue that a LOT model of human cognition could motivate Wright’s criterion. In section 4, I shall (...)
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  36.  77
    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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  37.  39
    Nonclassical connectionism should enter the decathlon.Francisco Calvo Garzón - 2003 - Behavioral and Brain Sciences 26 (5):603-604.
    In this commentary I explore nonclassical connectionism as a coherent framework for evaluation in the spirit of the Newell Test. Focusing on knowledge integration, development, real-time performance, and flexible behavior, I argue that NCC's “within-theory rank ordering” would place subsymbolic modeling in a better position. Failure to adopt a symbolic level of thought cannot be interpreted as a weakness.
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  38. Connectionism and the problem of systematicity: Why Smolensky's solution doesn't work.Jerry Fodor & Brian P. McLaughlin - 1990 - Cognition 35 (2):183-205.
  39.  12
    A Recurrent Connectionist Model of Melody Perception: An Exploration Using TRACX2.Daniel Defays, Robert M. French & Barbara Tillmann - 2023 - Cognitive Science 47 (4):e13283.
    Are similar, or even identical, mechanisms used in the computational modeling of speech segmentation, serial image processing, and music processing? We address this question by exploring how TRACX2, a recognition‐based, recursive connectionist autoencoder model of chunking and sequence segmentation, which has successfully simulated speech and serial‐image processing, might be applied to elementary melody perception. The model, a three‐layer autoencoder that recognizes “chunks” of short sequences of intervals that have been frequently encountered on input, is trained on the tone intervals of (...)
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  40.  54
    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.
  41. Classicism, Connectionism and the Concept of Level.Yu-Houng H. Houng - 1990 - Dissertation, Indiana University
    The debate between Classicism and Connectionism can be properly characterized as a debate concerning the appropriate levels of analysis for psychological theorizing. Classicists maintain that the level of analysis defined by the Classical architecture is the level of analysis at which psychological theorizing should reside. This level is called the symbolic level. On the other hand, Connectionists claim that the proper level of analysis for cognitive modeling is at the subsymbolic level which is considered a level lower than the (...)
     
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  42.  69
    Computation, connectionism and modelling the mind.Mary Litch - 1997 - Philosophical Psychology 10 (3):357-364.
    Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several versions of connectionism (...)
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  43.  37
    Putting together connectionism – again.Paul Smolensky - 1988 - Behavioral and Brain Sciences 11 (1):59-74.
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  44.  9
    Connectionist representations of tonal music: discovering musical patterns by interpreting artificial neural networks.Michael Robert William Dawson - 2018 - Edmonton, Alberta: AU Press.
    Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of networks is provided for each case study which together demonstrate that focusing on the internal structure of trained networks could yield important contributions to the field of music cognition.
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  45.  10
    Connectionist hashed associative memory.Ronald L. Greene - 1991 - Artificial Intelligence 48 (1):87-98.
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  46.  9
    Connectionism in Context.A. Clark & Ronald Lutz (eds.) - 1992 - Springer Verlag.
    Connectionism is currently one of the most flourishing and interdisciplinary areas of cognitive science. Drawing on research in neural computation and networks it has found applications in areas such as psychology and animal intelligence. By using types of network which attempt to mirror our own cognitive architecture, connectionism is making breakthroughs in the understanding of the human mind a real possibility.
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  47. Hybrid Connectionist -Symbolic Mo dels.Ron Sun - unknown
    During the two days of the workshop, various presentations and discussions brought to light many new ideas, controv ersies, and syntheses. The fo cus was on learning and architecture s that feature hybrid representations and supp ort hybrid learning. It was a general consensus among the workshop participants that hybrid connectionist-symb olic mo dels constitute a promising aven ue toward developing more robust, more p owerful, and more versatile architecture s b oth for cognitive mo deling and for intelligen t (...)
     
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  48. 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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  49.  18
    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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  50.  30
    Does connectionism suffice?Steven W. Zucker - 1985 - Behavioral and Brain Sciences 8 (2):301-302.
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