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  1. Jeffrey L. Elman, Elizabeth A. Bates, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi, and Kim Plunkett, (Eds.), Rethinking Innateness: A Connectionist Perspective on Development, Neural Network Modeling and Connectionism Series and Kim Plunkett and Jeffrey L. Elman, Exercises in Rethinking Innateness: A Handbook for Connectionist Simulations. [REVIEW]Kenneth Aizawa - 1999 - Minds and Machines 9 (3):447-456.
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  2. Explaining Systematicity.Kenneth Aizawa - 1997 - Mind and Language 12 (2):115-136.
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  3. Biology and Sufficiency in Connectionist Theory.Kenneth Aizawa - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 69--88.
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  4. Philosophy and Connectionist Theory.Kenneth Aizawa - 1992 - Mind and Language 7 (3):286-297.
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  5. A Survey of Parallel Distributed Genetic Algorithms.Enrique Alba & José M. Troya - 1999 - Complexity 4 (4):31-52.
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  6. Optimism for the Future of Unified Theories.John R. Anderson & Christian Lebiere - 2003 - Behavioral and Brain Sciences 26 (5):628-633.
    The commentaries on our article encourage us to believe that researchers are beginning to take seriously the goal of achieving the broad adequacy that Newell aspired to. The commentators offer useful elaborations to the criteria we suggested for the Newell Test. We agree with many of the commentators that classical connectionism is too restrictive to achieve this broad adequacy, and that other connectionist approaches are not so limited and can deal with the symbolic components of thought. All these approaches, including (...)
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  7. ACT-R: A Higher-Level Account of Processing Capacity.John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder - 1998 - 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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  8. Neural Networks and Psychopathology: An Introduction.Dan J. Stein Andjacques Ludik - 1998 - In Dan J. Stein & J. Ludick (eds.), Neural Networks and Psychopathology. Cambridge University Press.
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  9. From Paleo to Neo Connectionism.Daniel Andler - 1992 - In G. van der Vijve (ed.), New Perspectives on Cybernetics.
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  10. A Pieced Quilt: A Critical Discussion of Stephen Schiffer'sRemnants of Meaning.Louise Antony - 1991 - Philosophical Psychology 4 (1):119-137.
    Abstract Stephen Schiffer, in his recent book, Remnants of Meaning, argues against the possibility of any compositional theory of meaning for natural language. Because the argument depends on the premise that there is no possible naturalistic reduction of the intentional to the physical, Schiffer's attack on theories of meaning is of central importance for theorists of mind. I respond to Schiffer's argument by showing that there is at least one reductive account of the mental that he has neglected to consider?the (...)
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  11. On the Proper Treatment of the Connection Between Connectionism and Symbolism.Louise Antony & Joseph Levine - 1988 - Behavioral and Brain Sciences 11 (1):23.
  12. The Visual Language of Thought: Fodor Vs. Pylyshyn.Víctor Martín Verdejo Aparicio - 2012 - Teorema: International Journal of Philosophy 31 (1):59-74.
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  13. Semantic Networks.Michael A. Arbib - 2002 - In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. MIT Press.
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  14. What Some Concepts Might Not Be.S. L. Armstrong, L. R. Gleitman & H. Gleitman - 1983 - Cognition 13 (1):263--308.
  15. Syntax, Functionalism, Connectionism, and the Language of Thought.Murat Mustafa Aydede - 1993 - Dissertation, University of Maryland, College Park
    Fodor and Pylyshyn's critique of connectionism has posed a challenge to connectionists: Explain such cognitive regularities as systematicity and productivity without postulating a LOT architecture. Some connectionists took the challenge seriously. They developed some models that purport to show that they can explain the regularities without becoming classical. The key to their claim is that their models can and do provide non-concatenatively realized syntactically complex representations that can also be processed in a structure sensitive way. Surprisingly, Fodor and McLaughlin seem (...)
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  16. Rules Work on One Representation; Similarity Compares Two Representations.Todd M. Bailey - 2005 - Behavioral and Brain Sciences 28 (1):16-16.
    Rules and similarity refer to qualitatively different processes. The classification of a stimulus by rules involves abstract and usually domain-specific knowledge operating primarily on the target representation. In contrast, similarity is a relation between the target representation and another representation of the same type. It is also useful to distinguish associationist processes as a third type of cognitive process.
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  17. Los orígenes del problema de la articulació de niveles en el cognitivismo: Newell y Pylyshyn.José Rubia Barcia - 2012 - Estudios Filosóficos 61 (176):103-116.
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  18. Chaos, Symbols, and Connectionism.John A. Barnden - 1987 - Behavioral and Brain Sciences 10 (2):174.
  19. Connectionist Value Units: Some Concerns.John A. Barnden - 1986 - Behavioral and Brain Sciences 9 (1):92.
  20. Networks in Cognitive Science.Andrea Baronchelli, Ramon Ferrer-I.-Cancho, Romualdo Pastor-Satorras, Nick Chater & Morten H. Christiansen - 2013 - Trends in Cognitive Sciences 17 (7):348-360.
  21. Compatibility of Connectionist and Rule‐Based Systems.W. Bechtel - 1988 - Philosophical Psychology 1:5-16.
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  22. Looking Down, Around, and Up: Mechanistic Explanation in Psychology.William Bechtel - 2009 - Philosophical Psychology 22 (5):543-564.
    Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...)
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  23. Connectionism and the Philosophy of Mind: An Overview.William Bechtel - 1991 - In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer Academic Publishers. pp. 30--59.
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  24. Connectionism and Interlevel Relations.William Bechtel - 1988 - Behavioral and Brain Sciences 11 (1):24.
  25. Connectionism and the Philosophy of Mind: An Overview.William Bechtel - 1988 - Southern Journal of Philosophy 26 (S1):17-41.
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  26. Connectionism and the Mind: Parallel Processing, Dynamics, and Evolution in Networks.William Bechtel & Adele Abrahamsen - 2002 - Wiley-Blackwell.
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  27. Connectionism and the Mind.William Bechtel & Adele Abrahamsen - 1991 - Wiley-Blackwell.
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  28. What the is a Symbol?Istvan S. N. Berkeley - 2008 - Minds and Machines 18 (1):93-105.
    The notion of a ‘ symbol ’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘ symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that (...)
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  29. Peter Novak, Mental Symbols: A Defence of the Classical Theory of Mind. Studies in Cognitive Systems 19, Dordrecht, Netherlands: Kluwer Academic Publishers, 1997, XXII + 266 Pp., $114.00, ISBN 0-7923-4370-. [REVIEW]Istvan S. N. Berkeley - 2001 - Minds and Machines 11 (1):148-150.
  30. On Connectionism.Istvan Stephen Norman Berkeley - 1997 - Dissertation, University of Alberta (Canada)
    This dissertation opens with a discussion and clarification of the Classical Computational Theory of Mind . An alleged challenge to this theory, which derives from research into connectionist systems, is then briefly outlined and connectionist systems are introduced in some detail. Several claims which have been made on behalf of connectionist systems are then examined, these purport to support the conclusion that connectionist systems provide better models of the mind and cognitive functioning than the CCTM. It is argued that most (...)
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  31. Systematicity in the Vision to Language Chain.Niels Ole Bernsen - 1993 - Royal Institute of Philosophy Supplement 34:189-215.
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  32. Analysis of Neural Networks.Martin Berry & David Pymm - 1981 - In G. Adam, I. Meszaros & E. I. Banyai (eds.), Advances in Physiological Science. pp. 30--155.
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  33. On the Association Between Connectionism and Data: Are a Few Words Necessary?Derek Besner, Leslie Twilley, Robert S. McCann & Ken Seergobin - 1990 - Psychological Review 97 (3):432-446.
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  34. Symbolic Manipulations Via Subsymbolic Computations.D. S. Blank, L. A. Meeden & J. B. Marshall - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 113--148.
  35. Exploring the Symbolic/Subsymbolic Continuum: A Case Study of RAAM.Douglas S. Blank, Lisa A. Meeden & James B. Marshall - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 113--148.
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  36. Character-Principlism and the Particularity Objection.Blustein Jeffrey - 1997 - Metaphilosophy 28 (1-2):135-155.
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  37. Nonmonotonic Inferences and Neural Networks.Reinhard Blutner - 2004 - Synthese 142 (2):143-174.
    There is a gap between two different modes of computation: the symbolic mode and the subsymbolic (neuron-like) mode. The aim of this paper is to overcome this gap by viewing symbolism as a high-level description of the properties of (a class of) neural networks. Combining methods of algebraic semantics and non-monotonic logic, the possibility of integrating both modes of viewing cognition is demonstrated. The main results are (a) that certain activities of connectionist networks can be interpreted as non-monotonic inferences, and (...)
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  38. Clark, Andy, Associative Engines: Connectionism Concepts and Representational.Philotheus Boehner, Stephen F. Brown, Luigi Boscolo, Paolo Bertrando, David Boucher & Andrew Vincent - 1994 - Mind 103.
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  39. A New Theory of the Relationship of Mind and Matter.David Bohm - 1990 - Philosophical Psychology 3 (2 & 3):271 – 286.
    The relationship of mind and matter is approached in a new way in this article. This approach is based on the causal interpretation of the quantum theory, in which an electron, for example, is regarded as an inseparable union of a particle and afield. This field has, however, some new properties that can be seen to be the main sources of the differences between the quantum theory and the classical (Newtonian) theory. These new properties suggest that the field may be (...)
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  40. A Neural-Symbolic Perspective on Analogy.Rafael V. Borges, Artur S. D'Avila Garcez & Luis C. Lamb - 2008 - Behavioral and Brain Sciences 31 (4):379-380.
    The target article criticises neural-symbolic systems as inadequate for analogical reasoning and proposes a model of analogy as transformation (i.e., learning). We accept the importance of learning, but we argue that, instead of conflicting, integrated reasoning and learning would model analogy much more adequately. In this new perspective, modern neural-symbolic systems become the natural candidates for modelling analogy.
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  41. The Handbook of Brain Theory and Neural Networks.Hervé Bourlard & Samy Bengio - 2002
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  42. Neural Networks Learn Highly Selective Representations in Order to Overcome the Superposition Catastrophe.Jeffrey S. Bowers, Ivan I. Vankov, Markus F. Damian & Colin J. Davis - 2014 - Psychological Review 121 (2):248-261.
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  43. Dissociating Ideomotor and Spatial Compatibility: Empirical Evidence and Connectionist Models.Ty W. Boyer, Matthias Scheutz & Bennett I. Bertenthal - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 2280--2285.
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  44. What Connectionists Learn: Comparisons of Model and Neural Nets.Bruce Bridgeman - 1990 - Behavioral and Brain Sciences 13 (3):491-492.
  45. Systematicity and Objectivity in the Third Critique.Gordon G. Brittan - 1992 - Southern Journal of Philosophy 30 (S1):167-186.
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  46. A Connectionist Model of the Development of Velocity, Time, and Distance Concepts.David Buckingham & Thomas R. Shultz - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 72--77.
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  47. Connectionist Modelling of Spelling.John A. Bullinaria - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 78--83.
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  48. Internal Representations of a Connectionist Model of Reading Aloud.John A. Bullinaria - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 84--89.
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  49. The Many Ways to Distribute Distributed Representations.A. Mike Burton - 2000 - Behavioral and Brain Sciences 23 (4):472-473.
    Distributed representations can be distributed in very many ways. The specific choice of representation for a specific model is based on considerations unique to the area of study. General statements about the effectiveness of distributed models are therefore of little value. The popularity of these models is discussed, particularly with respect to reporting conventions.
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  50. Connectionism, Classical Cognitivism and Reduction: A Case for Parallel Distributed Processing.Keith Lawrence Butler - 1991 - Dissertation, The University of Wisconsin - Madison
    This essay will look at two approaches to psychological theory. The received view, the Classical theory, has it that psychological states are relations to representations that have syntactic structure, like sentences, and that functions from one state to another are computed with sensitivity to that structure, like computation of the sort found in digital computers. In the rival approach, Connectionism, functions are computed by spreading activation between neuron-like units. Typical networks consist of a set of input units, a set of (...)
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