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  1. Kenneth Aizawa (1999). 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] Minds and Machines 9 (3):447-456.
  2. Kenneth Aizawa (1992). Biology and Sufficiency in Connectionist Theory. In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. 69--88.
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  3. Kenneth Aizawa (1992). Philosophy and Connectionist Theory. Mind and Language 7 (3):286-297.
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  4. Enrique Alba & José M. Troya (1999). A Survey of Parallel Distributed Genetic Algorithms. Complexity 4 (4):31-52.
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  5. John R. Anderson & Christian Lebiere (2003). Optimism for the Future of Unified Theories. 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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  6. John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder (1998). ACT-R: A Higher-Level Account of Processing Capacity. Behavioral and Brain Sciences 21 (6):831-832.
    We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.
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  7. Dan J. Stein Andjacques Ludik (1998). Neural Networks and Psychopathology: An Introduction. In Dan J. Stein & J. Ludick (eds.), Neural Networks and Psychopathology. Cambridge University Press.
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  8. Daniel Andler (1992). From Paleo to Neo Connectionism. In G. van der Vijve (ed.), New Perspectives on Cybernetics.
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  9. Louise Antony (1991). A Pieced Quilt: A Critical Discussion of Stephen Schiffer'sRemnants of Meaning. 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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  10. Víctor Martín Verdejo Aparicio (2012). The Visual Language of Thought: Fodor Vs. Pylyshyn. Teorema: International Journal of Philosophy 31 (1):59-74.
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  11. S. L. Armstrong, L. R. Gleitman & H. Gleitman (1983). What Some Concepts Might Not Be. Cognition 13 (1):263--308.
  12. José Rubia Barcia (2012). Los orígenes del problema de la articulació de niveles en el cognitivismo: Newell y Pylyshyn. Estudios Filosóficos 61 (176):103-116.
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  13. William Bechtel (2009). Looking Down, Around, and Up: Mechanistic Explanation in Psychology. 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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  14. William Bechtel (1991). Connectionism and the Philosophy of Mind: An Overview. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer. 30--59.
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  15. William Bechtel (1988). Connectionism and the Philosophy of Mind: An Overview. Southern Journal of Philosophy 26 (S1):17-41.
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  16. William Bechtel & Adele Abrahamsen (2002). Connectionism and the Mind: Parallel Processing, Dynamics, and Evolution in Networks. Wiley-Blackwell.
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  17. Istvan S. N. Berkeley (2008). What the is a Symbol? 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 give (...)
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  18. Istvan S. N. Berkeley (2001). 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] Minds and Machines 11 (1):148-150.
  19. Istvan Stephen Norman Berkeley (1997). On Connectionism. 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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  20. Niels Ole Bernsen (1993). Systematicity in the Vision to Language Chain. Royal Institute of Philosophy Supplement 34:189-215.
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  21. Martin Berry & David Pymm (1981). Analysis of Neural Networks. In G. Adam, I. Meszaros & E. I. Banyai (eds.), Advances in Physiological Science. 30--155.
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  22. James Blackmon, David Byrd, Robert C. Cummins, Pierre Poirier, Martin Roth & George Schwarz (2001). Systematicity and the Cognition of Structured Domains. Journal of Philosophy 98 (4):1-19.
    The current debate over systematicity concerns the formal conditions a scheme of mental representation must satisfy in order to explain the systematicity of thought.1 The systematicity of thought is assumed to be a pervasive property of minds, and can be characterized (roughly) as follows: anyone who can think T can think systematic variants of T, where the systematic variants of T are found by permuting T’s constituents. So, for example, it is an alleged fact that anyone who can think the (...)
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  23. Douglas S. Blank, Lisa A. Meeden & James B. Marshall (1992). Exploring the Symbolic/Subsymbolic Continuum: A Case Study of RAAM. In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. 113--148.
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  24. Reinhard Blutner (2004). Nonmonotonic Inferences and Neural Networks. 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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  25. Philotheus Boehner, Stephen F. Brown, Luigi Boscolo, Paolo Bertrando, David Boucher & Andrew Vincent (1994). Clark, Andy, Associative Engines: Connectionism Concepts and Representational. Mind 103.
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  26. David Bohm (1990). A New Theory of the Relationship of Mind and Matter. 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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  27. Hervé Bourlard & Samy Bengio (2002). The Handbook of Brain Theory and Neural Networks.
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  28. Ty W. Boyer, Matthias Scheutz & Bennett I. Bertenthal (2009). Dissociating Ideomotor and Spatial Compatibility: Empirical Evidence and Connectionist Models. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2280--2285.
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  29. Gordon G. Brittan (1992). Systematicity and Objectivity in the Third Critique. Southern Journal of Philosophy 30 (S1):167-186.
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  30. John A. Bullinaria (1994). Connectionist Modelling of Spelling. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. 78--83.
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  31. John A. Bullinaria (1994). Internal Representations of a Connectionist Model of Reading Aloud. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. 84--89.
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  32. Francisco Calvo Garzon (2000). A Connectionist Defence of the Inscrutability Thesis. Mind and Language 15 (5):465-480.
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  33. Paco Calvo & John Symons (eds.) (2014). The Architecture of Cognition: Rethinking Fodor and Pylyshyn's Systematicity Challenge. The Mit Press.
    Philosophers and cognitive scientists reassess systematicity in the post-connectionist era, offering perspectives from ecological psychology, embodied and distributed cognition, enactivism, and other methodologies.
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  34. Franz Caspar (1998). A Connectionist View of Psychotherapy1. In Dan J. Stein & J. Ludick (eds.), Neural Networks and Psychopathology. Cambridge University Press. 88.
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  35. Frederic Aviolatt Daniel Cattani & Thierry Cornu (1996). Recognition of Meteorological Situations with Neural Networks. Esda 1996: Expert Systems and Ai; Neural Networks 7:41.
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  36. Ronald L. Chrisley, Non-Compositional Representation in Connectionist Networks.
    have context-sensitive constituents, but rather because they sometimes have no constituents at all. The argument to be rejected depends on the assumption that one can only assign propositional contents to representations if one starts by assigning sub-propositional contents to atomic representations. I give some philosophical arguments and present a counterexample to show that this assumption is mistaken.
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  37. Ronald L. Chrisley (1992). Connectionism, Cognitive Maps and the Development of Objectivity. School of Cognitive and Computing Sciences, University of Sussex.
    It is claimed that there are pre-objective phenomena, which cognitive science should explain by employing the notion of non-conceptual representational content. It is argued that a match between parallel distributed processing (PDP) and non-conceptual content (NCC) not only provides a means of refuting recent criticisms of PDP as a cognitive architecture; it also provides a vehicle for NCC that is required by naturalism. A connectionist cognitive mapping algorithm is used as a case study to examine the affinities between PDP and (...)
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  38. Gregory Christ (forthcoming). Toward a Model of Attention and Cognition, Using a Parallel Distributed Processing Approach: II. The Sweeping Model. Journal of Mind and Behavior.
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  39. Gregory Christ (forthcoming). Toward a Model of Attention and Cognition Using a Parallel Distributed Processing Approach: I. Background. Journal of Mind and Behavior.
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  40. M. H. Christiansen, N. Chater & M. S. Seidenberg (1999). Sonderband Connectionist Models of Human Language Processing. Cognitive Science 23 (4).
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  41. Morten H. Christiansen, Christopher M. Conway & Michelle R. Ellefson (2002). Raising the Bar for Connectionist Modeling of Cognitive Developmental Disorders. Behavioral and Brain Sciences 25 (6):752-753.
    Cognitive developmental disorders cannot be properly understood without due attention to the developmental process, and we commend the authors’simulations in this regard. We note the contribution of these simulations to the nascent field of connectionist modeling of developmental disorders and outline a set of criteria for assessing individual models in the hope of furthering future modeling efforts.
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  42. D. Christie (1993). The Case for Connectionism-Comments on Bechtel. Philosophical Studies 71 (2):155-162.
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  43. Paul M. Churchland (1995). Machine Stereopsis: A Feedforward Network for Fast Stereo Vision with Movable Fusion Plane. In Android Epistemology. Cambridge: MIT Press.
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  44. Andrew Clark (1991). Connectionism: The Structure Beneath the Symbols. In Raymond Tallis & Howard Robinson (eds.), The Pursuit of Mind. Carcanet. 129.
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  45. Andy Clark (1994). Introduction: Reinventing the Connectionist Challenge. Synthese 101 (3):301-303.
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  46. Andy Clark (1993). Critique of Rumelhart and McClelland. In Alvin Goldman (ed.), Readings in Philosophy and Cognitive Science. Cambridge: Mit Press.
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  47. Andy Clark (1991). Systematicity, Structured Representations and Cognitive Architecture: A Reply to Fodor and Pylyshyn. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer. 198--218.
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  48. Andy Clark & Peter Millican (eds.) (1999). Connectionism, Concepts, and Folk Psychology: The Legacy of Alan Turing, Volume Ii. Clarendon Press.
    This is the second of two volumes of essays on the ideas of Alan Turing, whose pioneering work in artificial intelligence and computer science made him one of the seminal thinkers of the century. A distinguished international cast of contributors offer original investigations of key issues in contemporary philosophy of mind and cognitive science, celebrating Turing's intellectual legacy in these fields. 'fascinating . . .we can all learn by reading these essays because they encourage us to explore issues beyond our (...)
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  49. Andy Clark & Josefa Toribio (1994). Doing Without Representing. Synthese 101 (3):401-31.
    Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in armchair theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper (...)
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  50. Axel Cleeremans, Applying Forward Models to Sequence Learning: A Connectionist Implementation.
    The ability to process events in their temporal and sequential context is a fundamental skill made mandatory by constant interaction with a dynamic environment. Sequence learning studies have demonstrated that subjects exhibit detailed — and often implicit — sensitivity to the sequential structure of streams of stimuli. Current connectionist models of performance in the so-called Serial Reaction Time Task (SRT), however, fail to capture the fact that sequence learning can be based not only on sensitivity to the sequential associations between (...)
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