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  1. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - manuscript
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  2. AISC 17 Talk: The Explanatory Problems of Deep Learning in Artificial Intelligence and Computational Cognitive Science: Two Possible Research Agendas.Antonio Lieto - 2018 - In Proceedings of AISC 2017.
    Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...)
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  3. Empiricism Without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...)
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  4. Construction of National Identity Through a Social Network: A Case Study of Ethnic Networks of Immigrants to Russia From Central Asia.Andrey P. Glukhov - 2017 - AI and Society 32 (1):101-108.
  5. Jerry A. Fodor and Xenon W. Pylyshyn: Minds Without Meanings: An Essay in the Content of Concepts.Sean Welsh - 2016 - Minds and Machines 26 (4):467-471.
  6. Connectionism, Confusion and Cognitive Science.M. R. W. Dawson & K. S. Shamanski - 1994 - Journal of Intelligent Systems 4 (3-4):215-262.
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  7. Associationism: Not the Cliff Over Which to Push Connectionism.R. J. Jorna & W. F. G. Haselager - 1994 - Journal of Intelligent Systems 4 (3-4):279-308.
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  8. Computability of Logical Neural Networks.T. B. Ludermir - 1992 - Journal of Intelligent Systems 2 (1-4):261-290.
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  9. Dreams and Connectionism: A Critique.D. Kuiken - 1994 - Journal of Intelligent Systems 4 (3-4):263-278.
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  10. Cytological Diagnosis Based on Fuzzy Neural Networks.D. Kontoravdis, A. Likas & P. Krakitsos - 1998 - Journal of Intelligent Systems 8 (1-2):55-80.
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  11. What Systematicity Isn’T.Robert Cummins, Jim Blackmon, David Byrd, Alexa Lee & Martin Roth - 2005 - Journal of Philosophical Research 30:405-408.
    In “On Begging the Systematicity Question,” Wayne Davis criticizes the suggestion of Cummins et al. that the alleged systematicity of thought is not as obvious as is sometimes supposed, and hence not reliable evidence for the language of thought hypothesis. We offer a brief reply.
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  12. Common and Distinct Neural Networks for Theory of Mind Reasoning and Inhibitory Control.Christoph Rothmayr - unknown
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  13. On the Systematicity of Language and Thought.Kent Johnson - 2004 - Journal of Philosophy 101 (3):111-139.
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  14. VI—Connectionist Minds.Andy Clark - 1990 - Proceedings of the Aristotelian Society 90 (1):83-102.
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  15. Multistability of Memristive Neural Networks with Time-Varying Delays.Ailong Wu & Zhang Jin-E. - 2016 - Complexity 21 (1):177-186.
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  16. Exponential State Estimator Design for Discrete-Time Neural Networks with Discrete and Distributed Time-Varying Delays.Qihui Duan, Ju H. Park & Zheng-Guang Wu - 2014 - Complexity 20 (1):38-48.
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  17. Analyzing Outcomes of Intrauterine Insemination Treatment by Application of Cluster Analysis or Kohonen Neural Networks.Anna Justyna Milewska, Dorota Jankowska, Urszula Cwalina, Teresa Więsak, Dorota Citko, Allen Morgan & Robert Milewski - 2013 - Studies in Logic, Grammar and Rhetoric 35 (1):7-25.
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  18. 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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  19. Critical Branching Neural Networks.Christopher T. Kello - 2013 - Psychological Review 120 (1):230-254.
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  20. Postscript: Parallel Distributed Processing in Localist Models Without Thresholds.David C. Plaut & James L. McClelland - 2010 - Psychological Review 117 (1):289-290.
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  21. Phonology, Reading Acquisition, and Dyslexia: Insights From Connectionist Models.Michael W. Harm & Mark S. Seidenberg - 1999 - Psychological Review 106 (3):491-528.
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  22. Connectionist and Diffusion Models of Reaction Time.Roger Ratcliff, Trisha Van Zandt & Gail McKoon - 1999 - Psychological Review 106 (2):261-300.
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  23. Models of Reading Aloud: Dual-Route and Parallel-Distributed-Processing Approaches.Max Coltheart, Brent Curtis, Paul Atkins & Micheal Haller - 1993 - Psychological Review 100 (4):589-608.
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  24. ALCOVE: An Exemplar-Based Connectionist Model of Category Learning.John K. Kruschke - 1992 - Psychological Review 99 (1):22-44.
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  25. 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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  26. A Recurrent Connectionist Model of Group Biases.Dirk Van Rooy, Frank Van Overwalle, Tim Vanhoomissen, Christophe Labiouse & Robert French - 2003 - Psychological Review 110 (3):536-563.
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  27. A Symbolic-Connectionist Theory of Relational Inference and Generalization.John E. Hummel & Keith J. Holyoak - 2003 - Psychological Review 110 (2):220-264.
  28. Purity Homophily in Social Networks.Morteza Dehghani, Kate Johnson, Joe Hoover, Eyal Sagi, Justin Garten, Niki Jitendra Parmar, Stephen Vaisey, Rumen Iliev & Jesse Graham - 2016 - Journal of Experimental Psychology: General 145 (3):366-375.
  29. Connectionism and Human Learning: Critique of Gluck and Bower.David R. Shanks - 1990 - Journal of Experimental Psychology: General 119 (1):101-104.
  30. Models of Cognitive Processing: A Single Flexible Workspace or a Distributed Process? Comment on Carlson, Khoo, Yaure, and Schneider.Diane F. Halpern - 1990 - Journal of Experimental Psychology: General 119 (3):331-332.
  31. The Architecture of Cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge.Matteo Colombo - 2016 - Philosophical Psychology 29 (3):476-478.
  32. Book Review : Systematicity: The Nature of Science. [REVIEW]Darrell Rowbottom - unknown
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  33. Systematicity and Arbitrariness in Novel Communication Systems.Carrie Ann Theisen-White, Jon Oberlander & Simon Kirby - 2010 - Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 11 (1):14-32.
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  34. Cognition, Systematicity and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-153.
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  35. Explaining Systematicity.Kenneth Aizawa - 1997 - Mind and Language 12 (2):115-136.
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  36. Can Connectionists Explain Systematicity?Robert J. Matthews - 1997 - Mind and Language 12 (2):154-177.
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  37. Networks with Attitudes.Paul Skokowski - 2009 - AI and Society 23 (4):461-470.
  38. Early-Connectionism Machines.Roberto Cordeschi - 2000 - AI and Society 14 (3-4):314-330.
    In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. (...)
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  39. Systematicity, Conceptual Truth, and Evolution.Brian P. McLaughlin - 1993 - Royal Institute of Philosophy Supplement 34:217-234.
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  40. Wittgenstein and Connectionism: A Significant Complementarity?Stephen Mills - 1993 - Royal Institute of Philosophy Supplement 34:137-157.
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  41. 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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  42. Tye on Connectionism.Brian McLaughlin - 1988 - Southern Journal of Philosophy 26 (S1):185-193.
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  43. Rules and Relations: Some Connectionist Implications for Cognitive Science and Language.Paul Cilliers - 2016 - In PaulHG Cilliers (ed.), Critical Complexity: Collected Essays. De Gruyter. pp. 39-54.
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  44. Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011).Vincent C. Müller - 2011 - The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
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  45. OSWI: A Consulting System for Pupils and Prospective Students on the Basis of Neural Networks.Christina Klüver, Jürgen Klüver & Björn Zurmaar - 2015 - AI and Society 30 (1):23-30.
  46. Connectionism and the Mind.William Bechtel & Adele Abrahamsen - 1991 - Wiley-Blackwell.
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  47. Connectionism and the Mind: Parallel Processing, Dynamics, and Evolution in Networks.William Bechtel & Adele Abrahamsen - 2002 - Wiley-Blackwell.
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  48. Function, Sufficiently Constrained, Implies Form: Commentary on Green on Connectionist Explanation.Robert M. French & Axel Cleeremans - unknown
    Green's target article is an attack on most current connectionist models of cognition. Our commentary will suggest that there is an essential component missing in his discussion of modeling, namely, the idea that the appropriate level of the model needs to be specified. We will further suggest that the precise form of connectionist networks will fall out as ever more detailed constraints are placed on their function.
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  49. How Much is Enough? A Connectionist Perspective on the Representation Debate.Christopher James Thornton - 1993 - School of Cognitive and Computing Sciences, University of Sussex.
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  50. The Case Against Connectionism: Productivity and Systematicity.Marin Marinov - unknown - Eidos: The Canadian Graduate Journal of Philosophy 12.
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