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  1. A Puzzle concerning Compositionality in Machines.Ryan M. Nefdt - 2020 - Minds and Machines 30 (1):47-75.
    This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general. The puzzle identified here touches on a larger debate in Artificial Intelligence related to epistemic opacity but specifically focuses on computational applications of human level linguistic abilities or properties and a special difficulty with relation to these. Thus, the resulting issue is both general and unique. A partial solution is suggested.
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  2. Literal Perceptual Inference.Alex Kiefer - 2017 - In Thomas Metzinger & Wanja Wiese (eds.), Philosophy and predictive processing. Frankfurt, Germany:
    In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. -/- In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the (...)
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  3. Representation in the Prediction Error Minimization Framework.Alex Kiefer & Jakob Hohwy - 2019 - In Sarah K. Robins, John Symons & Paco Calvo (eds.), The Routledge Companion to Philosophy of Psychology: 2nd Edition. London, UK: pp. 384-409.
    This chapter focuses on what’s novel in the perspective that the prediction error minimization (PEM) framework affords on the cognitive-scientific project of explaining intelligence by appeal to internal representations. It shows how truth-conditional and resemblance-based approaches to representation in generative models may be integrated. The PEM framework in cognitive science is an approach to cognition and perception centered on a simple idea: organisms represent the world by constantly predicting their own internal states. PEM theories often stress the hierarchical structure of (...)
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  4. Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  5. Explainable AI is Indispensable in Areas Where Liability is an Issue.Nelson Brochado - manuscript
    What is explainable artificial intelligence and why is it indispensable in areas where liability is an issue?
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  6. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2019 - Synthese:arXiv:1901.02918v1.
    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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  7. 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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  8. 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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  9. 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.
  10. Systematicity, Conceptual Truth, and Evolution*: Brian P. McLaughlin.Brian P. McLaughlin - 1993 - Royal Institute of Philosophy Supplement 34:217-234.
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  11. Wittgenstein and Connectionism: A Significant Complementarity?*: Stephen Mills.Stephen Mills - 1993 - Royal Institute of Philosophy Supplement 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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  12. 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 0-7923-4370-0. [REVIEW]Istvan S. N. Berkeley - 2001 - Minds and Machines 11 (1):148-150.
  13. The Combinatorial-Connectionist Debate and the Pragmatics of Adjectives.Ran Lahav - 1993 - Pragmatics and Cognition 1 (1):71-88.
    Within the controversy between the combinatorial and the connectionist approaches to cognition it has been argued that our semantic and syntactic capacities provide evidence for the combinatorial approach. In this paper I offer a counter-weight to this argument by pointing out that the same type of considerations, when applied to the pragmatics of adjectives, provide evidence for connectionism.
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  14. Noel Starkey , Connectionist Natural Language Processing: Readings From 'Connection Science'.Ephraim Nissan - 1997 - Pragmatics and Cognition 5 (2):383-384.
  15. What Makes Connectionism Different?James H. Fetzer - 1994 - Pragmatics and Cognition 2 (2):327-348.
  16. Connectionism, Concepts, and Folk Psychology: The Legacy of Alan Turing, Volume 2. [REVIEW]Daniel N. Robinson - 1998 - Review of Metaphysics 51 (4):919-919.
  17. Rules and Representations. [REVIEW]L. J. - 1981 - Review of Metaphysics 34 (3):603-604.
    Here find four chapters as the first publication of the Woodbridge Lectures and Kant Lectures ; chapters 5 and 6, "On the Biological Basis of Language Capacities" and "Language and Unconscious Knowledge," have, substantively, appeared as parts of other books. This is an excellent book for philosophers, almost wholly given to theoretical-philosophical issues. A complementary process to the one William James outlined respecting the reception of a new idea is that the purveyor of the new idea, stung by dismissive and (...)
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  18. Smolensky’s Interpretation of Connectionism: The Implications for Symbolic Theory.Stephen Mills - 1990 - Irish Philosophical Journal 7 (1/2):104-118.
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  19. Jerry A. Fodor and Xenon W. Pylyshyn: Minds Without Meanings: An Essay in the Content of Concepts: MIT Press, Cambridge, MA, 2015, 208 Pp, $32.00, ISBN 978-0-262-02790-8.Sean Welsh - 2016 - Minds and Machines 26 (4):467-471.
  20. Clinical Diagnosis of Creutzfeldt-Jakob Disease Using a Multi-Layer Perceptron Neural Network Classifier.Κ Sutherland, R. de Silva & R. G. Will - 1997 - Journal of Intelligent Systems 7 (1-2):1-18.
  21. 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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  22. 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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  23. Computability of Logical Neural Networks.T. B. Ludermir - 1992 - Journal of Intelligent Systems 2 (1-4):261-290.
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  24. Dreams and Connectionism: A Critique.D. Kuiken - 1994 - Journal of Intelligent Systems 4 (3-4):263-278.
  25. 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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  26. 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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  27. 2.2 Grundlagen Neuronaler Netze.Klaus Mainzer - 1994 - In Computer - Neue Flügel des Geistes?: Die Evolution Computergestützter Technik, Wissenschaft, Kultur Und Philosophie. De Gruyter. pp. 247-275.
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  28. Common and Distinct Neural Networks for Theory of Mind Reasoning and Inhibitory Control.Christoph Rothmayr - unknown
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  29. On the Systematicity of Language and Thought.Kent Johnson - 2004 - Journal of Philosophy 101 (3):111-139.
  30. VI—Connectionist Minds.Andy Clark - 1990 - Proceedings of the Aristotelian Society 90 (1):83-102.
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  31. Multistability of Memristive Neural Networks with Time-Varying Delays.Ailong Wu & Zhang Jin-E. - 2016 - Complexity 21 (1):177-186.
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  32. 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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  33. 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.
    Intrauterine insemination is one of many treatments provided to infertility patients. Many factors such as, but not limited to, quality of semen, the age of a woman, and reproductive hormone levels contribute to infertility. Therefore, the aim of our study is to establish a statistical probability concerning the prediction of which groups of patients have a very good or poor prognosis for pregnancy after IUI insemination. For that purpose, we compare the results of two analyses: Cluster Analysis and Kohonen Neural (...)
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  34. 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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  35. Critical Branching Neural Networks.Christopher T. Kello - 2013 - Psychological Review 120 (1):230-254.
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  36. 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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  37. 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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  38. 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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  39. 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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  40. ALCOVE: An Exemplar-Based Connectionist Model of Category Learning.John K. Kruschke - 1992 - Psychological Review 99 (1):22-44.
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  41. 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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  42. 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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  43. A Symbolic-Connectionist Theory of Relational Inference and Generalization.John E. Hummel & Keith J. Holyoak - 2003 - Psychological Review 110 (2):220-264.
  44. 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.
  45. Connectionism and Human Learning: Critique of Gluck and Bower.David R. Shanks - 1990 - Journal of Experimental Psychology: General 119 (1):101-104.
  46. 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.
  47. The Architecture of Cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge.Matteo Colombo - 2016 - Philosophical Psychology 29 (3):476-478.
  48. Book Review : Systematicity: The Nature of Science. [REVIEW]Darrell Rowbottom - unknown
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  49. Systematicity and Arbitrariness in Novel Communication Systems.Carrie Ann Theisen-White, Jon Oberlander & Simon Kirby - 2010 - Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems 11 (1):14-32.
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  50. Cognition, Systematicity and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-153.
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