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  1. A Falsificationist Account of Artificial Neural Networks.Oliver Buchholz & Eric Raidl - forthcoming - The British Journal for the Philosophy of Science.
    Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and induction, the falsificationist component of machine learning has received minor attention. In this paper, we argue that the idea of falsification is central to the methodology of machine learning. It is commonly thought that machine learning algorithms infer general prediction rules from past (...)
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  2. Occam's Razor For Big Data?Birgitta Dresp-Langley - 2019 - Applied Sciences 3065 (9):1-28.
    Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...)
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  3. Walking Through the Turing Wall.Albert Efimov - forthcoming - In Teces.
    Can the machines that play board games or recognize images only in the comfort of the virtual world be intelligent? To become reliable and convenient assistants to humans, machines need to learn how to act and communicate in the physical reality, just like people do. The authors propose two novel ways of designing and building Artificial General Intelligence (AGI). The first one seeks to unify all participants at any instance of the Turing test – the judge, the machine, the human (...)
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  4. Black Boxes, or Unflattering Mirrors? Comparative Bias in the Science of Machine Behavior.Cameron Joseph Buckner - forthcoming - British Journal for the Philosophy of Science.
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  5. Environmental Variability and the Emergence of Meaning: Simulational Studies Across Imitation, Genetic Algorithms, and Neural Nets.Patrick Grim - 2006 - In Angelo Loula & Ricardo Gudwin (eds.), Artificial Cognition Systems. Idea Group. pp. 284-326.
    A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here we (...)
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  6. Ontology, Neural Networks, and the Social Sciences.David Strohmaier - 2020 - Synthese 199 (1-2):4775-4794.
    The ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks have made their way into (...)
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  7. Connectomes as Constitutively Epistemic Objects: Critical Perspectives on Modeling in Current Neuroanatomy.Philipp Haueis & Jan Slaby - 2017 - In Progress in Brain Research Vol 233: The Making and Use of Animal Models in Neuroscience and Psychiatry. Amsterdam: pp. 149–177.
    in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams (...)
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  8. Review of Starkey (1992): Connectionist Natural Language Processing: Readings From ‘Connection Science’. [REVIEW]Ephraim Nissan - 1997 - Pragmatics and Cognition 5 (2):383-384.
  9. 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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  10. 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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  11. 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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  12. Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  13. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2021 - Synthese 198 (March):2061-2081.
    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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  14. 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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  15. 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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  16. Systematicity, Conceptual Truth, and Evolution.Brian P. McLaughlin - 1993 - Royal Institute of Philosophy Supplement 34:217-234.
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  17. Wittgenstein and Connectionism: A Significant Complementarity?Stephen Mills - 1993 - Royal Institute of Philosophy Supplement 34:137-157.
    Between the later views of Wittgenstein and those of connectionism 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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  18. Peter Novak, Mental Symbols: A Defence of the Classical Theory of Mind. [REVIEW]Istvan S. N. Berkeley - 2001 - Minds and Machines 11 (1):148-150.
  19. 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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  20. What Makes Connectionism Different?James H. Fetzer - 1994 - Pragmatics and Cognition 2 (2):327-348.
  21. Connectionism, Concepts, and Folk Psychology: The Legacy of Alan Turing, Volume 2. [REVIEW]Daniel N. Robinson - 1998 - Review of Metaphysics 51 (4):919-919.
  22. What Systematicity Isn’T: Reply to Davis.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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  23. 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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  24. 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.
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  25. 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.
  26. 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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  27. 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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  28. Computability of Logical Neural Networks.T. B. Ludermir - 1992 - Journal of Intelligent Systems 2 (1-4):261-290.
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  29. Dreams and Connectionism: A Critique.D. Kuiken - 1994 - Journal of Intelligent Systems 4 (3-4):263-278.
  30. 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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  31. 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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  32. Common and Distinct Neural Networks for Theory of Mind Reasoning and Inhibitory Control.Christoph Rothmayr - unknown
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  33. On the Systematicity of Language and Thought.Kent Johnson - 2004 - Journal of Philosophy 101 (3):111-139.
  34. Connectionist Minds.Andy Clark - 1990 - Proceedings of the Aristotelian Society 90 (1):83-102.
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  35. 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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  36. Critical Branching Neural Networks.Christopher T. Kello - 2013 - Psychological Review 120 (1):230-254.
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  37. 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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  38. 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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  39. 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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  40. 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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  41. ALCOVE: An Exemplar-Based Connectionist Model of Category Learning.John K. Kruschke - 1992 - Psychological Review 99 (1):22-44.
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  42. 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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  43. 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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  44. A Symbolic-Connectionist Theory of Relational Inference and Generalization.John E. Hummel & Keith J. Holyoak - 2003 - Psychological Review 110 (2):220-264.
  45. The Architecture of Cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge.Matteo Colombo - 2016 - Philosophical Psychology 29 (3):476-478.
  46. 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 Systemsinteraction Studies / Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies 11 (1):14-32.
    Arbitrariness and systematicity are two of language’s most fascinating properties. Although both are characterizations of the mappings between signals and meanings, their emergence and evolution in communication systems has generally been explored independently. We present an experiment in which both arbitrariness and systematicity are probed. Participants invent signs from scratch to refer to a set of items that share salient semantic features. Through interaction, the systematic re-use of arbitrary signal elements emerges.
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  47. Cognition, Systematicity and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-153.
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  48. Can Connectionists Explain Systematicity?Robert J. Matthews - 1997 - Mind and Language 12 (2):154-177.
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  49. Networks with Attitudes.Paul Skokowski - 2009 - AI and Society 23 (4):461-470.
  50. 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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