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  1. The Exploratory Status of Postconnectionist Models.Miljana Milojevic & Vanja Subotić - 2020 - Theoria: Beograd 2 (63):135-164.
    This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...)
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  2. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  3. From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence.Catherine Stinson - 2020 - Philosophy of Science 87 (4):590-611.
    There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...)
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  4. Dead Reckoning in the Desert Ant: A Defence of Connectionist Models.Christopher Mole - 2014 - Review of Philosophy and Psychology 5 (2):277-290.
    Dead reckoning is a feature of the navigation behaviour shown by several creatures, including the desert ant. Recent work by C. Randy Gallistel shows that some connectionist models of dead reckoning face important challenges. These challenges are thought to arise from essential features of the connectionist approach, and have therefore been taken to show that connectionist models are unable to explain even the most primitive of psychological phenomena. I show that Gallistel’s challenges are successfully met by one recent connectionist model, (...)
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  5. “Machine” Consciousness and “Artificial” Thought: An Operational Architectonics Model Guided Approach.Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves - 2012 - Brain Research 1428:80-92.
    Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical Operational Architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis of the (...)
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  6. Empirical Modeling and Information Semantics.Gordana Dodig-Crnkovic - 2008 - Mind and Society 7 (2):157-166.
    This paper investigates the relationship between reality and model, information and truth. It will argue that meaningful data need not be true in order to constitute information. Information to which truth-value cannot be ascribed, partially true information or even false information can lead to an interesting outcome such as technological innovation or scientific breakthrough. In the research process, during the transition between two theoretical frameworks, there is a dynamic mixture of old and new concepts in which truth is not well (...)
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  7. Program Execution in Connectionist Networks.Martin Roth - 2005 - Mind and Language 20 (4):448-467.
    Recently, connectionist models have been developed that seem to exhibit structuresensitive cognitive capacities without executing a program. This paper examines one such model and argues that it does execute a program. The argument proceeds by showing that what is essential to running a program is preserving the functional structure of the program. It has generally been assumed that this can only be done by systems possessing a certain temporalcausal organization. However, counterfactualpreserving functional architecture can be instantiated in other ways, for (...)
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  8. Neurons Amongst the Symbols?C. Philip Beaman - 2000 - Behavioral and Brain Sciences 23 (4):468-470.
    Page's target article presents an argument for the use of localist, connectionist models in future psychological theorising. The “manifesto” marshalls a set of arguments in favour of localist connectionism and against distributed connectionism, but in doing so misses a larger argument concerning the level of psychological explanation that is appropriate to a given domain.
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  9. Elme És Evolúció.Bence Nanay - 2000 - Kávé..
  10. The Role of Implementation in Connectionist Explanation.Gerard O'Brien - 1998 - Psycoloquy 9 (6).
  11. Connectionist Modelling Strategies.Jonathan Opie - 1998 - Psycoloquy 9 (30).
    Green offers us two options: either connectionist models are literal models of brain activity or they are mere instruments, with little or no ontological significance. According to Green, only the first option renders connectionist models genuinely explanatory. I think there is a third possibility. Connectionist models are not literal models of brain activity, but neither are they mere instruments. They are abstract, IDEALISED models of the brain that are capable of providing genuine explanations of cognitive phenomena.
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  12. Consciousness and Connectionism--The Problem of Compatability of Type Identity Theory and of Connectionism.Matjaz Potrc - 1998 - Acta Analytica 13:175-190.
  13. Internal Recurrence.Don Ross - 1998 - Dialogue 37 (1):155-161.
    It is crucial, first of all, to stress the importance Churchland attaches to the idea that the neural networks whose assemblages he holds to be “engines of reason” must be recurrent. Non-recurrent networks, of the sort best known among philosophers, simply discover patterns in input data presented to them as sets of features. The learning capacities of such networks, extensively discussed since the publication of Rumelhart and McClelland et al., are indeed impressive; and Churchland describes them clearly and gracefully as (...)
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  14. Trading Spaces: Computation, Representation, and the Limits of Uninformed Learning.Andy Clark & S. Thornton - 1997 - Behavioral and Brain Sciences 20 (1):57-66.
    Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large type-2 problems. they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding (...)
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  15. Relational Learning Re-Examined.Chris Thornton & Andy Clark - 1997 - Behavioral and Brain Sciences 20 (1):83-83.
    We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”.
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  16. How a Neural Net Grows Symbols.James Franklin - 1996 - In Peter Bartlett (ed.), Proceedings of the Seventh Australian Conference on Neural Networks, Canberra. Canberra, Australia: ACNN '96. pp. 91-96.
    Brains, unlike artificial neural nets, use symbols to summarise and reason about perceptual input. But unlike symbolic AI, they “ground” the symbols in the data: the symbols have meaning in terms of data, not just meaning imposed by the outside user. If neural nets could be made to grow their own symbols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as to combine the good features (...)
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  17. Thoughts as Activation Vectors in Recurrent Nets, or Concentric Epicenters, Or..Stevan Harnad - 1995 - Http.
    Churchland underestimates the power and purpose of the Turing Test, dismissing it as the trivial game to which the Loebner Prize (offered for the computer program that can fool judges into thinking it's human) has reduced it, whereas it is really an exacting empirical criterion: It requires that the candidate model for the mind have our full behavioral capacities -- so fully that it is indistinguishable from any of us, to any of us (not just for one Contest night, but (...)
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  18. Representational Trajectories in Connectionist Learning.Andy Clark - 1994 - Minds and Machines 4 (3):317-32.
    The paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to miss the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we (...)
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  19. Connectionism and Psychiatry: A Brief Review.S. B. G. Park & A. H. Young - 1994 - Philosophy, Psychiatry, and Psychology 1 (1):51-58.
  20. Connectionism and Artificial Intelligence: History and Philosophical Interpretation.Kenneth Aizawa - 1992 - Journal for Experimental and Theoretical Artificial Intelligence 4:1992.
    Hubert and Stuart Dreyfus have tried to place connectionism and artificial intelligence in a broader historical and intellectual context. This history associates connectionism with neuroscience, conceptual holism, and nonrationalism, and artificial intelligence with conceptual atomism, rationalism, and formal logic. The present paper argues that the Dreyfus account of connectionism and artificial intelligence is both historically and philosophically misleading.
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  21. Autonomous Processing in Parallel Distributed Processing Networks.Michael R. W. Dawson & Don P. Schopflocher - 1992 - Philosophical Psychology 5 (2):199-219.
    This paper critically examines the claim that parallel distributed processing (PDP) networks are autonomous learning systems. A PDP model of a simple distributed associative memory is considered. It is shown that the 'generic' PDP architecture cannot implement the computations required by this memory system without the aid of external control. In other words, the model is not autonomous. Two specific problems are highlighted: (i) simultaneous learning and recall are not permitted to occur as would be required of an autonomous system; (...)
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  22. Computational Neuroethology: A Provisional Manifesto.D. Cliff - 1990 - In Jean-Arcady Meyer & Stewart W. Wilson (eds.), From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems). Cambridge University Press.
  23. What Connectionist Models Learn.Susan Hanson & D. Burr - 1990 - Behavioral and Brain Sciences.
  24. Active Symbols and Internal Models: Towards a Cognitive Connectionism. [REVIEW]Stephen Kaplan, Mark Weaver & Robert French - 1990 - AI and Society 4 (1):51-71.
    In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...)
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  25. From Animals to Animats: Proceedings of The First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems).Jean-Arcady Meyer & Stewart W. Wilson (eds.) - 1990 - Cambridge University Press.
    These sixty contributions from researchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fields delve into the behaviors and underlying mechanisms that allow animals and, potentially, robots to adapt and survive in uncertain environments. They focus in particular on simulation models in order to help characterize and compare various organizational principles or architectures capable of inducing adaptive behavior in real or artificial animals. Jean-Arcady Meyer is Director of Research at CNRS, Paris. Stewart W. Wilson is a Scientist at The (...)
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  26. Connectionism, Classical Cognitive Science and Experimental Psychology.Mike Oaksford, Nick Chater & Keith Stenning - 1990 - AI and Society 4 (1):73-90.
    Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this (...)
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  27. Connectionism, the Classical Theory of Cognition, and the Hundred Step Constraint.Stephen L. Mills - 1989 - Acta Analytica 4 (4):5-38.
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  28. Philosophical Issues in Edelman's Neural Darwinism.Raymond J. Nelson - 1989 - Journal of Experimental and Theoretical Artificial Intelligence 1:195-208.
  29. The Relation Between Linguistic Structure and Associative Theories of Language Learning.J. Lachter & Thomas G. Bever - 1988 - Cognition 28:195-247.
  30. On Language and Connectionism: Analysis of a Parallel Distributed Processing Model of Language Acquisition.Steven Pinker & Alan Prince - 1988 - Cognition 28 (1-2):73-193.
  31. Connectionism in Pavlovian Harness.George Graham - 1987 - Southern Journal of Philosophy (Suppl.) 73 (S1):73-91.
  32. Putting a Price on Cognition.David Kirsh - 1987 - Southern Journal of Philosophy Supplement 26 (S1):119-35.
  33. Connectionism Reconsidered: Minds, Machines and Models.Istvan S. N. Berkeley - 1998
    In this paper the issue of drawing inferences about biological cognitive systems on the basis of connectionist simulations is addressed. In particular, the justification of inferences based on connectionist models trained using the backpropagation learning algorithm is examined. First it is noted that a justification commonly found in the philosophical literature is inapplicable. Then some general issues are raised about the relationships between models and biological systems. A way of conceiving the role of hidden units in connectionist networks is then (...)
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