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  1. Kenneth Aizawa (1992). Connectionism and Artificial Intelligence: History and Philosophical Interpretation. Journal for Experimental and Theoretical Artificial Intelligence 4:1992.
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  2. C. Philip Beaman (2000). Neurons Amongst the Symbols? 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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  3. Istvan S. N. Berkeley, Connectionism Reconsidered: Minds, Machines and Models.
    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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  4. Andy Clark (1994). Representational Trajectories in Connectionist Learning. 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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  5. Andy Clark & Chris Thornton (1997). Relational Learning Re-Examined. Behavioral and Brain Sciences 20 (1):83-90.
    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 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.
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  6. Andy Clark & S. Thornton (1997). Trading Spaces: Computation, Representation, and the Limits of Uninformed Learning. 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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  7. D. Cliff (1990). Computational Neuroethology: A Provisional Manifesto. 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.
  8. Michael R. W. Dawson & D. P. Schopflocher (1992). Autonomous Processing in Parallel Distributed Processing Networks. 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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  9. Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (2012). “Machine” Consciousness and “Artificial” Thought: An Operational Architectonics Model Guided Approach. 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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  10. James Franklin (1996). How a Neural Net Grows Symbols. Proc 7.
    Brains, unlike artificial neural nets, use sym- bols to summarise and reason about percep- tual input. But unlike symbolic AI, they “ground” the symbols in the data: the sym- bols have meaning in terms of data, not just meaning imposed by the outside user. If neu- ral nets could be made to grow their own sym- bols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as (...)
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  11. George Graham (1987). Connectionism in Pavlovian Harness. Southern Journal of Philosophy (Suppl.) 73 (S1):73-91.
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  12. Susan Hanson & D. Burr (1990). What Connectionist Models Learn. Behavioral and Brain Sciences.
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  13. Stevan Harnad (1995). Thoughts as Activation Vectors in Recurrent Nets, or Concentric Epicenters, Or.. 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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  14. S. Kaplan, M. Weaver & Robert M. French (1990). Active Symbols and Internal Models: Towards a Cognitive Connectionism. [REVIEW] 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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  15. David Kirsh (1987). Putting a Price on Cognition. Southern Journal of Philosophy Supplement 26 (S1):119-35.
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  16. J. Lachter & Thomas G. Bever (1988). The Relation Between Linguistic Structure and Associative Theories of Language Learning. Cognition 28:195-247.
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  17. Jean-Arcady Meyer & Stewart W. Wilson (eds.) (1990). From Animals to Animats: Proceedings of The First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems). Cambridge University Press.
  18. Stephen L. Mills (1989). Connectionism, the Classical Theory of Cognition, and the Hundred Step Constraint. Acta Analytica 4 (4):5-38.
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  19. Raymond J. Nelson (1989). Philosophical Issues in Edelman's Neural Darwinism. Journal of Experimental and Theoretical Artificial Intelligence 1:195-208.
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  20. Gerard O'Brien (1998). The Role of Implementation in Connectionist Explanation. Psycoloquy 9 (6).
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  21. Mike Oaksford, Nick Chater & Keith Stenning (1990). Connectionism, Classical Cognitive Science and Experimental Psychology. 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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  22. Jonathan Opie (1998). Connectionist Modelling Strategies. 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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  23. Steven Pinker & Alan Prince (1988). On Language and Connectionism. Cognition 28 (1-2):73-193.
  24. Matjaz Potrc (1995). Consciousness and Connectionism--The Problem of Compatability of Type Identity Theory and of Connectionism. Acta Analytica 13 (13):175-190.
  25. Don Ross (1998). Internal Recurrence. Dialogue 37 (1):155-161.
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  26. Martin Roth (2005). Program Execution in Connectionist Networks. 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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