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Connectionism versus symbolism in high-level cognition

In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer Academic Publishers. pp. 382--416 (1991)

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  1. A neural network for creative serial order cognitive behavior.Steve Donaldson - 2008 - Minds and Machines 18 (1):53-91.
    If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler systems. Its (...)
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  • The connectionism/classicism battle to win souls.Brian P. McLaughlin - 1993 - Philosophical Studies 71 (2):163-190.
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  • The Allure of Connectionism Reexamined.B. P. McLaughlin & T. A. Warfield - 1994 - Synthese 101 (3):365 - 400.
    There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...)
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  • A nonclassical framework for cognitive science.Terence E. Horgan & John L. Tienson - 1994 - Synthese 101 (3):305-45.
    David Marr provided a useful framework for theorizing about cognition within classical, AI-style cognitive science, in terms of three levels of description: the levels of (i) cognitive function, (ii) algorithm and (iii) physical implementation. We generalize this framework: (i) cognitive state transitions, (ii) mathematical/functional design and (iii) physical implementation or realization. Specifying the middle, design level to be the theory of dynamical systems yields a nonclassical, alternative framework that suits (but is not committed to) connectionism. We consider how a brain's (...)
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  • The allure of connectionism reexamined.Brian P. McLaughlin & F. Warfield - 1994 - Synthese 101 (3):365-400.
    There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...)
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