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  1. Neural-Symbolic Cognitive Reasoning.Artur S. D'Avila Garcez, Luís C. Lamb & Dov M. Gabbay - 2009 - Berlin and Heidelberg: Springer.
    This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
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  • The language of thought hypothesis.Murat Aydede - 2010 - Stanford Encyclopedia of Philosophy.
    A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...)
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  • Context-sensitive inference, modularity, and the assumption of formal processing.Mitch Parsell - 2005 - Philosophical Psychology 18 (1):45-58.
    Performance on the Wason selection task varies with content. This has been taken to demonstrate that there are different cognitive modules for dealing with different conceptual domains. This implication is only legitimate if our underlying cognitive architecture is formal. A non-formal system can explain content-sensitive inference without appeal to independent inferential modules.
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  • 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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