Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation and those employing symboliccomputation can in fact strengthen one another. To substantiate this controversialclaim, this landmark work develops in depth a cognitive architecture based in neuralcomputation but supporting formally explicit higher-level symbolic descriptions, including new grammar formalisms.Detailed studies in both phonology and syntaxprovide arguments that these grammatical theories and their neural networkrealizations enable deeper explanations of early acquisition, processing difficulty, cross-linguistic typology, and the possibility of genomically encoding universalprinciples of grammar. Foundational questions concerning the explanatory status ofsymbols for central problems such as the unbounded productivity of higher cognitionare also given proper treatment.The work is made accessible to scholars in differentfields of cognitive science through tutorial chapters and numerous expository boxesproviding background material from several disciplines. Examples common to differentchapters facilitate the transition from more basic to more sophisticated treatments.Details of method, formalism, and foundation are presented in later chapters, offering a wealth of new results to specialists in psycholinguistics, languageacquisition, theoretical linguistics, computational linguistics, computationalneuroscience, connectionist modeling, and philosophy of mind.
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