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Imaging neural correlates of syntactic complexity in a naturalistic context

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dc.contributor.advisor John Gabrieli and Alex Marantz. en_US
dc.contributor.author Bachrach, Asaf en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Linguistics and Philosophy. en_US
dc.date.accessioned 2009-06-30T16:34:35Z
dc.date.available 2009-06-30T16:34:35Z
dc.date.copyright 2008 en_US
dc.date.issued 2008 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/45900
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2008. en_US
dc.description Includes bibliographical references (p. 253-280). en_US
dc.description.abstract The aim of this thesis, and the research project within which it is embedded, is to delineate a neural model of grammatical competence. For this purpose, we develop here a novel integrated, multi-disciplinary experimental paradigm that endorses the fundamental premise of generative grammar, that the study of language is in essence, the study of the mind. We use functional Magnetic Resonance Imaging (fMRI) to monitor brain activation while subjects listen to short narratives. The texts have been written so as to introduce various syntactic complexities (relative clauses, embedded questions, etc.) not usually found (in such density) in actual corpora. We have calculated a number of complexity measures (both at the level of the single word and at that of the phrase) based on current linguistic and psycholinguistic theory and with the use of a computationally implemented probabilistic parser. By correlating these measures with observed brain activity, we are able to identify the different brain networks that support linguistic processing and characterize their particular function. Conversely, we use the rich brain data to inform our cognitive, and linguistic, theory. We report here the neural correlates of surprisal (based on contextual predictions), syntactic complexity, structural ambiguity and disambiguation, Theory of Mind and non-local dependencies. This work made use of novel solutions to compute numerical predictions for these linguistic dimensions, which are often tested only qualitatively, and of a novel parametric fMRI design that allowed for the use of single subject unaveraged data as the dependent variable. The thesis ends with a synthesis of the results in the form of a blue print for a neural model of grammatical competence. en_US
dc.description.statementofresponsibility by Asaf Bachrach. en_US
dc.format.extent 280 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Linguistics and Philosophy. en_US
dc.title Imaging neural correlates of syntactic complexity in a naturalistic context en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Linguistics and Philosophy. en_US
dc.identifier.oclc 320526571 en_US


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