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Dissertation/Thesis Abstract

Towards a Mechanistic Account of Speech Comprehension in the Human Brain
by Gwilliams, Laura, Ph.D., New York University, 2020, 182; 28029854
Abstract (Summary)

Humans understand speech with such speed and accuracy, it belies the complexity of transforming sound into meaning. The goal of my research is to develop a theoretically grounded, empirically tested and computationally explicit account of how the brain achieves this feat. In the work presented here, I overview a set of magneto-encephalography studies that describe (i) what linguistic representations the brain uses to bridge between sound and meaning; (ii) how those representations are combined to form hierarchical structures (e.g. phonemes into morphemes; morphemes into words); (iii) how information is exchanged across structures to guide comprehension from the bottom-up and top-down. The research also contributes to a broader analytical framework — informed by machine-learning and classic statistics — which allows neural signals to be decomposed into an interpretable sequence of operations. Overall, this dissertation showcases the utility of combining theoretical linguistics, machine-learning and cognitive neuroscience for developing empirically- and performance-optimised models of spoken language processing.

Indexing (document details)
Advisor: Marantz, Alec, Poeppel, David
Commitee: Simoncelli, Eero, Mesgarani, Nima, Pylkkanen, Liina
School: New York University
Department: Psychology
School Location: United States -- New York
Source: DAI-A 82/5(E), Dissertation Abstracts International
Subjects: Neurosciences, Cognitive psychology, Linguistics, Language, Speech therapy
Keywords: Human brain function, Decoding, MEG, Phonology, Speech comprehension, Machine learning, Spoken language processing
Publication Number: 28029854
ISBN: 9798691231858
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