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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.
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 |
Source Type: | DISSERTATION |
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 |