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Laura Gwilliams, University of California, San Francisco (UCSF)
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Title: Decoding the neural architecture of speech comprehension
Abstract: 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 this talk, I will first present an analytical framework — informed by machine-learning and classic statistics — which allows neural signals to be decomposed into an interpretable sequence of operations.
Next, utilising this framework, I will 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 the top-down. Overall, this body of work showcases the utility of combining theoretical linguistics, machine-learning and cognitive neuroscience for developing neurally-constrained models of spoken language processing.