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The Society for the Neurobiology of Language is pleased to announce the 2020 Dissertation Award winner: Laura Gwilliams.
The Dissertation Award is generously sponsored by Brain & Language.
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 empirically- and performance-optimised models of spoken language processing.
About Laura Gwilliams
Laura Gwilliams received her PhD in Psychology with a focus in Cognitive Neuroscience from New York University in May 2020. Currently she is a post-doctoral researcher at UCSF, using MEG and ECoG data to understand how linguistic structures are parsed and composed while listening to continuous speech. The ultimate goal of Laura’s research is to describe speech comprehension in terms of what operations are applied to the acoustic signal; which representational formats are generated and manipulated (e.g. phonetic, syllabic, morphological), and under what processing architecture.
Dr. Gwilliams’ PhD research focused on questions of phonemic representation in lexical contexts, on ambiguity, and on sentence level phenomena in online processing. For example, using an innovative combination of psychophysics and MEG, Laura tackled how ambiguously perceived phonemes inform lexical-level processing in an influential article published in the Journal of Neuroscience. The work makes a significant contribution to when in time, where in the brain, and how ambiguous representations are maintained versus ‘discarded’ to facilitate categorical level processing. Further and quite impressive are her quantitative and data analytic skills that go far beyond what most researchers attain. In fact, she has developed her own integrative approach to cognitive neuroscience, which spans advances in data analysis, theory, and computational modeling – showcased in her recent paper on phoneme sequence processing of naturalistic speech. Furthermore, together with Jean-Remi King, Laura provided machine-learning and data analysis tools for M/EEG that serve multiple labs at NYU. Most impressively she has written a sole authored paper in Philosophical Transactions, which informs the community in an incredibly differentiated manner about the integration of contemporary linguistic theory with the sketch of a computational model, while also encompassing the latest neuroscience data on morphological processing. We congratulate Laura on these impressive achievements.
Reem Al Yahya, MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; King Fahad Medical City, Saudi Arabia
Esti Blanco-Elorrieta, New York University; Harvard University
Gabriela Meade, San Diego State University; University of California, San Diego
Sarah Solomon, University of Pennsylvania