I study the mental structures and computations used to understand words and sentences, with a focus on how these processes are implemented in the brain. My research uses formal, computationally grounded models of language comprehension to probe neural signals collected with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI).
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I have a particular interest in experimental methods that are as natural as possible, such as having participants read or listen to a story. Naturalistic techniques are especially suitable with populations for which standard experimental tasks may be inappropriate, such as with children with developmental disorders.
PhD in Linguistics, 2010
New York University
BA in Cognitive Science & Classics, 2003
This breezy guide introduces readers to the state-of-the-art neuroscientific research that is revolutionizing our understanding of language.
We investigate how the brain supports building meaning that combines what people say with who is saying it, guided by the understanding that stereotypes and biases can shape (and misshape) comprehension.
We model and test the neural mechanisms that allow us to recognize the meanings of spoken and written words rapidly and efficiently.
We build computational models of sentence understanding and evaluate their fit against neural signals collected from people performing a relatively natural task, like listening to a story. (NSF #1607251, 2017-2019)
We use magnetoencephalography to probe processing of sounds, words, and sentences in school-aged children with high-functioning Autism Spectrum Disorder. (U-M M-Cubed Initative, 2013-2015)