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).
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
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, 2017-2020)
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)