Recently there has been an increase in psycholinguistics and neurolinguistics research using naturalistic stimulus following Willems’ (2015) encouragement to probe the neural bases of language comprehension with greater ecological validity. Along with naturalistic stimulus, applying tools from computational linguistics to neuroimaging data can help us gain further insight into real time language processing.
In this talk, I will focus on two topics: noncompositional expressions (MWEs) and verbal argument structure. Using computational models and neuroimaging data, I investigate questions such as: Are multiword expressions processed differently from compositional phrases and do they have different neural bases? Are all multiword expressions equally “frozen” and how can we distinguish between them? Does subcategorization and selectional restrictions on a verb have different neural bases? How does argument structure influence the entropy during sentence processing?
Based on these two studies, I will show how we can utilize various models and metrics from computational linguistics to help us better understand the neurocognitive bases of language processing.
Shohini Bhattasali is a Ph.D candidate in the Department of Linguistics at Cornell University. Her research brings together computational linguistics and neurolinguistics. Using fMRI data, she explores how different syntactic, semantic, and pragmatic cues are utilized during language comprehension across different levels of linguistic analysis. Previously, she graduated from Bryn Mawr College with a major in Linguistics and minors in Computer Science and Chinese.