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    THE COMPUTATION OF VERB-ARGUMENT RELATIONS IN ONLINE SENTENCE COMPREHENSION
    (2020) Liao, Chia-Hsuan; Lau, Ellen; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Understanding how verbs are related to their arguments in real time is critical to building a theory of online language comprehension. This dissertation investigates the incremental processing of verb-argument relations with three interrelated approaches that use the event-related potential (ERP) methodology. First, although previous studies on verb-argument computations have mainly focused on relating nouns to simple events denoted by a simple verb, here I show by investigating compound verbs I can dissociate the timing of the subcomputations involved in argument role assignment. A set of ERP experiments in Mandarin comparing the processing of resultative compounds (Kid bit-broke lip: the kid bit his lip such that it broke) and coordinate compounds (Store owner hit-scolded employee: the store owner hit and scolded an employee) provides evidence for processing delays associated with verbs instantiating the causality relation (breaking-BY-biting) relative to the coordinate relation (hitting-AND-scolding). Second, I develop an extension of classic ERP work on the detection of argument role-reversals (the millionaire that the servant fired) that allows me to determine the temporal stages by which argument relations are computed, from argument identification to thematic roles. Our evidence supports a three-stage model where an initial word association stage is followed by a second stage where arguments of a verb are identified, and only at a later stage does the parser start to consider argument roles. Lastly, I investigate the extent to which native language (L1) subcategorization knowledge can interfere with second language (L2) processing of verb-argument relations, by examining the ERP responses to sentences with verbs that have mismatched subcategorization constraints in L1 Mandarin and L2 English (“My sister listened the music”). The results support my hypothesis that L1 subcategorization knowledge is difficult for L2 speakers to override online, as they show some sensitivity to subcategorization violations in offline responses but not in ERPs. These data indicate that computing verb-argument relations requires accessing lexical syntax, which is vulnerable to L1 interference in L2. Together, these three ERP studies allow us to begin to put together a full model of the sub-processes by which verb-argument relations are constructed in real time in L1 and L2.
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    The Temporal Dimension of Linguistic Prediction
    (2013) Chow, Wing Yee; Phillips, Colin; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis explores how predictions about upcoming language inputs are computed during real-time language comprehension. Previous research has demonstrated humans' ability to use rich contextual information to compute linguistic prediction during real-time language comprehension, and it has been widely assumed that contextual information can impact linguistic prediction as soon as it arises in the input. This thesis questions this key assumption and explores how linguistic predictions develop in real-time. I provide event-related potential (ERP) and reading eye-movement (EM) evidence from studies in Mandarin Chinese and English that even prominent and unambiguous information about preverbal arguments' structural roles cannot immediately impact comprehenders' verb prediction. I demonstrate that the N400, an ERP response that is modulated by a word's predictability, becomes sensitive to argument role-reversals only when the time interval for prediction is widened. Further, I provide initial evidence that different sources of contextual information, namely, information about preverbal arguments' lexical identity vs. their structural roles, may impact linguistic prediction on different time scales. I put forth a research framework that aims to characterize the mental computations underlying linguistic prediction along a temporal dimension.