The Temporal Dimension of Linguistic Prediction

Abstract

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.