Syntactic Priming in Comprehension

University Of California-Davis, Davis CA

Investigators

Abstract

0446618 Traxler With National Science Foundation support, Dr. Matthew J. Traxler will conduct three years of linguistic research examining how readers access and use stored grammatical knowledge while comprehending sentences. The project will use a highly accurate eye-tracker to monitor readers' eye movements as they process sentences of various grammatical types. The experiments will assess the degree to which processing of one sentence facilitates the processing of a following sentence with a similar syntactic structure or overlapping lexical (word-related) information. Because eye-movements are highly sensitive to small changes in processing demands, the readers' patterns of eye-movements will indicate whether reading one sentence speeds the processing of a subsequent sentence. The goals of this research are to (a) determine whether building a "context-free" syntactic structure for one sentence can speed processing of a following sentence, (b) determine whether argument-structure information that is tied to specific words that appear in two consecutive sentences provides the only means to speed processing of the second sentence, and (c) to determine whether facilitated processing occurs across a range of sentence types or is limited to only those sentences that normally cause substantial processing difficulty. The results of this research will be important for understanding how grammatical knowledge is stored and used to determine how words in sentences are related to one another. Further, the results will inform ongoing discussion of the extent to which readers discover the syntactic structure of a sentence by using an autonomous parsing module versus accessing and applying a wide range of probabilistic cues, including lexically stored frequencies. A fully articulated model of normative language processing, particularly those processes used to parse sentences, may be useful to practitioners working with aphasic populations and computer scientists developing computer-based language understanding systems.