Dissertation/Thesis Abstract

A neurophysiological study on probabilistic grammatical learning and sentence processing
by Hsu, Hsin-jen, Ph.D., The University of Iowa, 2009, 148; 3356264
Abstract (Summary)

Syntactic anomalies reliably elicit P600 effects in natural language processing. A survey of previous work converged on a conclusion that the mean amplitude of the P600 seems to be associated with the goodness of fit of a target word with expectation generated based on already unfolded materials. Based on this characteristic of the P600 effects, the current study aimed to look for evidence indicating the influence of input statistics in shaping grammatical knowledge/representations, and as a result leading to probabilistically-based competition/expectation generation processes of online sentence processing. An artificial grammar learning (AGL) task with 4 different conditions varying in probabilities were used to test this hypothesis. Results from this task indicated graded mean amplitude of the P600 effects across conditions, and the pattern of gradience is consistent with the variation of the input statistics. The use of the artificial language to simulate natural language learning process was further justified with statistically undistinguishable P600 effects elicited in a natural language sentence processing (NLSP) task. Together, the results indicate that the same neural mechanisms are recruited for both syntactic processing of natural language stimuli and sentence strings in an artificial language.

Indexing (document details)
Advisor: Tomblin, J. Bruce
Commitee: McGregor, Karla K., McMurray, Bob, Mordkoff, J. Toby, Owen, Amanda J.
School: The University of Iowa
Department: Speech & Hearing Science
School Location: United States -- Iowa
Source: DAI-B 70/05, Dissertation Abstracts International
Subjects: Speech therapy
Keywords: Event-related potentials, Grammar, Grammatical learning, P600, Sentence processing, Statistical learning
Publication Number: 3356264
ISBN: 978-1-109-16371-1
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