Document Type


Date of Degree

Spring 2009

Degree Name

PhD (Doctor of Philosophy)

Degree In

Speech and Hearing Science

First Advisor

Tomblin, J. Bruce

First Committee Member

Owen, Amanda J.

Second Committee Member

McGregor, Karla K.

Third Committee Member

McMurray, Bob

Fourth Committee Member

Mordkoff, J Toby


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.


ERP, Grammar, P600, probability, Statistical leanring


ix, 135 pages


Includes bibliographical references (pages 129-135).


Copyright 2009 Hsin-jen Hsu