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Learning Grammar via Statistical Mechanism
thesis
posted on 2013-04-10, 00:00 authored by WonJae ShinA novel word-by-word prediction paradigm, which employs a task similar to the one given to Elman's (1990, 1991, 1993) simple recurrent networks, is used in the current study to investigate whether adults exhibit learning higher-order contingencies corresponding to structural contingencies. The results indicate that token and category contingencies are learnable regardless of training conditions, but higher-order constituent contingency was only learnable in conditions that facilitated initial learning of bigram and trigram word frequencies. Our results are consistent with previous studies that involve both human subjects and computer simulations, and provide a guide toward identifying factors that may enhance learning of higher-order statistical relations in human subjects.
History
Date Modified
2017-06-05Research Director(s)
Kathleen M. EberhardCommittee Members
Jill Lany James R. BrockmoleDegree
- Master of Arts
Degree Level
- Master's Thesis
Language
- English
Alternate Identifier
etd-04102013-025151Publisher
University of Notre DameProgram Name
- Psychology
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