Date of Award

12-14-2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Psychology

First Advisor

Christopher M. Conway, PhD

Second Advisor

Rose A. Sevcik, PhD

Third Advisor

David A. Washburn, PhD

Fourth Advisor

Samuel Fernandez-Carriba, PhD

Abstract

Structured Sequence Processing (SSP) is a fundamental, general-purpose neurocognitive mechanism used to learn patterns of information in the environment over time. SSP helps us make predictions about upcoming events and is vital for grammatical language processing. The primary aim of my doctoral research is using cognitive training techniques with brain imaging to better understand and to improve attention and learning mechanisms that support (and could, in turn, enhance) language. The focus has been on SSP and grammatical/predictive language processing. To summarize main findings across three studies, 1) the results from a mediation analysis (N=60) demonstrate that computerized SSP training has an underlying effect on predictive language processing by way of its effect on SSP, 2) the results from source localization of electrophysiological activity in the brain (N=32) suggest the neurocognitive mechanisms recruited for a non-linguistic SSP task and a grammatical language processing task share marked overlap in the left anterior superior temporal gyrus, and 3) the preliminary findings of a second computerized SSP training study with EEG/ERP recording (N=34) establish the feasibility of improving SSP as a way to improve language, possibly modulated by changes to attention and working memory. These outcomes inform future investigations of how SSP and language mechanisms can be enhanced in individuals with atypical SSP and language processing, either developmental or acquired. This approach could lead to collaborative opportunities for exploring the potential to improve SSP and language by functionally reorganizing their shared neural circuitry.

Available for download on Thursday, November 21, 2019

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