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A computational account of bilingual aphasia rehabilitation*

Published online by Cambridge University Press:  22 October 2012

SWATHI KIRAN*
Affiliation:
Department of Speech and Hearing Sciences, Boston University & Department of Communication Sciences and Disorders, The University of Texas at Austin
ULI GRASEMANN
Affiliation:
Department of Computer Science, The University of Texas at Austin
CHALEECE SANDBERG
Affiliation:
Department of Speech and Hearing Sciences, Boston University
RISTO MIIKKULAINEN
Affiliation:
Department of Computer Science, The University of Texas at Austin
*
Address for correspondence: Swathi Kiran, Speech Language and Hearing Sciences, Boston University Sargent College, 635 Commonwealth Ave., Boston, MA 02215, USAkirans@bu.edu

Abstract

Current research on bilingual aphasia highlights the paucity in recommendations for optimal rehabilitation for bilingual aphasic patients (Edmonds & Kiran, 2006; Roberts & Kiran, 2007). In this paper, we have developed a computational model to simulate an English–Spanish bilingual language system in which language representations can vary by age of acquisition (AoA) and relative proficiency in the two languages to model individual participants. This model is subsequently lesioned by varying connection strengths between the semantic and phonological networks and retrained based on individual patient demographic information to evaluate whether or not the model's prediction of rehabilitation matches the actual treatment outcome. In most cases the model comes close to the target performance subsequent to language therapy in the language trained, indicating the validity of this model in simulating rehabilitation of naming impairment in bilingual aphasia. Additionally, the amount of cross-language transfer is limited both in the patient performance and in the model's predictions and is dependent on that specific patient's AoA, language exposure and language impairment. It also suggests how well alternative treatment scenarios would have fared, including some cases where the alternative would have done better. Overall, the study suggests how computational modeling could be used in the future to design customized treatment recipes that result in better recovery than is currently possible.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

*

The computational component and portion of the treatment component of this research was supported by NIDCD # R21DC009446 to the first and last author. Also, a Clinical Research Grant from American Speech Language Hearing Foundation to the first author supported another component of the treatment project. The authors would like to thank the reviewers for their valuable comments and Danielle Tsibulskly, Anne Alvarez and Ellen Kester for their assistance in data collection.

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