UTCS Artificial Intelligence

One goal of the Healthy People 2010 program is to reduce health disparities across different segments of the population. Diagnosis and treatment of bilingual aphasia is one area where disparities continue to exist even though this topic is of great importance in an increasingly bilingual world. The current research on this topic, however, lacks specific recommendations on which languages should be trained in a bilingual aphasic individual and to what extent cross-language transfer occurs subsequent to rehabilitation. Factors contributing to the paucity of research in this area relate to the multitude of possible language combinations in a bilingual individual, the relative competency of the two languages of the bilingual individual and the effect of focal brain damage on bilingual language representation. It is, however, unfeasible to examine these issues without undertaking a large scale longitudinal study in this population.

As a potential solution, this project will systematically examine the extent of cross-language transfer subsequent to rehabilitation using a computational model. This model will be developed to simulate a bilingual language system in which language representations can vary by age of acquisition and relative proficiency, and will be subsequently lesioned and retrained to improve output. The training will be provided in one language and the extent of cross-language transfer will be examined. It is predicted that age of acquisition, the level of pre-morbid language proficiency and post-morbid language performance will influence the nature and degree of cross-language transfer. Further, the model's power to predict the optimal language to be treated will be compared to data obtained from behavioral interventions from a sample of patients with bilingual aphasia. The work is innovative because it uses a computational model to predict optimal rehabilitation protocols to facilitate the greatest amount of language recovery in bilingual aphasia. The successful completion of this project is expected to have an important impact on rehabilitation of stroke and bilingual aphasia as well as on the applications of computational modeling.

This research is supported by the National Institutes of Health under grant R21-DC009446, with Swathi Kiran of Boston University as a co-PI.