Abstract
Aphasia is a common speech and language disorder that often occurs after acquired brain damage. Bilinguals with aphasia may present varying degrees of impairment across their two languages, yet both have potential for recovery. Since therapy is often provided in only one language, identifying the language to be targeted in treatment is a current challenge for bilingual research and clinical practice. Computational models that accurately simulate impairment and recovery in bilinguals with aphasia can offer a suitable solution to this problem by predicting individual response to therapy provided in one versus the other language. In this lecture, I will present BiLex, a computational model developed to simulate lexical access in healthy bilinguals, and naming deficits and treatment outcomes in bilinguals with aphasia. I will also discuss how BiLex is currently being used to predict individual rehabilitation outcomes and identify the language that when targeted in treatment may lead to maximum treatment gains across the two languages in bilinguals with aphasia.
Mandatory readings
Pe?aloza, C., Grasemann, U., Dekhtyar, M., Miikkulainen, R., & Kiran, S. (2019). BiLex: A computational approach to the effects of age of acquisition and language exposure on bilingual lexical access. Brain and Language, 195: 104643. doi: 10.1016/j.bandl.2019.104643. (PDF)
Pe?aloza, C., Dekhtyar, M., Scimeca, M., Carpenter, E., Mukadam, N., & Kiran, S. (2020). Predicting treatment outcomes for bilinguals with aphasia using computational modeling: Study protocol for the PROCoM randomised controlled trial. BMJ Open, 10(11): e040495. doi: 10.1136/bmjopen-2020-040495. (PDF)
Optional reading
Kiran, S., Grasemann, U., Sandberg, C., Miikkulainen, R. (2013). A computational account of bilingual aphasia rehabilitation. Bilingualism, Language and Cognition, 16(2):325-342. doi: 10.1017/S1366728912000533. (PDF)