The aim of the proposed project is to improve state-of-the-art knowledge on aphasia, focusing on verb-related (morpho)syntactic production. This deficit is considered to be the hallmark of agrammatic aphasia, which usually occurs following damage to Broca’s area and neighbouring areas in the left hemisphere. Although many studies on (morpho)syntactic production in agrammatic aphasia have been conducted thus far, little is known on the factors that determine the relative preservation or impairment of a given verb-related morphosyntactic category (e.g., Tense, subject-verb Agreement, grammatical Mood) in a given person with agrammatic aphasia (PWAA) in a given language. Inspired by the developments in machine learning, the proposed project aims at filling this gap by taking an original and innovative methodological approach. Machine Learning Aphasia addresses two important, yet unanswered questions: (1) Which factors determine the performance accuracy of a given PWAA, native speaker of a given language, on verb-related morphosyntactic production? (2) What is the hierarchy of factors/predictors of successful verb-related morphosyntactic production in agrammatic aphasia? Addressing questions 1 and 2 will advance our understanding of the complexities underlying morphosyntactic production in agrammatic aphasia, which will be a significant contribution to cognitive science (in particular to psycholinguistics, neurolinguistics, theoretical linguistics, and cognitive neuropsychology). Importantly, achieving goals 1 and 2 will also have significant clinical implications, as the findings about the best predictors of morphosyntactic production in agrammatic aphasia will inform and improve treatment programmes for PWAA. For instance, if the proposed study finds that verbal working memory capacity is one of the best predictors of performance on (morpho)syntactic production, treatment programmes should also include cognitive training targeting verbal working memory.
Project leader: Valantis Fyndanis
Institution: Center for Multilingualism in Society Across the Lifespan (MultiLing)