Per the 2020 US Census (data.census.gov), there are approximately 62 million Hispanic/Latino individuals living in the US, and of these 15% are individuals aged 55 years and older 2. The US Census also projects that by 2030, this Hispanic population will increase to 74 million and by this time the number of older adults will outnumber children. A separate but concerning statistic is that the WHO predicts a rising incidence of dementia (78 million individuals by 2030) 3 and strokes (70 million survivors by 2030) 4 suggesting that there is an increased urgency to provide clinical services for these populations. In order to do that, it is necessary to understand the interaction between bilingualism (in Spanish-English speaking Hispanic individuals) and neurological/neurodegenerative disorders. The problem is that bilingual speakers vary widely in how effectively they process their two languages and how these processes may break down in neurological disorders. Thus, to fully understand the nature of bilingual impairment (in stroke) and decline (in dementia), it would be necessary to conduct prohibitively large-scale cross-sectional and longitudinal examinations of hundreds of bilingual individuals with varying degrees of proficiency to accurately capture the variation in bilingual speakers. Our central hypothesis is that computational simulations of bilingual language processing (in healthy aging), language impairment and recovery (in stroke), and decline (in neurodegenerative disorders) is a powerful approach in lieu of such studies. Computational modeling makes it possible to study an adult bilingual language system that can vary by any language combination and proficiency at any single time point and characterize change over time. BILEX, our computational model for bilingual language processing, has an already-proven ability to simulate bilingual post-stroke aphasia (BPSA) and bilingual semantic dementia (BSD) as well as rehabilitation outcomes for post-stroke individuals. Computational simulations can, thus, be used to effectively represent not just known patient cases but also generalize to cases for which we do not yet have any patient data. Consequently, our specific aims are to explain how different types of observed impairments in BPSA and BSD arise. For that reason, we need to first characterize these impairments in more detail, and understand how they may arise using a computational structure of maps and connections (Aim 1). Armed with such an understanding, the second aim is to extend them over time, i.e. to explain how these mechanisms result in recovery after a stroke (in aphasia) and decline (in dementia) (Aim 2). The third aim, then, is to understand how these processes interact with language selection and control, by including subcortical conditional routing mechanisms to our BILEX model (Aim 3). Each aim allows for progressively more detailed characterizations and explanations of modeling impairment and recovery in bilingual post-st...