台大心理系

Efficient and accurate prediction of Alzheimer’s disease in the preclinical stage is vital for individual, family, social, and medical reasons. However, increased behavioral variability at old ages has increased difficulty investigating issues concerning early detection for dementia at-risk individuals. In this talk, I will present data from our work on how a sound neuropsychological approach can improve the detection of heterogeneity among older adults with a high risk of dementia and improve the predictive value of dementia progression. I will also present data from our effort to investigate culturally relevant aspects, such as language and social information processing, and the application of deep learning and human-robot interaction techniques in the realm of early detection of dementia.