Department of Psychology
Seoul National University
Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning, and adaptive design optimization (ADO) is a promising machine-learning method that might lead to rapid, precise, and reliable markers of individual differences. In this talk, I will first discuss the importance of reliability of (bio)markers. Then, I will present a series of studies that utilized ADO in the area of decision-making and for the development of ADO-based digital phenotypes for addiction and related behaviors. Lastly, I will discuss other promising approaches that might allow us to develop (bio)markers with clinical utility.
View a recording of this session here.