- Thursday, April 15th 2021 at 16:00 - 17:00 UK (Other timezones)
- General participation info | Participate online | + Phone in Meeting ID: 972 4297 4350 Passcode: 205293 Find your local number: https://ucl.zoom.us/u/aedyEiW1A6
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 introduce an open-source Python package, ADOpy, which we developed to increase the accessibility of ADO to even researchers who have limited background in Bayesian statistics or cognitive modeling.
Woo-Young Ahn, PhD
Department of Psychology
Seoul National University