Tuesday, October 19 at 1:00pm to 2:00pmVirtual Event
Kathleen Gates, Ph.D.
The University of North Carolina at Chapel Hill
Tue., October 19, 2021, 1:00pm-2:00pm PDT
Applications of Data-Driven Methods for Studying Dynamic, Person-Specific processes
The use of time series data in psychological and neural research is on the rise. This increase largely can be attributed to greater ease in gathering numerous observations per person via the use of smart phones, automated behavioral coding, wearable technologies, and psychophysiological data. Often the end goal is to arrive at a better understanding of individuals’ processes. From these individual-level findings, we can get closer to personalized interventions as well as obtain insights into meaningful similarities across individuals and aspects of human processes that may generalize to the population. Arriving at personalized dynamic models often requires the use and adaptation of machine learning methods such as unsupervised classification, feature selection, and model-building. This talk focuses on the application of such methods on a few data sets collected across various domains of psychological inquiry including self-report data and functional MRI.