“Emotions, Interaction, and Autism: Leveraging Big Behavioral Data” is a Department of Computer Science Pizza Seminar Series event. Join us on Wednesday, August 31, 2016, at 5:00 PM in the Abess Center, Ungar Building Room 230C_D for this lecture by Dr. Daniel Messinger from the Department of Psychology.
Infants develop in the context of social interaction with parents and others. Study of these social interactions typically involves behavioral observation using expert coding systems that rely on subjective judgments. To produce objective measurements of infant and parent behavior, my lab employs computer vision and pattern recognition approaches. These methods produce “big behavioral data” fine-grained records of expression and other movements in time. To make sense of the underlying interactive process in these observations, we employ computational approaches such as inverse optimal control and statistical approaches such as dynamic time-series models. The ultimate goal is understanding and predicting both typical development and developmental disturbances such as autism spectrum disorder (ASD). To this end, we incorporate common genetic (e.g., dopaminergic) variants in our models of interaction and development.