LEARNING SHORT - Neuroimaging of Pain and Affect: Generalizable models for prediction and explanation, feat. Dr. Tor Wager
Affective processes—pain and pleasure, desire and dread—are fundamental motivators of behavior and drivers of learning. How are they represented and organized in the brain? In this talk, I provide a picture of the new neuroscience of human affect grounded in a multivariate predictive modeling approach. In the past years, neuroimaging has undergone a renaissance, combining machine learning with open sharing of data across laboratories. The multivariate predictive modeling approach uses machine learning to construct brain-based models of behavioral outcomes, clinical symptoms, and psychological states. Such models can have much larger effect sizes and be dramatically more reproducible and reliable than effects using traditional analysis approaches. They can also serve both predictive and explanatory goals, providing evidence on the brain features and systems that are necessary and sufficient to explain specific types of human feeling and behavior. Speaker Bio: Tor Wager is the Diana L. Taylor Distinguished Professor in Neuroscience at Dartmouth College. He received his Ph.D. from the University of Michigan in Cognitive Psychology in 2003, and served as an Assistant (2004-2008) and Associate Professor (2009) at Columbia University, and as Associate (2010-2014) and Full Professor (2014-2019) at the University of Colorado, Boulder. Since 2004, he has directed the Cognitive and Affective Neuroscience laboratory, a research lab devoted to work on the neurophysiology of affective processes—pain, emotion, stress, and empathy—and how they are shaped by cognitive and social influences. Dr. Wager and his lab are also dedicated to developing analysis methods for functional neuroimaging and sharing ideas, tools, and scientific data with the scientific community and public.