**Summary:** Pain and emotion are central to human life. Their experience defines our wellbeing, and the brain processes that underlie them drive behavior and learning. Developing the capacity to influence them, and sometimes to accept them, motivates human endeavors ranging from spiritual practices to medical interventions. Developing models of the brain systems that generate pain and emotion could transform how we understand their neurophysiological origins, and how we understand interventions ranging from psychotherapy to self-regulation to drug effects. However, developing such models will require computational advances, particularly in our ability to model how emergent properties like pain arise from complex interactions among brain systems, and how to construct such models so that they have high neuroscientific interpretability, predictive validity, and reproducibility. In this talk, I describe a series of studies aimed addressing these goals. Combining functional neuroimaging with machine learning techniques, we have developed brain models capable of predicting the intensity of pain, negative affect, empathy, and autonomic activity in individual participants, with no prior knowledge about the individual's experience. I will show how these models can serve as measures of the brain processes that generate pain and emotion, and how interrogating the structure of these models and relationships among them can provide insight into how the brain represents multiple varieties of affective experience. And, finally, I will show how these models allow us to compare diverse interventions on a level playing field, shedding light on how both cognitive and drug interventions work and how they might be inter-related.
Thursday, 2/21/2019
11:00am-12:20pm
Kent 120
A light lunch will be provided by Noodles, Etc.
**Tor Wager** is a Professor of Psychology and Neuroscience and a faculty member in the Institute for Cognitive Science at the University of Colorado, Boulder. He received his Ph.D. from the University of Michigan in cognitive psychology in 2003, and served as an Assistant and Associate Professor at Columbia University from 2004-2009. Since 2010, he has directed Boulder’s Cognitive and Affective Neuroscience laboratory. He has a deep interest in how thinking influences affective experiences, affective learning, and brain-body communication. His laboratory also focuses on the development and deployment of analytic methods, and has developed several publicly available software toolboxes for fMRI analysis.
The 2018-2019 Computational Social Science Workshop meets Thursdays from 11 a.m. to 12:20 p.m. in Kent 120. All interested faculty and graduate students are welcome.
Students in the Masters of Computational Social Science program are expected to attend and join the discussion by posting a comment on the issues page of the workshop's public repository on GitHub. Further instructions are documented in the Computational Social Science Workshop's README on Github.