Modeling Social Information in the Human Brain

Date(s):

Location:
Jacobs Hall, Room 2512, Jacobs School of Engineering, 9500 Gilman Dr, La Jolla, San Diego, California 92093

Sponsored By:
Professor Piya Pal

Speaker(s):
R. McKell Carter
University of Colorado Boulder
R. McKell Carter

Abstract:

Social interactions provide support for individuals and heavily influence the decisions they make. Understanding how information is extracted from social interactions and how that information influences our decisions could have a tremendous public health benefit. In this talk, I will describe experiments designed to elucidate how the brain constructs representations of complex social interactions and the network modeling and machine learning methods developed to study those processes. First, I will focus on the hierarchical construction of social information and tests of those mechanisms using support vector machines and hitting-time network measures. This model of social information processing has implications for the study of autism and for human computer interaction. Second, I will report on results from a multiplayer game that provides an experimental setting for the assessment of social influences on risky decision making. Risky choices made in the game correlate with reported drug and alcohol abuse. For each individual, the extent of influence by the play of others correlates with the likelihood of shoplifting. Neuroimaging data collected during game play provides early support for multiple mechanisms underlying social effects on risk taking. Finally, I will describe ongoing work to extend our research to the classroom with the target of improving student learning and engagement. This research provides a better understanding of how the brain represents social information and new methods for the study of information processing in the brain with implications for autism, interaction with autonomous agents, and the cultivation of healthy risk.


Speaker Bio:
R. McKell Carter is a computational cognitive neuroscientist who studies how the brain weighs social factors that affect decision making. He applies statistical learning methods and network models to large-scale fMRI datasets in order to better understand social behavior and the relationship between network integration and decision making. He has developed novel applications of statistical learning algorithms to fMRI data in order to predict participant behavior and identify brain areas that uniquely encode social influences on economic models of choice. Through this work, he seeks to understand social processes in both healthy individuals and in individuals with mental disorders, like autism and addiction. Dr. Carter completed his Ph.D. with Christof Koch at the California Institute of Technology before doing postdoctoral work with Scott Huettel at Duke University. He then joined the Institute of Cognitive Science and Department of Psychology and Neuroscience at the University of Colorado Boulder where he started the Social Neuroscience and Games lab

Contact:
Bethany Carson
bacarson@eng.ucsd.edu