The Neural Mechanisms of Peer Influence on Risky Decision-Making



 DESCRIPTION (provided by applicant): Risky behaviors such as smoking, drug and alcohol use, and teenaged sexual intercourse are all heavily influenced by the behaviors of one's peers. While much is known about the neural networks involved in risky decision-making, this research has largely been conducted in the absence of the rich network of social information available when making risky decisions in real-life. Given the large body of evidence suggesting the importance of information about peers' choices when making risky decisions, it is of great public-health relevance to understand how the integration of this information into the risky decision takes place at the neural level, and, particularly, what differences in processing of this information lead some people to be more susceptible to peer influence than others. The proposed research aims to answer these questions by having a sample of 50 14-19 year olds perform a novel real-stakes social gambling task in the scanner. In this task, participants will make choices about a series of risky gambles, on each trial receiving false information about the decisions of four of their friends who previously completed the task. Using the reported percentage of risk acceptance as a parametric modulator, we will identify regions in the brain where BOLD activation encodes information about others' choice behavior. Second, we will calculate a peer susceptibility score for each subject based on their choices in the task, reflecting how much their choices were swayed by the reported actions of others. We will then use a machine-learning based regression technique to generate a spatial pattern of regression weights that can predict each participant's behavioral susceptibility to peer influence. The strength of these regression weights in different areas of the brain reflects how strongly encoding of others' choices in that region predicts individuals' peer susceptibility scores. These data allow us to: 1) Identify patterns of encoding of others' choice information that are most strongly predictive of being highly susceptible to peer influence, and 2) Generate a neurologic signature that can be used in new samples of participants to predict their level of susceptibility to peer influence. We will then compare predictions of peer susceptibility derived from the brain data to patterns of real-world risk-taking of the participant and their peers. We believe that thes findings can set the stage for future work examining developmental changes in these networks, and how responses in these networks can be altered by exposure to different types of peers or by potential interventions designed to reduce susceptibility to peer influence in specific domains. These avenues of research will allow for the accelerated development of more precise and effective intervention strategies to reduce susceptibility to peer influence in the domain of publi-health relevant risk-taking.