Risky decision-making and social influence are associated with many health-risk behaviors. However, more work is necessary to understand risky decision-making and social influence. Additionally, to begin identifying ways to change individuals' engagement in health-risk behaviors, more work is necessary to understand whether and how risky decision-making and social influence can be modulated. Using computational modeling in conjunction with other techniques, this dissertation 1) explores mechanisms underlying risky decision-making under social influence (Study 1) and 2) examines how individuals could modulate risky decision-making and social influence (Studies 2 and 3). Study 1 identifies a novel social heuristic decision-making process whereby individuals who are more uncertain about risky decisions follow others and proposes dorsolateral prefrontal cortex (dlPFC) as a 'controller' of this heuristic. Study 2 finds that giving individuals agency in viewing social information increases the utility of that information. Study 3 finds that some individuals can modulate brain patterns associated with risky decision-making using a real-time fMRI (rt-fMRI) neurofeedback paradigm, and preliminarily shows that this leads to behavior change in risky decision-making. In sum, these studies expand on previous work elucidating mechanisms of risky decision-making under social influence and suggest two possible avenues (agency and real-time fMRI neurofeedback) by which individuals can be taught to change their behavior when making risky decisions under social influence. / Doctor of Philosophy / Risky decision-making and social influence are associated with many health-risk behaviors such as smoking and alcohol use. However, more work is necessary to understand risky decision-making and social influence. Additionally, to identify ways to change individuals' engagement in health-risk behaviors, more work is necessary to understand how risky decision-making and social influence can be changed. Here, computational modeling, a way to quantify individual's behavior, is used in a series of studies to 1) understand how individuals make risky decisions under social influence (Study 1) and 2) test ways in which individuals can be guided to change the way they respond to social influence (Study 2) and make risky decisions (Study 3). Study 1 shows that individuals who do not have strong preferences respond to social information in a different way than those who do and utilizes neuroimaging to identify a particular brain region which may be responsible for this process. Study 2 shows that individuals are more influenced by others when they ask to see their choices, as compared to passively viewing others' choices. Study 3 shows that a brain–computer interface can be used to guide individuals to change their brain activity related to risky decision-making and preliminarily demonstrates that following this training individuals change their risky decisions. Together, these studies further the field's understanding of how individuals make risky decisions under social influence and suggest avenues for behavior change in risky decision-making under social influence.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/114066 |
| Date | 15 September 2021 |
| Creators | Orloff, Mark Andrew |
| Contributors | Graduate School, Chiu, Pearl H., Chung, Dongil, LaConte, Stephen M., Casas, Brooks |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation, Text |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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