{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/114066"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/114066","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Risky Decision-Making Under Social Influence","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Orloff, Mark Andrew"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Translational Biology, Medicine and Health","degree_department":"Graduate School","school":null,"contributors":[],"advisors":[],"committee_chairs":["Chiu, Pearl H."],"committee_members":["Chung, Dongil","LaConte, Stephen M.","Casas, Brooks"],"year":2021,"date_issued":"2021-09-15","date_published":"2021-09-15","updated_at":"2026-07-22T22:19:39Z","subjects":["risky decision-making","social influence","health-risk behaviors","computational modeling","functional magnetic resonance imaging","lesion"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:32603"],"render_values":[{"text":"vt_gsexam:32603","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/114066","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Chiu, Pearl H."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Chung, Dongil","LaConte, Stephen M.","Casas, Brooks"]},{"key":"dc:contributor.department","label":"Department","values":["Graduate School"]},{"key":"dc:creator","label":"Author","values":["Orloff, Mark Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-10T07:00:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-10T07:00:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-09-15"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Translational Biology, Medicine and Health"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["risky decision-making","social influence","health-risk behaviors","computational modeling","functional magnetic resonance imaging","lesion"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:32603"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/114066"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Risky Decision-Making Under Social Influence"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Chiu, Pearl H."],"dc:contributor.committeemember":["Chung, Dongil","LaConte, Stephen M.","Casas, Brooks"],"dc:contributor.department":["Graduate School"],"dc:creator":["Orloff, Mark Andrew"],"dc:date.accessioned":["2023-03-10T07:00:07Z"],"dc:date.available":["2023-03-10T07:00:07Z"],"dc:date.issued":["2021-09-15"],"dc:description.abstract":["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."],"dc:description.abstractgeneral":["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."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["vt_gsexam:32603"],"dc:identifier.uri":["http://hdl.handle.net/10919/114066"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["risky decision-making","social influence","health-risk behaviors","computational modeling","functional magnetic resonance imaging","lesion"],"dc:title":["Risky Decision-Making Under Social Influence"],"dc:type":["Dissertation"],"dc:type.dcmitype":["Text"],"thesis:degree_discipline":["Translational Biology, Medicine and Health"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:39Z"}