CAREER: Understanding how knowledge of social networks shapes thoughts, feelings, and behaviors

人类形成了复杂的社会网络。这些网络由许多紧密的联系组成，包括家庭成员、朋友、同事、邻居和许多其他人之间的联系。人们从根本上想知道别人在现实社会网络中的位置。发现“分离度”和知道谁的关系特别好，需要大量的注意力。在这种网络中追踪人际关系的认知需求，并相应地调整自己的行为，很可能是人类大脑进化的推动力。此外，人类理解社会网络的能力对日常功能的许多方面至关重要，包括人际信任、同理心和社会影响。然而，对于人类大脑如何编码嵌入其中的社会网络结构，或者这些知识如何塑造下游的心理处理和行为，人们知之甚少。这个项目旨在了解他人在一个人的社会网络中的地位如何影响思想、感情和行为。该研究考虑了个体对这些信息的调节差异，以及环境如何塑造社会网络的表征。该项目的相关教育和培训部分包括为来自社区大学的代表性不足的学生提供指导研究和专业发展机会，以及开发一门新的研究生课程，重点关注社会神经科学的先进方法。该项目整合了社会心理学、认知神经科学和社会网络分析的方法，以阐明社会网络的知识如何塑造影响、行为和认知。有几项研究考察了一系列有限社区的社会网络。社区成员的子集完成行为和/或神经成像实验，试图描述(1)在一个人的社会网络中了解他人位置的行为、认知和情感后果，以及(2)这些知识的心理表征如何在不同的环境和个体之间变化。一个具体的焦点是如何感知距离的社会网络和网络中心性影响亲社会行为。研究结果将为更好地理解社会影响和人际信任提供更广泛的信息。了解这些现象还将为国家安全部门的工作提供信息，以减轻社交网络上有针对性的错误信息传播。该项目由认知神经科学和人类网络与数据科学（HNDS-R）项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Human beings form complex social networks. Those networks are composed of numerous intense bonds, including those between family members, friends, co-workers, neighbors, and many others. People are fundamentally interested in knowing where others sit in their real-world social networks. Discovering “degrees of separation” and knowing who is especially well-connected consumes significant attention. The cognitive demands of tracking relationships in such networks, and calibrating one’s behavior accordingly, are likely to have been a driving force in human brain evolution. Additionally, the human capacity for understanding social networks is central to many aspects of everyday functioning, including interpersonal trust, empathy, and social influence. Yet, little is known about how the human brain encodes the structure of the social networks in which it is embedded, or how this knowledge shapes downstream mental processing and behavior. This project seeks to understand how knowledge of others’ positions in one’s social network shapes thoughts, feelings, and behavior. The research considers individual differences in attunement to this information, and how context shapes representations of social networks. The related educational and training components of the project include the creation of mentored research and professional development opportunities for underrepresented students from community colleges and the development of a new graduate course focused on advanced methods in social neuroscience. This project integrates approaches from social psychology, cognitive neuroscience, and social network analysis to illuminate how knowledge of social networks shapes affect, behavior, and cognition. Several studies examine the social networks of a series of bounded communities. Subsets of community members complete behavioral and/or neuroimaging experiments that seek to delineate (1) the behavioral, cognitive, and affective consequences of knowledge of others’ positions in one’s social network, and (2) how the mental representation of such knowledge varies across contexts and individuals. One specific focus is on how perceptions of distance in social networks and network centrality influences prosocial behavior. The results of the research will inform broader efforts to better understand social influence and interpersonal trust. Understanding these phenomena will also inform efforts in the national security community to mitigate the targeted spread of misinformation in social networks. This project is supported with co-funding from the Cognitive Neuroscience and Human Networks and Data Science (HNDS-R) Programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.