NCS-FO: Cognitive maps as a framework for organizing relationships in large-scale real social networks

人类的生活在我们社交网络的广阔图景中展开。成功的社交导航可以帮助缓冲孤独感和负面互动，但它需要了解谁与谁有联系：潜在的社会关系结构（例如，小集团或中心）。这个项目由布朗大学的一组研究人员领导，目的是揭示人们如何学习和推理现实社会网络的行为和神经特征。尽管了解人们如何学习相关的社会知识很重要，但关于大脑如何组织这些知识仍有很多未知之处。空间认知研究为这个问题提供了一个窗口：大量的动物研究表明，大脑在“认知地图”中代表了物理空间的信息，该地图将世界上实体及其关系的信息联系在一起。认知地图已被证明可以在新的或不断变化的环境中加速学习。这个项目测试的是，在心理上导航他们的社交网络时，人们是否会吸收类似于导航外部物理空间的认知地图表征。通过整合先进的人类成像方法、计算建模、社会网络科学和纵向抽样，研究人员将对一大批大一本科生进行研究，因为他们在一学年的过程中发展了新的友谊，以调查关于现实社会网络的新兴知识的行为和神经特征。研究者将在这个复杂和动态的环境中识别社会信息的神经表征。这些研究的结果有可能改变我们对人类如何学习和驾驭社会世界的认识。这对促进我们对导致本科生在STEM领域坚持不懈的社会因素的理解具有重要意义。该项目由理解神经和认知系统的综合策略（NCS）资助，这是一个由生物学（BIO）、计算机与信息科学与工程（CISE）、教育与人力资源（EHR）、工程学（ENG）和社会、行为和经济科学（SBE）联合支持的多学科项目。这项工作旨在为理解真实社会背景下的人类学习提供基础。即将到来的本科班级（由尚未建立大学社交网络的个人组成）提供了一个独特的测试平台，以跟踪社交网络的出现及其在学生第一学年课程中的变化配置。主要的假设是，一个人的社会网络的认知地图使他们能够更熟练地在他们的网络中导航。本研究将通过功能磁共振成像（fMRI）对海马体和眶额/内嗅皮层进行研究，探索这种认知结构的神经表征。将建立不同类别的计算模型（即，基于状态转换，基于后继表示和基于潜在原因）来测试社会网络学习的竞争性账户和社会认知地图的格式。这些模型将探索个人层面的参数，以评估个人的可变性是否会影响这一学习过程，包括个人在社区中的地位，以及他们收集信息的能力，是否会影响他们构建社会环境认知地图的能力。通过利用横断面和纵向设计，该研究将提供一个组织框架，可以识别大脑如何代表复杂环境的社会知识，同时精确地模拟社会网络的认知地图如何实现有效的社会导航。这项工作将为揭示现实世界中社会学习过程背后的神经生物学机制提供一个窗口。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 life plays out in the vast landscape of our social networks. Successful social navigation can help buffer against loneliness and negative interactions, but it requires learning who is connected to whom: the latent structure underlying social relationships (e.g., cliques or hubs). The goal of this project, led by a team of researchers at Brown University, is to reveal the behavioral and neural signatures of how people learn and reason about real-world social networks. Despite the importance of understanding how people learn relational social knowledge, much remains unknown about how the brain organizes this knowledge. Spatial cognition research offers a window into this problem: A long line of animal research has demonstrated that the brain represents information about physical space in a ‘cognitive map’ that binds information about entities in the world and their relationships. Cognitive maps have been shown to speed learning in new or changing environments. This project tests whether, in mentally navigating their social networks, people recruit cognitive map-like representations similar to those used to navigate external physical space. By integrating advanced human imaging methods, computational modeling, social network science, and longitudinal sampling, the investigators will study a large cohort of first-year undergraduate students as they develop new friendships over the course of an academic year to investigate the behavioral and neural signatures of emerging knowledge about real-world social networks. The investigators will identify the neural representations of social information in this complex and dynamic environment. The results of these studies have the potential to transform our knowledge of how humans learn about and navigate through their social world. It has implications for advancing our understanding of social factors that contribute to the persistence of undergraduates in STEM fields. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). This work aims to provide a foundation for understanding human learning in real social contexts. An incoming undergraduate class (comprised of individuals who have yet to establish their college social network) provides a unique testbed to track the emergence of a social network and its shifting configuration over the course of the students’ first academic year. The main hypothesis is that an individual’s cognitive map of their social network enables them to navigate more adeptly through their network. The study will probe neural representations of this cognitive structure by means of function magnetic resonance imaging (fMRI) focusing on the hippocampus and orbitofrontal/entorhinal cortex). Different classes of computational models (i.e., state transition-based, successor representation-based, and latent-cause based) will be built to test competing accounts of social network learning and the format of social cognitive maps. These models will probe for person-level parameters to assess whether individual variability biases this learning process, including whether an individual’s position in their community, and thus their capacity to gather information, shapes their ability to build cognitive maps of their social environment. By harnessing both a cross-sectional and longitudinal design, the research will provide an organizing framework that can identify how the brain represents social knowledge about complex environments while precisely modelling how cognitive maps of a social network enable efficient social navigation. This work will provide a window into the neurobiological mechanisms underlying the social learning processes that unfold in the real world.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.