Collaborative Research: From Brains to Society: Neural Underpinnings of Collective Behaviors Via Massive Data and Experiments

合作研究：从大脑到社会：通过大量数据和实验研究集体行为的神经基础

• 批准号: 1939459
• 负责人: Jie Gao
• 金额: $ 21.89万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939459&HistoricalAwards=false
• 关键词: Collaborative; Research; Brains; Society; Neural

Despite thousands of investigations on the neural basis of individual behaviors and even more studies on collective behaviors, a clear bridge between the organization of individual brains and their combinational impact on group behaviors, such as cooperation and conflict and ultimately collective action, is lacking. To address the grand challenge of inferring group cooperation from the functional neuroarchitecture of individual brains, this project will harness advances in data, experiment and computation. Specifically, it will integrate, for the first time, existing large-scale human functional neuroimaging data, prospectively collected individual and group behavioral data from a large cohort, with cutting-edge machine learning tools, hierarchical models and large-scale simulations. This is a collaborative effort between a team of neuroscientists, social scientists and data scientists, that aims to elucidate the neural basis of cooperation, a fundamental process in a functioning society and at the core of social environments. The project will first harness the combined wealth of existing neuroimaging and behavioral data from large-scale studies, including the Human Connectome-Lifespan (HCP-L) and the Adolescent Brain Cognitive Development (ABCD) and will leverage recent breakthroughs in machine learning to characterize the diversity, individuality and commonality of neural circuits (the connectome) supporting cognitive function across the lifespan. It will then conduct large-scale (~10,000 individuals) online behavioral experiments to identify connections between individual behaviors, decisions and group behaviors during a Public Goods Game. The experiments will measure individual proclivity towards cooperation and the social welfare obtained by cooperation, leading to potentially transformative insights into the emergence of cooperation within groups via individual behaviors. The resulting first-of-its-kind dataset may become a very valuable resource to the research community. Large-scale simulations based on statistical models estimated from this and the assembled neuroimaging datasets will then assess the direct or indirect relationships between individual connectomes and cooperation in group settings, and will elucidate the role of group processes in amplifying or ameliorating individual differences towards collective outcomes. Findings from this project may have a transformative impact on the scientific community's currently incomplete understanding of how individual brains shape societal behavior via cognitive, social, and interactive mechanisms.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.

尽管对个体行为的神经基础进行了数千次调查，对集体行为的研究甚至更多，但在个体大脑的组织与其对群体行为的组合影响（如合作和冲突，最终是集体行动）之间缺乏明确的桥梁。为了解决从个体大脑的功能神经结构推断群体合作的巨大挑战，该项目将利用数据，实验和计算方面的进步。具体而言，它将首次整合现有的大规模人类功能神经成像数据，从大型队列中前瞻性收集的个人和群体行为数据，以及尖端的机器学习工具，分层模型和大规模模拟。这是一个由神经科学家、社会科学家和数据科学家组成的团队之间的合作，旨在阐明合作的神经基础，这是一个正常运作的社会的基本过程，也是社会环境的核心。该项目将首先利用来自大规模研究的现有神经成像和行为数据的组合财富，包括人类连接体寿命（HCP-L）和青少年大脑认知发展（ABCD），并将利用机器学习的最新突破来表征支持整个生命周期认知功能的神经回路（连接体）的多样性，个性和共性。然后，它将进行大规模（约10，000人）在线行为实验，以确定公共产品游戏中个人行为、决策和群体行为之间的联系。这些实验将测量个人对合作的倾向以及通过合作获得的社会福利，从而对通过个人行为出现的群体内合作产生潜在的变革性见解。由此产生的首个同类数据集可能成为研究界非常宝贵的资源。基于统计模型的大规模模拟，从这个和组装的神经成像数据集估计，然后将评估个体连接体和合作之间的直接或间接关系，在组设置，并将阐明在放大或改善个体差异对集体结果的作用组过程。这个项目的发现可能会对科学界目前对个体大脑如何通过认知、社会、该项目是国家科学基金会利用数据革命（HDR）的一部分。大创意活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

项目成果

Curvature Graph Network

• 发表时间: 2020-04
• 作者: Ze Ye;Kin Sum Liu;Tengfei Ma;Jie Gao;Chao Chen