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.

尽管对个人行为的神经基础进行了成千上万的调查，甚至还对集体行为进行了更多研究，但缺乏个人大脑的组织及其对群体行为的组合影响之间的清晰桥梁，例如合作和冲突，最终缺乏集体行动。为了解决从单个大脑的功能神经结构来推断群体合作的巨大挑战，该项目将利用数据，实验和计算的进步。具体而言，它将首次整合现有的大规模人类功能神经影像数据，并从大型队列中收集的个人和组行为数据，以及尖端的机器学习工具，分层模型和大规模模拟。这是神经科学家，社会科学家和数据科学家团队之间的合作努力，旨在阐明合作的神经基础，这是一个运转良好的社会和社会环境核心的基本过程。该项目将首先利用大规模研究的现有神经影像学和行为数据的综合财富，包括人类连接组合（HCP-L）和青少年的脑认知发展（ABCD），并将在机器学习中利用最近的突破性的突破来表征神经通用（connectome）的多样性，个性和共同点，支持COPNIS COMPATIN COMPATIN COMPECTINE COMPECTINE COMPECTINEFERDSEFESPESPEDS跨越成熟的功能。然后，它将进行大规模（约10,000个人）在线行为实验，以确定公共物品游戏期间个人行为，决策和团体行为之间的联系。这些实验将衡量个人对合作和合作获得的社会福利的倾向，从而通过个人行为对群体内部合作的出现有潜在的变革见解。由此产生的首先数据集可能会成为研究界非常宝贵的资源。基于从此估计的统计模型和组装神经成像数据集的大规模仿真将评估单个连接组与组设置中的个人连接之间的直接或间接关系，并将阐明小组过程在扩大或改善各个单个差异方面对集体灭绝的作用。该项目的发现可能会对科学界通过认知，社会和互动机制塑造社会行为的目前不完整的理解具有变革性的影响。该项目是国家科学基金会（National Science Foundation）利用数据革命（HDR）的大思想活动的一部分。这项奖项奖励NSF的法定任务，并通过评估委员会的范围来反映出该奖励的范围，并已被评估构成群体的范围和众所周知。

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