US-German Collaboration: Computational Modeling of Cooperative Success using Neural Signals and Networks

随着时间的推移，社会进步要求人类在合作方面表现出色。当人们成功合作时，团队中的每个人都会团结起来实现一个目标，同时对整个团队公平。理想情况下，成功的合作还包括实现团队中每个人的利益。为了维持成功的合作，人们需要协调他们的所见所闻、所思所行，特别是关于在任何给定时刻谁处于最佳位置，以便为整个团队做出最佳决策或采取最佳行动。专业知识随着环境的变化而变化，团队成员需要快速灵活地做出反应。这个项目将加强对大脑如何在快速变化的世界中启动和维持成功的合作的理解。这项研究将揭示大脑的各个方面，它们最能支持合作成功的制胜策略。它将把团队协调的计算模型与成功合作期间的大脑网络的测量和模型结合起来。这项研究的结果可以帮助团队加强技术设计的创新，帮助疾病控制中心遏制突发疫情，并创造新的方法来帮助机器人助理更好地响应人类的指示。研究提案试图回答以下主要问题：1)团队成员对与其合作的其他代理人的心理表征是什么，特别是在合作成功的时期？2)这些表征如何有助于了解其他人的决策过程和改变专业知识？以及3)预测认知更新和成功的合作行动的神经信号是什么？要对合作中的认知过程进行建模，需要明确地建模社会推理能力，特别是考虑其他合作伙伴的决策过程以及他们在理解团队成员之间的任务和沟通方面的复杂程度的能力。这些特征不能直接观察，将使用多代理设置的计算方法来估计(即，交互的部分可观察的马尔可夫决策过程)。基本的神经网络将通过密集阵列EEG和对大脑内部和跨大脑的功能连接模式的分析而显现。网络连通性测量包括循环相关系数和使用与选择结果和/或认知建模参数密切相关的独立分量的时变、自适应多变量自回归建模。这些衡量大脑之间连通性的指标不太容易受到潜在任务需求引起的虚假关联的影响。德国联邦教育和研究部(BMBF)正在资助一个配套项目。

Societal progress requires humans to excel at cooperation over time. When people cooperate successfully, everyone on the team comes together to achieve a goal while being fair to the entire team. Successful cooperation ideally includes achieving the good of each person on the team, as well. To sustain successful cooperation, people need to coordinate what they see, hear, think, and do, especially about who is in the best position at any given moment to make the best decision or to take the best action for the team as a whole. Expertise changes with changing circumstances, and team members need to respond quickly and flexibly. This project will enhance understanding about how the mind and brain work to initiate and sustain successful cooperation in a rapidly changing world. The research will reveal aspects of the mind and brain that best support winning strategies for cooperative success. It will combine computational models of team coordination with measurements and models of brain networks during successful cooperation. The outcomes of the research could help enhance innovation in teams for technology design, assist centers of disease control to contain sudden outbreaks, and create new methods to help robotic assistants better respond to human direction.The research proposal seeks to answer the following primary questions: 1) What is a team member's mental representation of other agents with whom that person is cooperating, especially during periods of cooperative success?; 2) How do these representations contribute to knowledge of others' decision-making processes and changing expertise?; and 3) What are the neural signals that predict cognitive updating and successful cooperative action? To model the cognitive processes during cooperation requires explicitly modeling social reasoning capabilities, especially capacities to think about other partners's decision process and their sophistication in understanding both the task and communication among team members. These features cannot be observed directly and will be estimated using computational approaches for multi-agent settings (i.e., interactive partially observable Markov decision processes). The underlying neural networks will emerge via dense array EEG and analysis of functional connectivity patterns within and across brains. Network connectivity measures include the circular correlation coefficient and time-varying, adaptive multivariate autogressive modeling, using independent components that covary strongly with choice outcomes and/or cognitively modeled parameters. These measures of between-brain connectivity are less susceptible to spurious correlations induced by underlying task demands. A companion project is being funded by Federal Ministry of Education and Research, Germany (BMBF).