The neurophysiological analysis of cooperation has evolved over the past 20 years, moving towards the research of common patterns in neurophysiological signals of people interacting. Social Physiological Compliance (SPC) and Hyperscanning represent two frameworks for the joint analysis of autonomic and brain signals respectively. Each of the two approaches allows to know about a single layer of cooperation according to the nature of these signals: SPC provides information mainly related to emotions, and Hyperscanning that related to cognitive aspects. In this work, after the analysis of the state of the art of SPC and Hyperscanning, we explored the possibility to unify the two approaches creating a complete neurophysiological model for cooperation considering both affective and cognitive mechanisms. We synchronously recorded electrodermal activity, cardiac and brain signals of 14 cooperative dyads. Time series from these signals were extracted and Multivariate Granger Causality was computed. The results showed that only when subjects in a dyad cooperate there is a statistically significant causality between the multivariate variables representing each subject. Moreover, the entity of this statistical relationship correlates with the dyad’s performance. Finally, given the novelty of this approach and its exploratory nature, we provided its strengths and limitations.

中文翻译：

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合作的多元模型：弥合社会生理依从性和超扫描。

合作的神经生理学分析在过去的20年中已经发展起来，正朝着研究人们互动的神经生理信号中常见模式的方向发展。社会生理顺应性（SPC）和超扫描代表分别对植物神经和大脑信号进行联合分析的两个框架。两种方法中的每一种都允许根据这些信号的性质来了解单层合作：SPC提供主要与情感有关的信息，以及与认知方面有关的超扫描。在这项工作中，在分析了SPC和Hyperscanning的技术水平之后，我们探索了将两种方法统一起来的可能性，从而创建了一个完整的神经生理学模型进行合作，同时考虑了情感和认知机制。我们同步记录了皮肤电活动，14个双联的心脑信号。从这些信号中提取时间序列，并计算多元Granger因果关系。结果表明，只有当一个二元组中的对象合作时，代表每个对象的多元变量之间才存在统计上显着的因果关系。而且，这种统计关系的实体与二元组的性能相关。最后，鉴于这种方法的新颖性和探索性，我们提供了它的优点和局限性。这种统计关系的实体与二元组的性能相关。最后，鉴于这种方法的新颖性和探索性，我们提供了它的优点和局限性。这种统计关系的实体与二元组的性能相关。最后，鉴于这种方法的新颖性和探索性，我们提供了它的优点和局限性。