Multivariate model for cooperation: bridging social physiological compliance and hyperscanning

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.

Automated summary

The neurophysiological analysis of cooperation has evolved over the past 20 years towards studying common patterns in neurophysiological signals during interactions. Social Physiological Compliance (SPC) and hyperscanning are two frameworks that analyze autonomic and brain signals, providing information on emotions and cognitive aspects respectively. This study explores the possibility of unifying these two approaches to create a comprehensive neurophysiological model for cooperation, taking into account affective and cognitive mechanisms. Results show a statistically significant causality between variables representing individuals in a dyad only when they cooperate, and the strength of this relationship correlates with the dyad's performance.