Effect of social network properties on neural and behavioral dynamics in real-time social interaction

Key question: How do our social connections relate to the way we interact with each other in real time and use our brains in such interactions?

We live in a complex fabric of social connections. In this fabric, each person has a relatively stable pattern of ties to others: Some people are interwoven into a closely-knit community of friends and family where everybody knows each other. Others might bridge multiple groups of individuals who have never met. What happens when individuals with such different patterns of social connections “collide” in interactions unfolding here and now (for example, when carrying a table together)? Do such people behave in a different way? Do they use their brains differently?

Real-time interaction – as common as a conversation or a handshake – is particularly relevant for understanding and predicting how human social networks are being built and rebuilt. These interactions are the basis for many types of vital social ties: from friendships and romantic relationships to business partnerships. Despite the never-declining scientific interest in the ways human brains are “wired” to navigate in social structures, there is still little known about the relationships between the social network characteristics of individuals, the way they interact with each other in real time, and the underlying neural processes.

In this project, we combine the cutting-edge methods of network science and cognitive neuroscience to advance our knowledge of human social networks and interactions. First, we ask the participants of our studies to list people they know personally (family, friends, coworkers, and so on) so that we can model their personal social networks. Then pairs of participants perform an interactive task, while the EEG signal is recorded simultaneously from both individuals. We use these types of data to investigate whether the differences in social network parameters of interacting individuals predict the degree of alignment in behavior (such as synchrony of body movements) and thinking (for example, convergence on a specific opinion when asked to decide jointly). Besides, relying on the EEG-recording of two interacting brains, we explore how well one brain can predict the partner’s brain: For example, can your brain regions responsible for raising a hand predict the activity in the perception areas of your partner’s brain when they see your hand being raised? Finally, we explore the relationship between the social network patterns of individuals and the patterns of activity in their brain networks during social interaction.

The results will help us gain a deeper understanding of the functioning of human social networks and allow us to better predict collective cognitions and behaviors.