Symbolic dynamics of joint brain states during dyadic coordination

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Understanding neural mechanisms of human interaction is crucial.
• Existing methods often analyze individual brain activity separately.
• Coordination requires integrated analysis of multiple brains.

Purpose of the Study:

• To introduce a novel approach for analyzing joint brain states in interacting individuals.
• To explore neural mechanisms underlying multi-person motor coordination.
• To investigate how different interaction conditions affect brain dynamics.

Main Methods:

• Developed symbolic representations of joint brain states from functional connectivity.
• Applied recurrence and network analysis to electroencephalographic (EEG) data.
• Examined finger-tapping tasks (synchronization, syncopation) under varying interaction conditions (uncoupled, leader-follower, mutual).

Main Results:

• Interaction primarily affects symbolic dynamics, not the joint symbols themselves.
• Recurrence analysis showed interaction alters dwell times and motif lengths in joint symbols.
• Synchronization with feedback increased stability; syncopation showed complex responses to leader-follower and mutual interactions.
• Network analysis revealed distinct topological changes in joint brain states based on task and feedback.

Conclusions:

• Symbolic representations of metastable joint brain states offer new insights into human interaction.
• The analytic tools developed can advance the study of neural dynamics in coordination.
• Findings highlight the differential impact of interaction types on brain network organization.