2026/07/23 by Luca A. Naudszus, Ryssa Moffat, Emily S. Cross · 1 voice
Engineering · Neuroscience · Psychology · #Action Observation and Synchronization #Neural and Behavioral Psychology Studies #Spatial Cognition and Navigation
paper · pdf · doi:10.1016/j.actpsy.2026.107252
openalex publication_date 2026/07/23 · openalex created_date 2026/07/24 · openalex updated_date 2026/07/31
Engaging with others in social scenarios can result in the alignment of brain activity between individuals. Dyadic (i.e., hyperscanning) studies typically estimate average region-specific levels of connectivity between brains (as opposed to within brains) across a given task to quantify brain activity alignment. This approach assumes symmetric interactions with equal and mutual adaptation from both dyad members, excluding asymmetric (e.g., leader-follower) contexts. Such approaches also obscure spatial dynamics (i.e., relationship between different brain regions) and temporal dynamics during unfolding interactions. To overcome these challenges, we took a data-driven approach to quantify within- and between-brain connectivity during collaborative drawing among dyads. Specifically, we used sliding windows and Riemannian-geometry-based k-means clustering to identify recurrent two-brain states while 61 dyads drew alone and together at 6 weekly timepoints. Thirty dyads comprised young adults only (same generation) and 31 dyads comprised one older and one younger individual (intergenerational). We identified 7 two-brain states, 3 of which were specific to real (not pseudo) dyads. One two-brain state showed convincing evidence of sensitivity to collaboration context: During collaborative drawing, low-to-medium between-brain connectivity and prominent within-brain connectivity in bilateral IFG in a single dyad member arose for longer periods in intergenerational than same generation dyads (co-occurring with reduced turn-taking behaviour). No two-brain state showed evidence of longitudinal changes across sessions. These findings inform recent accounts of neural dynamics that emphasize the complementary roles of within-brain and between-brain connectivity. Furthermore, they suggest that state-based analyses can inform neural dynamics in a way not captured by traditional analysis techniques.