Human to Human sensorimotor interaction can only be fully understood by modeling the patterns of bodily synchronization and reconstructing the underlying mechanisms of optimal cooperation. We designed a tower-building task to address such a goal. We recorded upper body kinematics of dyads and focused on the velocity profiles of the head and wrist. We applied Recurrence Quantification Analysis to examine the dynamics of synchronization within, and across the experimental trials, to compare the roles of leader and follower. Our results show that the leader was more auto-recurrent than the follower to make his/her behavior more predictable. When looking at the cross-recurrence of the dyad, we find different patterns of synchronization for head and wrist motion. On the wrist, dyads synchronized at short lags, and such pattern was weakly modulated within trials, and invariant across them. Head motion instead, synchronized at longer lags and increased both within and between trials: a phenomenon mostly driven by the leader. Our findings point at a multilevel nature of human to human sensorimotor synchronization, and may provide an experimentally solid benchmark to identify the basic primitives of motion, which maximize behavioral coupling between humans and artificial agents.
|Numero di pagine||11|
|Rivista||IEEE Transactions on Cognitive and Developmental Systems|
|Stato di pubblicazione||Pubblicato - 2016|
- Human-human interaction, Human-robot interaction, Body motion capture, Automatic imitation, Sensorimotor convergence, Joint action, Mirror neurons, Cross- recurrence quantification analysis, Dynamical systems