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Published on: March 21, 2019
Transition from an antiphase error-correction mode to a synchronization mode in mutual hand tracking.
Yoshikatsu Hayashi1, Yasuji Sawada
1Cybernetics Group, School of Systems Engineering, University of Reading, PO Box 225, Whiteknights, Reading RG6 6AY, United Kingdom*
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 17, 2013
Summary
Human movement often anticipates fast-paced signals, shifting from reactive to proactive modes. When two people track each other, they synchronize when both use this proactive anticipatory mode, transitioning from error correction.
Area of Science:
- Human motor control
- Human-robot interaction
- Neuroscience
Background:
- The human sensory-motor system exhibits proactive (anticipatory) behavior in response to fast periodic signals.
- This anticipatory tendency suggests a preference for proactive over reactive modes in rapid motion tracking.
Purpose of the Study:
- To investigate the modes of human sensory-motor system interaction during dyadic (two-person) hand tracking.
- To determine the transition frequencies between different interaction modes in a paired tracking task.
Main Methods:
- Participants were tasked with tracking their partner's hand position across various mean tracking frequencies.
- Experimental data were analyzed to identify shifts in interaction modes.
- Mathematical models were developed to describe observed behaviors.
Main Results:
- A transition from a mutual error-correction mode to a synchronization mode was observed.
- This transition occurred at similar frequency regions as the shift from reactive to proactive modes in single-subject tracking.
- Synchronization of body motion was contingent upon both participants operating in a proactive anticipatory mode.
Conclusions:
- Dyadic tracking reveals distinct interaction modes: error correction and synchronization.
- The human sensory-motor system transitions to synchronization when both individuals adopt a proactive anticipatory strategy.
- Mathematical models provide insights into the dynamics of these interaction modes.
