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Synergic practice with a body-machine interface: implications for individual and collective motor learning
Amy Bellitto1,2,3, Ferdinando A Mussa-Ivaldi4,5, Camilla Pierella1
1Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Journal of Neural Engineering
|July 11, 2025
Summary
In synergic practice using body-machine interfaces (BoMIs), higher individual contributions lead to better sustained motor learning. Lower contributions initially hinder performance but can be overcome with further practice.
Area of Science:
- Neuroscience
- Robotics
- Motor Control
Background:
- Body-machine interfaces (BoMIs) translate body movements into device commands.
- Synergic practice in dyadic tasks shows potential for enhancing motor learning.
- The impact of individual contributions within synergic practice is not well understood.
Purpose of the Study:
- To investigate how varying contribution levels in synergic practice affect individual and collective motor learning.
- To assess the influence of contribution levels on individual performance after returning to solo practice.
Main Methods:
- Forty participants engaged in solo, dyadic synergic, and post-dyadic solo practice with a BoMI.
- Participants were categorized as high or low contributors based on their role in dyadic cursor control.
- Analysis focused on performance, motor strategies, and internal models.
Main Results:
- High contributors maintained consistent motor strategies during dyadic practice.
- Low contributors exhibited strategy deviations during dyadic practice.
- Post-dyadic solo practice showed high contributors retaining performance, while low contributors initially regressed before improving.
Conclusions:
- Contribution levels in synergic practice significantly impact motor learning trajectories.
- Understanding these dynamics is crucial for optimizing dyadic training protocols.
- Synergic practice benefits subsequent individual performance, with contribution level modulating the effect.
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