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The Readiness-Efficiency Coupling: Synergy of High Preparatory and Reduced Execution Fractal Complexity in Motor
Russell W Chan1, Marcel Hildebrandt2, Julian Elias Reiser2
1Faculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, The Netherlands.
Journal of Motor Behavior
|May 12, 2026
Summary
Expertise in motor sequence learning (MSL) involves a balance between preparatory complexity and execution efficiency. This study reveals that optimal skill acquisition couples high readiness with reduced movement complexity.
Area of Science:
- Neuroscience
- Motor Control
- Skill Acquisition
Background:
- Motor sequence learning (MSL) transitions from reactive to predictive control.
- Traditional expertise metrics (speed, accuracy) overlook fractal movement variability.
- The Readiness-Efficiency hypothesis proposes expertise links preparatory and execution complexity.
Purpose of the Study:
- To investigate the fractal dynamics of movement variability during motor skill acquisition.
- To test the Readiness-Efficiency hypothesis in a whole-body motor task.
- To establish a mechanistic framework for profiling skill acquisition.
Main Methods:
- Participants (n=22) performed a whole-body Dance Discrete Sequence Production task.
- Center of Mass (CoM) kinematics were recorded.
- Hurst exponents (H) quantified fractal complexity of preparatory and execution dynamics.
Main Results:
- Preparatory complexity (Hprep) increased, while execution complexity (Hexec) decreased with practice.
- Fastest response times occurred when high preparatory readiness coupled with reduced execution complexity.
- This coupling was task-specific, collapsing upon transfer to novel sequences.
- State-space analysis showed a five-fold increase in occupying the Readiness-Efficiency state.
Conclusions:
- Expertise in MSL is characterized by dynamic flexibility, decoupling and pairing control regimes.
- The Readiness-Efficiency configuration serves as a functional attractor for skilled performance.
- This provides a mechanistic framework for understanding and profiling skill acquisition.
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