From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

Area of Science:

• Behavioral and brain sciences
• Neuroscience
• Motor control

Background:

• Motor variability is a complex construct with varied definitions and meanings in scientific literature.
• Existing research often uses 'motor variability' monolithically, lacking precise operational definitions.
• Understanding the sources and functional significance of motor variability is crucial in behavioral and brain sciences.

Purpose of the Study:

• To establish a precise lexicon for motor variability, moving beyond generic terminology.
• To model distinct domains and sub-domains of motor variability based on computational elements.
• To address theoretical, experimental, and clinical challenges in modeling motor variability.

Main Methods:

• Comprehensive literature review to build a precise lexicon.
• Modeling three domains of motor variability: noise, differentiation, and adaptability.
• Focusing on adaptability, including sub-domains like learning, social fitting, and co-adaptability.

Main Results:

• Proposed a model categorizing motor variability into noise, differentiation, and adaptability domains.
• Defined adaptability as variation within the same motor representation, encompassing learning and social influences.
• Highlighted co-adaptability as a key sub-domain within adaptability.
• Identified significant theoretical and experimental challenges in modeling motor variability.

Conclusions:

• Motor variability is neither inherently detrimental nor beneficial; its interpretation depends on context.
• Studying fluctuations in motor variability offers valuable insights for future research.
• The proposed lexicon and model can guide future research and clinical applications in neurorehabilitation.