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Updated: Feb 28, 2026

Corticospinal Excitability Modulation During Action Observation

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

Entropy-dependent human motor modulation consistent with morphological computation in a single subject.

Tsubasa Wakatsuki¹, Norimasa Yamada²

¹Institute of Engineering, Tokyo University of Agriculture and Technology, Koganei, Japan.

Frontiers in Robotics and AI

|February 26, 2026

Summary

Morphological computation (MC) principles suggest body mechanics aid processing. This study found evidence in human motor control, where limb movement adapted to temporal uncertainty, unlike simple button presses.

Keywords:

coefficient of variation embodied intelligence foreperiod low pass filter motor control reaction time shannon entropy temporal uncertainty

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Last Updated: Feb 28, 2026

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Area of Science:

Neuroscience
Robotics
Human Motor Control

Background:

Morphological computation (MC) explores how body mechanics influence information processing, with prior research in robotics and human physiology.
Human motor control studies have yet to fully explore MC's behavioral implications, especially under temporal uncertainty.

Purpose of the Study:

To investigate behavioral signatures of morphological computation (MC) in human motor control within a temporal-preparation paradigm.
To examine how temporal uncertainty affects movement variability and reaction time in tasks with differing embodiment.

Main Methods:

A single participant performed button-pressing and reaching movements across four levels of temporal uncertainty (0 to 2.0 bits).
Response variability (coefficient of variation) and reaction time (RT) were analyzed for each task and uncertainty level.

Main Results:

Reaching movements showed decreased response variability with increased temporal uncertainty, unlike button presses.
Reaction times diverged: longer foreperiods lengthened RTs in reaching but shortened them in button presses.
Spatial accuracy improved across foreperiods in the reaching task, suggesting adaptation to temporal context.

Conclusions:

Behavioral patterns observed in reaching movements are consistent with MC, suggesting limb mechanics filter temporal uncertainty.
Findings provide preliminary evidence for embodied intelligence principles in human motor control, linking biological and robotic systems.
The study highlights potential commonalities in brain-body-environment dynamics across biological and artificial systems.