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

## TL;DR

This study explores how human motor control adapts to uncertainty in a way that resembles mechanical computation, using a single participant's movements.

## Contribution

The study provides preliminary evidence that human motor control may exhibit morphological computation-like behavior under temporal uncertainty.

## Key findings

- Reaching movements showed decreasing response variability with increasing temporal uncertainty.
- Reaction times diverged between reaching and button-pressing tasks under different entropy levels.
- Spatial accuracy in reaching improved across foreperiods without explicit instructions.

## Abstract

Morphological computation (MC)—the idea that body mechanics contribute to computation—has been widely explored in robotics and examined in humans from a physiological perspective. In this study, we report a behavioral pattern consistent with MC under temporal uncertainty. This proof-of-concept single-subject study examined whether human motor control shows behavioral signatures consistent with MC within a temporal-preparation paradigm. One participant completed 160 trials across four entropy levels (0, 1.0, 1.5, 2.0 bits) in two tasks: a low-embodiment button-pressing movement and a high-embodiment reaching movement. The reaching movement tended to show decreasing response variability (coefficient of variation, CV) with increasing temporal uncertainty, whereas the button-pressing movement tended to remain flat or slightly increase. Reaction time (RT) patterns also diverged: RTs tended to lengthen with longer foreperiods in the reaching condition but shortened in the button-pressing movement. Moreover, spatial accuracy in the reaching movement tended to improve across foreperiods. These adaptations emerged without explicit strategy instructions, may reflect sensitivity to temporal context. Taken together, these patterns appear consistent with MC-inspired accounts in which limb mechanics and modest co-contraction may filter temporal uncertainty rather than amplify it. Although constrained by a single-subject, four-level design, the findings offer preliminary evidence that is suggestive of embodied-intelligence principles that may generalize to human motor control, highlighting commonalities between biological and robotic systems in brain–body–environment dynamics.