Discharge patterns from a population of motor units (MUs) were estimated with multi-channel surface electromyogram and signal processing techniques to investigate parametric differences in low-frequency force fluctuations, MU discharges, and force-discharge relation during static force-tracking with varying sizes of execution error presented via visual feedback. Fourteen healthy adults produced isometric force at 10% of maximal voluntary contraction through index abduction under three visual conditions that scaled execution errors with different amplification factors. Error-augmentation feedback that used a high amplification factor (HAF) to potentiate visualized error size resulted in higher sample entropy, mean frequency, ratio of high-frequency components, and spectral dispersion of force fluctuations than those of error-reducing feedback using a low amplification factor (LAF). In the HAF condition, MUs with relatively high recruitment thresholds in the dorsal interosseous muscle exhibited a larger coefficient of variation for inter-spike intervals and a greater spectral peak of the pooled MU coherence at 13–35 Hz than did those in the LAF condition. Manipulation of the size of error feedback altered the force-discharge relation, which was characterized with non-linear approaches such as mutual information and cross sample entropy. The association of force fluctuations and global discharge trace decreased with increasing error amplification factor. Our findings provide direct neurophysiological evidence that favors motor training using error-augmentation feedback. Amplification of the visualized error size of visual feedback could enrich force gradation strategies during static force-tracking, pertaining to selective increases in the discharge variability of higher-threshold MUs that receive greater common oscillatory inputs in the β-band.

Error amplification (EA) feedback is a promising approach to advance visuomotor skill. As error detection and visuomotor processing at short time scales decline with age, this study examined whether older adults could benefit from EA feedback that included higher-frequency information to guide a force-tracking task. Fourteen young and 14 older adults performed low-level static isometric force-tracking with visual guidance of typical visual feedback and EA feedback containing augmented high-frequency errors. Stabilogram diffusion analysis was used to characterize force fluctuation dynamics. Also, the discharge behaviors of motor units and pooled motor unit coherence were assessed following the decomposition of multi-channel surface electromyography (EMG). EA produced different behavioral and neurophysiological impacts on young and older adults. Older adults exhibited inferior task accuracy with EA feedback than with typical visual feedback, but not young adults. Although stabilogram diffusion analysis revealed that EA led to a significant decrease in critical time points for both groups, EA potentiated the critical point of force fluctuations <ΔFc2>, short-term effective diffusion coefficients (Ds), and short-term exponent scaling only for the older adults. Moreover, in older adults, EA added to the size of discharge variability of motor units and discharge regularity of cumulative discharge rate, but suppressed the pooled motor unit coherence in the 13–35 Hz band. Virtual EA alters the strategic balance between open-loop and closed-loop controls for force-tracking. Contrary to expectations, the prevailing use of closed-loop control with EA that contained high-frequency error information enhanced the motor unit discharge variability and undermined the force steadiness in the older group, concerning declines in physiological complexity in the neurobehavioral system and the common drive to the motoneuronal pool against force destabilization.

Although error amplification (EA) feedback has been shown to improve performance on visuomotor tasks, the challenge of EA is that it concurrently magnifies task-irrelevant information that may impair visuomotor control. The purpose of this study was to improve the force control in a static task by preclusion of high-oscillatory components in EA feedback that cannot be timely used for error correction by the visuomotor system. Along with motor unit behaviors and corticomuscular coherence, force fluctuations (Fc) were modeled with non-linear SDA to contrast the reliance of the feedback process and underlying neurophysiological mechanisms by using real feedback, EA, and low-frequency error amplification (LF-EA). During the static force task in the experiment, the EA feedback virtually potentiated the size of visual error, whereas the LF-EA did not channel high-frequency errors above 0.8 Hz into the amplification process. The results showed that task accuracy was greater with the LF-EA than with the real and EA feedback modes, and that LF-EA led to smaller and more complex Fc. LF-EA generally led to smaller SDA variables of Fc (critical time points, critical point of Fc, the short-term effective diffusion coefficient, and short-term exponent scaling) than did real feedback and EA. The use of LF-EA feedback increased the irregularity of the ISIs of MUs but decreased the RMS of the mean discharge rate, estimated with pooled MU spike trains. Beta-range EEG–EMG coherence spectra (13–35 Hz) in the LF-EA condition were the greatest among the three feedback conditions. In summary, amplification of low-frequency errors improves force control by shifting the relative significances of the feedforward and feedback processes. The functional benefit arises from the increase in the common descending drive to promote a stable state of MU discharges.