Protocol design for the quantification of variability practice load in a visuomotor coordination task 

Sandra R. Reynoso1, David Barbado1,2, Raquel Bernabéu1, Francisco J. Moreno1 and Carla Caballero1,2 ¹ Department of Sports Sciences, Miguel Hernández University, Spain 2 Instituto de Investigación Sanitaria y Biomédica De Alicante (ISABIAL) 

Introduction 

Motor variability has been linked to enhanced learning rates (Barbado et al., 2017) by fostering exploratory behaviors (higher number of adjustments). Based on these findings, practicing in variable conditions could facilitate motion adjustments and increase learning. However, the benefits of external variability depend on its interaction with the individual’s intrinsic motor variability (Caballero et al., 2017). While some findings suggest an optimal level of variability load for maximizing learning based on learner profiles, the lack of quantitative external and internal indexes limits the choice of the optimal training dose. This study aimed to design a protocol to measure the effect of quantifiable external variability loads on internal indexes related to individuals’ motion adjustment ability. 

Methods 

Eighteen participants (22.9±3.2 years) performed a two-dimensional visuomotor coordination task, tracking a moving target on a computer screen using a mouse. After 3 familiarization trials (no variability), they completed 8 trials with variability loads manipulated from 5% to 40% (5% increments/decrements relative to baseline). To assess reliability, a second session (after ≥24 h rest) was conducted under the same conditions but with variability loads in reverse order. Target and mouse trajectories were recorded at 100 Hz, and distances between them were computed. The effect of variability loads was assessed using Detrended Fluctuation Analysis (DFA) to examine short-term (DFAST) and long-term (DFALT) motion adjustments. Intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were used for relative and absolute reliability, respectively. Repeated-measures ANOVA and Bonferroni-corrected post hoc tests analyzed differences between variability loads (α = 0.05). 

Results 

The loading protocol showed good relative (DFAST: 0.59<ICC<0.88; DFALT: 0.76<ICC<0.88) and absolute reliability (DFAST: 0.01<SEM<0.05; DFALT: 0.03<SEM<0.07) for most load conditions. ANOVAs indicated that increasing variability load generally increased DFAST up to the 20% load and decreased DFALT up to the 30% load. 

Conclusion 

Increasing variability loads to 20% and 30% reduced the ability to perform short-term postural adjustments (passive and involuntary control) while increasing the ability to perform long-term adjustments (voluntary control), respectively. This study is the first to analyze the acute impact of measurable variability loads on participants’ motion adjustment ability, opening opportunities to assess the dose-response relationship between variable practice and learning. 

References 

Barbado, D., Caballero, C., Moreside, J., Vera-García, F. J., & Moreno, F. J. (2017). Can the structure of motor variability predict learning rate? Journal of Experimental Psychology: Human Perception and Performance, 43(3), 596. 

Caballero, C., Moreno, F. J., Reina. R., Roldán, A., Coves, Á., & Barbado, D. (2017). The role of motor variability in motor control and learning depends on the nature of the task and the individual’s capabilities. European Journal of Human Movement, 38, 12-26.