Abstract

There is some evidence that an improved understanding of executive control in the human movement system could be gained from explorations based on scale-free, fractal analysis of cyclic motor time series. Such analyses capture non-linear fractal dynamics in temporal fluctuations of motor instances that are believed to reflect how executive control enlist a coordination of multiple interactions across temporal scales between the brain, the body and the task environment, an essential architecture for adaptation. Here by recruiting elite rugby players with high motor skills and submitting them to the execution of rhythmic motor tasks involving legs and arms concurrently, the main attempt was to build on the multifractal formalism of movement control to show a marginal need of effective adaptation in concurrent tasks, and a preserved adaptability despite complexified motor execution. The present study applied a multifractal analytical approach to experimental time series and added surrogate data testing based on shuffled, ARFIMA, Davies&Harte and phase-randomized surrogates, for assessing scale-free behavior in repeated motor time series obtained while combining cycling with finger tapping and with circling. Single-tasking was analyzed comparatively. A focus-based multifractal-DFA approach provided Hurst exponents (H) of individual time series over a range of statistical moments H(q), q = [−15 15]. H(2) quantified monofractality and H(-15)-H(15) provided an index of multifractality. Despite concurrent tasking, participants showed great capacity to keep the target rhythm. Surrogate data testing showed reasonable reliability in using multifractal formalism to decipher movement control behavior. The global (i.e., monofractal) behavior in single-tasks did not change when adapting to dual-task. Multifractality dominated in cycling and did not change when cycling was challenged by upper limb movements. Likewise, tapping and circling behaviors were preserved despite concurrent cycling. It is concluded that the coordinated executive control when adapting to dual-motor tasking is not modified in people having developed great motor skills through physical training. Executive control likely emerged from multiplicative interactions across temporal scales which puts emphasis on multifractal approaches of the movement system to get critical cues on adaptation. Extending such analyses to less skilled people is appealing in the context of exploring healthy and diseased movement systems.

Highlights

  • Modern conceptions of the movement system in humans are closely related to the notion of a multifractal architecture of executive control (Dixon et al, 2012; Stephen et al, 2012; Ihlen and Vereijken, 2013; Delignières et al, 2016; Bell et al, 2019)

  • Fractal dynamics has been inferred an essential background for a typical architecture of control, providing the movement system with essential properties of flexibility and robustness reflected in aiming stabilization

  • Performance was assessed by two variables, Absolute Error (AE) and Coefficient of Variation (CV, %) of the motor time series

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Summary

Introduction

Modern conceptions of the movement system in humans are closely related to the notion of a multifractal architecture of executive control (Dixon et al, 2012; Stephen et al, 2012; Ihlen and Vereijken, 2013; Delignières et al, 2016; Bell et al, 2019). The identification of fractal dynamics with homogeneous sized fluctuation as a function of temporal scales (monofractal) has evolved over years to include the conception of finer multifractal properties (Ihlen and Vereijken, 2013). The strength of interactions across scales could be captured by measures of multifractality in motor time series, as said measures index variations in fractal scaling exponents which themselves describe how fluctuations are related to observational scales

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