## 2 Materials and Methods
### Data Acquisition and Reduction  
Devices were connected by BNC cables (Digitimer, Welwyn Garden City, UK) to a Biopac MP150 and a CED Micro 1401-3 (Cambridge Electronic Design, Cambridge, UK) interfaced with a personal computer. Data were collected in Spike2 software (Version 7, Cambridge Electronic Design, Cambridge, UK). Raw data were processed using customized code in MATLAB R2017a (Mathworks, Natick, MA, USA).  
For all variability and complexity analyses, the steadiest five seconds of each 50% MVE trial was identified as the five-second epoch with the lowest SD [20]. Variability and complexity were, therefore, analyzed using 5000 data points. The CV was used as a magnitude-based variable, representing force variability normalized to the mean force [20]. Multiple complexity metrics are recommended for probing subtly different aspects of physiological signals [21]. Approximate entropy (ApEn) [22] was used to determine the regularity/randomness of the force signal and temporal fractal scaling was estimated using detrended fluctuation analysis (DFA) \(\alpha\)[35]. Sample entropy [36] was also considered; however, as shown by [PERSON] et al. [20], this measure does not differ from muscle force ApEn when 5000 data points are used in their calculation. The full details of the calculation of ApEn and DFA \(\alpha\) can be found in [PERSON] et al. [20].  
In short, ApEn quantifies the negative natural logarithm of the conditional probability that a template of length \(m\) is repeated during a time series. If the data are highly regular, then templates similar for \(m\) points (within the tolerance \(r\)) are likely to be similar for \(m+1\) points. In this situation, the conditional probability will be close to 1, and the negative logarithm, and therefore the entropy, will be close to zero. This reflects low complexity and high predictability. ApEn was calculated with the template length, \(m\), set at two and the tolerance, \(r\), set at 10% of the SD of knee extension moment output [20; 37]; 10% was chosen over other percentages following the recommendations of [PERSON] et al. [37], who sought to identify a \"gold standard\" for signal acquisition and processing parameters in the context of the ApEn analyses of isometric muscle force control records. In the DFA algorithm, the time-series of interest is integrated, then divided into boxes of equal length, \(n\), and a least-squares line (representing the trend in each box) is fitted. The integrated time series is detrended by subtracting the local trend in each box, and the root mean square of this integrated, detrended series, F(\(n\)), is calculated. This calculation is then repeated over all timescales or box sizes. The slope of the line relating log F(\(n\)) to log \(n\) determines the DFA \(\alpha\) scaling exponent [21]. Subsequently, DFA was calculated using 57 boxes, ranging from 1250 to 4 data points. The log-log plot of fluctuation size versus box size was plotted for each participant to identify any significant crossover (as shown by an \(r<0.95\)) and the presence of two trends [38]. No cases of significant crossover were observed. Typically, DFA \(\alpha\) ranges from \(\sim\)0.5 to \(\sim\)1.5 and acts as an indicator of the \"roughness\" of the time series; the larger the value of \(\alpha\), the more regular the time series [21].