Abstract
Complex systems typically demonstrate a mixture of regularity and flexibility in their behavior, which would make them adaptive. At the same time, adapting to perturbations is a core characteristic of resilience. The first aim of the current research was therefore to test the possible relation between complexity and resilient motor performance (i.e., performance while being perturbed). The second aim was to test whether complexity and resilient performance improve through differential learning. To address our aims, we designed two parallel experiments involving a motor task, in which participants moved a stick with their non-dominant hand along a slider. Participants could score points by moving a cursor as fast and accurately as possible between two boxes, positioned on the right- and left side of the screen in front of them. In a first session, we determined the complexity by analyzing the temporal structure of variation in the box-to-box movement intervals with a Detrended Fluctuation Analysis. Then, we introduced perturbations to the task: We altered the tracking speed of the cursor relative to the stick-movements briefly (i.e., 4 s) at intervals of 1 min (Experiment 1), or we induced a prolonged change of the tracking speed each minute (Experiment 2). Subsequently, participants had three sessions of either classical learning or differential learning. Participants in the classical learning condition were trained to perform the ideal movement pattern, whereas those in the differential learning condition had to perform additional and irrelevant movements. Finally, we conducted a posttest that was the same as the first session. In both experiments, results showed moderate positive correlations between complexity and points scored (i.e., box touches) in the perturbation-period of the first session. Across the two experiments, only differential learning led to a higher complexity index (i.e., more prominent patterns of pink noise) from baseline to post-test. Unexpectedly, the classical learning group improved more in their resilient performance than the differential learning group. Together, this research provides empirical support for the relation between complexity and resilience, and between complexity and differential learning in human motor performance, which should be examined further.
Highlights
Human motor performance can be considered as inherently complex and dynamic
Our research question was: What is the relation between complexity and resilient motor performance, and can they be improved through differential learning? In order to answer this question, we developed a motor task that allowed us to (1) let participants perform repetitive movements, which are useful for the analysis of complexity in time series (e.g., Wijnants et al, 2009, 2012), and (2) introduce perturbations while participants are performing the task
We aimed to answer the question: What is the relation between complexity and resilient motor performance, and can they be improved through differential learning? In order to answer this question, we developed a task in which participants moved a stick from left-to-right to touch two boxes on a screen in an alternate fashion with a cursor
Summary
Whether it is about rhythmical finger tapping, leg movements, or more complicated movements on the sports field, an individual’s motor performance emerges out of simultaneous processes at different levels of the motor system, including cells, muscles, limbs, and the brain (e.g., Thelen et al, 1987; Beek et al, 1995; Kelso, 1995; Davids et al, 2014; Den Hartigh et al, 2015) Through these complex dynamics, the human motor system typically organizes itself around metastable states, meaning that its behavior demonstrates a mixture of order (regularity) and disorder (flexibility) (e.g., Kello et al, 2007). Researchers examining cyclic movements in sports found that skilled athletes demonstrate more prominent patterns of pink noise in their movements than their less-skilled counterparts (e.g., Den Hartigh et al, 2015 in rowing ergometer performance; Nourrit-Lucas et al, 2015 in ski simulator performance)
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