The relation between complexity and resilient motor performance and the effects of differential learning

• Verfasst von: Den Hartigh, Ruud [VerfasserIn]
• Verfasst von: Otten, Sem [VerfasserIn]
• Verfasst von: Gruszczynska, Zuzanna M. [VerfasserIn]
• Verfasst von: Hill, Yannick [VerfasserIn]
• Titel: The relation between complexity and resilient motor performance and the effects of differential learning
• Verf.angabe: Ruud J.R. Den Hartigh, Sem Otten, Zuzanna M. Gruszczynska and Yannick Hill
• E-Jahr: 2021
• Jahr: 12 August 2021
• Umfang: 10 S.
• Teil: volume:15
• Teil: year:2021
• Teil: elocationid:715375
• Teil: pages:1-10
• Teil: extent:10
• Fussnoten: Gesehen am 06.10.2021
• Titel Quelle: Enthalten in: Frontiers in human neuroscience
• Ort Quelle: Lausanne : Frontiers Research Foundation, 2008
• Jahr Quelle: 2021
• Band/Heft Quelle: 15(2021), Artikel-ID 715375, Seite 1-10
• ISSN Quelle: 1662-5161
• DOI: doi:10.3389/fnhum.2021.715375
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: adaptability
• Sach-SW: behavior
• Sach-SW: complex systems
• Sach-SW: coordination
• Sach-SW: detrended fluctuation analysis
• Sach-SW: fractal dynamics
• Sach-SW: models
• Sach-SW: movement systems
• Sach-SW: noise
• Sach-SW: non-linear dynamics
• Sach-SW: nonlinear dynamics
• Sach-SW: pink noise
• Sach-SW: skill
• Sach-SW: sports
• Sach-SW: variability
• K10plus-PPN: 1772577456

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.