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Published on: May 3, 2012
Creating a memory-based automated system for skillful performance through cognitive, neurophysiological, and
Narges Abdoli1, Alireza Saberi Kakhki1
1Department of Motor Behavior, Faculty of Sport Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran.
Objectives:
The fundamental mechanisms underlying skilled and automated behavior remain poorly understood; to uncover these underlying mechanisms, we examined cognitive structures, alpha power, alpha connectivity, and motor performance outcomes in three groups of golfers: inexperienced (n = 15), less experienced (n = 15), and experienced (n = 15).
Methods:
Cognitive structures were analyzed using SDA-M software, while EEG data provided insights into alpha power and neural connectivity. Motor performance was evaluated through accuracy metrics (Mean Radial Error) and consistency measures (Bivariate Variable Error).
Results:
Findings reveal that experienced golfers possess significantly more complex cognitive structures than their less experienced counterparts. Moreover, neurophysiological data indicated that experienced golfers exhibited higher alpha power and connectivity in the frontal, central, and parietal regions than less experienced golfers (p < .001). Additionally, experienced golfers committed significantly fewer motor performance errors (p < .001).
Conclusions:
The results of this study, backed by extensive literature, suggest that cognitive, neurophysiological, and behavioral adaptations in motor learning develop a memory-based automated motor control system that underlies skilled and automated behavior. This research provides a holistic and integrated framework that enhances our understanding of skillful and automatic behavior.
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