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EEG BIOMARKERS OF ADAPTIVE DECISION-MAKING UNDER UNCERTAINTY IN ELITE JUDOKAS
1Yerevan State University; Armenian State Institute of Physical Culture and Sport, Armenia.
None:
This study investigated the neural oscillatory correlates of decision-making under uncertainty in elite athletes. Specifically, it aimed to identify the electrophysiological signatures underlying successful performance on the Iowa Gambling Task (IGT) and to construct a neurocognitive profile of optimal decision-makers. A small cohort of elite judokas (n=7) performed 100 trials of the IGT while high-density electroencephalography (EEG) was recorded. Behavioral measures included final bank balance, advantageous versus disadvantageous deck selection, and learning slope across blocks. Neural indices were analyzed in the theta (4-8 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (>30 Hz) frequency bands. Superior performers were characterized by enhanced frontal midline theta power (ρ=-0.86 with rank), reflecting sustained engagement of cognitive control, and by right-frontal activation in the alpha band, indicative of a punishment-sensitive motivational style. Positive theta-beta coupling further distinguished successful decision-makers, supporting efficient translation of strategic analysis into motor execution. In contrast, lower performers exhibited attenuated theta activity, negative FAAI (approach dominance), and higher global gamma power, suggestive of inefficient neural resource allocation. The findings reveal a tripartite profile of optimal decision-making: (1) strategic left-hemispheric analytical processing with right-frontal punishment sensitivity, (2) strong cognitive-motor coupling via theta-beta communication, and (3) efficient neural economy marked by low gamma activity. This integrated neural signature provides a framework for understanding elite decision-making under uncertainty and may inform future neurofeedback interventions to enhance performance. However, given the small sample size, these findings are exploratory and require validation in a larger, more powered cohort to confirm their robustness and generalizability.
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