Neuromechanical proficiency in elite performance decision-making: An event-related potential (ERP) analysis

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2024-07-25 DOI:10.1016/j.slast.2024.100171
Qiang Xiong , Dan-Li Song
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Abstract

This research focuses intensively on the neural mechanisms of action selection responses in elite athletes during high-stakes decision-making. It emphasizes the neuromechanical dimensions of these processes, contrasting the responses of expert and novice players within a theoretical framework of neural information processing in high-performance contexts. Utilizing advanced EEG technologies, including event-related potential (ERP) analysis, the study captures a comprehensive view of both behavioral and neurophysiological data. The central aim is to unravel the intricate neural underpinnings that distinguish elite athletes in their decision-making strategies. Key findings highlight: (1) Enhanced accuracy and swifter reaction times in elite athletes during the action selection phase; (2) Significant neurophysiological differences, marked by pronounced N1 peak amplitudes with prolonged latencies, reduced P2 peak amplitudes with stable latencies, decreased P3 peak amplitudes with reduced latencies, and increased average PSW amplitudes. These discoveries significantly advance our understanding of the neural foundations of expert decision-making in high-performance sports. This study not only sheds light on the cognitive and neural dynamics of elite sports performance but also provides a foundation for developing training and performance enhancement techniques in various high-stakes domains.

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精英表演决策中的神经机械能力:事件相关电位(ERP)分析。
这项研究重点关注精英运动员在高风险决策过程中做出动作选择反应的神经机制。它强调了这些过程的神经机械层面,在高性能环境下神经信息处理的理论框架内对比了专家和新手的反应。该研究利用先进的脑电图技术,包括事件相关电位(ERP)分析,对行为和神经生理学数据进行了全面观察。研究的核心目的是揭示精英运动员决策策略的复杂神经基础。主要发现突出表明:1)精英运动员在行动选择阶段的准确性更高,反应时间更快;2)神经生理差异明显,N1 峰值振幅明显,潜伏期延长,P2 峰值振幅降低,潜伏期稳定,P3 峰值振幅降低,潜伏期缩短,PSW 平均振幅增加。这些发现极大地推动了我们对高性能运动中专家决策的神经基础的理解。这项研究不仅揭示了精英体育成绩的认知和神经动态,还为开发各种高风险领域的训练和成绩提升技术奠定了基础。
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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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