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Cognitive Neurophysiologic Synchronies: What Can They Contribute to the Study of Teamwork

机译:认知神经生理同步:他们可以为团队合作研究做出什么贡献

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Objective: Cognitive neurophysiologic synchronies (NS) are low-level data streams derived from electroencephalography (EEG) measurements that can be collected and analyzed in near real time and in realistic settings. The objective of this study was to relate the expression of NS for engagement to the frequency of conversation between team members during Submarine Piloting and Navigation (SPAN) simulations. Background: If the expression of different NS patterns is sensitive to changes in the behavior of teams, they may be a useful tool for studying team cognition. Method: EEG-derived measures of engagement (EEG-E) from SPAN team members were normalized and pattern classified by self-organizing artificial neural networks and hidden Markov models. The temporal expression of these patterns was mapped onto team events and related to the frequency of team members' speech. Standardized models were created with pooled data from multiple teams to facilitate comparisons across teams and levels of expertise and to provide a framework for rapid monitoring of team performance. Results: The NS expression for engagement shifted across task segments and internal and external task changes.These changes occurred within seconds and were affected more by changes in the task than by the person speaking. Shannon entropy measures of the NS data stream showed decreases associated with periods when the team was stressed and speaker entropy was high. Conclusion: These studies indicate that expression of neurophysiologic indicators measured by EEG may complement rather than duplicate communication metrics as measures of team cognition. Application: Neurophysiologic approaches may facilitate the rapid determination of the cognitive status of a team and support the development of novel adaptive approaches to optimize team function.
机译:目的:认知神经生理同步(NS)是源自脑电图(EEG)测量值的低级数据流,可以在接近实时和真实环境下进行收集和分析。这项研究的目的是将NS的参与表达与潜艇驾驶和导航(SPAN)模拟期间团队成员之间的交谈频率联系起来。背景:如果不同NS模式的表达对团队行为的变化敏感,那么它们可能是研究团队认知的有用工具。方法:使用自组织人工神经网络和隐马尔可夫模型对来自SPAN团队成员的EEG参与度(EEG-E)进行标准化和模式分类。这些模式的时间表达被映射到团队事件上,并与团队成员讲话的频率有关。使用来自多个团队的汇总数据创建了标准化模型,以促进团队之间的比较和专业水平,并提供用于快速监控团队绩效的框架。结果:参与度的NS表达式跨任务段以及内部和外部任务更改而变化,这些更改在几秒钟内发生,并且受任务更改的影响比受讲话者影响更大。 NS数据流的Shannon熵测度显示,与团队压力和说话者熵高时的时期相关的减少。结论:这些研究表明,通过脑电图测量的神经生理指标的表达可以补充而不是重复交流指标作为团队认知的指标。应用:神经生理学方法可能有助于快速确定团队的认知状态,并支持开发新的自适应方法来优化团队功能。

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