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Neurophysiologic Collaboration Patterns During Team Problem Solving

机译:团队问题解决过程中的神经生理协作模式

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We have explored using neurophysiologic collaboration patterns as an approach for developing a deeper understanding of how teams collaborate when solving time-critical, complex real-world problems. Teams of three students solved substance abuse management simulations using IMMEX software while measures of mental workload (WL) and engagement (E) were generated by electroencephalography (EEG). Levels of high and low workload and engagement were identified for each member at each epoch statistically and the vectors consisting of these measures were clustered by self organizing artificial neural networks. The resulting cognitive teamwork patterns, termed neural synchronies, were different across six different teams. When the neural synchronies were compared across the team members of individual teams segments were identified where different synchronies were preferentially expressed. Some were expressed early in the collaboration when the team members were forming mental models of the problem, others were expressed later in the collaboration when the team members were sharing their mental models and converging on a solution. These studies indicate that non-random patterns of neurophysiologic synchronies can be observed across teams and members of a team when they are engaged in problem solving. This approach may provide an approach for monitoring the quality of team work during complex, real-world and possible one of a kind problem solving.
机译:我们已经探索了使用神经生理学协作模式作为一种方法,以加深对团队在解决时间紧迫,复杂的现实世界中的问题时如何进行协作的理解。三名学生组成的小组使用IMMEX软件解决了药物滥用管理模拟问题,而脑电图(EEG)产生了精神工作量(WL)和参与度(E)的量度。统计每个时期每个成员的高,低工作量和参与程度,并通过自组织人工神经网络对由这些度量组成的向量进行聚类。在六个不同的团队中,由此产生的认知团队合作模式(称为神经同步)是不同的。当在各个团队的团队成员之间比较神经同步时,会识别出优先表达不同同步的段。一些在团队成员形成问题的心理模型时在协作中表达过,另一些在团队成员共享他们的心理模型并融合于解决方案时在协作中表达了。这些研究表明,当团队和团队成员参与解决问题时,可以观察到神经生理同步的非随机模式。这种方法可以提供一种在复杂,现实世界中以及可能的一种问题解决方案中监视团队工作质量的方法。

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