a Department of Psychology , Arizona State University , Tempe , USA.
Soc Neurosci. 2014;9(3):219-34. doi: 10.1080/17470919.2014.882861. Epub 2014 Feb 12.
The quality of a team depends on its ability to deliver information through a hierarchy of team members and negotiate processes spanning different time scales. That structure and the behavior that results from it pose problems for researchers because multiply-nested interactions are not easily separated. We explored the behavior of a six-person team engaged in a Submarine Piloting and Navigation (SPAN) task using the tools of dynamical systems. The data were a single entropy time series that showed the distribution of activity across six team members, as recorded by nine-channel electroencephalography (EEG). A single team's data were analyzed for the purposes of illustrating the utility of multifractal analysis and allowing for in-depth exploratory analysis of temporal characteristics. Could the meaningful events experienced by one of these teams be captured using multifractal analysis, a dynamical systems tool that is specifically designed to extract patterns across levels of analysis? Results indicate that nested patterns of team activity can be identified from neural data streams, including both routine and novel events. The novelty of this tool is the ability to identify social patterns from the brain activity of individuals in the social interaction. Implications for application and future directions of this research are discussed.
团队的质量取决于其通过团队成员的层次结构传递信息和协商跨越不同时间尺度的过程的能力。这种结构及其产生的行为给研究人员带来了问题，因为多重嵌套的相互作用不容易分离。我们使用动力系统的工具研究了一个由六个人组成的团队在潜艇驾驶和导航 (SPAN) 任务中的行为。数据是一个单一的熵时间序列，显示了通过九通道脑电图 (EEG) 记录的六个团队成员的活动分布。为了说明多重分形分析的实用性并对时间特征进行深入探索性分析，对单个团队的数据进行了分析。是否可以使用多重分形分析来捕获这些团队中的一个团队所经历的有意义的事件，多重分形分析是一种专门用于在分析层次上提取模式的动力系统工具？结果表明，可以从神经数据流中识别出团队活动的嵌套模式，包括常规和新颖事件。该工具的新颖之处在于能够从社交互动中个体的大脑活动中识别出社会模式。讨论了该研究的应用和未来方向的影响。
