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神经工效学:个体、共享和团队神经动力学信息的定量建模。

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information.

机构信息

University of California, Los Angeles, California.

The Learning Chameleon, Los Angeles, California.

出版信息

Hum Factors. 2018 Nov;60(7):1022-1034. doi: 10.1177/0018720818781623. Epub 2018 Jun 15.

DOI:10.1177/0018720818781623
PMID:29906201
Abstract

OBJECTIVE

The aim of this study was to use the same quantitative measure and scale to directly compare the neurodynamic information/organizations of individual team members with those of the team.

BACKGROUND

Team processes are difficult to separate from those of individual team members due to the lack of quantitative measures that can be applied to both process sets.

METHOD

Second-by-second symbolic representations were created of each team member's electroencephalographic power, and quantitative estimates of their neurodynamic organizations were calculated from the Shannon entropy of the symbolic data streams. The information in the neurodynamic data streams of health care ( n = 24), submarine navigation ( n = 12), and high school problem-solving ( n = 13) dyads was separated into the information of each team member, the information shared by team members, and the overall team information.

RESULTS

Most of the team information was the sum of each individual's neurodynamic information. The remaining team information was shared among the team members. This shared information averaged ~15% of the individual information, with momentary levels of 1% to 80%.

CONCLUSION

Continuous quantitative estimates can be made from the shared, individual, and team neurodynamic information about the contributions of different team members to the overall neurodynamic organization of a team and the neurodynamic interdependencies among the team members.

APPLICATION

Information models provide a generalizable quantitative method for separating a team's neurodynamic organization into that of individual team members and that shared among team members.

摘要

目的

本研究旨在使用相同的定量测量和量表,直接比较个体团队成员与团队的神经动力学信息/组织。

背景

由于缺乏可同时应用于两个过程集的定量测量方法,因此很难将团队过程与个体团队成员的过程分开。

方法

为每个团队成员的脑电图功率创建了逐秒的符号表示,并从符号数据流的香农熵中计算出他们神经动力学组织的定量估计。医疗保健(n=24)、潜艇导航(n=12)和高中解决问题(n=13)双人组的神经动力学数据流中的信息被分为每个团队成员的信息、团队成员共享的信息和整个团队的信息。

结果

大部分团队信息是每个个体神经动力学信息的总和。其余的团队信息在团队成员之间共享。这些共享信息平均约占个体信息的 15%,瞬时水平为 1%至 80%。

结论

可以从共享的、个体的和团队的神经动力学信息中连续定量估计不同团队成员对团队整体神经动力学组织的贡献以及团队成员之间的神经动力学相互依存关系。

应用

信息模型为将团队的神经动力学组织分解为个体团队成员的组织和团队成员之间共享的组织提供了一种可推广的定量方法。

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Hum Factors. 2018 Nov;60(7):1022-1034. doi: 10.1177/0018720818781623. Epub 2018 Jun 15.
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Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty.解析神经动力学信息流以估计团队不确定性的频率、幅度和持续时间。
Front Syst Neurosci. 2021 Feb 1;15:606823. doi: 10.3389/fnsys.2021.606823. eCollection 2021.
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Neural Correlates of Group Versus Individual Problem Solving Revealed by fMRI.
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