Wilson Glenn F, Russell Christopher A
Air Force Research Laboratory, Wright Patterson Air Force Base, OH, USA.
Hum Factors. 2007 Dec;49(6):1005-18. doi: 10.1518/001872007X249875.
We show that psychophysiologically driven real-time adaptive aiding significantly enhances performance in a complex aviation task. A further goal was to assess the importance of individual operator capabilities when providing adaptive aiding.
Psychophysiological measures are useful for monitoring cognitive workload in laboratory and real-world settings. They can be recorded without intruding into task performance and can be analyzed in real time, making them candidates for providing operator functional state estimates. These estimates could be used to determine if and when system intervention should be provided to assist the operator to improve system performance.
Adaptive automation was implemented while operators performed an uninhabited aerial vehicle task. Psychophysiological data were collected and an artificial neural network was used to detect periods of high and low mental workload in real time. The high-difficulty task levels used to initiate the adaptive automation were determined separately for each operator, and a group-derived mean difficulty level was also used.
Psychophysiologically determined aiding significantly improved performance when compared with the no-aiding conditions. Improvement was greater when adaptive aiding was provided based on individualized criteria rather than on group-derived criteria. The improvements were significantly greater than when the aiding was randomly provided.
These results show that psychophysiologically determined operator functional state assessment in real time led to performance improvement when included in closed loop adaptive automation with a complex task.
Potential future applications of this research include enhanced workstations using adaptive aiding that would be driven by operator functional state.
我们证明，心理生理驱动的实时自适应辅助显著提高了复杂航空任务中的表现。另一个目标是评估提供自适应辅助时个体操作员能力的重要性。
心理生理测量方法在实验室和现实环境中对监测认知工作量很有用。它们可以在不干扰任务表现的情况下进行记录，并且可以实时分析，这使其成为提供操作员功能状态估计的候选方法。这些估计可用于确定是否以及何时应提供系统干预以协助操作员提高系统性能。
在操作员执行无人机任务时实施自适应自动化。收集心理生理数据，并使用人工神经网络实时检测高心理工作量和低心理工作量时期。用于启动自适应自动化的高难度任务级别是为每个操作员单独确定的，同时也使用了基于群体得出的平均难度级别。
与无辅助条件相比，心理生理确定的辅助显著提高了表现。基于个性化标准而非基于群体得出的标准提供自适应辅助时，表现提升更大。这些提升显著大于随机提供辅助时的提升。
这些结果表明，在复杂任务的闭环自适应自动化中，心理生理实时确定的操作员功能状态评估可提高表现。
本研究未来的潜在应用包括使用由操作员功能状态驱动的自适应辅助的增强型工作站。
