Devlin Shannon P, Brown Noelle L, Drollinger Sabrina, Sibley Ciara, Alami Jawad, Riggs Sara L
U.S. Naval Research Laboratory, Washington, D.C, USA; University of Virginia, Charlottesville, VA, USA.
U.S. Naval Research Laboratory, Washington, D.C, USA.
Appl Ergon. 2022 Nov;105:103829. doi: 10.1016/j.apergo.2022.103829. Epub 2022 Aug 2.
Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research.
鉴于目前尚无关于工作负荷转换的统一理论或设计指导，这项研究通过了解基于扫视的眼动追踪指标能否预测在与实际场景相关领域中的工作负荷转换性能趋势，来探究视觉注意力分配模式如何为这两个主题提供信息。在一个无人机测试平台上，当工作负荷以慢速、中速和快速进行转换时，对60名海军飞行学员的眼动进行了追踪。在反应时间和准确性的不同增长曲线模型中，有四个基于扫视的指标是显著的预测因子。静态注视熵（一种衡量视觉注意力转换在不同任务间分散程度的指标）在所有三种转换速率下都具有预测性。其他三个基于扫视的预测指标则捕捉了视觉注意力的不同方面，包括其分布范围、直接程度和持续时间。这些研究结果明确了理论和设计指导中几个缺失的细节，这是前所未有的，并为未来的工作负荷转换研究奠定了基础。
