Grundgeiger Tobias, Michalek Annabell, Hahn Felix, Wurmb Thomas, Meybohm Patrick, Happel Oliver
Institute Human-Computer-Media, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Würzburg, Germany.
Hum Factors. 2023 Dec;65(8):1689-1701. doi: 10.1177/00187208211060586. Epub 2021 Dec 27.
To investigate the effect of a cognitive aid on the visual attention distribution of the operator using the Salience Effort Expectancy Value (SEEV) model.
Cognitive aids aim to support an operator during the execution of a task. The effect of cognitive aids on performance is frequently evaluated but whether a cognitive aid improved, for example, attention distribution has not been considered.
We built the Expectancy Value (EV) model version which can be considered to indicate optimal attention distribution for a given event. We analyzed the eye tracking data of emergency physicians while using a cognitive aid application versus no application during a simulated in-hospital cardiac arrest scenario.
The EV model could fit the attention distribution in such a simulated emergency situation. Partially supporting our hypothesis, the cognitive aid application group showed a significantly better EV model fit than the no application group in the first phases of the event, but a worse fit in the last phase.
We demonstrated that a cognitive aid affected attention distribution and that the SEEV model provides the means of capturing these effects. We suggest that the aid supported and improved visual attention distribution in the stressful first phases of a cardiopulmonary resuscitation but may have focused attention on objects that are relevant for lower priority goals in the last phase.
The SEEV model can provide insights into expected and unexpected effects of cognitive aids on visual attention distribution and may help to design better artifacts.
使用显著努力期望价值（SEEV）模型研究认知辅助工具对操作者视觉注意力分配的影响。
认知辅助工具旨在在任务执行过程中支持操作者。人们经常评估认知辅助工具对绩效的影响，但尚未考虑认知辅助工具是否改善了注意力分配等问题。
我们构建了期望价值（EV）模型版本，该版本可被视为指示给定事件的最佳注意力分配。我们分析了急诊医生在模拟院内心脏骤停场景中使用认知辅助应用程序与不使用该程序时的眼动追踪数据。
EV模型能够拟合这种模拟紧急情况下的注意力分配。部分支持我们的假设，在事件的第一阶段，认知辅助应用组的EV模型拟合度明显优于无应用组，但在最后阶段拟合度较差。
我们证明了认知辅助工具会影响注意力分配，并且SEEV模型提供了捕捉这些影响的方法。我们建议，该辅助工具在心肺复苏压力较大的第一阶段支持并改善了视觉注意力分配，但在最后阶段可能将注意力集中在了与较低优先级目标相关的物体上。
SEEV模型可以深入了解认知辅助工具对视觉注意力分配的预期和意外影响，并可能有助于设计更好的人工制品。
