Young M S, Stanton N A
Ergonomics Research Group, School of Engineering and Design, Brunel University, Uxbridge, Middlesex, UK.
Ergonomics. 2007 Aug;50(8):1324-39. doi: 10.1080/00140130701318855.
Previous research has found that vehicle automation systems can reduce driver mental workload, with implications for attentional resources that can be detrimental to performance. The present paper considers how the development of automaticity within the driving task may influence performance in underload situations. Driver skill and vehicle automation were manipulated in a driving simulator, with four levels of each variable. Mental workload was assessed using a secondary task measure and eye movements were recorded to infer attentional capacity. The effects of automation on driver mental workload were quite robust across skill levels, but the most intriguing findings were from the eye movement data. It was found that, with little exception, attentional capacity and mental workload were directly related at all levels of driver skill, consistent with earlier studies. The results are discussed with reference to applied theories of cognition and the design of automation.
先前的研究发现，车辆自动化系统可以减轻驾驶员的心理负荷，这对注意力资源有影响，而注意力资源可能对驾驶表现不利。本文探讨了驾驶任务中自动化程度的发展如何影响低负荷情况下的驾驶表现。在驾驶模拟器中对驾驶员技能和车辆自动化进行了操控，每个变量都有四个水平。使用一项次要任务测量来评估心理负荷，并记录眼动以推断注意力容量。自动化对驾驶员心理负荷的影响在各个技能水平上都相当显著，但最有趣的发现来自眼动数据。结果发现，几乎毫无例外，在所有驾驶员技能水平上，注意力容量和心理负荷都直接相关，这与早期研究一致。将结合应用认知理论和自动化设计对结果进行讨论。
