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首页> 外文期刊>International Journal of Industrial Ergonomics >A measure of mental workload during multitasking: Using performance-based Timed Petri Nets
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A measure of mental workload during multitasking: Using performance-based Timed Petri Nets

机译：多任务处理期间的心理工作量：使用基于性能的定时Petri网

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摘要

Multitasking is common in emergency, which will result in man-made error due to many factors such as inappropriate Mental Workload (MW). To improve the performance under multitasking, the automatic equipment or assistive systems carry out task/interface optimizations according to the MW evaluation results, and the effectiveness of these technologies depends on how to choose an appropriate MW evaluation model. This paper has developed a performance-based Timed Petri Nets (TPN) model to evaluate MW during multitasking. The concrete functioned places and transitions, were proposed to structure a human resource allocation and recovery processes in TPN. The characteristics of operator are interpreted as initial markings of places and delay of transitions. Task performance is calculated as the difference between the triggering time of two transitions and a behavior process structure, respectively. The real-time MW is quantified and expressed as the remaining percentage of tokens in the attention resource place. A case study of triple-task execution was examined for verification of the model. Furthermore, the distinguishability and stability of the model, its comparison with ACT-R, and the application in task optimization were discussed.
机译：多任务处理在紧急情况下是常见的，这将导致人为错误，因为许多因素，例如未恰当的心理工作负载（MW）。为了提高多任务处理的性能，自动设备或辅助系统根据MW评估结果进行任务/接口优化，这些技术的有效性取决于如何选择适当的MW评估模型。本文开发了一种基于性能的定时Petri网（TPN）模型，可在多任务处理期间评估MW。建议在TPN中构建人力资源分配和恢复过程的具体功能的地点和转变。操作员的特征被解释为初始标记和过渡的延迟。任务性能分别计算为两个转换的触发时间与行为过程结构之间的差异。实时MW被量化，并表示为注意资源位置中的令牌的剩余百分比。检查了对三重任务执行的案例研究进行验证模型。此外，讨论了模型的区分性和稳定性，其与ACT-R的比较以及任务优化中的应用。

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