The cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety in emerging human-in-the-loop systems. In driving, cognitive overload, due to various secondary tasks, such as texting, results in driver distraction. On the other hand, cognitive underload may result in fatigue. In automated manufacturing systems, a distracted operator may be prone to muscle injuries. Similar outcomes are possible in many other fields of human performance such as aviation, healthcare, and learning environments. The challenge with such human-centred applications is that the cognitive load is not directly measurable. Only the change in cognitive load is measured indirectly through various physiological, behavioural, performance-based and subjective means. A method to objectively assess the performance of such diverse measures of cognitive load is lacking in the literature. In this paper, a performance metric for the comparison of different measures to determine the cognitive workload is proposed in terms of the signal-to-noise ratio. Using this performance metric, several measures of cognitive load, that fall under the four broad groups were compared on the same scale for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent -back tasks of varying difficulty. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load.

中文翻译：

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n-back 任务期间并发认知负荷测量的比较

人类经历的认知负荷是影响其表现的重要因素。认知过载或负载不足可能会导致人类表现不佳，并可能危及新兴人机交互系统的安全性。在驾驶过程中，由于发短信等各种次要任务，认知超载会导致驾驶员分心。另一方面，认知负荷不足可能会导致疲劳。在自动化制造系统中，分心的操作员可能容易受伤。在人类表现的许多其他领域，例如航空、医疗保健和学习环境，也可能出现类似的结果。这种以人为中心的应用程序面临的挑战是认知负荷无法直接测量。只有认知负荷的变化是通过各种生理、行为、基于表现和主观手段间接测量的。文献中缺乏客观评估多种认知负荷测量表现的方法。本文提出了一种根据信噪比来比较不同测量方法以确定认知工作量的性能指标。使用这一绩效指标，对属于四大组的几种认知负荷测量方法进行了相同规模的比较，以了解它们测量认知负荷变化的能力。使用所提出的指标，根据 28 名参与者在接受不同难度的背任务时收集的数据，对认知负荷测量进行了比较。结果表明，所提出的绩效评估方法可用于单独评估不同的认知负荷指标。