Cognitive Efficiency in Human–Machine Systems: Metrics of Display Effectiveness for Supporting Multitask Performance

Abstract

In this study, we define a metric for quantifying the cognitive efficiency (CE) of displays in human-machine systems and examine correlations between the metric and multitasking performance in a driving simulation. The CE metric uses existing theory and methods to quantify both display informativeness (increasing CE when displays convey more useful information to human operators) and required mental resources (increasing CE when fewer human mental resources must be allocated to the display). A divided-attention task set involved processing different visual displays to inform route selection while concurrently avoiding obstacles in a simulated driving study. Measures of multitasking performance as well as informativeness and resources required were collected while participants processed each display. These measures were combined in different ways to construct several CE metrics, which were then evaluated with correlation analyses to determine which combinations were most predictive of multitask performance. Generally, CE constructs involving measures of subjective workload correlated with performance indices more strongly than did physiological indicators. Importantly, some CE metrics showed higher correlations with performance indices than did constituent component measures, illustrating the value of the combined construct. This work describes a simple yet powerful way to quantify the efficiency of displays for human-machine systems in demanding multitask environments.

The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection

“…In addition to temporal variation, cognitive workload is difficult to measure because of individual differences (Matthews et al, 2015), such as differences in expertise (Sarkar et al, 2019), stress (Conway et al, 2013), and cognitive efficiency (Yang & Ferris, 2018). Due to individual differences, drivers may perform differently in the same cognitive task, thereby impacting the generalization of driver cognitive workload classification to different individuals.…”

Evaluating Driver Features for Cognitive Distraction Detection and Validation in Manual and Level 2 Automated Driving

“…Classification algorithms may be fooled by drivers performing “attentively” in the CD conditions. Attentive driving may be achievable for some drivers in the distraction tasks, at least, for a short period when the drivers either have sufficient cognitive resources to process multitask loads, or more constantly if the drivers have higher cognitive efficiency—processing the same task with fewer cognitive resources (Yang & Ferris, 2018). Therefore, although many studies implicitly assume CD to be identical with the distraction task (Young, 2012), it is not guaranteed due to the dynamic fluctuations and individual differences in cognitive workload.…”