The association between physiological and eye-tracking metrics and cognitive load in drivers: A meta-analysis

Abstract

Driving performance can be impaired by a high cognitive load of drivers. Thus, it is important to estimate drivers’ cognitive load. Although physiological and eye-tracking metrics have been widely used in many studies to assess cognitive load while driving, conflicts still exist regarding the association between physiological and eye-tracking metrics and different levels of cognitive load. Through a meta-analysis, our study aims to quantify the association between physiological, eye-tracking metrics and cognitive load induced by n-back tasks. A total of 18 articles met the inclusion criteria for the meta-analysis. The results indicate four types of metrics, including the sensitive-to-low ones that can only differentiate the low to medium level of cognitive load (i.e., the power spectrum of θ wave of electroencephalogram at Fp1 channel); high-resolution ones that can differentiate all levels of cognitive load (including pupil size, heart rate, and skin conductance); and low-resolution ones that can only differentiate low and high cognitive load (including the total power spectrum of electrocardiogram, eye blink rate, and respiration rate) and others (the power spectrum of θ wave of electroencephalogram at Fp2 channel). Furthermore, the association between metrics and cognitive load can be modulated by the n-back version, modality of n-back task, automation level, and percentage of male participants. In summary, this study contributes to the literature by quantifying associations between physiological and eye-tracking metrics and different cognitive load levels. Practically, we provide evidence for the selection of physiological and eye-tracking metrics for future driving cognitive load monitoring system design.