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

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 49 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 49 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.