A link between attentional function, effective eye movements, and driving ability.

Abstract

The misallocation of driver visual attention has been suggested as a major contributing factor to vehicle accidents. One possible reason is that the relatively high cognitive demands of driving limit the ability to efficiently allocate gaze. We present an experiment that explores the relationship between attentional function and visual performance when driving. Drivers performed 2 variations of a multiple-object tracking task targeting aspects of cognition including sustained attention, dual-tasking, covert attention, and visuomotor skill. They also drove a number of courses in a driving simulator. Eye movements were recorded throughout. We found that individuals who performed better in the cognitive tasks exhibited more effective eye movement strategies when driving, such as scanning more of the road, and they also exhibited better driving performance. We discuss the potential link between an individual’s attentional function, effective eye movements, and driving ability. We also discuss the use of a visuomotor task in assessing driving behavior.

Driver’s emotional state and detection of vulnerable road users: Towards a better understanding of how emotions affect drivers’ perception using cardiac and ocular metrics

“…In other words, they would probably focus their attention more quickly on relevant space areas (i.e., cyclists they had to detect). On the other hand, according to Mackenzie and Harris (2017), cognitive load could impair visual scanning during driving. Consequently, it is also feasible that the conservation of the coping strategies used during driving previously mentioned carried a cognitive cost, which, in turn, manifested itself in smaller saccadic amplitudes.…”

Adaptive response criteria in road hazard detection among older drivers

“…Similarly, when performing a video-based hazard perception task, participants also freely move their eyes. When eye movements are allowed, participants’ hazard detection is a result of attentional allocation in a gist (Ball et al, 1993), covert attentional allocation (i.e., without eye movements; Mackenzie & Harris, 2017) and visual scanning strategies (Romoser et al, 2013; Romoser & Fisher, 2009). Using the fixated eye instruction, the current DAT allows of the isolation of effects from attentional processing of static driving scenes without eye movements on hazard detection.…”

“…Using the fixated eye instruction, the current DAT allows of the isolation of effects from attentional processing of static driving scenes without eye movements on hazard detection. Admittedly, when applying these findings about attentional processing without eye movements to understand hazard detection on road, it is critical to investigate how these attentional processes translate to effective visual scanning in driving (e.g., Mackenzie & Harris, 2017), and the interplay between these attentional processes and practice and learning of visual scanning. In addition, the current DAT used only three types of potential hazards on the road: vehicle, pedestrian, traffic light/sign (e.g., a pedestrian crossing in the right periphery could be a hazard for a driver turning right at an intersection).…”

Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups

“…Given the distribution of such psychophysical data, a logarithmic transformation was applied to the scores to permit conducting bivariate correlations between those scores and the driving measures. It has been suggested that multiple object tracking is a task correlated with some measures of driving ability [ 35 , 46 ]. Therefore, it is conceivable that 3D-MOT might be a better predictor of risky driving behaviour than age and naturally adopted mean driving speed.…”

“…In the present experiment, we assessed the influence of mental workload on driving measures between different age groups by manipulating the situation complexity in distinct simulator scenarios, each one representing a different driving environment with a different mental workload. Additionally, we used a psychophysical task known as 3-Dimensional Multiple Object Tracking (3D-MOT) to link an individual’s ability to capture and integrate relevant information in a highly complex visual environment [ 33 , 34 ] to measures of driving performance [ 35 ] under different mental workloads.…”