Cognitive Effort Measures Driven by Fixation Induced Retinal Flow in Visual Scanning Behavior during Virtual Driving

TL;DR

This work links visual scanning in dynamic sensorimotor tasks to cognitive effort by introducing fixation-induced retinal flow as a quantitative basis. It defines two objective measures: $CEM_{VI}$, based on the entropy of grid-view importance, and $CEM_{IQ}$, based on the information quantity of perceived visual motion via SRJSD between fixation and retinal-flow distributions, including fixation-transition considerations. Psychophysical experiments in a virtual driving setup show that $CEM_{IQ}$ reliably correlates with pupil size change and fixation rate, while $CEM_{VI}$ correlates with pupil size; traditional metrics like SGE and entropy rate show weaker associations. The results suggest that eye-tracking-derived, information-theoretic metrics can robustly assess cognitive effort in sensorimotor tasks, offering a path toward more objective behaviometric evaluations in ergonomics and safety-critical contexts. Future work aims to generalize to real driving, investigate illumination effects, and integrate additional physiological signals for a richer understanding of cognitive load.

Abstract

In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks, such as walking and driving, in dynamic environments. We exploit eye tracking data for offering two new cognitive effort measures in visual scanning behavior of virtual driving. By utilizing the retinal flow induced by fixation, two novel measures of cognitive effort are proposed through the importance of grids in the viewing plane and the concept of information quantity, respectively. Psychophysical studies are conducted to reveal the effectiveness of the two proposed measures. Both these two cognitive effort measures have shown their significant correlation with pupil size change. Our results suggest that the quantitative exploitation of eye tracking data provides an effective approach for the evaluation of sensorimotor activities.

Figures (3)

• Figure 1: An illustration of retinal flow $I_n$ induced by fixation $f_n$.
• Figure 2: A participant is performing a driving task in virtual reality.
• Figure 3: Correlation coefficients of eye movement measures with pupil size change/fixation rate (*: $p<0.05$, **: $p<0.01$, ***: $p<0.001$)