PT  - JOURNAL ARTICLE
AU  - Zezhong Lv
AU  - Qing Xu
AU  - Klaus Schoeffmann
AU  - Simon Parkinson
TI  - New Measures of Visual Scanning Efficiency and Cognitive Effort
AID  - 10.1101/2020.11.17.386185
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.11.17.386185
4099  - http://biorxiv.org/content/early/2021/04/28/2020.11.17.386185.short
4100  - http://biorxiv.org/content/early/2021/04/28/2020.11.17.386185.full
AB  - Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as walking and car driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measures in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely-known information theoretic tool, namely the square root of Jensen-Shannon divergence. Based on the proposed efficiency metric, a cognitive effort measure (CEM) is developed by using the concept of quantity of information. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. In addition, the effectiveness of the proposed cognitive effort measure is demonstrated by a strong correlation between this measure and pupil size change. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activity.Competing Interest StatementThe authors have declared no competing interest.