New Measures of Visual Scanning Efficiency and Cognitive Effort

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.