Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as 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 measure 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 widelyknown information theoretic tool, namely the square root of Jensen-Shannon divergence. 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. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activities.
Eye movement behavior, which provides the visual information acquisition and processing, plays an important role in performing sensorimotor tasks, such as driving, by human beings in everyday life. In the procedure of performing sensorimotor tasks, eye movement is contributed through a specific coordination of head and eye in gaze changes, with head motions preceding eye ovements. Notably we believe that this coordination in essence indicates a kind of causality. In this paper, we investigate transfer entropy to set up a quantity for measuring an unidirectional causality from head motion to eye movement. A normalized version of the proposed measure, demonstrated by virtual reality based psychophysical studies, behaves very well as a proxy of driving performance, suggesting that quantitative exploitation of coordination of head and eye may be an effective behaviometric of sensorimotor activity.
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 biomarker, 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, 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 biomarker for sensorimotor behaviors.
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