Human Eyes Move to the Target Earlier When Performing an Aiming Task with Increasing Difficulties

Abstract

We examined whether human operators move their eyes earlier to a target before hands when the level of task difficulty increases. We hypothesized that participants would perform less proactive eye movements in the difficult task than in the easy one, as they would need to focus more on their current hand movements. Sixteen university students were recruited to perform the aiming and touching task reciprocally on three paired targets (circles) differing in sizes and distances, while had their eye movements tracked. The movement time, the early eye engagement time (EEET), and the number of eye adjustments were recorded. The EEET was defined as the time that a subject’s eyes fix on a target to the moment that the tool reaches out for it. The movement time increased as the index of difficulty (ID) value increased, echoing with the Fitts’ Law prediction. When aiming to a target with a higher ID, participants’ EEET was longer comparing to when reaching for a target with a lower ID. Participants reduced the movement speed to give themselves a longer time in searching visually for the target information before moving their hands. In contrast to our hypothesis, results suggested a proportional relationship between the task difficulty and the early eye engagement time. Participants also performed an increasing number of eye adjustments over the course of moving from the easy to the hard target. Future research is needed to examine eye hand coordination under the regulation of Fitts’ Law.

Disclosure statement

Drs. Liu, Jiang, Zheng, and Ms. Zhang have no conflicts of interest or financial ties to disclose.

Additional information

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant [NSERC RGPIN-2016-06462]; the Royal College of Physicians and Surgeons of Canada under Grant [RCPSC MERG16-09]; the National Natural Science Foundation of China under Grant 61801019 and the China Scholarship Council under Grant [201906465021].

Notes on contributors

Xin Liu

Xin Liu is currently an assistant professor at the Computer Science and Technology Department, University of Science and Technology Beijing (USTB). Her research interests include high-level data analysis, intelligent information processing, data mining, and decision making in medicine. She received her PhD from the Control Science of the USTB.

Yao Zhang

Yao Zhang currently proceed to the PhD degree at the Department of Surgery, University of Alberta. Her research interest is on using data analysis and machine learning methods to analyze team behaviors during surgical procedures.

Xianta Jiang

Xianta Jiang is currently an assistant professor in the Computer Science of Memorial University of Newfoundland. His research interests include intelligent human-machine interaction, ubiquitous computing in healthcare, human behavior recognition, bio-signal processing, and human factors. He received his PhD from the Computer Science of Simon Fraser University and Zhejiang University.

Bin Zheng

Bin Zheng is currently an associate professor in Surgery and the Endowed Research Chair in Surgical Simulation at the Department of Surgery of the University of Alberta. He studies the performance and cognition of physicians and surgeons during surgery. He received his PhD from the Kinesiology of Simon Fraser University.