ABSTRACT

Eye tracking, that encompasses the scienti˜c study and measurement of eye movements, has been evolving for over a century (Delabarre, 1898; Dodge & Cline, 1901). Initially, it was developed for observing basic visual behaviors, but early technology was invasive, painful, and of low accuracy. As the technology advanced, eye tracking has become more accurate, user friendly, and much more affordable (Hiley, Redekopp, & Fazel-Rezai, 2006). It has extended to various ˜elds and developed into a wide range of applications (Mele & Federici, 2012). Eye tracking applications generally fall into two categories: diagnostic and interactive (Duchowski, 2002). Diagnostic applications aim to monitor eye movements and pupil constriction and evaluate user’s visual attention and cognitive load (Tokuda, Obinata, Palmer, & Chaparro, 2011). In the medical ˜eld, eye tracking has been used to assess and improve surgeon training (Atkins, Tien, Khan, Meneghetti, & Zheng, 2013). Wilson suggested that gaze control parameters extracted from eye movement data can be used to differentiate novices from experts when performing laparoscopic surgery in a virtual reality (VR) simulator (Wilson et al., 2010). Tomizawa, using similar reasoning, found signi˜cant differences in eye scan patterns between expert and novice perfusionists when conducting extracorporeal circulation surgery (Tomizawa, Aoki, Suzuki, Matayoshi, & Yozu, 2012). Recent reports have shown the utility of eye tracking in the diagnosis of progressive supranuclear palsy in which decreased ability to carry out vertical saccades is a key symptom (Marx et al., 2012). Eye tracking has also been used to diagnose driver distraction or fatigue in real time and to mitigate driver error or accident (Qiang, Zhiwei, & Lan, 2004). In marketing, eye tracking has also been used to examine the effectiveness of warning labels (Strasser, Tang, Romer, Jepson, & Cappella, 2012).