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1. Estimating Perceptual Depth Changes with Eye Vergence and Interpupillary Distance using an Eye Tracker in Virtual Reality

   https://doi.org/10.1145/3517031.3529632  

   Arefin, Mohammed Safayet; Swan II, J. Edward; Cohen Hoffing, Russell A.; Thurman, Steven M. (June 2022, ACM Symposium on Eye Tracking Research and Applications)

   Blascheck, Tanja; Bradshaw, Jessica; Vrzakova, Hana (Ed.)

   Virtual Reality (VR) technology has advanced to include eye-tracking, allowing novel research, such as investigating how our visual system coordinates eye movements with changes in perceptual depth. The purpose of this study was to examine whether eye tracking could track perceptual depth changes during a visual discrimination task. We derived two depth-dependent variables from eye tracker data: eye vergence angle (EVA) and interpupillary distance (IPD). As hypothesized, our results revealed that shifting gaze from near-to-far depth significantly decreased EVA and increased IPD, while the opposite pattern was observed while shifting from far-to-near. Importantly, the amount of change in these variables tracked closely with relative changes in perceptual depth, and supported the hypothesis that eye tracker data may be used to infer real-time changes in perceptual depth in VR. Our method could be used as a new tool to adaptively render information based on depth and improve the VR user experience. 

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2. Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

   https://doi.org/10.1038/s41598-024-74244-4  

   Cui, Zixin; Sato, Tetsuya; Jackson, Austin; Jayarathna, Sampath; Itoh, Makoto; Yamani, Yusuke (December 2024, Scientific Reports)

   Abstract Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently. 

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3. How Coordinated Visual Attention on a Target Area of Interest is Impacted by a Change in Workload Over Time

   https://doi.org/10.1177/1071181320641083  

   Devlin, Shannon P.; Flynn, Jake R.; Riggs, Sara L. (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Data-rich environments rely on operators to collaborate, especially in light of workload changes. This work explores the relationship between the operators’ shared visual attention patterns on a target area of interest (AOI), i.e. the AOI causing a workload change, and how it affects collaborative performance. Eye tracking data was collected from ten pairs of participants who completed two scenarios, the first being low workload and the second being high workload, in an unmanned aerial vehicle (UAV) command and control testbed. Then, best and worst performing pairs were compared in terms of two shared visual attention metrics: (1) percent gaze overlap and (2) the phi coefficient for the target AOI. The results showed that coordinated visits to and from the target AOI were associated with better performance during high workload. These results suggest including quantitative measures of visual attention can be indicators of the adaptation process in real- time. 

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4. Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

   https://doi.org/10.1145/3449066  

   Devlin, Shannon P.; Byham, Jennifer K.; Riggs, Sara Lu (June 2021, ACM Transactions on Applied Perception)

   null (Ed.)

   Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments. 

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5. WebGazer: Scalable Webcam Eye Tracking Using User Interactions

   Papoutsaki, Alexandra; Sangkloy, Patsorn; Laskey, James; Daskalova, Nediyana; Huang, Jeff; Hays, James (January 2016, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence - IJCAI 2016)

   We introduce WebGazer, an online eye tracker that uses common webcams already present in laptops and mobile devices to infer the eye-gaze locations of web visitors on a page in real time. The eye tracking model self-calibrates by watching web visitors interact with the web page and trains a mapping between features of the eye and positions on the screen. This approach aims to provide a natural experience to everyday users that is not restricted to laboratories and highly controlled user studies. WebGazer has two key components: a pupil detector that can be combined with any eye detection library, and a gaze estimator using regression analysis informed by user interactions. We perform a large remote online study and a small in-person study to evaluate WebGazer. The findings show that WebGazer can learn from user interactions and that its accuracy is sufficient for approximating the user's gaze. As part of this paper, we release the first eye tracking library that can be easily integrated in any website for real-time gaze interactions, usability studies, or web research. 

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