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1. For Your Eyes Only: Privacy-preserving eye-tracking datasets

   https://doi.org/10.1145/3517031.3529618  

   David-John, Brendan; Butler, Kevin; Jain, Eakta (June 2022, ACM Symposium on Eye Tracking Research and Applications (ETRA 2022))

   Eye-tracking is a critical source of information for understanding human behavior and developing future mixed-reality technology. Eye-tracking enables applications that classify user activity or predict user intent. However, eye-tracking datasets collected during common virtual reality tasks have also been shown to enable unique user identification, which creates a privacy risk. In this paper, we focus on the problem of user re-identification from eye-tracking features. We adapt standardized privacy definitions of k-anonymity and plausible deniability to protect datasets of eye-tracking features, and evaluate performance against re-identification by a standard biometric identification model on seven VR datasets. Our results demonstrate that re-identification goes down to chance levels for the privatized datasets, even as utility is preserved to levels higher than 72% accuracy in document type classification. 

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2. A Preliminary Investigation into Learning Behaviors in Complex Environments for Human-in-the-Loop Cyber-Physical Systems

   https://doi.org/10.1177/1071181321651222  

   Luster, Maya S.; Pitts, Brandon J. (September 2021, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The field of Cyber-Physical Systems (CPS) is increasingly recognizing the importance of integrating Human Factors for Human-in-the-loop CPS (HiLCPS) developments. This is because psychological, physiological, and behavioral characteristics of humans can be used to predict human-machine interactions. The goal of this pilot study is to collect initial data to determine whether driving and eye tracking metrics can provide evidence of learning for a CPS project. Six participants performed a series of 12 repeated obstacle avoidance tasks in manual driving. Lane deviations and fixation-related eye data were recorded for each trial. Overall, participants displayed either conservation/safe or aggressive/risky in their lateral position with respect to the obstacle during successive trials. Also, eye tracking metrics were not significantly affected by trial number, but observational trends suggest their potential for aiding in understanding adjustments humans make in learning. Results can inform predictive modeling algorithms that can anticipate and mitigate potential problems in real-time. 

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3. Scan-based eye tracking measures are predictive of workload transition performance

   Shannon P Devlin, Noelle L (November 2022, Applied Ergonomics)

   Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research. 

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4. Scan-based eye tracking measures are predictive of workload transition performance

   https://doi.org/10.1016/j.apergo.2022.103829  

   Devlin, S.P; Brown, N.L.; Drollinger, S.; Sibley, C.; Alami, J.; Riggs, S.L. (November 2022, Applied Ergonomics)

   Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research. 

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5. What you see is what you get? – Relating eye-tracking metrics to students’ attention to game elements

   Shojaee, Amirbahador; Kim, Hyeon Woo; Cook-Chennault, Kimberly; Villanueva Alarcon, Idalis (January 2021, Conference proceedings Frontiers in Education Conference)

   Though engineering digital game inclusion in undergraduate classrooms has steadily increased over the last two decades for in-person courses, their use has exponentially increased in remote and contactless higher education learning environments. Studies exploring student technological acceptance of and content mastery from the use of engineering digital games have provided mixed results in terms of student enjoyment, engagement, and game effectiveness. The majority of these studies have relied on pre- and post-questionnaires to assess differences in students’ gaming experiences and performance in the game and learning environment. However, quantitative methods such as the measurement of physiological responses during gameplay have been less explored for the exploration of student engagement and education. The goal of this work is to explore how a set of eye - tracking metrics can be related to gamer attention to in-game stimuli and game interface areas of interest. 

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