Gaze dynamics are sensitive to target orienting for working memory encoding in virtual reality.

Numerous studies have demonstrated that visuospatial attention is a requirement for successful working memory encoding. It is unknown, however, whether this established relationship manifests in consistent gaze dynamics as people orient their visuospatial attention toward an encoding target when searching for information in naturalistic environments. To test this hypothesis, participants' eye movements were recorded while they searched for and encoded objects in a virtual apartment (Experiment 1). We decomposed gaze into 61 features that capture gaze dynamics and a trained sliding window logistic regression model that has potential for use in real-time systems to predict when participants found target objects for working memory encoding. A model trained on group data successfully predicted when people oriented to a target for encoding for the trained task (Experiment 1) and for a novel task (Experiment 2), where a new set of participants found objects and encoded an associated nonword in a cluttered virtual kitchen. Six of these features were predictive of target orienting for encoding, even during the novel task, including decreased distances between subsequent fixation/saccade events, increased fixation probabilities, and slower saccade decelerations before encoding. This suggests that as people orient toward a target to encode new information at the end of search, they decrease task-irrelevant, exploratory sampling behaviors. This behavior was common across the two studies. Together, this research demonstrates how gaze dynamics can be used to capture target orienting for working memory encoding and has implications for real-world use in technology and special populations.

Privacy-preserving datasets of eye-tracking samples with applications in XR.

Virtual and mixed-reality (XR) technology has advanced significantly in the last few years and will enable the future of work, education, socialization, and entertainment. Eye-tracking data is required for supporting novel modes of interaction, animating virtual avatars, and implementing rendering or streaming optimizations. While eye tracking enables many beneficial applications in XR, it also introduces a risk to privacy by enabling re-identification of users. We applied privacy definitions of it-anonymity and plausible deniability (PD) to datasets of eye-tracking samples and evaluated them against the state-of-the-art differential privacy (DP) approach. Two VR datasets were processed to reduce identification rates while minimizing the impact on the performance of trained machine-learning models. Our results suggest that both PD and DP mechanisms produced practical privacy-utility trade-offs with respect to re-identification and activity classification accuracy, while k-anonymity performed best at retaining utility for gaze prediction.

A privacy-preserving approach to streaming eye-tracking data.

Eye-tracking technology is being increasingly integrated into mixed reality devices. Although critical applications are being enabled, there are significant possibilities for violating user privacy expectations. We show that there is an appreciable risk of unique user identification even under natural viewing conditions in virtual reality. This identification would allow an app to connect a user's personal ID with their work ID without needing their consent, for example. To mitigate such risks we propose a framework that incorporates gatekeeping via the design of the application programming interface and via software-implemented privacy mechanisms. Our results indicate that these mechanisms can reduce the rate of identification from as much as 85% to as low as 30%. The impact of introducing these mechanisms is less than 1.5° error in gaze position for gaze prediction. Gaze data streams can thus be made private while still allowing for gaze prediction, for example, during foveated rendering. Our approach is the first to support privacy-by-design in the flow of eye-tracking data within mixed reality use cases.