What are the visuo-motor tendencies of omnidirectional scene free-viewing in virtual reality?

A tutorial: Analyzing eye and head movements in virtual reality

This tutorial provides instruction on how to use the eye tracking technology built into virtual reality (VR) headsets, emphasizing the analysis of head and eye movement data when an observer is situated in the center of an omnidirectional environment. We begin with a brief description of how VR eye movement research differs from previous forms of eye movement research, as well as identifying some outstanding gaps in the current literature. We then introduce the basic methodology used to collect VR eye movement data both in general and with regard to the specific data that we collected to illustrate different analytical approaches. We continue with an introduction of the foundational ideas regarding data analysis in VR, including frames of reference, how to map eye and head position, and event detection. In the next part, we introduce core head and eye data analyses focusing on determining where the head and eyes are directed. We then expand on what has been presented, introducing several novel spatial, spatio-temporal, and temporal head-eye data analysis techniques. We conclude with a reflection on what has been presented, and how the techniques introduced in this tutorial provide the scaffolding for extensions to more complex and dynamic VR environments.

Influence of open-source virtual-reality based gaze training on navigation performance in Retinitis pigmentosa patients in a crossover randomized controlled trial

METHODS

A group of RP patients (n = 8, aged 20-60) participated in a study consisting of two 4-week-phases, both carried out by the same patient group in randomized order: In the 'training phase', participants carried out a Virtual-Reality gaze training for 30 minutes per day; In the 'control phase', no training occurred. Before and after each phase, participants were tasked to move through a randomized real-world obstacle course. Navigation performance in the obstacle course as well as eye-tracking data during the trials were evaluated. The study is registered at the German Clinical Trials Register (DRKS) with the ID DRKS00032628.

RESULTS

On average, the time required to move through the obstacle course decreased by 17.0% after the training phase, the number of collisions decreased by 50.0%. Both effects are significantly higher than those found in the control phase (p < 0.001 for required time, p = 0.0165 for number of collisions), with the required time decreasing by 5.9% and number of collisions decreasing by 10.4% after the control phase. The average visual area observed by participants increases by 4.41% after training, however the effect is not found to be significantly higher than in the control phase (p = 0.394).

CONCLUSION

The performance increase over the training phase significantly surpasses the natural learning effect found in the control phase, suggesting that Virtual-Reality based gaze training can have a positive effect on real-world navigation tasks for patients with RP. The training is available as work-in-progress open-source software.

Flipping the world upside down: Using eye tracking in virtual reality to study visual search in inverted scenes

Image inversion is a powerful tool for investigating cognitive mechanisms of visual perception. However, studies have mainly used inversion in paradigms presented on twodimensional computer screens. It remains open whether disruptive effects of inversion also hold true in more naturalistic scenarios. In our study, we used scene inversion in virtual reality in combination with eye tracking to investigate the mechanisms of repeated visual search through three-dimensional immersive indoor scenes. Scene inversion affected all gaze and head measures except fixation durations and saccade amplitudes. Our behavioral results, surprisingly, did not entirely follow as hypothesized: While search efficiency dropped significantly in inverted scenes, participants did not utilize more memory as measured by search time slopes. This indicates that despite the disruption, participants did not try to compensate the increased difficulty by using more memory. Our study highlights the importance of investigating classical experimental paradigms in more naturalistic scenarios to advance research on daily human behavior.

Eye and head movements while encoding and recognizing panoramic scenes in virtual reality

One approach to studying the recognition of scenes and objects relies on the comparison of eye movement patterns during encoding and recognition. Past studies typically analyzed the perception of flat stimuli of limited extent presented on a computer monitor that did not require head movements. In contrast, participants in the present study saw omnidirectional panoramic scenes through an immersive 3D virtual reality viewer, and they could move their head freely to inspect different parts of the visual scenes. This allowed us to examine how unconstrained observers use their head and eyes to encode and recognize visual scenes. By studying head and eye movement within a fully immersive environment, and applying cross-recurrence analysis, we found that eye movements are strongly influenced by the content of the visual environment, as are head movements-though to a much lesser degree. Moreover, we found that the head and eyes are linked, with the head supporting, and by and large mirroring the movements of the eyes, consistent with the notion that the head operates to support the acquisition of visual information by the eyes.

Eye Tracking in Virtual Reality

This chapter explores the current state of the art in eye tracking within 3D virtual environments. It begins with the motivation for eye tracking in Virtual Reality (VR) in psychological research, followed by descriptions of the hardware and software used for presenting virtual environments as well as for tracking eye and head movements in VR. This is followed by a detailed description of an example project on eye and head tracking while observers look at 360° panoramic scenes. The example is illustrated with descriptions of the user interface and program excerpts to show the measurement of eye and head movements in VR. The chapter continues with fundamentals of data analysis, in particular methods for the determination of fixations and saccades when viewing spherical displays. We then extend these methodological considerations to determining the spatial and temporal coordination of the eyes and head in VR perception. The chapter concludes with a discussion of outstanding problems and future directions for conducting eye- and head-tracking research in VR. We hope that this chapter will serve as a primer for those intending to implement VR eye tracking in their own research.