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

No robust evidence for an effect of head-movement propensity on central bias in head-constrained scene viewing, despite an effect on fixation duration

When viewing natural scenes, participants tend to direct their gaze towards the image center, the so-called "central bias." Unless the head is fixed, gaze shifts to peripheral targets are accomplished by a combination of eye and head movements, with substantial individual differences in the propensity to use the head. We address the relation of central bias and head-movement propensity. In one part of the experiment, participants viewed natural scenes of two different sizes without moving their head. We found that the central bias of each individual scaled with image size. In another experimental part, the same participants stood in the center of a panoramic screen and shifted their gaze to peripheral targets. Target eccentricities were either instructed by text (endogenous mode) or by a bar appearing at the target location (exogenous mode). In this "peripheral-target" task, we found a strong correlation between the exogenous and the endogenous mode, indicating that they provide a robust measure of an individual's head-movement propensity. Despite substantial inter-individual variability in both tasks, no significant correlation was found between head-movement propensity and central bias, and a trend toward significance for a specific measure was brittle. However, individuals with a higher head-movement propensity tended to have shorter fixation durations in scene viewing. Our results suggest that central bias in free scene viewing on typical screen sizes is predominately determined by visual properties. Although head-movement propensity seems to affect some aspects of scene-viewing behavior (fixation durations), individual differences in central bias are not explained by head-movement propensity.

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.

Large eye-head gaze shifts measured with a wearable eye tracker and an industrial camera

We built a novel setup to record large gaze shifts (up to 140∘ ). The setup consists of a wearable eye tracker and a high-speed camera with fiducial marker technology to track the head. We tested our setup by replicating findings from the classic eye-head gaze shift literature. We conclude that our new inexpensive setup is good enough to investigate the dynamics of large eye-head gaze shifts. This novel setup could be used for future research on large eye-head gaze shifts, but also for research on gaze during e.g., human interaction. We further discuss reference frames and terminology in head-free eye tracking. Despite a transition from head-fixed eye tracking to head-free gaze tracking, researchers still use head-fixed eye movement terminology when discussing world-fixed gaze phenomena. We propose to use more specific terminology for world-fixed phenomena, including gaze fixation, gaze pursuit, and gaze saccade.

Using XR (Extended Reality) for Behavioral, Clinical, and Learning Sciences Requires Updates in Infrastructure and Funding

Extended reality (XR, including augmented and virtual reality) creates a powerful intersection between information technology and cognitive, clinical, and education sciences. XR technology has long captured the public imagination, and its development is the focus of major technology companies. This article demonstrates the potential of XR to (1) deliver behavioral insights, (2) transform clinical treatments, and (3) improve learning and education. However, without appropriate policy, funding, and infrastructural investment, many research institutions will struggle to keep pace with the advances and opportunities of XR. To realize the full potential of XR for basic and translational research, funding should incentivize (1) appropriate training, (2) open software solutions, and (3) collaborations between complementary academic and industry partners. Bolstering the XR research infrastructure with the right investments and incentives is vital for delivering on the potential for transformative discoveries, innovations, and applications.