Eye tracking in virtual reality

Abstract


 
 
 
 The intent of this paper is to provide an introduction into the bourgeoning field of eye tracking in Virtual Reality (VR). VR itself is an emerging technology on the consumer market, which will create many new opportunities in research. It offers a lab environment with high immersion and close alignment with reality. An experiment which is using VR takes place in a highly controlled environment and allows for a more in-depth amount of information to be gathered about the actions of a subject. Techniques for eye tracking were introduced more than a century ago and are now an established technique in psychological experiments, yet recent development makes it versatile and affordable. In combination, these two techniques allow unprecedented monitoring and control of human behavior in semi-realistic conditions. This paper will explore the methods and tools which can be applied in the implementation of experiments using eye tracking in VR following the example of one case study. Accompanying the technical descriptions, we present research that displays the effectiveness of the technology and show what kind of results can be obtained when using eye tracking in VR. It is meant to guide the reader through the process of bringing VR in combination with eye tracking into the lab and to inspire ideas for new experiments. 
 
 
 

Identification of Target Objects from Gaze Behavior during a Virtual Navigation Task

“…This is comparable to previous work by Zelinksy (2008) who found a Mean Number of Fixations of 4.8 on targets when searching for military tanks in a realistic scene. We found that subjects looked at objects with a Mean Dwell Time of 2.60 seconds per each object, which is comparable to work by Clay et al (2019), who found a Mean Dwell Time of 5.53 seconds on visual objects for subjects who freely navigated and observed houses (considerably larger than most our targets) in a virtual environment town. We found that fixations on the surrounding terrain and sky compromised, on average, about 47% of all fixations.…”

“…As noted previously, our virtual environment allowed subjects to view objects down the path or to look around to their surroundings. Here, subjects appeared to fixation on objects relatively further away in their environment, which was previously noted for studies measuring gaze in a virtual setting (Clay et al, 2019). However, we would expect there to be some discrepancy between our findings and what occurs in real-world ambulation.…”

MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation

“…We have shown that the benefits of the MAD estimator mainly accrue at high noise levels. This is particularly important in non-ideal experimental conditions that can lead to noisier data, such as unrestrained viewing, or when working with younger, older, or clinical populations (Bargary et al, 2017;Rütsche et al, 2006), as well as in more realistic virtual or game-like settings (Clay et al, 2019). The other benefit of MAD is that it allows experimentalists to define a confidence level (lambda parameter), as required by the experimental questions/equipment.…”

A Simple and Effective Way to Study Executive Functions by Using 360° Videos

“…ET allows constantly measuring the eyes' displacements within the monitor screen's spatial working area (Cipresso et al, 2013b). In particular, it will enable calculating the subject gaze's direction in the 3D environment, defining regions of interest in 3D space and fixation time of each region (Clay et al, 2019). Over the years, several studies showed that antisaccade (AS) eye movements obtained with an AS task (AST) can be considered a good indicator of executive dysfunction (e.g., inhibition control) in patients with neurological disease and in elderly subjects (Mirsky et al, 2013).…”