A Case for Studying Naturalistic Eye and Head Movements in Virtual Environments

Using head mounted displays (HMDs) in conjunction with virtual reality (VR), vision researchers are able to capture more naturalistic vision in an experimentally controlled setting. Namely, eye movements can be accurately tracked as they occur in concert with head movements as subjects navigate virtual environments. A benefit of this approach is that, unlike other mobile eye tracking (ET) set-ups in unconstrained settings, the experimenter has precise control over the location and timing of stimulus presentation, making it easier to compare findings between HMD studies and those that use monitor displays, which account for the bulk of previous work in eye movement research and vision sciences more generally. Here, a visual discrimination paradigm is presented as a proof of concept to demonstrate the applicability of collecting eye and head tracking data from an HMD in VR for vision research. The current work's contribution is 3-fold: firstly, results demonstrating both the strengths and the weaknesses of recording and classifying eye and head tracking data in VR, secondly, a highly flexible graphical user interface (GUI) used to generate the current experiment, is offered to lower the software development start-up cost of future researchers transitioning to a VR space, and finally, the dataset analyzed here of behavioral, eye and head tracking data synchronized with environmental variables from a task specifically designed to elicit a variety of eye and head movements could be an asset in testing future eye movement classification algorithms.

Automated summary

By combining head mounted displays (HMDs) with virtual reality (VR), researchers are able to capture more naturalistic vision in an experimentally controlled setting, accurately tracking eye movements as they occur in concert with head movements. This approach allows for easier comparison between HMD studies and previous research that used monitor displays, providing insights into the strengths and weaknesses of recording and classifying eye and head tracking data in VR.