A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Abstract

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

Experiments as Code and its application to VR studies in human-building interaction

“…In the future, we plan to further develop our proof of concept, providing better user-friendliness, as currently the instantiation of new experiments is performed through the command line. Previous work on VR has already produced robust frameworks for data collection (e.g., EVE 62 , 63 ). We would like to integrate these frameworks to provide improved participant management, but also tools to aid in the rapid and effective design of experiments and multi-user environments 63 .…”

“…Previous work on VR has already produced robust frameworks for data collection (e.g., EVE 62 , 63 ). We would like to integrate these frameworks to provide improved participant management, but also tools to aid in the rapid and effective design of experiments and multi-user environments 63 . Additionally, we would like to improve the analysis and data management of experiments by integrating complete machine learning lifecycles 20 .…”

“…The issue is even starker because a large part of the infrastructure remains unspecified including the data processing, content delivery system, analysis equipment, and researcher equipment. Only unusual configurations produce more detailed documentation, such as multi-user VR 63 , 67 but do not provide automation in the form of provisioning or deployment instructions. The rise of IaC offers an opportunity to remove a major hurdle to equitably running VR experiments that to our knowledge so far remain untapped for VR, but see 40 , 41 for non-VR approaches.…”

“…Monitoring and managing experiment participants in real time requires a definition and API of how the researcher can interact with the experiment as it happens. Only two frameworks offer out of the box solutions for monitoring 63 , 75 . Especially, when studying crowd behavior, it may not be enough to conduct single-user experiments, but it becomes necessary to immerse multiple users at once requiring additional infrastructure and experiment integration 63 , 67 .…”

“…Only two frameworks offer out of the box solutions for monitoring 63 , 75 . Especially, when studying crowd behavior, it may not be enough to conduct single-user experiments, but it becomes necessary to immerse multiple users at once requiring additional infrastructure and experiment integration 63 , 67 .…”