The representative study was funded by the Swiss National Science Foundation as part of the grant &#34;Wayfinding in Social Environments&#34; (No. 100014_162428). We want to thank M. Moussaid for insightful discussions. We also want to thank C. Wilhelm, F. Thaler, H. Abdelrahman, S. Madjiheurem, A. Ingold, and A. Grossrieder for their work during the software development.

All methods described here have been approved by Research Ethics Committee of ETH Z&#252;rich as part of the proposal EK 2015-N-37.
1. Recruit Participants for the Planned Experimental Session.
<ol>
	<li>Sample the participants within particular constraints (e.g., age, gender, educational background) using the participant recruitment system.</li>
	<li>Send invitations by email to the randomly selected participants using the contact information provided by the recruitment system.</li...

<ol>
	<li>Waller, D., Nadel, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Handbook of Spatial Cognition. , (2013).</li><li>Denis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Space and Spatial Cognition: A Multidisciplinary Perspective. , (2017).</li><li>Moussa&#239;d, M., Kapadia, M., Thrash, T., Sumner, R. W., Gross, M., Helbing, D., H&#246;lscher, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Crowd+behaviour+during+high-stress+evacuations+in+an+immersive+virtual+environment.">Crowd behaviour during high-stress evacuations in an immersive virtual environment.</a> Journal of the Royal Society Interface. 13 (122), 20160414 (2016).</li><li>Gr&#252;bel, J., Weibel, R., Jiang, M. H., H&#246;lscher, C., Hackman, D. A., Schinazi, V. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EVE:+A+Framework+for+Experiments+in+Virtual+Environments.">EVE: A Framework for Experiments in Virtual Environments.</a> Spatial Cognition X: Lecture Notes in Artificial Intelligence. , 159-176 (2017).</li><li>Loomis, J. M., Blascovich, J. J., Beall, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immersive+virtual+environment+technology+as+a+basic+research+tool+in+psychology.">Immersive virtual environment technology as a basic research tool in psychology.</a> Behavior Research Methods, Instruments, &amp; Computers. 31 (4), 557-564 (1999).</li><li>Brooks, F. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What's+Real+About+Virtual+Reality?.">What's Real About Virtual Reality?.</a> Proceedings IEEE Virtual Reality. , (1999).</li><li>Moorthy, K., Munz, Y., Jiwanji, M., Bann, S., Chang, A., Darzi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+and+reliability+of+a+virtual+reality+upper+gastrointestinal+simulator+and+cross+validation+using+structured+assessment+of+individual+performance+with+video+playback.">Validity and reliability of a virtual reality upper gastrointestinal simulator and cross validation using structured assessment of individual performance with video playback.</a> Surgical Endoscopy and Other Interventional Techniques. 18 (2), 328-333 (2004).</li><li>Weisberg, S. M., Schinazi, V. R., Newcombe, N. S., Shipley, T. F., Epstein, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Variations+in+cognitive+maps:+Understanding+individual+differences+in+navigation.">Variations in cognitive maps: Understanding individual differences in navigation.</a> Journal of Experimental Psychology: Learning Memory and Cognition. 40 (3), 669-682 (2014).</li><li>Schinazi, V. R., Nardi, D., Newcombe, N. S., Shipley, T. F., Epstein, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hippocampal+size+predicts+rapid+learning+of+a+cognitive+map+in+humans.">Hippocampal size predicts rapid learning of a cognitive map in humans.</a> Hippocampus. 23 (6), 515-528 (2013).</li><li>Ruddle, R. A., Payne, S. J., Jones, D. 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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+a+conceptual+framework+for+predicting+navigation+performance+in+virtual+reality.">Evaluation of a conceptual framework for predicting navigation performance in virtual reality.</a> PLoS One. 12 (9), (2017).</li><li>Thrash, T., Kapadia, M., Moussaid, M., Wilhelm, C., Helbing, D., Sumner, R. W., H&#246;lscher, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+control+interfaces+for+desktop+virtual+environments.">Evaluation of control interfaces for desktop virtual environments.</a> Presence. 24 (4), (2015).</li><li>Ruddle, R. A., Volkova, E., B&#252;lthoff, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Learning+to+walk+in+virtual+reality.">Learning to walk in virtual reality.</a> ACM Transactions on Applied Perception. 10 (2), 1-17 (2013).</li><li>Moussa&#239;d, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+walking+behaviour+of+pedestrian+social+groups+and+its+Impact+on+crowd+dynamics.">The walking behaviour of pedestrian social groups and its Impact on crowd dynamics.</a> PLoS One. 5 (4), e10047 (2010).</li><li>Bode, N. W. F., Franks, D. W., Wood, A. J., Piercy, J. J. B., Croft, D. P., Codling, E. 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The authors have nothing to disclose.

In this article, we described a multi-user desktop virtual reality laboratory in which up to 36 participants can interact and simultaneously navigate through various virtual environments. The experimental protocol details the steps necessary for this type of research and unique to multi-user scenarios. Considerations specific to these scenarios include the number of participants in attendance, the cost of seemingly small experimenter errors, rendering and networking capacities (both server- and client-side), training wit...

Research on spatial cognition and navigation typically studies the spatial decision-making (e.g., turning left or right at an intersection) and mental representation of individuals in real and virtual environments1,2. The advantages of virtual reality (VR) include the prevention of ethical and safety issues (e.g., during a dangerous evacuation3), the automatic measurement and analysis of spatial data4, and a balanced combination of internal and external validity5,6,7. For example, Weisberg and colleagues extended previous research on individual differences in spatial knowledge acquisition by demonstrating that spatial tasks in VR can provide an objective behavioral measure of spatial ability8. This study also suggested that the navigation behavior in VR approximates real-world navigation because the virtual environment was modeled after the university campus used by Schinazi and colleagues9 (see also the study of Ruddle and colleagues10). VR has also been applied to psychotherapy11, clinical assessment12, consumer behavior13, and surgery14,15. However, most VR systems lack proprioceptive and audio feedback that may improve presence and immersion16,17,18,19, require training with the control interface20,21,22, and lack social cues. Indeed, people in the real world often move in groups23, avoid or follow other people3,24, and make decisions based on social context25,26.
At the same time, research on crowd behavior often focuses on emergent characteristics of crowds (e.g., lane formation, congestion at bottlenecks) that are simulated on a computer or observed in the real world. For example, Helbing and colleagues used a combination of real-world observations and computer simulations in order to suggest improvements to traffic flow in an intersection by separating inflow and outflow with physical barriers and placing an obstacle in the center27. Moussa&#239;d and colleagues used a heuristics-based model to study high-density situations during a crowd disaster28. This approach suggested improvements to an environmental setting for mass events in order to avoid crowd disasters. With the aid of an existing open source framework, the implementation of such simulations could be relatively easy. SteerSuite is an open source framework that allows users to simulate steering algorithms and crowd behavior easily by providing tools for facilitating, benchmarking, and testing29. This framework can provide the core of an agent&#39;s navigation rationale, which is critical for successful crowd simulation. In addition, Singh and colleagues demonstrated a single platform that combines a variety of steering techniques30. While researchers can propose design interventions using such simulations, they are rarely validated with human participants in a controlled setting. Controlled experiments are rare in crowd research because they can be difficult to organize and dangerous to the participants.
VR has been employed to investigate social behavior using simple and complex virtual environments with one or more computer-simulated agents. In the study of Bode and colleagues31,32, the participants were asked to evacuate a simple virtual environment from a top-down perspective among several agents and found that exit choice was affected by static signage and motivation. Presenting participants with a more complex environment from a first-person perspective, Kinateder and colleagues found that the participants were more likely to follow a single computer-simulated agent during the escape from a virtual tunnel fire25. In a complex virtual environment with multiple agents, Drury and colleagues found that the participants tended to assist a fallen agent during an evacuation when they identified with the crowd26. Collectively, these findings suggest that VR can be an effective way of eliciting social behaviors, even with computer-simulated agents. However, some crowd behaviors may only be observed when there is a realistic social signal (i.e., when the participants are aware that the other avatars are controlled by people3). In order to address this shortcoming, the present protocol describes a method for conducting controlled experiments with multiple users in a networked VR setup. This approach has been employed in a recent study by Moussaid and colleagues in order to investigate the evacuation behavior of 36 networked participants3.
Research on networked VR has focused on topics unrelated to navigation strategies33,34 and/or relied on existing online gaming platforms such as Second Life. For example, Molka-Danielsen and Chabada investigated evacuation behavior in terms of exit choice and spatial knowledge of the building using participants recruited among existing users of Second Life35. While the authors provide some descriptive results (e.g., visualizations of trajectories), this study had difficulties with participant recruitment, experimental control, and generalization beyond this specific case. More recently, Normoyle and colleagues found that existing users of Second Life and participants in a laboratory were comparable in terms of evacuation performance and exit choice and different in terms of self-reported presence and frustration with the control interface36. The findings from these two studies highlight some of the challenges and opportunities afforded by online and laboratory experiments. Online studies are capable of drawing from a much larger and motivated population of potential participants. However, laboratory studies allow for more experimental control of the physical environment and potential distractions. In addition, online studies may pose some ethical concerns regarding data anonymity and confidentiality.
As a networked desktop VR laboratory, the Decision Science Laboratory (DeSciL) at ETH Z&#252;rich is primarily used to study economic decision-making and strategic interactions in a controlled environment. The technical infrastructure at the DeSciL consists of hardware, software for laboratory automation, and software that supports the multi-user desktop VR setup. The hardware includes high-performance desktop computers with Microsoft Windows 10 Enterprise operating system, control interfaces (e.g., mouse and keyboard, joysticks), headphones, and eye trackers (Table of Materials). All client computers are connected with Ethernet of one gigabit per second to the university network and the same network file share. There is no visible delay or lag when there are 36 clients connected. The number of frames per second is consistently above 100. The experiments are also managed and controlled with laboratory automation software based on Microsoft PowerShell (i.e., PowerShell Desired State Configuration and PowerShell Remoting). All relevant steps of the protocol are preprogrammed with PowerShell scripts called Cmdlets (e.g., Start-Computer, Stop-Computer). During the experiment, these scripts can be executed simultaneously and remotely on all client computers. This type of laboratory automation ensures an identical state of the client computers, reduces potential errors and complexity during scientific testing, and prevents researchers from having to perform repetitive manual tasks. For the navigation experiments, we use the Unity game engine (&#60;https://unity3d.com/&#62;) in order to support the development of 2D and 3D environments for multi-user, interactive desktop VR. The 36 client computers are connected to a server via an authoritative server architecture. At the start of every experiment, each client sends an instantiation request to the server, and the server responds by instantiating an avatar for that user on all of the connected machines. Each user&#39;s avatar has a camera with a 50 degrees field of view. Throughout the experiment, the clients send user&#39; input to the server, and the server updates the movement of all of the clients.
In the physical laboratory, each computer is contained in a separate cubicle within three semi-independent rooms (Figure 1). The overall size of the laboratory is 170 m2 (150 m2 for experiment room and 20 m2 for control room). Each of these rooms is equipped with audio and video recording devices. Experiments are controlled from a separate adjacent room (i.e., by providing instructions and initiating the experimental program). From this control room, the experimenters can also observe the participants in both physical and virtual environments. Together with the Department of Economics at the University of Z&#252;rich, the DeSciL also maintains the University Registration Center for Study Participants, which was implemented based on h-root37.
Although similar systems have been described in the literature38, the DeSciL is the first functional laboratory that is suitable for multi-user desktop VR experiments on navigation and crowd behavior to our knowledge. Here, we describe the protocol for conducting an experiment in the DeSciL, present representative results from one study on social navigation behavior and discuss the potential and limitations of this system.

<table>
	<tbody>
		<tr>
			<td>PC</td>
			<td>Lenovo</td>
			<td>IdeaCentre AIO 700</td>
			<td>24&#8217;&#8217; screen, 16 GB RAM, and SSDs. CPU: Intel core i7. GPU:NVidia GeForce GTX 950A</td>
		</tr>
		<tr>
			<td>Keyboard</td>
			<td>Lenovo</td>
			<td>LXH-EKB-10YA</td>
			<td></td>
		</tr>
		<tr>
			<td>Mouse</td>
			<td>Lenovo</td>
			<td>SM-8825</td>
			<td></td>
		</tr>
		<tr>
			<td>Eye tracker</td>
			<td>Tobii Technology</td>
			<td>Tobii EyeX</td>
			<td>Data rate: 60 Hz. Tracking screen size: Up to 27&#8243;</td>
		</tr>
		<tr>
			<td>Communication audio system</td>
			<td>Biamp Systems</td>
			<td>Networked paging station - 1</td>
			<td>Ethernet:100BaseTX</td>
		</tr>
	</tbody>
</table>

a networked desktop virtual reality setup for decision science and navigation experiments with multiple participants

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.

For each client on each trial, the experiment data from the DeSciL typically include trajectories, time stamps, and measures of performance (e.g., whether the participant turned in the &#34;correct&#34; direction at a particular intersection). A representative study investigated the effects of signage complexity on the route choice for a crowd of human participants (with virtual avatars) in a simple Y-shaped virtual environment. In this experiment, 28 participants (12 women and 1...

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

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

This method can help answer key questions in the decision science and spatial cognition fields, such as how to use desktop virtual reality to conduct wayfinding studies with multiple participants. The main advantage of this technique is that the experimental protocol can be adapted and applied by other researchers in order to set up a network desktop virtual reality laboratory. The implications of this technique extend toward behavioral science and spatial cognition, because experimenters can efficiently monitor the behavior of participants that are simultaneously immersed in a desktop virtual environment.Generally individuals new to this method will struggle because such a laboratory requires maintenance and strict protocols to collect data in a controlled setting. The visual demonstration of this method is critical because of difficulties associated with multi-user desktop virtual reality experiments, such as setting up the experiment and managing participants. Demonstrating this procedure will be Alvaro Ingold, a technician from our laboratory.Begin by turning on the server and the lights in the control room at the lab. Set up the testing rooms according to the required number of participants. Copy the executable experiment program and its corresponding configuration files on the network drive.Then open PowerShell Integrated Scripting Environment on the Windows desktop. In the PowerShell console, specify an array of computer names to create a client pool object. Type Start-Pool pool to start the client computers, and Register-Pool pool to connect the server to the client computers.Next, prepare the computers on the client side before launching the program. Type Invoke-Pool Mount-NetworkShare path}to direct the computers to enter the right folder path. Then, execute the prepared functions on the server by typing in Start-GameServer, and typing on the client's computer Invoke-Pool Start-GameClient}Specify the IP Address of the server as parameter of the function.Wait for a message on the server's monitor that indicates a successful connection. Finally, distribute the consent forms and pens in each cubicle. Shuffle the deck of seating cards that indicate the seating arrangement of the participants.Five minutes before the official start time, start checking each participant's ID to ensure that they match the list of registered participants. Then let the participants pick a card that indicates their seat number. Have the participants walk to the corresponding cubicle, and to read and sign the consent forms.Pick up these before conducting the experiment. Broadcast the instructions with a microphone to all the participants. Inform them of the basic rules, including no communication to other participants, and no personal electronic devices permitted.Ask the participants to raise their hands if they have any questions regarding the experiment. Begin the experiment by presenting the demographic questionnaire. Deploy the training scene to teach the participants how to maneuver through the virtual environment and monitor progress by looking at screenshots from all computers until all participants have finished the training session.After the training session, begin the testing phase of the experiment. Ensure that there is a short waiting period before each trial for loading the next scene and allowing the participants to read the instructions. Observe the participants'behaviors from the birds-eye interface on the server computer.After the experiment is complete, close the server and client program by typing Stop-GameClient and Stop-GameServer in the PowerShell console. Then, ask the participants to remain seated until their number is called over the microphone. Extract the participants'final scores from the file score.txt in the Project Folder on the server's computer, and convert their scores into a monetary payment. Copy and save the experiment data from the server to an external disk for future analysis. Then, call the cubicle numbers one at at time, and meet each participant at the reception desk.Thank the participant and give them the corresponding payment. Finally, examine the empty cubicles and collect any remaining pens or forms. Here, the maximum and minimum times are the times required by the fastest and slowest participants to reach the destination on each trial.These descriptive statistics indicate the extent to which the participants became familiar with the environment over trials. The obtained data also allows for the visualization of trajectories generated but the virtual crowd in Trial 1 and Trial 16. Spatial statistics can then be used to analyze the changes in trajectories over trials.While attempting this procedure, it's important to remember to always follow the strict steps of the designed experiments because of the cost associated with each experimental session.

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5.9K ビュー.  ETH Zürich. This method can help answer key questions in the decision science and spatial cognition fields, such as how to use desktop virtual reality to conduct wayfinding studies with multiple participants. The main advantage of this technique is that the experimental protocol can be adapted and applied by other researchers in order to set up a network desktop virtual reality laboratory. The implications of this technique extend toward behavioral science and spatial cognition, because experimenters can...