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>
	<li>Wait for these participants to register via the online system. Make sure that more participants than required (e.g., 4 overbooked participants for a session that requires 36 people) register. Overbooked participants help ensure that a session is viable in the event of no-shows.</li>
	<li>Ensure that a confirmation email is sent to registered participants automatically.</li>
</ol>
2. Prepare the Experimental Session.
<ol>
	<li>Prepare the laboratory environment.
	<ol>
		<li>Print the participant list from the recruitment system.</li>
		<li>Turn on the server and the lights in the control room of the DeSciL and organize the testing rooms according to the required number of participants.</li>
		<li>Copy the executable experiment program and its corresponding configuration files on the network drive. This executable program deploys a custom-written software framework based on the Unity game engine to support client-server communication among different computers through a local area network. For navigation experiments, the framework provides a bird-eye observer server system for monitoring the client&#8217;s behaviors during the experiment.</li>
		<li>Open PowerShell Integrated Scripting environment on the Windows desktop. In the PowerShell console, specify an array of computer names (e.g., $pool = &#8220;descil-w01&#8221;, &#8220;descil-w02&#8221;&#8230;) to create a client pool object. Next, type Start-Pool $pool to start the client computers and Register-Pool $pool to connect the server to the client computers.</li>
		<li>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.</li>
		<li>Execute the prepared functions on the server (i.e., Start-GameServer) and on the clients (i.e., Invoke-Pool { Start-GameClient }). Specify the IP address of the server as parameter of the function.</li>
		<li>Wait for a message on the server&#8217;s monitor that indicates a successful connection.</li>
		<li>Distribute the consent forms and pens in each cubicle. The consent forms contains the information regarding the study (e.g., the purpose of the study, potential risks and benefits of the experiment), the contact information for the experimenter, and a legal disclaimer.</li>
		<li>Shuffle the deck of seating cards that indicate the seating arrangement of the participants.</li>
	</ol>
	</li>
	<li>Welcome the participants.
	<ol>
		<li>Ask the participants to wait outside of the laboratory. 5 min before the official start time, check the participants&#8217; identity documents to ensure that they match the list of registered participants. At the same time, let the participants pick a card that indicates their seat number. Have the participants walk to the corresponding cubicle and wait for the experiment to begin.</li>
		<li>Wait a few minutes for the participants to read and sign the consent forms. Collect these forms before conducting the experiment.</li>
	</ol>
	</li>
</ol>
3. Conduct the Experiment.
<ol>
	<li>Broadcast the experiment instructions with the microphone to all of 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.</li>
	<li>Begin the experiment by presenting the demographic questionnaire (e.g., gender and age) on each client.</li>
	<li>Deploy the training scene to teach the participants how to maneuver through the virtual environment. If the participants have trouble using the control interface (e.g., mouse and keyboard), walk towards their cubicle in order to assist them. Keep monitoring the participants&#8217; progress by requesting screenshots from all of the clients (i.e., type Get-ScreenShots on PowerShell console) until all of the participants have finished the training session.</li>
	<li>After the training session, begin the testing phase of the experiment. Observe the participants&#8217; behaviors from the bird&#8217;s-eye interface on the server computer. Send warning messages to the participants through the program if they are doing something abnormal by clicking on their avatar. Otherwise, try not to interfere with the participants during the experiment.</li>
	<li>Ensure that there is a short waiting period before each trial for loading the next scene and allowing the participants to read the instructions.</li>
</ol>
4. Finalize the Experiment.
<ol>
	<li>Close the server and client program by typing Stop-GameClient and Stop-GameServer in the PowerShell console.</li>
	<li>Ask the participants to remain seated until their number is called over the microphone.</li>
	<li>Extract the participants&#8217; final scores from the file &#8220;Score.txt&#8221; in the project folder on the server computer and convert their scores into a monetary payment.</li>
	<li>Call the cubicle numbers one at a time and meet each participant at the reception desk. Thank the participants and give them the corresponding payment.</li>
	<li>Examine the cubicles and collect any remaining pens or forms.</li>
	<li>Copy and save the experiment data from the server to an external disk for future analysis.</li>
</ol>

<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. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Navigating+Large-Scale+&amp;#34;Desk-+Top&amp;#34;+Virtual+Buildings:+Effects+of+orientation+aids+and+familiarity.">Navigating Large-Scale &amp;#34;Desk- Top&amp;#34; Virtual Buildings: Effects of orientation aids and familiarity.</a> Presence. 7 (2), 179-192 (1998).</li><li>Riva, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+Reality+in+Psychotherapy:+Review.">Virtual Reality in Psychotherapy: Review.</a> CyberPsychology &amp; Behavior. 8 (3), 220-230 (2005).</li><li>Ruse, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+a+Virtual+Reality+Assessment+of+Everyday+Living+Skills.">Development of a Virtual Reality Assessment of Everyday Living Skills.</a> Journal of Visualized Experiments. (86), 1-8 (2014).</li><li>Ploydanai, K., van den Puttelaar, J., van Herpen, E., van Trijp, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+a+Virtual+Store+As+a+Research+Tool+to+Investigate+Consumer+In-store+Behavior.">Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior.</a> Journal of Visualized Experiments. (125), 1-15 (2017).</li><li>Satava, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+reality+surgical+simulator+-+The+first+steps.">Virtual reality surgical simulator - The first steps.</a> Surgical Endoscopy. 7 (3), 203-205 (1993).</li><li>Stanney, K. M., Hale, K. S. <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 virtual environments: Design, implementation, and applications. , 811-834 (2014).</li><li>Ryu, J., Kim, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+a+vibro-tactile+display+for+enhanced+collision+perception+and+presence.">Using a vibro-tactile display for enhanced collision perception and presence.</a> Proceedings of the ACM symposium on Virtual reality software and technology VRST 04. , 89 (2004).</li><li>Louison, C., Ferlay, F., Mestre, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatialized+vibrotactile+feedback+contributes+to+goal-directed+movements+in+cluttered+virtual+environments.">Spatialized vibrotactile feedback contributes to goal-directed movements in cluttered virtual environments.</a> 2017 IEEE Symposium on 3D User Interfaces (3DUI). , 99-102 (2017).</li><li>Knierim, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tactile+Drones+-+Providing+Immersive+Tactile+Feedback+in+Virtual+Reality+through+Quadcopters.">Tactile Drones - Providing Immersive Tactile Feedback in Virtual Reality through Quadcopters.</a> Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '17. , 433-436 (2017).</li><li>Serafin, S., Nordahl, R., De G&#246;tzen, A., Erkut, C., Geronazzo, M., Avanzini, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sonic+interaction+in+virtual+environments.">Sonic interaction in virtual environments.</a> 2015 IEEE 2nd VR Workshop on Sonic Interactions for Virtual Environments (SIVE). , 1-2 (2015).</li><li>Gr&#252;bel, J., Thrash, T., H&#246;lscher, C., 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=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. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinguishing+Social+from+Nonsocial+Navigation+in+Moving+Animal+Groups.">Distinguishing Social from Nonsocial Navigation in Moving Animal Groups.</a> The American Naturalist. 179 (5), 621-632 (2012).</li><li>Kinateder, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Social+influence+on+route+choice+in+a+virtual+reality+tunnel+fire.">Social influence on route choice in a virtual reality tunnel fire.</a> Transportation Research Part F: Traffic Psychology and Behaviour. 26, 116-125 (2014).</li><li>Drury, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cooperation+versus+competition+in+a+mass+emergency+evacuation:+A+new+laboratory+simulation+and+a+new+theoretical+model.">Cooperation versus competition in a mass emergency evacuation: A new laboratory simulation and a new theoretical model.</a> Behavior Research Methods. 41 (3), 957-970 (2009).</li><li>Helbing, D., Buzna, L., Johansson, A., Werner, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-Organized+Pedestrian+Crowd+Dynamics:+Experiments,+Simulations,+and+Design+Solutions.">Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions.</a> Transportation Science. 39 (1), 1-24 (2005).</li><li>Moussa&#239;d, M., 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=How+simple+rules+determine+pedestrian+behavior+and+crowd+disasters.">How simple rules determine pedestrian behavior and crowd disasters.</a> Proceedings of the National Academy of Sciences of the United States of America. 108 (17), 6884-6888 (2011).</li><li>Singh, S., Kapadia, M., Faloutsos, P., Reinman, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+open+framework+for+developing,+evaluating,+and+sharing+steering+algorithms.">An open framework for developing, evaluating, and sharing steering algorithms.</a> International Workshop on Motion in Games. , 158-169 (2009).</li><li>Singh, S., Kapadia, M., Hewlett, B., Reinman, G., Faloutsos, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+modular+framework+for+adaptive+agent-based+steering.">A modular framework for adaptive agent-based steering.</a> Symposium on Interactive 3D Graphics and Games. , (2011).</li><li>Bode, N., Codling, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+exit+route+choice+in+virtual+crowd+evacuations.">Human exit route choice in virtual crowd evacuations.</a> Animal Behaviour. 86, 347-358 (2013).</li><li>Bode, N. W. F., Kemloh Wagoum, A. U., Codling, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+responses+to+multiple+sources+of+directional+information+in+virtual+crowd+evacuations.">Human responses to multiple sources of directional information in virtual crowd evacuations.</a> Journal of the Royal Society Interface. 11 (91), 20130904 (2014).</li><li>Pandzic, I. S., Capin, T., Lee, E., Thalmann, N. M., Thalmann, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+flexible+architecture+for+virtual+humans+in+networked+collaborative+virtual+environments.">A flexible architecture for virtual humans in networked collaborative virtual environments.</a> Computer Graphics Forum. 16, (1997).</li><li>Joslin, C., Pandzic, I. S., Thalmann, N. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trends+in+networked+collaborative+virtual+environments.">Trends in networked collaborative virtual environments.</a> Computer Communications. 26 (5), 430-437 (2003).</li><li>Molka-Danielsen, J., Chabada, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+the+3D+multi+user+virtual+environment+of+Second+Life+to+emergency+evacuation+simulation.">Application of the 3D multi user virtual environment of Second Life to emergency evacuation simulation.</a> System Sciences (HICSS), 2010 43rd Hawaii International Conference. , 1-9 (2010).</li><li>Normoyle, A., Drake, J., Safonova, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Egress online: Towards leveraging massively, multiplayer environments for evacuation studies. , (2012).</li><li>Bock, O., Baetge, I., Nicklisch, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=hroot:+Hamburg+registration+and+organization+online+tool.">hroot: Hamburg registration and organization online tool.</a> European Economic Review. 71, 117-120 (2014).</li><li>Tanvir Ahmed, D., Shirmohammadi, S., Oliveira, J., Bonney, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Supporting+large-scale+networked+virtual+environments.">Supporting large-scale networked virtual environments.</a> Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2007. IEEE Symposium. , 150-154 (2007).</li><li>Cipresso, P., Bessi, A., Colombo, D., Pedroli, E., Riva, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+psychometrics+for+modeling+system+dynamics+during+stressful+disasters.">Computational psychometrics for modeling system dynamics during stressful disasters.</a> Frontiers in Psychology. 8, 1-6 (2017).</li><li>Bernold, E., Gsottbauer, E., Ackermann, K., Murphy, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Social framing and cooperation: The roles and interaction of preferences and beliefs. , 1-26 (2015).</li><li>Balietti, S., Goldstone, R. L., Helbing, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peer+review+and+competition+in+the+Art+Exhibition+Game.">Peer review and competition in the Art Exhibition Game.</a> Proceedings of the National Academy of Sciences of the United States of America. 113 (30), 8414-8419 (2016).</li><li>Gomez-Marin, A., Paton, J. J., Kampff, A. R., Costa, R. M., Mainen, Z. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Big+behavioral+data:+Psychology,+ethology+and+the+foundations+of+neuroscience.">Big behavioral data: Psychology, ethology and the foundations of neuroscience.</a> Nature Neuroscience. 17 (11), 1455-1462 (2014).</li></ol>

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&amp;rsquo;&amp;rsquo; 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&amp;Prime;</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...

Watch this Scientific Journal Video about A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants at JoVE.com

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 ...

a-networked-desktop-virtual-reality-setup-for-decision-science

Universidad San Sebastian

Research

JoVE Journal

Behavior

5.9K Views.  ETH Zürich. This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

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

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality 

Fear conditioning is a widely used paradigm in non-human animal research to investigate the neural mechanisms underlying fear and anxiety. A major challenge in conducting conditioning studies in humans is the ability to strongly manipulate or simulate the environmental contexts that are associated with conditioned emotional behaviors. In this regard, virtual reality (VR) technology is a promising tool. Yet, adapting this technology to meet experimental constraints requires special accommodations. Here we address the methodological issues involved when conducting fear conditioning in a fully immersive 6-sided VR environment and present fear conditioning data.
In the real world, traumatic events occur in complex environments that are made up of many cues, engaging all of our sensory modalities. For example, cues that form the environmental configuration include not only visual elements, but aural, olfactory, and even tactile. In rodent studies of fear conditioning animals are fully immersed in a context that is rich with novel visual, tactile and olfactory cues. However, standard laboratory tests of fear conditioning in humans are typically conducted in a nondescript room in front of a flat or 2D computer screen and do not replicate the complexity of real world experiences. On the other hand, a major limitation of clinical studies aimed at reducing (extinguishing) fear and preventing relapse in anxiety disorders is that treatment occurs after participants have acquired a fear in an uncontrolled and largely unknown context. Thus the experimenters are left without information about the duration of exposure, the true nature of the stimulus, and associated background cues in the environment1. In the absence of this information it can be difficult to truly extinguish a fear that is both cue and context-dependent. Virtual reality environments address these issues by providing the complexity of the real world, and at the same time allowing experimenters to constrain fear conditioning and extinction parameters to yield empirical data that can suggest better treatment options and/or analyze mechanistic hypotheses.
In order to test the hypothesis that fear conditioning may be richly encoded and context specific when conducted in a fully immersive environment, we developed distinct virtual reality 3-D contexts in which participants experienced fear conditioning to virtual snakes or spiders. Auditory cues co-occurred with the CS in order to further evoke orienting responses and a feeling of "presence" in subjects 2 . Skin conductance response served as the dependent measure of fear acquisition, memory retention and extinction.

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality

Classical fear conditioning paradigm was adapted for human participants in a fully immersive virtual reality setting. Using a discrimination paradigm, conditioned fear, cue and context memory retention, and extinction was measured with skin conductance response to dynamic virtual snakes and spiders (the conditioned stimuli) in two distinct virtual contexts.

Fear conditioning is a widely used paradigm in non-human animal research to investigate the neural mechanisms underlying fear and anxiety. A major ...

Neuroscience

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

The increasing development of computing (hardware and software) in the last decades has impacted scientific research in many fields including materials science, biology, chemistry and physics among many others. A new computational system for the accurate and fast simulation and 3D/VR visualization of nanostructures is presented here, using the open-source molecular dynamics (MD) computer program LAMMPS. This alternative computational method uses modern graphics processors, NVIDIA CUDA technology and specialized scientific codes to overcome processing speed barriers common to traditional computing methods. In conjunction with a virtual reality system used to model materials, this enhancement allows the addition of accelerated MD simulation capability. The motivation is to provide a novel research environment which simultaneously allows visualization, simulation, modeling and analysis. The research goal is to investigate the structure and properties of inorganic nanostructures (e.g., silica glass nanosprings) under different conditions using this innovative computational system. The work presented outlines a description of the 3D/VR Visualization System and basic components, an overview of important considerations such as the physical environment, details on the setup and use of the novel system, a general procedure for the accelerated MD enhancement, technical information, and relevant remarks. The impact of this work is the creation of a unique computational system combining nanoscale materials simulation, visualization and interactivity in a virtual environment, which is both a research and teaching instrument at UC Merced.

A new computational system featuring GPU-accelerated molecular dynamics simulation and 3D/VR visualization, analysis and manipulation of nanostructures has been implemented, representing a novel approach to advance materials research and promote innovative investigation and alternative methods to learn about material structures with dimensions invisible to the human eye.

The increasing development of computing (hardware and software) in the last decades has impacted scientific research in many fields including materials ...

Engineering

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional rubber-hand illusion or the more recently discovered enfacement illusion. However, these examples require rather artificial experimental setups, in which the artificial effector needs to be stroked in synchrony with the participants&#39; real hand or face&#8212;a situation in which participants have no control over the stroking or the movements of their real or artificial effector. Here, we describe a technique to establish ownership illusions in a setup that is more realistic, more intuitive, and of presumably higher ecological validity. It allows creating the virtual-hand illusion by having participants control the movements of a virtual hand presented on a screen or in virtual space in front of them. If the virtual hand moves in synchrony with the participants&#39; own real hand, they tend to perceive the virtual hand as part of their own body. The technique also creates the virtual-face illusion by having participants control the movements of a virtual face in front of them, again with the effect that they tend to perceive the face as their own if it moves in synchrony with their real face. Studying the circumstances that illusions of this sort can be created, increased, or reduced provides important information about how people create and maintain representations of themselves.

Here, we describe virtual-hand and virtual-face illusion paradigms that can be used to study body-related self-perception/-representation. They have already been used in various studies to demonstrate that, under specific conditions, a virtual hand or face can be incorporated into one&#39;s body representation, suggesting that body representations are rather flexible.

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional ...

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Path integration is a process in which self-motion is integrated over time to obtain an estimate of one's current position relative to a starting point 1. Humans can do path integration based exclusively on visual 2-3, auditory 4, or inertial cues 5. However, with multiple cues present, inertial cues - particularly kinaesthetic - seem to dominate 6-7. In the absence of vision, humans tend to overestimate short distances (&lt;5 m) and turning angles (&lt;30&deg;), but underestimate longer ones 5. Movement through physical space therefore does not seem to be accurately represented by the brain.
Extensive work has been done on evaluating path integration in the horizontal plane, but little is known about vertical movement (see 3 for virtual movement from vision alone). One reason for this is that traditional motion simulators have a small range of motion restricted mainly to the horizontal plane. Here we take advantage of a motion simulator 8-9 with a large range of motion to assess whether path integration is similar between horizontal and vertical planes. The relative contributions of inertial and visual cues for path navigation were also assessed.
16 observers sat upright in a seat mounted to the flange of a modified KUKA anthropomorphic robot arm. Sensory information was manipulated by providing visual (optic flow, limited lifetime star field), vestibular-kinaesthetic (passive self motion with eyes closed), or visual and vestibular-kinaesthetic motion cues. Movement trajectories in the horizontal, sagittal and frontal planes consisted of two segment lengths (1st: 0.4 m, 2nd: 1 m; &plusmn;0.24 m/s2 peak acceleration). The angle of the two segments was either 45&deg; or 90&deg;. Observers pointed back to their origin by moving an arrow that was superimposed on an avatar presented on the screen.
Observers were more likely to underestimate angle size for movement in the horizontal plane compared to the vertical planes. In the frontal plane observers were more likely to overestimate angle size while there was no such bias in the sagittal plane. Finally, observers responded slower when answering based on vestibular-kinaesthetic information alone. Human path integration based on vestibular-kinaesthetic information alone thus takes longer than when visual information is present. That pointing is consistent with underestimating and overestimating the angle one has moved through in the horizontal and vertical planes respectively, suggests that the neural representation of self-motion through space is non-symmetrical which may relate to the fact that humans experience movement mostly within the horizontal plane.

An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.

Path integration is a process in which self-motion is integrated over time to obtain an estimate of one's current position relative to a starting point 1. ...

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

Virtual reality (VR) experiments can be difficult to implement and require meticulous planning. This protocol describes a method for the design and implementation of VR experiments that collect physiological data from human participants. The Experiments in Virtual Environments (EVE) framework is employed to accelerate this process.

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and ...

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to use. Therefore, new approaches like immersive analytics are welcome. Immersive analytics promise to experience high-dimensional data sets in a convenient manner for various user groups and data sets. Technically, virtual-reality devices are used to enable immersive analytics. In Industry 4.0, for example, scenarios like the identification of outliers or anomalies in high-dimensional data sets are pursued goals of immersive analytics. In this context, two important questions should be addressed for any developed technical solution on immersive analytics: First, is the technical solutions being helpful or not? Second, is the bodily experience of the technical solution positive or negative? The first question aims at the general feasibility of a technical solution, while the second one aims at the wearing comfort. Extant studies and protocols, which systematically address these questions are still rare. In this work, a study protocol is presented, which mainly investigates the usability for immersive analytics in Industry 4.0 scenarios. Specifically, the protocol is based on four pillars. First, it categorizes users based on previous experiences. Second, tasks are presented, which can be used to evaluate the feasibility of the technical solution. Third, measures are presented, which quantify the learning effect of a user. Fourth, a questionnaire&#160;evaluates the stress level when performing tasks. Based on these pillars, a technical setting was implemented that uses mixed reality smartglasses to apply the study protocol. The results of the conducted study show the applicability of the protocol on the one hand and the feasibility of immersive analytics in Industry 4.0 scenarios on the other. The presented protocol includes a discussion of discovered limitations.

This protocol delineates the technical setting of a developed mixed reality application that is used for immersive analytics. Based on this, measures are presented, which were used in a study to gain insights into usability aspects of the developed technical solution.

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to ...

Game-Based Exploration: Unlocking Human Spatial Navigation Abilities

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. This capacity is known to rely on the sequential firing of place cells within the hippocampus. SN is an important behavior to investigate as this process deteriorates with age, especially in neurodegenerative disorders. However, the investigation of SN is limited by the lack of sophisticated behavioral techniques to assess this hippocampal-dependent task. Therefore, the goal of this protocol was to develop a novel, real-world approach to studying SN in humans. Specifically, an active virtual SN task was developed using a cross-platform game engine. During the encoding phase, participants navigated their way through a virtual city to locate landmarks. During the remembering phase, participants remembered where these reward locations were and delivered items to these locations. Time to find each location was captured and episodic memory was assessed by a free recall phase, including aspects of place, order, item, and association. Movement behavior (x, y, and z coordinates) was assessed through an asset available in the game engine. Importantly, results from this task demonstrate that it accurately captures both spatial learning and memory abilities as well as episodic memory. Further, findings indicate that this task is sensitive to exercise, which improves hippocampal functioning. Overall, the findings suggest a novel way to track human hippocampal functioning over the course of time, with this behavior being sensitive to physical activity training paradigms.

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Here, we present a novel, brief, and active spatial navigation task that assesses both spatial navigation and episodic memory ability. Importantly, spatial navigation and episodic memory were associated with one another, and this task demonstrated sensitivity to exercise.

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. ...

Behavioral Training Procedures for Head-fixed Virtual Reality in Mice

Virtual reality (VR) combined with head-fixation is increasingly being utilized in behavioral neuroscience studies as it allows complex behavioral assays to be performed in head-fixed mice. This enables precise behavioral recordings while incorporating various neurophysiological techniques that require head-fixation to minimize movement-related signal noise during neural recordings. However, despite the growing use of VR, there is little published data on the detailed methodology of how to implement it. In this study, a training protocol is developed whereby male and female C57B16/J mice are trained to run down a virtual linear corridor, the length of which is increased from 1-3 m over multiple training sessions. Building upon this foundation, this study investigated the feasibility of mice performing complex behaviors within VR using a Y-maze paradigm. The task required navigating to the arm with black walls from the choice point in the Y-maze. After reaching a criterion of two consecutive days equal to or greater than 70% correct, the mice progressed to increasingly difficult sensory discrimination. The findings provide important details on the methodologies useful for the successful training of mice in VR and demonstrate that mice exhibit learning capabilities in navigating the Y-maze. The methodology presented not only offers insights into training duration in VR-based assays but also underscores the potential for probing intricate behaviors in mice, opening avenues for more comprehensive neuroscience investigations.

The article describes the experimental procedures for the commonly used linear track virtual reality (VR) paradigm in mice as well as determining the feasibility of running complex VR tasks by testing a Y-shaped signal discrimination task.

Virtual reality (VR) combined with head-fixation is increasingly being utilized in behavioral neuroscience studies as it allows complex behavioral assays ...

WebVR-Based Virtual Reality Laboratory System for Immersive Online Experiments

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education

Online laboratories play an important role in engineering education. This work discusses a WebVR-based virtual laboratory system. The user enters the simulated laboratory environment through a virtual reality (VR) device and interacts with the experimental equipment, similar to hands-on experiments in a physical laboratory. In addition, the proposed system allows users to design their own control algorithms and observe the effects of different control parameters to enhance their understanding of the experiment. To illustrate the features of the proposed virtual laboratory, an example is provided in this paper, which is an experiment on a double inverted pendulum system. The experimental results show that the proposed system allows users to conduct experiments in an immersive and interactive manner and provides users with a complete experimental process from principal design to experimental operation. A solution is also provided to change any virtual laboratory into a WebVR-based virtual laboratory for education and training.

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

This study describes a WebVR-based online virtual reality (VR) laboratory system that provides users with immersive and interactive experimentation capabilities supported by VR devices. The proposed system not only helps to enhance the realism of user participation in online experiments but is also applicable to a wide range of online laboratory frameworks.

Online laboratories play an important role in engineering education. This work discusses a WebVR-based virtual laboratory system. The user enters the ...

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

Summary

Explore More Videos

Chapters in this video

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Virtual Reality Experiments with Physiological Measures

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Behavioral Training Procedures for Head-fixed Virtual Reality in Mice

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education