WEB-BASED VR EXPERIMENTS POWERED BY THE CROWD

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1 WEB-BASED VR EXPERIMENTS POWERED BY THE CROWD Xiao Ma [1,2] Megan Cackett [2] Leslie Park [2] Eric Chien [1,2] Mor Naaman [1,2] The Web Conference 2018 [1] Social Technologies Lab, Cornell Tech [2] Cornell University maxiao.info

2 VIRTUAL REALITY The Big Bang Theory S9E20 3

3 A VISION Virtual Reality (VR) Crowdsourcing + =? 4

4 COMBINING THE BEST OF BOTH WORLDS Virtual Reality (as of 2017) Crowdsourcing Crowdsourced VR (this work) Manipulation Realism Measurement Granularity Participant Diversity Reproducibility 5

5 COMBINING THE BEST OF BOTH WORLDS Virtual Reality (as of 2017) Crowdsourcing Crowdsourced VR (this work) Manipulation Realism + High - Low Measurement Granularity + High - Low Participant Diversity - Low + High Reproducibility - Low + High 6

6 COMBINING THE BEST OF BOTH WORLDS Virtual Reality (as of 2017) Crowdsourcing Crowdsourced VR (this work) Manipulation Realism + High - Low + High Measurement Granularity + High - Low + High Participant Diversity - Low + High + High Reproducibility - Low + High + High 7

7 HOW 8

8 RESEARCH QUESTIONS RQ1: Are there VR-eligible reachable workers? RQ2: What is a good user flow? RQ3: What types of experiment manipulations can we deliver remotely? RQ4: What are the limitations and challenges? 9

9 CONTRIBUTIONS 1. Validated a VR-eligible panel of 242 workers 2. Implemented a user flow between desktop and VR 3. Replicated three previous studies with different experiment manipulation remotely 4. Limitations and challenges Source code and data are available at: bit.ly/vrcrowdexperiments 10

10 OUTLINE 1. Validated a VR-eligible panel of 242 workers 2. Implemented a user flow between desktop and VR 3. Replicated three previous studies with different experiment manipulation remotely 4. Limitations and challenges Source code and data are available at: bit.ly/vrcrowdexperiments 11

11 CONSTRUCTING VR-READY PANEL Please take a picture of your device with lasts 4 digits of your worker ID handwritten on a piece of paper in view. 12

12 (MORE) DIVERSE PARTICIPANTS N =

13 (MORE) DIVERSE PARTICIPANTS N = 242 WHITE 70% HIGH SCHOOL OR BELOW 14% ASIAN 14% SOME / 2-YEAR COLLEGE 29% BLACK 6% BACHELOR'S 29% OTHER 10% MASTER'S AND ABOVE 13% Age: (Median 32) U.S. OTHER 0% 25% 50% 75% 100% 90% 10% BELOW $30K 21% 10% 32.5% 0% 25% 50% 75% 100% $30K - $80K 57% MALE 61% SUBURBAN 52% ABOVE $80K 22% FEMALE* 39% URBAN 30% 0% 25% 50% 75% 0% 25% 50% 75% 100% RURAL 18% * One worker self-identified 0% 25% as other. 50% 75% 100% 14

14 OUTLINE 1. Validated a VR-eligible panel of 242 workers 2. Implemented a user flow between desktop and VR 3. Replicated three previous studies with different experiment manipulation remotely 4. Limitations and challenges Source code and data are available at: bit.ly/vrcrowdexperiments 15

15 DESIGN GOALS OF USER FLOW Usable Direct workers to participate in VR and complete survey on desktop Web-Based Allows for online data collection and thus remote participation Low Technical Barrier Allows for easier replication and adaptation 16

16 TECHNICAL PLATFORM CHOICE Source code and data are available at: bit.ly/vrcrowdexperiments 17

17 FLOW Experimenter approves payment Verification Code 2 VR Web App URL Verification Code 1 Worker accepts task through Amazon Mechanical Turk Worker completes experiment in VR Worker answers survey in Qualtrics 18

18 OUTLINE 1. Validated a VR-eligible panel of 242 workers 2. Implemented a user flow between desktop and VR 3. Replicated three previous studies with different experiment manipulation remotely 4. Limitations and challenges Source code and data are available at: bit.ly/vrcrowdexperiments 19

19 MODELS OF ILLUSIONS IN VR 20

20 MODELS OF ILLUSIONS IN VR Place Illusion A user s feeling of being transported into the rendered environment Embodiment Illusion A user s feeling of experiencing the virtual world through an avatar Plausibility Illusion A user s feeling that events happening in the virtual world are real Gonzalez-Franco, M., & Lanier, J. (2017). Model of Illusions and Virtual Reality. Frontiers in psychology, 8,

21 MODELS OF ILLUSIONS IN VR Study 1: Restorative Effects of Virtual Environments [Valtchanov et al. 2010], N = 22 (original) v.s. 55 (ours) Place Illusion A user s feeling of being transported into the rendered environment Study 2: Proteus Effect [Yee and Bailenson, 2007], N = 50 (original) v.s. 59 (ours) Embodiment Illusion A user s feeling of experiencing the virtual world through an avatar Study 3: Drawing Power of Crowds [Milgram et al. 1969], N = 1,424 (original) v.s. 56 (ours) Plausibility Illusion A user s feeling that events happening in the virtual world are real Gonzalez-Franco, M., & Lanier, J. (2017). Model of Illusions and Virtual Reality. Frontiers in psychology, 8,

22 MODELS OF ILLUSIONS IN VR Study 1: Restorative Effects of Virtual Environments [Valtchanov et al. 2010], N = 22 (original) v.s. 55 (ours) Place Illusion A user s feeling of being transported into the rendered environment Study 2: Proteus Effect [Yee and Bailenson, 2007], N = 50 (original) v.s. 59 (ours) Embodiment Illusion A user s feeling of experiencing the virtual world through an avatar Study 3: Drawing Power of Crowds [Milgram et al. 1969], N = 1,424 (original) v.s. 56 (ours) Plausibility Illusion A user s feeling that events happening in the virtual world are real Gonzalez-Franco, M., & Lanier, J. (2017). Model of Illusions and Virtual Reality. Frontiers in psychology, 8,

23 24

24 STUDY 2: PROTEUS EFFECT Van Der Heide, Brandon, et al. "The Proteus effect in dyadic communication: Examining the effect of avatar appearance in computer-mediated dyadic interaction." Communication Research 40.6 (2013):

25 26

26 STUDY 3: DRAWING POWER OF CROWDS N = 1,424 Milgram, S., Bickman, L., & Berkowitz, L. (1969). Note on the drawing power of crowds of different size. Journal of personality and social psychology, 13(2),

27 STUDY 3: DRAWING POWER OF CROWDS N = 1,424 Milgram, S., Bickman, L., & Berkowitz, L. (1969). Note on the drawing power of crowds of different size. Journal of personality and social psychology, 13(2),

28 29

29 RESULTS Default Field of View (101 ) Divided Into Four Zones Zone 1 Zone 2 Zone 4 Front Zone 3 30

30 FOUR CONDITIONS Participant Male avatar Female avatar Zero Low Medium High Size of stimulus crowd 31

31 0% 100% GAZE* DISTRIBUTION Participant Male avatar Female avatar Zero (N = 15) Low (N = 15) Medium (N = 13) High (N = 13) Size of stimulus crowd * Head rotation used as a proxy for gaze. 32

32 0% 100% GAZE* DISTRIBUTION Participant Male avatar Female avatar 60% 14% 18% 8% Zero (N = 15) Low (N = 15) Medium (N = 13) High (N = 13) Size of stimulus crowd * Head rotation used as a proxy for gaze. 33

33 0% 100% GAZE* DISTRIBUTION Participant Male avatar Female avatar *** statistically significant 60% 54% 47% 47% 14% 18% 12% 22% 16% 24% 16% 23% 8% 12% 13% 14% Zero (N = 15) Low (N = 15) Medium (N = 13) High (N = 13) Size of stimulus crowd * Head rotation used as a proxy for gaze. 34

34 RECAP Study 1: Restorative Effects of Virtual Environments [Valtchanov et al. 2010], 22 (original) v.s. 55 (ours) Place Illusion A user s feeling of being transported into the rendered environment Study 2: Proteus Effect [Yee and Bailenson, 2007], 50 (original) v.s. 59 (ours) Embodiment Illusion A user s feeling of experiencing the virtual world through an avatar Study 3: Drawing Power of Crowds [Milgram et al. 1969], 1,424 (original) v.s. 56 (ours) Plausibility Illusion A user s feeling that events happening in the virtual world are real Source code and data are available at: bit.ly/vrcrowdexperiments 35

35 DISCUSSION 36

36 COMBINING THE BEST OF BOTH WORLDS Virtual Reality (as of 2017) Crowdsourcing Crowdsourced VR (this work) Manipulation Realism + High - Low + High Measurement Granularity + High - Low + High Participant Diversity - Low + High + High Reproducibility - Low + High + High 37

37 1. MANIPULATION REALISIM Study 1: Restorative Effects of Virtual Environments [Valtchanov et al. 2010], N = 22 (original) v.s. 55 (ours) Place Illusion Illusion Delivered Study 2: Proteus Effect [Yee and Bailenson, 2007], N = 50 (original) v.s. 59 (ours) Embodiment Illusion Illusion Delivered Study 3: Drawing Power of Crowds [Milgram et al. 1969], N = 1,424 (original) v.s. 56 (ours) Plausibility Illusion Illusion Delivered 38

38 2. MEASUREMENT GRANULARITY Study 1: Restorative Effects of Virtual Environments [Valtchanov et al. 2010], N = 22 (original) v.s. 55 (ours) Place Illusion Illusion Delivered Study 2: Proteus Effect [Yee and Bailenson, 2007], N = 50 (original) v.s. 59 (ours) Embodiment Illusion Illusion Delivered Study 3: Drawing Power of Crowds [Milgram et al. 1969], N = 1,424 (original) v.s. 56 (ours) Plausibility Illusion Illusion Delivered Head Rotation 39

39 3. PARTICIPANT DIVERSITY N = 242 WHITE 70% HIGH SCHOOL OR BELOW 14% ASIAN 14% SOME / 2-YEAR COLLEGE 29% BLACK 6% BACHELOR'S 29% OTHER 10% MASTER'S AND ABOVE 13% Age: (Median 32) U.S. OTHER 0% 25% 50% 75% 100% 90% 10% BELOW $30K 21% 10% 32.5% 0% 25% 50% 75% 100% $30K - $80K 57% MALE 61% SUBURBAN 52% ABOVE $80K 22% FEMALE* 39% URBAN 30% 0% 25% 50% 75% 0% 25% 50% 75% 100% RURAL 18% * One worker self-identified 0% 25% as other. 50% 75% 100% 40

40 4. REPRODUCIBILITY Study 1: Restorative Effects of Virtual Environments [Valtchanov et al. 2010], N = 22 (original) v.s. 55 (ours) Place Illusion Illusion Delivered Partially replicated Study 2: Proteus Effect [Yee and Bailenson, 2007], N = 50 (original) v.s. 59 (ours) Embodiment Illusion Illusion Delivered Didn t replicate Study 3: Drawing Power of Crowds [Milgram et al. 1969], N = 1,424 (original) v.s. 56 (ours) Plausibility Illusion Illusion Delivered Replicated Head Rotation 41

41 4. REPRODUCIBILITY Cheaper $8 per task (study participation) $1,374 for 201 participants (50% reduction, estimating $3,600 for the same in-lab studies) No equipment cost Faster 201 study participation within a week Compared to three weeks estimation (67% reduction, estimating 10 in-lab studies per day) ~20 min per task Easier Pure web-based JavaScript easy to learn Open sourced Source code and data are available at: bit.ly/vrcrowdexperiments 42

42 CONTRIBUTIONS 1. Validated a VR-eligible panel of 242 workers 2. Implemented a user flow between desktop and VR 3. Replicated three previous studies with different experiment manipulation remotely 4. Limitations and challenges Source code and data are available at: bit.ly/vrcrowdexperiments 43

43 THE (LONG-DUE) PROMISE OF VIRTUAL REALITY... provide a compelling sense of personal, social, and environmental presence for users, while allowing the investigator near-perfect control over the experimental environment and actions within it. allow for more representative sampling. Whole experiments can be carried out concurrently in multiple laboratories via networked collaboratories. replications, or at least near-perfect replications, become quite possible. Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002). Immersive virtual environment technology as a methodological tool for social psychology. Psychological Inquiry, 13(2),

44 A VISION Virtual Reality (VR) Crowdsourcing + = Scaling Replicable VR Studies Source code and data are available at: bit.ly/vrcrowdexperiments 45

45 THANK YOU Xiao Ma [1,2] Megan Cackett [2] Leslie Park [2] Eric Chien [1,2] Mor Naaman [1,2] [1] Social Technologies Lab, Cornell Tech [2] Cornell University This work is partially supported by Oath through the Connected Experiences Lab at Cornell Tech. Source code and data are available at: bit.ly/vrcrowdexperiments We thank the crowdworkers who participated in our studies, and Oculus for providing the equipment used for developing the studies. We thank Dan Goldstein, Jake Hofman and Sid Suri for early feedback and direction. 46

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