Measuring egocentric distance perception in virtual reality: Influence of methodologies, locomotion and translation gains


Autoři: Philipp Maruhn aff001;  Sonja Schneider aff001;  Klaus Bengler aff001
Působiště autorů: Chair of Ergonomics, Department of Mechanical Engineering, Technical University of Munich, Munich, Bavaria, Germany aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224651

Souhrn

Virtual reality has become a popular means to study human behavior in a wide range of settings, including the role of pedestrians in traffic research. To understand distance perception in virtual environments is thereby crucial to the interpretation of results, as reactions to complex and dynamic traffic scenarios depend on perceptual processes allowing for the correct anticipation of future events. A number of approaches have been suggested to quantify perceived distances. While previous studies imply that the selected method influences the estimates’ accuracy, it is unclear how the respective estimates depend on depth information provided by different perceptual modalities. In the present study, six methodological approaches were compared in a virtual city scenery. The respective influence of visual and non-visual cues was investigated by manipulating the ratio between visually perceived and physically walked distances. In a repeated measures design with 30 participants, significant differences between methods were observed, with the smallest error occurring for visually guided walking and verbal estimates. A linear relation emerged between the visual-to-physical ratio and the extent of underestimation, indicating that non-visual cues during walking affected distance estimates. This relationship was mainly evident for methods building on actual or imagined walking movements and verbal estimates.

Klíčová slova:

Biological locomotion – Cognition – Distance measurement – Eyes – Sensory perception – Target detection – Virtual reality – Vision


Zdroje

1. Lehsing C, Feldstein IT. Urban interaction—getting vulnerable road users into driving simulation. In: Bengler K, Drüke J, Hoffmann S, Manstetten D, Neukum A, editors. UR:BAN Human Factors in Traffic. Wiesbaden: Springer Fachmedien Wiesbaden; 2018. p. 347–362.

2. Doric I, Frison AK, Wintersberger P, Riener A, Wittmann S, Zimmermann M, et al. A Novel Approach for Researching Crossing Behavior and Risk Acceptance. In: Green P, Pfleging B, Kun AL, Liang Y, Meschtscherjakov A, Fröhlich P, editors. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct—Automotive’UI 16. New York, New York, USA: ACM Press; 2016. p. 39–44.

3. Sobhani A, Farooq B. Impact of smartphone distraction on pedestrians’ crossing behaviour: An application of head-mounted immersive virtual reality. Transportation Research Part F: Traffic Psychology and Behaviour. 2018;58:228–241. doi: 10.1016/j.trf.2018.06.020

4. Renner RS, Velichkovsky BM, Helmert JR. The perception of egocentric distances in virtual environments—A review. ACM Computing Surveys. 2013;46(2):1–40. doi: 10.1145/2543581.2543590

5. Peer A, Ponto K. Evaluating perceived distance measures in room-scale spaces using consumer-grade head mounted displays. In: 2017 IEEE Symposium on 3D User Interfaces (3DUI). IEEE; 2017. p. 83–86.

6. Watson MR, Enns JT. Depth Perception. In: Ramachandran VS, editor. Encyclopedia of human behavior. Boston: Academic Press; 2012. p. 690–696.

7. Messing R, Durgin FH. Distance Perception and the Visual Horizon in Head-Mounted Displays. ACM Transactions on Applied Perception. 2005;2(3):234–250. doi: 10.1145/1077399.1077403

8. Armbrüster C, Wolter M, Kuhlen T, Spijkers W, Fimm B. Depth perception in virtual reality: Distance estimations in peri- and extrapersonal space. Cyberpsychology & behavior: the impact of the Internet, multimedia and virtual reality on behavior and society. 2008;11(1):9–15. doi: 10.1089/cpb.2007.9935

9. Wexler M, van Boxtel JJA. Depth perception by the active observer. Trends in cognitive sciences. 2005;9(9):431–438. doi: 10.1016/j.tics.2005.06.018 16099197

10. Harris LR, Jenkin M, Zikovitz DC. Visual and non-visual cues in the perception of linear self motion. Experimental brain research. 2000;135(1):12–21. doi: 10.1007/s002210000504 11104123

11. Finnegan DJ. Compensating for Distance Compression in Virtual Audiovisual Environments [Doctoral thesis]. University of Bath. Bath; 2017.

12. Battaglia PW, Kersten D, Schrater PR. How haptic size sensations improve distance perception. PLoS computational biology. 2011;7(6):e1002080. doi: 10.1371/journal.pcbi.1002080 21738457

13. Warren WH Jr, Kay BA, Zosh WD, Duchon AP, Sahuc S. Optic flow is used to control human walking. Nature Neuroscience. 2001;4:213 EP –. doi: 10.1038/84054 11175884

14. Wann JP, Rushton S, Mon-Williams M. Natural problems for stereoscopic depth perception in virtual environments. Vision research. 1995;35(19):2731–2736. doi: 10.1016/0042-6989(95)00018-u 7483313

15. Schneider S, Maruhn P, Bengler K. Locomotion, Non-Isometric Mapping and Distance Perception in Virtual Reality. In: Association for Computing Machinery, editor. Proceedings of the 2018 10th International Conference on Computer and Automation Engineering—ICCAE 2018. New York, New York, USA: ACM Press; 2018. p. 22–26.

16. Waller D, Richardson AR. Correcting distance estimates by interacting with immersive virtual environments: effects of task and available sensory information. Journal of experimental psychology Applied. 2008;14(1):61–72. doi: 10.1037/1076-898X.14.1.61 18377167

17. Kelly JW, Cherep LA, Klesel B, Siegel ZD, George S. Comparison of Two Methods for Improving Distance Perception in Virtual Reality. ACM Transactions on Applied Perception. 2018;15(2):1–11.

18. Kelly JW, Hammel WW, Siegel ZD, Sjolund LA. Recalibration of perceived distance in virtual environments occurs rapidly and transfers asymmetrically across scale. IEEE transactions on visualization and computer graphics. 2014;20(4):588–595. doi: 10.1109/TVCG.2014.36 24650986

19. Frenz H, Lappe M. Absolute travel distance from optic flow. Vision research. 2005;45(13):1679–1692. doi: 10.1016/j.visres.2004.12.019 15792843

20. Lappe M, Frenz H. Visual estimation of travel distance during walking. Experimental brain research. 2009;199(3-4):369–375. doi: 10.1007/s00221-009-1890-6 19533107

21. Sun HJ, Campos JL, Chan GSW. Multisensory integration in the estimation of relative path length. Experimental brain research. 2004;154(2):246–254. doi: 10.1007/s00221-003-1652-9 14685814

22. Campos JL, Butler JS, Bülthoff HH. Multisensory integration in the estimation of walked distances. Experimental brain research. 2012;218(4):551–565. doi: 10.1007/s00221-012-3048-1 22411581

23. Lappe M, Jenkin M, Harris LR. Travel distance estimation from visual motion by leaky path integration. Experimental brain research. 2007;180(1):35–48. doi: 10.1007/s00221-006-0835-6 17221221

24. Klein E, Swan JE, Schmidt GS, Livingston MA, Staadt OG. Measurement Protocols for Medium-Field Distance Perception in Large-Screen Immersive Displays. In: Steed A, editor. IEEE Virtual Reality Conference, 2009. Piscataway, NJ: IEEE; 2009. p. 107–113.

25. Kelly JW, Cherep LA, Siegel ZD. Perceived space in the HTC Vive. ACM Transactions on Applied Perception. 2017;15(1):1–16. doi: 10.1145/3106155

26. Grechkin TY, Nguyen TD, Plumert JM, Cremer JF, Kearney JK. How does presentation method and measurement protocol affect distance estimation in real and virtual environments? ACM Transactions on Applied Perception. 2010;7(4):1–18. doi: 10.1145/1823738.1823744

27. Plumert JM, Kearney JK, Cremer JF, Recker K. Distance perception in real and virtual environments. ACM Transactions on Applied Perception. 2005;2(3):216–233. doi: 10.1145/1077399.1077402

28. Ziemer CJ, Plumert JM, Cremer JF, Kearney JK. Estimating distance in real and virtual environments: Does order make a difference? Attention, perception & psychophysics. 2009;71(5):1095–1106.

29. Bruder G, Sanz FA, Olivier AH, Lecuyer A. Distance estimation in large immersive projection systems, revisited. In: 2015 IEEE Virtual Reality (VR). IEEE; 2015. p. 27–32.

30. Interrante V, Ries B, Lindquist J, Anderson L. Elucidating Factors that can Facilitate Veridical Spatial Perception in Immersive Virtual Environments. In: Sherman W, editor. IEEE Virtual Reality Conference, 2007. Piscataway, NJ: IEEE Service Center; 2007. p. 11–18.

31. IBM Corp. IBM SPSS Statistics for Windows, Version 24.0; Released 2016.

32. RStudio Team. RStudio: Integrated Development Environment for R; 2016. Available from: http://www.rstudio.com/.

33. Michael Waskom, Olga Botvinnik, Drew O’Kane, Paul Hobson, Saulius Lukauskas, David C Gemperline, et al. Mwaskom/Seaborn: V0.8.1 (September 2017); 2017.

34. Pinheiro J, Douglas B, DebRoy S, Sarkar D, R Core Team. nlme: Linear and Nonlinear Mixed Effects Models; 2018. Available from: https://CRAN.R-project.org/package=nlme.


Článek vyšel v časopise

PLOS One


2019 Číslo 10