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VR Experience from Data Science Point of View: How to Measure Inter-subject Dependence in Visual Attention and Spatial Behavior

  • Pawel KobylinskiEmail author
  • Grzegorz Pochwatko
  • Cezary Biele
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)

Abstract

Any Virtual Reality (VR) immersive experience inherently allows its subjects to choose their own paths of visual attention and/or spatial behavior. If a VR designer employs any system of attentional cues, they might be interested in measuring the system’s effectiveness. Eye tracking (ET) time series data can be used as a visual attention trail and positional time series data can be used as spatial behavior trails. In this paper we are addressing the issue of measuring inter-subject dependence in visual attention and spatial behavior. We are arguing why recently developed distance correlation coefficient [1, 2] might be both a proper and convenient choice to either measure the inter-subject dependence or test for the inter-subject independence in visual and behavioral data recorded during a VR experience.

Keywords

Virtual reality Narration Attention Behavior Eye tracking Positional tracking Data science Applied statistics Energy statistics Distance correlation Distance variance Human-Technology Interaction User experience Research methodology Social sciences Psychology 

References

  1. 1.
    Szekely, G.J., Rizzo, M.L., Bakirov, N.K., Nail, K.: Measuring and testing dependence by correlation of distances. Ann. Stat. 35, 2769–2794 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Szekely, G.J., Rizzo, M.L.: Brownian distance covariance. Ann. Appl. Stat. 3, 1236–1265 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Lowood, H.E.: Virtual Reality (2018). https://www.britannica.com/technology/virtual-reality
  4. 4.
    Sutherland, I.E.: A head-mounted three dimensional display. In: AFIPS Fall Joint computer Conference, pp. 757–764. ACM, New York (1968)Google Scholar
  5. 5.
    Fox, J., Arena, D., Bailenson, J.N.: Virtual reality: a survival guide for the social scientist. J. Media Psychol. Ger. 21, 95–113 (2009)CrossRefGoogle Scholar
  6. 6.
    Fisher, W.R.: Narration as a human communication paradigm: the case of public moral argument. Commun. Monogr. 51, 1–22 (1984)CrossRefGoogle Scholar
  7. 7.
    Aylett, R., Louchart, S.: Towards a narrative theory of virtual reality. Virtual Real. 7, 2–9 (2003)CrossRefGoogle Scholar
  8. 8.
    Godde, M., Gabler, F., Siegmund, D, Braun, A.: Cinematic narration in VR - rethinking film conventions for 360 Degrees. In: Chen, J., Fragomeni, G. (eds.) VAMR 2018. LNCS, vol. 10910, pp. 184–201. Springer, Cham (2018)CrossRefGoogle Scholar
  9. 9.
    Duchowski, A.T.: Eye Tracking Methodology: Theory and Practice. Springer, Berlin (2007)zbMATHGoogle Scholar
  10. 10.
    Clark, M.: A comparison of correlation measures. Technical report, University of Notre Dame (2013)Google Scholar
  11. 11.
    de Santos, S.S., Takahashi, D.Y., Nakata, A., Fujita, A.: A comparative study of statistical methods used to identify dependencies between gene expression signals. Brief. Bioinform. 15, 906–918 (2014)CrossRefGoogle Scholar
  12. 12.
    Davis, R.A., Matsui, M., Mikosch, T., Wan, P.: Applications of distance correlation to time series. Bernoulli 24, 3087–3116 (2018)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Szekely, G.J., Rizzo, M.L.: Energy statistics: a class of statistics based on distances. J. Stat. Plan. Infer. 143, 1249–1272 (2013)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Szekely, G.J., Rizzo, M.L.: The energy of data. Ann. Rev. Stat. Appl. 4, 447–479 (2017)CrossRefGoogle Scholar
  15. 15.
    Fan, Y., Lafaye de Micheaux, P., Penev, S., Salopek, D.: Multivariate nonparametric test of independence. J. Multivar. Anal. 153, 189–210 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Rizzo, M.L., Szekely G.J.: energy: E-statistics: multivariate inference via the energy of data. R package version 1.7-5 (2018). https://CRAN.R-project.org/package=energy
  17. 17.
    Lafaye de Micheaux P., Bilodeau, M.: Software: R Package, IndependeceTests, Version 0.2 (2012). https://CRAN.R-project.org/package=IndependenceTests
  18. 18.
    R Core Team: R: A Language and Environment for Statistical Computing, Vienna, Austria (2018). https://www.R-project.org

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pawel Kobylinski
    • 1
    Email author
  • Grzegorz Pochwatko
    • 2
  • Cezary Biele
    • 1
  1. 1.Laboratory of Interactive TechnologiesNational Information Processing InstituteWarsawPoland
  2. 2.Virtual Reality and Psychophysiology Lab, Institute of PsychologyPolish Academy of SciencesWarsawPoland

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