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

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Intelligent Human Systems Integration 2019 (IHSI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 903))

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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.

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Author information

Authors and Affiliations

  1. Laboratory of Interactive Technologies, National Information Processing Institute, al. Niepodleglosci 188b, 00-608, Warsaw, Poland

    Pawel Kobylinski & Cezary Biele

  2. Virtual Reality and Psychophysiology Lab, Institute of Psychology, Polish Academy of Sciences, ul. Jaracza 1, 00-378, Warsaw, Poland

    Grzegorz Pochwatko

Authors
  1. Pawel Kobylinski
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  2. Grzegorz Pochwatko
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  3. Cezary Biele
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Corresponding author

Correspondence to Pawel Kobylinski .

Editor information

Editors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Waldemar Karwowski

  2. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

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Kobylinski, P., Pochwatko, G., Biele, C. (2019). VR Experience from Data Science Point of View: How to Measure Inter-subject Dependence in Visual Attention and Spatial Behavior. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_60

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