VR Experience from Data Science Point of View

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

Cite as:
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

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 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

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,Science,Cinematic VR,R programming,