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ACM Transactions on Graphics
Article . 2024 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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DBLP
Article . 2024
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Identifying Behavioral Correlates to Visual Discomfort

Authors: David A. Tovar; James Wilmott; Xiuyun Wu; Daniel Martin; Michael J. Proulx; Dave Lindberg; Yang Zhao 0030; +2 Authors

Identifying Behavioral Correlates to Visual Discomfort

Abstract

Outside of self-report surveys, there are no proven, reliable methods to quantify visual discomfort or visually induced motion sickness symptoms when using head-mounted displays. While valuable tools, self-report surveys suffer from potential biases and low sensitivity due to variability in how respondents may assess and report their experience. Consequently, extreme visual-vestibular conflicts are generally used to induce discomfort symptoms large enough to measure reliably with surveys (e.g., stationary participants riding virtual roller coasters). An emerging area of research is the prediction of discomfort survey results from physiological and behavioral markers. However, the signals derived from experimental paradigms that are explicitly designed to be uncomfortable may not generalize to more naturalistic experiences where comfort is prioritized. In this work we introduce a custom VR headset designed to introduce significant near-eye optical distortion (i.e., pupil swim) to induce visual discomfort during more typical VR experiences. We evaluate visual comfort in our headset while users play the popular VR title Job Simulator and show that eye-tracked dynamic distortion correction improves visual comfort in a multi-session, within-subjects user study. We additionally use representational similarity analysis to highlight changes in head and gaze behavior that are potentially more sensitive to visual discomfort than surveys.

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150, 620

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
hybrid