Brightness and contrast do not affect visually induced motion sickness in a passively-flown fixed-base flight simulator

Shahal, Avner; Hemmerich, Wanja; Hecht, Heiko

doi:10.1016/j.displa.2016.05.007

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…Since we measured a large number of variables, including balance control across five sensory conditions, vestibular thresholds, and motion sickness susceptibility, we elected a priori to extract only one measurement of balance control, namely, sway path length. Sway path length is a popular measure of balance control in research and has been used in several previous studies of motion sickness (e.g., Lubeck et al 2015; Palmisano et al 2014; Shahal et al 2016). There are, however, numerous balance control measures that we did not assess here, the analysis of which would have significantly increased the number of statistical tests conducted at the possible cost of an increased false positive count.…”

Section: Discussionmentioning

confidence: 99%

Estimating the sensorimotor components of cybersickness

Weech

Varghese

Barnett‐Cowan

2018

Journal of Neurophysiology

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The user base of the virtual reality (VR) medium is growing, and many of these users will experience cybersickness. Accounting for the vast inter-individual variability in cybersickness forms a pivotal step in solving the issue. Most studies of cybersickness focus on a single factor (e.g., balance, sex, vection), while other contributors are overlooked. Here, we characterize the complex relationship between cybersickness and several measures of sensorimotor processing. In a single session, we conducted a battery of tests of balance control, vection responses, and vestibular sensitivity to self-motion. Following this, we measured cybersickness after VR exposure. We constructed a principal components regression model using the measures of sensorimotor processing. The model significantly predicted 37% of the variability in cybersickness measures, with 16% of this variance being accounted for by a principal component that represented balance control measures. The strongest predictor was participants' sway path length during vection, which was inversely related to cybersickness (r(28) = -.53, p = .002) and uniquely accounted for 7.5% of the variance in cybersickness scores across participants. Vection strength reports and measures of vestibular sensitivity were not significant predictors of cybersickness. We discuss the possible role of sensory reweighting in cybersickness that is suggested by these results, and we identify other factors that may account for the remaining variance in cybersickness. The results reiterate that the relationship between balance control and cybersickness is anything but straightforward.

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Section: Discussionmentioning

confidence: 99%

Estimating the sensorimotor components of cybersickness

Weech

Varghese

Barnett‐Cowan

2018

Journal of Neurophysiology

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“…It also remains to be seen if visual noise manipulations result in a symmetric effect on CS, such that visual noise delays the process of reaching visual dominance for self-motion perception, resulting in more sensory conflicts. While evidence suggests that visual brightness and contrast manipulations do not affect CS (Shahal et al, 2016), other manipulations should be tested (e.g., visual motion coherence, blurring) before conclusive statements can be made about visual noise and CS.…”

Section: Practical Significancementioning

confidence: 99%

Reduction of cybersickness during and immediately following noisy galvanic vestibular stimulation

2020

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The data that support the findings of this study will be made freely available upon publication on the Open Science Framework: Weech, S., Wall, T., & Barnett-Cowan, M. (2018). Reduction of cybersickness during and immediately following noisy galvanic vestibular stimulation. Retrieved from osf.io/md36t/ Acknowledgements: This research was supported by grants to MBC from Oculus Research, the Ontario Research Fund and Canadian Foundation for Innovation's John R. Evans Leaders Fund, and the Natural Sciences and Engineering Research Council of Canada. The industry sponsor had no influence in the design or execution of the current research. All authors declare that there are no conflicts of interests. Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Séamas Weech and Travis Wall.

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“…The maximum value of the FMS scores, FMS 𝑚𝑎𝑥 , was taken as a representation of the experienced MS on each trial. The SSQ (specifically SSQ-N) and FMS 𝑚𝑎𝑥 should represent the same construct, and therefore the scores should be correlated Shahal et al, 2016), which was found to be the case: FMS 𝑚𝑎𝑥 correlated with the total SSQ score SSQ 𝑡𝑜𝑡𝑎𝑙 (Spearman 𝜌 = 0.682, 𝑝 < 0.001), as well as with its subscales (SSQ-N: 𝜌 = 0.658, 𝑝 < 0.001; SSQ-O: 𝜌 = 0.579, 𝑝 < 0.001; SSQ-D: 𝜌 = 0.572, 𝑝 < 0.001). Spearman correlation coefficients between 0.50-0.70 may be considered a moderately strong relationship (Mukaka, 2012).…”

Section: Analysis Of Motion Sickness Scoresmentioning

confidence: 99%