The Unexpected Vection Hypothesis: Examining the Relationship Between Vection and Cybersickness

The Unexpected Vection Hypothesis: Examining the Relationship Between Vection and Cybersickness

Abstract

This report examined the unexpected vection hypothesis for cybersickness, which is the proposal that cybersickness is caused by a certain type of visually induced illusions of self-motion – known as unexpected vection. The differences between unexpected and expected vection in Head Mounted Displays (HMDs) in Virtual Reality (VR) were explored. Sixty participants were recruited from the University of Wollongong (UOW) and the general population. Participants were placed into one of three conditions which included ‘always consistent’, ‘sometimes inconsistent’, or ‘unrelated’ auditory priming cues. The experiment required the participants to be exposed to the HMD-VR video game, Aircar, for ten minutes. Fast Motion Sickness (FMS) and vection measures were obtained every two minutes from each participant in each condition. A one-way between-subjects analysis of variance (ANOVA) was used to investigate group type differences and the probability of unexpected vection. Two separate repeated-measures ANOVAs were performed to examine the effects of group type and time in trial on cybersickness severity and vection strength. Results indicated significant differences between groups. It was concluded that there were effects of group on vection type, and that the “sometimes inconsistent” auditory primes led to an increased likelihood of unexpected vection. In turn, an increased likelihood of unexpected vection led to more severe cybersickness, thus, providing strong support for the unexpected vection hypothesis.

The interactive experience of VR by way of computer technology creates a simulated environment that immerses users into a virtual world in which they can realistically interact. VR provides multi-sensory feedback by simulating many senses, such as vision, hearing,?touch and smell (Skarbez et al., 2018). This gives users a powerful sense of presence in a 3D world which enables users to feel like they are “really there” (Chertoff & Schatz, 2014; Keshavarz et al., 2015; Weech et al., 2019). Vection is a physiological effect and will be referred to as a visual illusion of self-motion in the absence of true motion in the current study (Keshavarz et al., 2015).

Experiencing VR through HMDs is becoming increasingly popular for a variety of reasons. Technological advances in HMD-VR have led to widespread use in entertainment, business, education, rehabilitation, healthcare, and research (Rizzo et al., 2014; Greenwood-Ericksen et al., 2014; Powell et al., 2018; Wang et al., 2018; Ger?ak et al., 2020; Saredakis et al., 2020). VR has the potential to allow greater access to psychological treatment for mental health disorders (Donker et al., 2019). HMD-VR training can transfer technical skills to the operating room for surgeons (Khor et al., 2016; Luca et al., 2020) and is routinely applied to aviation and military training (Rizzo et al., 2014; Luca et al., 2020). The possibilities from the benefits of VR are truly endless.

Unfortunately, VR does come with some drawbacks; it may induce unpleasant sensations like motion-sickness in a car (Rebenitsch & Owen, 2016). These sensations are a side effect of cybersickness (Chang et al., 2020), which can lead to non-trivial symptoms, such as vertigo, nausea, vomiting, retching and more HMD specific oculomotor symptoms, such as headaches, fatigue, and eyestrain (Rebenitsch & Owen, 2016; Palmisano et al., 2017). It has also been found that, the longer the exposure time to VR, the greater degree of cybersickness (Liu & Uang, 2012; Dennison et al., 2016). Cybersickness intensity has been shown to be more severe in HMD-VR than other types of VR (Dennison, 2016) due to conflicts between non-visual and visual information about self-motion (Rebenitsch & Owen, 2016; Rissi & Palmisano, 2019). HMD-VR has huge potential; however, there is this unpleasant drag-effect of cybersickness which is limiting the spread. Therefore, it is important to evaluate the causes of HMD-VR cybersickness to guide future design and use.

Moreover, there are different explanations of cybersickness; some of these include visual illusions of self-motion (Hettinger et al., 1990; Keshavarz & Hecht, 2011), increased postural instability (Riccio & Stoffregen, 1991; Palmisano et al., 2018; Rissi & Palmisano, 2019; Clifton & Palmisano, 2019), and misperceptions of poisoning (Hettinger et al., 1990; Iskander et al., 2019). However, the primary explanation is Sensory Conflict Theory (SCT), which proposes that motion-sickness is triggered due to conflict between sensory inputs, where deviations in motion information conflict with expected information (Keshavarz & Hecht, 2011; Iskander et al., 2019). Nonetheless, it is quite difficult to determine whether one is experiencing SC, and how much is being experienced; thus, it does not produce a strongly testable hypothesis.

The Vection Conflict Hypothesis (VCH) of Visually Induced Motion Sickness (VIMS) predicts that as vection strength increases, so too should VIMS symptoms. However, this general measure of vection strength does not consider the type of vection. VIMS is what happens when an individual is physically stationary and is being exposed to visual motion (Keshavarz et al., 2015). Since motion-sickness requires self-motion (apparent or real), Hettinger et al. (1990) argued that vection is a prerequisite for VIMS. There is a strong belief that there is a positive relationship connecting VIMS and vection, and that stronger vection leads to more severe motion-sickness (Keshavarz et al., 2015; Risi & Palmisano, 2019). Subsequently, studies have also reported negative relationships between VIMS and vection (Palmisano et al., 2017; Weech et al., 2019). Thus, vection alone may not be a prerequisite for VIMS, but instead, may be one factor of many (Keshavarz et al., 2015; Teixeira & Palmisano, 2021). Therefore, with such mixed evidence for and against the VCH of VIMS, it seems unlikely that it is a strong theory.

Furthermore, a small number of studies have investigated the relationship between vection and cybersickness during HMD-VR (Palmisano et al., 2017; Clifton & Palmisano, 2019; Risi & Palmisano, 2019; Teixeria & Palmisano, 2021). However, their results were not consistent, with some reporting that, the stronger the vection experienced, the more severe cybersickness experienced (Risi & Palmisano, 2019), whilst others did not report this relationship (Teixeria & Palmisano, 2021). This may suggest that there is a problem with the vection explanation of cybersickness. Notably, the type of vection that the participant was exposed to was not specified in any of the previously mentioned studies. This is problematic, because vection is an illusion of self-motion, and one can experience different illusions of self-motion (Teixeira et al., 2021). This may be a crucial distinction that needs to be made when studying the relationship between vection and cybersickness in HMD-VR.

To examine this distinction between types of vection, a new refined hypothesis is required. Recently, Teixeira et al. (2021) proposed the unexpected vection hypothesis; namely only unexpected vection is provocative for cybersickness, and that vection by itself is not a negative experience; however, vection that is unexpected will more likely induce cybersickness. Thus, the strength of vection does not matter, only if it is expected or unexpected. Previous studies have not specified vection type; however, they do report that when a user is in control of their self-motion, they are less likely to experience motion-sickness (Israel et al., 2017; Iskander et al., 2019). This may also explain why passengers in physical vehicles might be more prone to motion-sickness because they have no control over the vehicle; this is referred to as the driver-passenger effect (Rolnick & Lubow, 1991; Iskander et al., 2019). Furthermore, driving simulation studies have investigated the driver-passenger effect in virtual vehicles, with reports of?stronger feelings of cybersickness in passengers (Sharples et al., 2008; Dong et al., 2011). However, the SCT and the VCH of VIMS have not produced insight into the phenomenon that for physical and virtual vehicles, the occurrence of motion-sickness differs between passenger and driver. (Rolnick & Lubow 1991; Dong et al., 2011). Thus, it is important to discriminate between different types of vection in HMD-VR to investigate whether a certain type of vection is more likely to induce cybersickness.

Therefore, the current study will test the unexpected vection hypothesis by examining the proposal that HMD-VR cybersickness is caused by a certain type of visually induced illusion of self-motion, referred to as unexpected vection. Participants’ expectations were experimentally manipulated about vection while they were passively exposed to an HMD-VR game for ten minutes (Aircar). Ratings of sickness severity and vection (strength and type) were obtained every two minutes during the exposure session. Firstly, it was hypothesised that there will be effects of group on vection type. It was also hypothesised that an increased likelihood of unexpected vection will lead to more severe cybersickness. Lastly, it was hypothesised that there should be similar vection strength between groups.

?Method

Participants

30 male and 30 female subjects participated in this experiment, with a mean age of 22.5 years and a standard deviation of five years. They were recruited from UOW and the general population. All participants had no self-reported visual, vestibular, or neurological impairments and had normal or corrected-to-normal vision. At the start of the experiment, all reported feeling well. Note: data was not analysed for two subjects, as they discontinued the experiment early (both experienced ‘frank sickness’).

Apparatus

Video footage was stereoscopically recorded from the commercial HMD-VR video game Aircar and was viewed through an Oculus Rift S HMD. This HMD had a resolution of 1080 x 1200 pixels for each eye and a refresh rate of 90 Hz using organic light-emitting diode (OLED) technology. The video was played on a high-performance Microsoft Windows 10 Dell Precision 5820 computer, which contained a NVidia GeForce GTX1080 graphics card with the latest stable driver software installed and an Intel 7th generation CPU.

Design

GROUP TYPE (3 groups: “Always Consistent” Prime, “Sometimes Inconsistent”, Prime, and “Unrelated” Prime”) and TIME IN TRIAL (5 levels: 2, 4, 6, 8 and 10 minutes) are the independent variables examined. At each TIME IN TRIAL three dependent variables were measured: (a) vection strength [rated from 0 = “no vection/stationary” to 10 = “strong vection”); (b) vection type (rated as either “expected” or “unexpected”); and c) the cybersickness severity [rated using the FMS scale; with “0” = “well” and “20” = “frank sickness”; Keshavarz & Hecht, 2011].

Procedure

Participants were assigned to three (sex balanced) groups of 20.?The audio cues provided to the first group always correctly primed them to the direction of each imminent turn (for example, ‘we will be turning left now’).?The second group viewed the same video footage and had audio primes about future turn directions; however, these primes indicated the wrong turn direction 60% of the time. The third group also viewed the same video footage, they were always given unrelated auditory primes prior to each turn (for example, “you have now travelled X km”).?FMS and vection measures were obtained every two minutes during each trial following turn.

Results

A one-way between-subjects analysis of variance (ANOVA) investigated group type and the percent of unexpected vection. Assumption of normality was supported for all conditions. Levene’s statistic was non-significant, F(2, 59) = 5.4, p = .007; thus homogeneity of variance was not violated. The ANOVA revealed that there was a statistically significant difference in the percent of unexpected vection between at least two groups, showing that there is an overall effect of group F(2, 59) = 130.18, p = < .001, ηp2 =?.82. Post-hoc analysis using Bonferroni test, using multiple comparisons, found that the mean value percent of unexpected vection showed the “sometimes inconsistent” group had significantly more unexpected vection ratings than the “unrelated” auditory prime (p = .000, 95% CI[6.33,25.67]), and the “consistent” auditory prime (p = .000, 95% CI[51.33,70.67]). Figure 1 shows the means and standard errors for percentage of unexpected vection for each group.


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Figure 1. Mean percentage of unexpected vection reports based on group. Error bars indicate confidence intervals using the Standard Error of the Mean (SEM).

?

A repeated measures ANOVA investigated group and time in trial effects on cybersickness severity. To adjust for violation of sphericity, the Greenhouse-Geisser correction was used. A significant main effect of time in trial was obtained F(2.88,5.75) = 185.69, p < .001, ηp2 ?= .765, which shows a large-medium effect of time in trial on cybersickness. A significant main effect of group on cybersickness severity was reported F(1,57) = 525.70, p = < .001, ηp2 ?= .902, which shows a large effect of group on cybersickness severity. An examination of group means indicated that cybersickness was more severe for the “sometimes inconsistent” group (M = 6.27) compared to the “unrelated” (M = 3.80), and “always consistent” groups (M = 3.30). Pairwise comparisons showed that there was a statistically significant difference in cybersickness severity between the “sometimes inconsistent’ the “unrelated” (p = .000, 95% CI[1.30,3.64]), and the “always consistent” groups (p = .000, 95% CI[1.81,4.15]). There is a non-significant difference between the “unrelated” and “always consistent” groups (p = .865 , 95% CI[-.644,1.68]). Pairwise comparisons show a significant difference between time 1 and 2 (p = .000), time 2 and 3 (p = .000), time 3 and 4 (p = .000), and time 4 and 5 (p = .000) ?in trial. Figure 2 shows these differences between the cybersickness severity for each group and their time in trial in minutes.

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Figure 2. Mean cybersickness severity as a function of time in trial by group. Error bars indicate confidence intervals using the SEM.

A repeated measures ANOVA investigated vection strength in each of the five times in trial for all groups. To adjust for violation of sphericity, the Greenhouse-Geisser correction was used. The ANOVA results showed a significant main effect of time in trial F(3.33,6.65) = 120.51, p = < .001, ηp2 ?= .679. The main effect of group on vection strength is non-significant F(2,57) = .682, p = > .005, ηp2?= .023, which can be characterised as a small effect size of group on vection strength. Pairwise comparisons show a significant difference between time 1 and 2 (p = .000), time 2 and 3 (p = .000), time 3 and 4 (p = .000), and time 4 and 5 (p = .000) in trial. Pairwise comparisons are not reported for group effects, because the results are non-significant. Figure 3 shows the means and standard errors.

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Figure 3. Mean vection strength as a function of time in trial by group. Error bars indicate confidence intervals using the SEM.

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Discussion

The results support the unexpected vection hypotheses that cybersickness should be more common and more severe during unexpected vection. As expected, the results for effects of group on vection type confirmed that the “sometimes inconsistent” auditory prime had more unexpected vection experiences than the “always consistent” and “unrelated” auditory primes. However, it is worth noting that whilst the “always consistent” and “unrelated” groups experienced significantly less unexpected vection, there were occasional unexpected vection experiences. An explanation may be due to some idiosyncrasy in how the individual is experiencing self-motion. Further, cybersickness severity for the “sometimes inconsistent” group was more significantly severe than the other groups. This indicates that when an individual is not in control of their self-motion, they are not only more likely to become sick, just like when an individual is a passenger in a vehicle (Sharples et al., 2008; Dong et al., 2011), but also more likely to experience a greater severity of cybersickness.

?Importantly, all participants experienced illusions of self-motion: expected or unexpected. However, the “sometimes inconsistent” group experienced a much higher percentage of unexpected vection and higher cybersickness severity. Thus, it seems that just because a user experiences an illusion of self-motion, it does not always mean that they will become sick, instead it depends on an unexpected illusion of self-motion. The current findings can also be linked back to the driver-passenger effect (Sharples et al., 2008; Dong et al., 2011), which states that the passenger’s absence of control of the vehicle may contribute to their higher susceptibility to motion-sickness (Rolnick & Lubow 1991). For all groups, as time in VR increases, so does their cybersickness severity, which is consistent with findings from past studies (Liu & Uang, 2012; Risi & Palmisano, 2019).

As expected, there was no difference in the strength of the vection that was experienced between groups, just the nature of the vection. Overall, there is a group difference in cybersickness severity, and unexpected vection trials, but there is not a group difference in vection strength. This suggests that the strength of vection does not matter, rather the nature of the vection, contrary to Hettinger et al.’ (1990) findings. All participants reported no accounts of vestibular impairments; however, because these were self-reported measures, future research might consider measuring postural instability and vection type. An implication of the obtained results may be that the moderating effect of cybersickness severity may also be explained by an individual’s moderating effect of illusion of self-motion and individual differences in VIMS susceptibility.

Possible limitations of this study include the use of FMS and vection measures which are self-reporting. If a participant’s tendency to report the type of vection or cybersickness severity may not accurately reflect their true experience, it is therefore important to consider in future studies the possibility that measures of vection and cybersickness may be modulated by response bias. The insight gained from the current study should encourage future studies to further examine the relationship between cybersickness and unexpected vection, and the broader implications of this, whilst devising strategies to mitigate cybersickness.

In conclusion, the current study was aimed at examining the unexpected vection hypothesis for cybersickness. There were significant effects of group on vection type and cybersickness severity. However, there were no significant effects of group on vection strength. Thus, the results of the current study do not show strong evidence for the VCH of VIMS; they instead show strong evidence for and suggest an explanation of cybersickness based on the unexpected vection hypothesis. However, this is the first study to discriminate between the type of vection that induces cybersickness in HMD-VR; therefore, there may be some practical limitations.


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