Prediction and Prevention of Simulator Sickness: an Examination of Individual Differences, Participant Behaviours, and Controlled Interventions

Prediction and Prevention of Simulator Sickness: An Examination of Individual Differences, Participant Behaviours, and Controlled Interventions

James G. Reed Jones

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Doctor of Philosophy in Psychology

Guelph, Ontario, Canada

ABSTRACT

PREDICTION AND PREVENTION OF SIMULATOR SICKNESS: AN EXAMINATION OF INDIVIDUAL DIFFERENCES, PARTICIPANT BEHAVIOURS, AND CONTROLLED INTERVENTIONS

James G. Reed Jones Advisor: University of Guelph, 2011 Professor Lana Trick

Fixed-base driving simulators are commonplace in research and training.

Simulators provide safe and controlled environments to train users on vehicle and device operation, to evaluate the safety of devices and controls, and to conduct research on driving and driving behaviours. One drawback to simulators is simulator sickness. As with motion sickness, simulator sickness can cause nausea, but additionally it has symptoms such as headache and eyestrain. Simulator sickness is a problem for multiple reasons: it can skew experimental results, it can waste participants’ and experimenter’s time, and it can limit testable populations.

In addition, participants may modify their behaviour to avoid sickness, affecting experimental results or impeding learning. While sickness can reduce over multiple exposures, it is not known if any observable behaviours accompany these reductions. It is also not known why there are such marked individual differences in susceptibility. To test for behaviours that could be responsible for reducing sickness,

I examined participants across two sessions in a fixed-base driving simulator. I found that gaze behaviour (eye and head movements) changed along with sickness. To determine the cause for this finding I instructed participants (pre-drive) to fixate their gaze during the curves of a simulated drive. This gaze modification was effective in reducing sickness during a first-time experience in the simulator, supporting a causal link. Next, I attempted to replace the missing vestibular input in a fixed-base simulator, so that the visual and vestibular perceptions of motion matched. This experiment showed that by providing vestibular stimulation appropriate or opposite of what would occur in the real world reduced sickness.

This provided support for the theory that distracting stimulation (electrical in this case) could reduce attention to visual motion cues and therefore reduce conflict, a novel finding for simulator sickness research. Finally, I tested for any correlations between individual differences and sickness. I found that history of motion sickness and current illness both correlated with sickness, potentially useful as a pre- screening tool. In addition, driving behaviours such as speed, braking, and acceleration all correlated with sickness, showing that how a person behaves in a simulation could also contribute to sickness.

iii

Dedication

I would like to dedicate this work to my grandfather. Jimmy was a man who I greatly admired and I wish he could have seen me finish. I miss you every day Grandpa; I hope

this makes you proud.

Acknowledgments

First of all I would like to acknowledge Lana Trick. Lana, without your tireless

efforts none of this would have been possible. To my committee, your advice and input

has been invaluable. I would like to thank Robin Fraser, without whom I could never

have navigated the ins and outs of graduate school. I would like to acknowledge the

support of my family, both emotional and physical. Without all of your help, I wouldn’t

have even been able to come back to collect my data! Last, but by no means least, I would like to acknowledge my wife Rebecca. Rebecca, your love and support has given me the strength to accomplish this.

Table of Contents

Chapter 1: Introduction

Introduction……………………………………………………………….1

Rationale…………………………………………………………………..9

Chapter 2: Experiment 1

Introduction………………………………………………………………15

Method…………………………………………………………………...20

Data Manipulation and Results………………………………………...... 26

Discussion………………………………………………………………..46

Chapter 3: Experiment 2

Introduction………………………………………………………………52

Method…………………………………………………………………...56

Data Manipulation and Results…………………………………………..59

Discussion………………………………………………………………..70

Chapter 4: Experiment 3

Introduction………………………………………………………………76

Method…………………………………………………………………...80

Data Manipulation and Results………………………………………...... 84

Discussion………………………………………………………………..94

Chapter 5: Experiment 4

Introduction…………………………………………………………...... 101

Method………………………………………………………………….106

Results…………………………………………………………………..107

Discussion………………………………………………………………111

Chapter 6: General Discussion

General Discussion……………………………………………………..115

Limitations……………………………………………………………...123

Future Directions……………………………………………………….125

Conclusions……………………………………………………………..129

References …………………………………………………………………...... 131

Appendices

Appendix A Medical screening questionnaire………………………….138

Appendix B Simulator sickness pre-screening questionnaire…………..139

Appendix C Simulator sickness questionnaire…………………………140

Appendix D Participant briefing………………………………………..141

Appendix E Instructions used for visual training………………………142

Appendix F Data capture questionnaire………………………………...143

Appendix G Immersive tendency questionnaire………………………..146

Appendix H Presence questionnaire…………………………………....148

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List of Tables

Chapter 2: Experiment 1

Table 1. Participant Characteristics……………………………………..21

Table 2. Summary of Experimental Results……………………………...46

Chapter 3: Experiment 2

Table 3. Participant Characteristics……………………………………..56

Table 4. Summary of Experimental Results……………………………...70

Chapter 4: Experiment 3

Table 5. Participant Characteristics……………………………………..81

Table 6. Summary of Experimental Results...... 94

Chapter 5: Experiment 4

Table 7. Descriptive Statistics for Participant Characteristics and

Measures of Immersion, Presence, and Sickness……………………….107

Table 8. Correlations for Participant Characteristics and Measures of

Immersion, Presence, and Sickness…………………………….………108

Table 9. Descriptive Statistics for Balance, Participant Behaviours During

Simulated Driving, and Measures of Sickness………………………….110

Table 10. Significant Correlations for Participant Behaviours During

Simulated Driving, and Measures of Sickness……………...…………..111

Table 11. Summary of Hypotheses Made and Correlations……………112

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List of Figures

Chapter 2: Experiment 1

Figure 1. Drive Safety DS-600c fixed-base driving simulator…………..23

Figure 2. Drive Safety DS-600c fixed-base driving simulator interior with

view of virtual environment………………………...…………………....24

Figure 3. Mean Simulator Sickness Questionnaire Percentage Scores for

Total and all Subscales across day (+/- SEM)………………...………….29

Figure 4. Average time to hold the Tandem Romberg test (seconds) pre

and post-drive, by day (+/- SEM)………………………..…………….....30

Figure 5. Approximate representation of Tangent gaze zone……………31

Figure 6. Percentage of time spent looking at zone Tangent during gradual

turns, across day (+/- SEM). Expected real world gaze percentage included

for comparison…………………………………………..……………….35

Figure 7. Visualization of one participant’s mean head position over time,

during one gradual turn...... 37

Figure 8. Mean variability of head movement on the X and Y-axis for the

gradual turns across day, measured in pixels (+/- SEM)………...……….39

Figure 9. Average speeds during each area of the sharp turns, across day

(+/- SEM)……………………………………………….………………..42

Figure 10. Average percent of accelerator press during the overall drive

and while exiting the gradual and sharp turns, across day (+/- SEM)...…43

Figure 11. Average percent of brake press during the overall drive and

while entering the gradual and sharp turns, across day (+/- SEM)……....44

Figure 12. Average steering variability (degrees of wheel turn) during the

overall drive and while negotiating the gradual and sharp turns, across day

(+/- SEM)...... 45

Chapter 3: Experiment 2

Figure 13. Diagram of focal point shown to Gaze Instruction – Horizon

participants……………………………………………………………….58

Figure 14. Diagram of focal point shown to Gaze Instruction – Road

participants……………………………………………………………….58

Figure 15. Position of a billboard reminder at the onset of a curve.

Billboard as shown is blank, in the simulation the billboard depicted a text

reminder………………………………………………………………….59

Figure 16. Mean Simulator Sickness Questionnaire total and subscale

percentage scores, across all three experimental conditions (+/- SEM).....63

Figure 17. Mean Simulator Sickness Questionnaire total and subscale

percentage scores for the Control and combined Gaze Instruction

conditions (+/- SEM)…...... 64

Figure 18. Average time to hold the Tandem Romberg test (seconds) pre

and post-drive, by condition (+/- SEM)………………………………….65

Figure 19. Mean variability of head movement on the X and Y-axis for the

curved portions of the drive across condition, measured in pixels

(+/- SEM)………………………………………………………………..67

Chapter 4: Experiment 3

Figure 20. Mean Simulator Sickness Questionnaire total and subscale

percentage scores, across all three experimental conditions (+/- SEM)….86

Figure 21. Average time to hold the Tandem Romberg test (seconds) pre

and post-drive, by condition (+/- SEM)………………………………….87

Figure 22. Mean variability of head movement on the X and Y-axis for the

curved portions of the drive across condition, measured in pixels

(+/- SEM)………………………………………………………………..90

Figure 23. Mean average speeds during the gradual turns, by area

(+/- SEM)………………………………………………………………..91

Figure 24. Mean average speeds during the sharp turns, by area

(+/- SEM)………………………………………………………………..92

Chapter 1

Introduction

Virtual environments are becoming increasingly commonplace tools in research and training. As vehicles and in-vehicle devices become more complex, there is a growing need for a safe and controlled environment to train users on vehicle and device operation and to evaluate the safety of new devices and vehicle controls. In fact, many of the situations most critical to personnel training and evaluation, such as the use of vehicles and in vehicle devices, would be considered too dangerous to test in the field.

Take for example the evaluation of a new navigation system to be included in a car. Of paramount importance to research and design are the critical moments when the distraction of this device could be fatal to the driver or other road users. However, it is precisely at these moments when testing the device in the real world would be extremely difficult, and unsafe. Simulators in their various forms offer an avenue to carry out training and testing in a safe and controlled manner. Pilots can train for hours to practice making emergency landings, and car companies can test the effectiveness of crash warning systems without ever putting people or equipment in harm’s way. While simulators offer researchers and trainers unprecedented control and safety, there are nonetheless certain critical drawbacks to their use.

Simulators can range from something as simple as a steering wheel and pedals attached to a computer monitor, to something as complex as a moving capsule designed to mimic the visual and motion aspects of an environment. The key to providing a realistic simulation however is to have users feel as if they are moving within the simulated environment (as opposed to just watching it move). To achieve this immersion,

1 all simulations, at least in part, induce a feeling of self-motion through visual cues; this feeling of self-motion is called vection. In the real world, an example of the vection illusion is when you are in a car stopped at a stoplight and the cars around you begin to move forward; in this situation, you may feel as if you are moving backwards. Some research suggests that the cues to vection require stimulation of peripheral visual field

(beyond 30° from straight ahead) while other research suggests that with the proper stimuli vection can occur across the entire field of vision (Brandt, Dichgans, & Koenig,

1973; Andersen, & Braunstein, 1985; respectively). While some simulators use moving platforms (and sometimes rooms) to augment vection with real movement, many simulators rely on vection as the sole source of the feeling of motion. For example, research performed for this dissertation used a fixed-platform driving simulator to immerse the participants. Because the simulator was stationary, it relied solely on vection to simulate motion.

One of the greatest drawbacks to simulator use in research, training, or even recreation is simulator sickness. Simulator sickness is similar in manifestation to motion sickness (from travel in planes, trains, boats, etc). Those suffering from simulator sickness exhibit one or more of a multitude of symptoms: eye strain, headache, sweating, dryness of mouth, fullness of stomach, disorientation, vertigo, lack of coordination, nausea, or vomiting (Stanney, Kingdon, Graeber and Kennedy, 2002). While it has been thought that up to 95% of simulator users show some level of simulator sickness (Stanney et al., 1998), the number that cannot tolerate long term exposure to a simulation is closer to 5% and the number who have an emetic response is approximately 1% (Stanney &

Kennedy, 2009). In addition to the immediate effects of simulator sickness, research has

2 shown that these symptoms can sometimes linger for several days after the virtual environment exposure (Ungs, 1989; Stanney & Kennedy, 1998).

When a participant in a research study or a trainee experiences simulator sickness, it can have severe ramifications to the person involved, the simulation session, and the continued use of the simulator for that individual. In research, simulator sickness can skew experimental results by becoming itself a distractor to the task under examination

(Kolasinski, 1995; Lawson, Graeber, Mead, & Muth, 2002; Stanney, Kingdon, &

Kennedy, 2002). In addition, it can waste time for both participants and experimenters and also squanders experimental resources. Simulator sickness can also limit the populations that can be tested in a simulator. This may remove potential populations of interest or systematically neglect populations who are at higher risk of simulator sickness

(Stanney et al., 2002), this may be extremely deleterious to research; many special populations are both at higher risk of simulator sickness and are a population of interest for driving research. An example of this is elderly adults.

Simulator sickness also affects the use of simulations in training. In many simulation applications for training, such as in military flight training, there is a real need to keep up a skill set. When trainees in this situation become sick, they lose valuable training time and the trainer loses a tool to keep its employees at peak performance levels

(Biocca, 1992). There is also the potential that trainees will learn improper behaviours to avoid becoming sick. For example, a trainee may avoid high-speed turns in a driving simulator because they make them feel ill (Kennedy, Hettinger, & Lilienthal, 1990).

At present, the exact percentage of simulator users that are affected by simulator sickness is still only a rough estimate. This is mainly due to the variety of factors that can

3 affect the incidence of simulator sickness. These factors can be broken down into four main categories: room conditions; display and technology issues; experimental/simulation design; and individual factors. First, simulator sickness can vary depending on the characteristics of the room where the simulation takes place. For example, if the temperature of the room increases simulator sickness will also increase

(Bertin, Collet, Espié, & Graf, 2005). In addition, unpleasant odours in the room can increase the rate of simulator sickness (Bertin, et al., 2005).

The next factor, display and technology issues, refers to aspects of the technology that can affect simulator sickness. In brief, sensory distortions and system consistency across all of the parts of the simulation, lag in the visual, auditory, or motion systems, flicker in the visual displays (too slow a refresh rate), and the size (too large), resolution

(too low) and brightness of the displays (too high) used can all increase simulator sickness symptoms if not controlled appropriately (Harwood & Foley 1987; Kolasinski,

1995; Pausch, Crea, & Conway, 1992; So & Griffin, 1995; Uliano, Kennedy, & Lambert,

1986; Welch, 1978).

The third factor, experimental/simulation design issues, refers to any considerations related to the simulation that can be controlled by the researcher or trainer.

Duration of the simulation positively correlates with sickness levels (Kennedy, Stanney,

& Dunlap, 2000). Length of time between simulation sessions affects simulator sickness levels, with a 2-5 day gap being optimal to avoid simulator sickness (Kennedy, Lane,

Berbaum, & Lilienthal, 1993; Watson, 1998). In addition, a scene that is too complex (for example having a driving simulation comprised entirely of inner city driving) can increase sickness - especially emetic responses (Kennedy & Fowlkes, 1992; McCauley &

Sharkey, 1992). Scene complexity is itself a complex issue as simulator sickness can be

increased even more when a complex scene is combined with other factors. For example,

combining a complex scene with a large field of view, a high spatial frequency

background, or too much vection (perceived motion) can exponentially increase

simulator sickness (Kennedy & Fowlkes, 1992; Dichgans & Brandt, 1978; Kennedy,

Berbaum, Dunlap, & Hettinger, 1996).

Finally, individual factors refer to characteristics of participants or trainees

themselves that may make them more prone to simulator sickness. Age is a possible

factor in simulator sickness, with older adults being more prone to sickness than younger

adults are (Stanney & Kennedy, 2009). With regard to gender, research has shown that

females are up to three times more susceptible than males (Biocca, 1992; Stanney et al.,

2002). Individuals with a history of motion sickness are at increased risk, and those that

have had an emetic response to a carnival ride have double the risk (Stanney et al., 2002).

Factors such as drug and alcohol consumption, fatigue/sleeplessness, and current ailments

(such as a cold or flu) have all been associated with higher levels of simulator sickness

(Stanney & Kennedy, 2009). Finally, a person’s history of successful adaption to novel

sensory environments has been shown to reduce the risk of simulator sickness (for

example being able to get their “sea legs” quickly; Stanney et al., 2002).

Researchers have postulated three main theories to explain the mechanism

responsible for both motion sickness and simulator sickness. The first is postural

instability theory (Ricco & Stoffregen, 1991). Ricco and Stoffregen define postural

control as the coordinated stabilization of all body segments. This theory holds that when

an animal encounters a destabilizing environment, it must try to regain and maintain

5 postural control. If the animal does not possess or cannot learn a strategy to maintain postural control and the instability continues, sickness results. This theory does not explain why postural instability results in the symptoms of motion sickness. It only posits that animals must regain postural control or they will become sick. Looking specifically at simulations, and simulator sickness, users receive visual information reflecting movement, yet their bodies remain stable. Therefore, a perception of postural instability may occur that results in central nervous system confusion and sickness. In a simulation, users could potentially need to adopt a strategy that both controls the vehicle within the norms of the simulated world and controls their posture with regard to the real world. If these two worlds are different, and users adopt a control strategy appropriate for the simulation that does not match their real environment, then this too could result in instability and sickness.

The second theory is sensory conflict theory (Reason, 1970, 1978; Reason &

Brand, 1975). Sensory conflict theory holds that sickness arises when the visual, vestibular, or proprioceptive system receives input that does not match with the “normal” expected situational norms that have been encoded in the brain. This mismatch is not between the individual senses (for example visual input conflicting with vestibular input).

It is rather a conflict between past experience/memory of the sensory input of a given situation, and what a person actually perceives in a situation. For example, individuals have a past experience/memory of what sensory input comes from driving a real car.

Therefore, when people are exposed to a driving simulation they compare the simulated experience to the encoded representation of what the sensory input of driving should be like. If the expectation does not match the input, the result is sickness. The scenario need

6 not be this specific however; in fact, this theory does not distinguish between specific situations such as driving and general situations such as “moving”. For example, if the normal sensory arrangement while moving is to receive visual, proprioceptive, and vestibular input, then a situation where a person is moving but the senses do not match this arrangement sickness would also occur. Under this theory, conflict can be as simple as one input being different (for example no vestibular input from a fixed base driving simulator) or it could be due to all of the inputs being different from those expected (as in the case with space sickness). However, it is important to note that the brain is always encoding new sensory information. Therefore, a consequence of this theory, which the authors point out, is that the longer people are exposed to a conflicting situation the less they should be sick. This is because input from the novel situation eventually becomes encoded as the new norm for that situation. While this might be the case for motion sickness where there is acclimatization (for example individuals get their “sea legs”), this does not fully account for simulator sickness. This is because simulator sickness increases with the duration of a single simulation session (Kennedy et al., 2000).

Nonetheless, this theory is consistent with the finding that sickness decreases over days of exposure (Kennedy, Berbaum, Williams, Brannan, & Welch, 1987; Sharma & Aparna,

1997). As a point to note, research has not readily explored why simulator sickness continues to increase during a session. A simple explanation however, could be that simulations are usually ended if a user becomes sick. For example, a person on a cruise has no choice but to “stick it out” until the cruise ends or they acclimatize. With a simulation however, sessions are usually ended as soon as a participant becomes ill. This early termination effectively eliminates any chance of determining the point at which

acclimatization occurs, if at all, within a session. As with postural instability theory,

sensory conflict theory does not explain why the symptoms of simulator sickness or

motion sickness manifest in the way they do, it only postulates the mechanism

responsible.

The final theory of motion and simulator sickness is the evolutionary hypothesis

of motion sickness (Treisman, 1977). Treisman bases this theory on the fact that humans

have evolved to use input from their visual, vestibular, and proprioceptive systems (or a

subset thereof) to move their eyes/head to a target or their body about an environment.

However, because this system uses none of the three inputs in isolation, when one of the

inputs is at odds with another sickness results. Unlike the sensory conflict theory, this

conflict is not between present and previous experience. It is a conflict between two or

more senses in a situation that requires close monitoring of input for motor control

purposes. For example, in a fixed based driving simulator the driver must move their eyes

about the scene to navigate the road accurately. However, the visual system is sensing

motion and the vestibular and proprioceptive systems are not. This conflict results in

sickness. Unlike the previous two theories of simulator sickness, Treisman hypothesizes

that the most important symptom of simulator sickness (the emetic response) occurs

because of an evolved response to the ingestion of various toxins. Vomiting purges toxins

from an animal’s system. Many toxins affect the visual, vestibular, and proprioceptive

systems and this can cause conflict, and consequently vomiting in the face of inconsistent

sensory information is adaptive. The by-product of this poison expulsion mechanism is that when faced with sensory conflict, the body reacts as if it was poisoned. An important point to note is that if simulator sickness occurs because of this type of mechanism, then

there should be no acclimatization to a simulation. Such a poison expulsion mechanism

would be a hard-wired evolutionary response; sickness should always be present as long

as there is mismatch. The data on simulator sickness is consistent with the Evolutionary

Hypothesis insofar as simulator sickness does not decrease within a single session - it increases (Kennedy, Stanney, & Dunlap, 2000). However, this account does not explain why sickness would reduce over multiple exposures across days (Kennedy et al., 1987;

Sharma & Aparna, 1997).

Rationale

Any of the three proposed theories of motion sickness and simulator sickness could explain simulator sickness. However, there are some problems with the work done on these theories thus far. To begin, all of the theories started as theories of motion sickness and were adapted to cover simulator sickness. Because the symptoms are similar

(aside from symptoms such as headache that could be due to the oculomotor discomfort of staring at computer screens), it is easy to see how parallels could be drawn. However, there has been little in the way of explicit testing of these theories with specific regard to virtual environments. In fact, one main problem with using a motion sickness explanation for simulator sickness is that in many simulators there is no motion. This point, on first glance, seems to be acceptable as the simulators simulate motion. However, when fitting a theory of motion sickness to an environment without motion there is a definite need for further explanation. In fact, one major clue to the potential difference between motion sickness and simulator sickness, which is overlooked in much of the simulator sickness research, is that people who have vestibular loss do not show classic motion sickness. To my knowledge there has been little in the way of explicit testing to see if this holds for

simulator sickness as well. In fact, in a recent article Bertin, Collet, Espié, and Graf

(2005) found that of the six vestibular loss patients they studied, three showed some form

of simulator sickness (all had intact visual and proprioceptive systems). If this holds true,

then fitting classic theories of motion sickness to simulator sickness becomes

problematic.

Another difficulty with the three theories proposed, is that for any given

simulation they should be able to predict whether sickness should result in the people

exposed to it. However, in practise only some people actually get sick in simulators while

others seem immune. In the literature thus far, this difficulty is only minimally addressed.

This is a problem because there are marked individual differences in how people react to

simulations, and it is important to predict who will get sick before they step into a

simulator. Although there has been much work examining the risk factors that can

increase simulator sickness, no one to date has identified any reliable predictors of

simulator sickness susceptibility. The fact that there is still an average of 5% sickness in

any given study or training session indicates that there needs to be more work done on

developing a reliable tool for predicating simulator sickness.

Furthermore, the work on simulator sickness thus far has neglected the day effect observed with virtual environment exposure. The day effect appears to be acclimatization to a virtual environment. This acclimatization, specifically repeated exposures to a virtual environment across different days, results in the subsequent reduction in simulator sickness symptoms observed (Kennedy et al., 1987; Sharma & Aparna, 1997). Unlike classic motion sickness research, which does not widely report such an effect over multiple exposures, the day effect phenomenon is widely reported in the simulator

research literature. However, while it is reported, little is actually known about what

changes with repeated exposures other than the incidence of simulator sickness. If there

are measurable changes in participant behaviour over sessions, this knowledge may assist in finding a way to prevent simulator sickness from occurring at all.

In order to address some of the shortcomings of previous work on simulator sickness, and to attempt to gain a clearer understanding of the mechanism responsible for simulator sickness, I undertook four experiments, each building on the next. In

Experiment 1, I investigated whether there are measurable participant characteristics/behaviours that change between the first exposure to a simulation and the second (two days later). Specifically, sickness, balance, eye movements, head movements, and driving behaviours were measured and compared across two exposures to a virtual environment driving simulation. The intent behind this experiment was to twofold. The first goal was to determine if one repeated exposure to a virtual driving simulation could induce a measurable day effect. The second goal of this experiment was to examine participant behaviours to identify changes that could be responsible for the day effect (eye movements, head movements, and driving behaviours). Any changes in these measures could explain the decline in sickness reported with repeated exposures and in turn help identify the causes of simulator sickness. In addition, any findings related to changes in behaviour may help identify possible interventions to prevent sickness from occurring.

Experiment 2 focused on taking the findings from Experiment 1 and using them to design a pre-exposure treatment to prevent simulator sickness. Specifically this training program was designed to have participants “behave” as if they had already been

11 exposed to a simulation. The training program was administered to participants before they began a drive through a novel virtual environment. There were three main goals of this experiment. First, it was designed as a follow up to experiment 1 – evaluating the relative influence of eye and head movements on sickness. Second, it was used to test whether participants could be trained in eye/head movement behaviours. This was important as an intervention that attempts to modify these behaviours relies on participants being able to learn them without exposure to the virtual environment (and running the risk of getting sick). The third goal was to determine if a training program that modifies eye/head behaviours would result in an artificial day effect – reducing sickness on an initial virtual environment exposure.

In Experiment 3, we examined the application of Galvanic Vestibular Stimulation during a simulated drive, an intervention technique to reduce sickness in fixed-base simulators. Results from our previous work suggest that Galvanic Vestibular Stimulation reduces sickness by adding the missing vestibular input in a fixed-base driving simulator

(Reed-Jones, Reed-Jones, Trick, & Vallis, 2007; Reed-Jones, Vallis, Reed-Jones, &

Trick, 2008; Reed-Jones, Reed-Jones, Trick, Toxopeus, & Vallis, 2009). The first goal was to test if a Galvanic Vestibular Stimulation intervention would reduce sickness during a novel virtual environment exposure. In addition, this experiment tested whether the observable participant behaviours associated with Galvanic Vestibular Stimulation were of a similar nature to ones associated with the day effect. Following from the three theories of motion sickness, the addition of vestibular input to a simulation could reduce sickness for three reasons. First, it could help provide the perception of a stable environment, matching the visual cues to motion (postural instability theory). Second, it

could reduce conflict between previous encoding of “driving” and the simulation of

driving (sensory conflict theory). Finally, it could help to reduce the conflict between the

visual and vestibular inputs (evolutionary theory). However, there is a difficulty in using

this intervention. Even though Galvanic Vestibular Stimulation has been theorized to be

effective by alleviating the sickness conditions proposed by one or more of the three

models of sickness, an appropriate control condition has never been used to test this.

Therefore, the hypothesised mechanisms behind each intervention may not actually be

responsible for the outcomes observed in previous studies. For example, the reductions in

sickness observed in previous work could have just been placebo effects, or alternatively,

it is possible that the stimulation worked because it distracted the participants from the

simulator sickness symptoms. Therefore, an additional goal of Experiment 3 was to

validate Galvanic Vestibular Stimulation with an appropriate control, Opposing

Vestibular Stimulation. Opposing Vestibular Stimulation induced a vestibular experience

opposite to what would be felt in the real world. This stimulation matched Galvanic

Vestibular Stimulation as a potential placebo or distraction, but it did not provide vestibular input that matched visual cues to motion.

The first three studies concentrated on both the identification of participant behaviours related to simulator sickness and the evaluation of interventions designed to reduce simulator sickness. What these studies did not address is one of the most intriguing aspects of simulator sickness, specifically that there are notable individual differences in propensity to illness. To address this issue Experiment 4 investigated two aspects of participant’s individual differences. First, I examined pre-exposure measures that could be used to predict sickness. This involved a detailed examination of all

13 participant data collected across experiments 1 through 3. Specifically, we focused on identifying any factors that could be related to predicting sickness prior to participation in an experimental session. These factors included measures of immersive tendencies, presence, risk factors, sickness, video game use, and driving history. The goal of this part of the experiment was to identify any correlations between these factors that researchers or trainers could use to identify high-risk individuals prior to entering a virtual environment.

A second set of participant characteristics were then examined, these included measures of how participants actually drove in the simulator; these behaviours were examined for any relationships to participants’ response to the simulation (sickness) and/or the interventions used. Specifically head movements, balance, and driving style were examined. The goal of this part of experiment 4 was to determine if certain participant behaviours during a simulated drive could be identified that could have bearing on how effective an intervention may be, or alternatively how a person may respond to multiple simulator exposures.

Chapter 2

Experiment 1

This experiment was an examination of the day effect: the reduction in simulator sickness symptoms observed with repeated exposure to a virtual environment across different days (Kennedy et al., 1987; Sharma & Aparna, 1997). In this experiment, the main goal was to attempt to isolate any specific changes in participant behaviour between an initial exposure and a re-exposure two days later. The hope was to determine the

reason why multiple exposures to a virtual environment result in reduced simulator

sickness.

In this experiment, the analyses consisted of within subject comparisons on the

effect of the variable day (first or second) on a number of dependent measures. The

Simulator Sickness Questionnaire measured level of sickness (Kennedy, Lane, Berbaum,

& Lilienthal, 1993). This test is the current standard for evaluating simulator sickness in

simulator research, and the comparison between days was done to determine whether

there was in fact a day effect, as previous research has suggested. The hypothesis was

that simulator sickness scores would be lower on day two compared to day one. This

result is based on previous research that has identified the effect of repeated exposures

across different days lowering level of sickness (Kennedy et al., 1987; Sharma & Aparna,

1997).

Sickness is a direct measure of acclimatization to the sensory conflict experienced

in a novel environment (virtual or otherwise). Postural instability and sensory conflict

theory both postulate that as people overcome or adapt to a novel arrangement of sensory

inputs sickness abates. In addition, while the evolutionary hypothesis of sickness posits

that conflict generates sickness (without addressing acclimatization directly), it could be

argued that if one acclimatizes and starts to consider a conflicting sensory situation as

“normal”, sickness would reduce in this case as well. However, sickness is not the only

indicator of acclimatization. Postural instability (balance) after exposure to a virtual

environment has also been shown to be an indicator of acclimatization (Reed-Jones,

Vallis, Reed-Jones, & Trick, 2008). Upon emergence from a simulation, people who

adapt best to the simulation need to re-acclimatize to the “real world” and during this

period of acclimatization, their balance suffers. For people who do not acclimatize to

simulations we see the opposite response. Because these individuals do not adapt their

balance to the simulation, their post-drive balance is the same as their pre-drive balance.

In addition, our research has shown that participants who show high levels of sickness are

more stable post-drive, then participants with lower levels of sickness (Reed-Jones,

Vallis, Reed-Jones, & Trick, 2008). Because postural instability is an indicator of acclimatization, it was examined for any changes across days. The hypothesis was that post-drive postural instability on a second day exposure would be higher than post-drive postural instability on day one. If acclimatization to sensory conflict drives the day effect, then post-drive postural instability would be observed.

To determine whether there were behavioural changes across session that might explain any reductions in simulator sickness observed, participant behaviours were measured. The first two behaviours examined were gaze direction and head movements.

If a sensory mismatch (as outlined in any of the three theories of motion sickness) is responsible for sickness, then in a fixed-base driving simulator the conflict would be between visual input (moving), vestibular input (resting), and proprioceptive input

(resting). Therefore, modification of head and eye movements could be at least partially responsible for acclimatization. Because of this, both gaze position and head movements were recorded and analyzed during the gradual curve portions of the drive. It made sense to examine these regions because it seemed probable that there would be more sensory conflict at these challenging points in the drive. In real-world driving, turning produces

medial/lateral accelerations that elicit proprioceptive and vestibular responses, which the

brain combines with the visual information perceived to form a representation of the

body in space. Specifically, when negotiating a turn, sensory systems provide drivers

with three sources of information. First, the visual system tells drivers that they are going

through a medial/lateral shift in the view of the world. Second, the vestibular system

informs the drivers that they are tilting based on the lateral forces that they are

experiencing as they go around the curve. Third, the proprioceptive system provides

information to drivers about the forces against to their bodies based on the pulls from the

seatbelt, steering wheel, etc. In addition, if drivers change speed in response to the curve,

anterior/posterior accelerations activate both the proprioceptive and vestibular systems.

Thus, drivers’ brains combine vestibular and proprioceptive inputs with the

corresponding visual information (in this case a change in the speed of the optic flow). In

a fixed-base driving simulator the visual input reflects what is observed in the real world.

However, the lack of acceleration means that proprioceptive and vestibular responses do

not match the visual input. Therefore, a conflict arises when combining information from

these sensory systems to form a coherent representation of the position of the body in

space. This does not happen when drivers travel down a straight road. In this case, vision

is predominant because the body is stationary and the proprioceptive and vestibular

systems are not being stimulated (although the proprioceptive system is still receiving

feedback from the steering wheel, seat, etc.). As a result, more sensory conflict and

changes across days should be more apparent in the curved sections of the road than the

straight. As well, it is useful to collect gaze information in simulated turns because there are norms about how the eyes move during actual real world turns (Land 2006).

I hypothesised that given a novel sensory environment, participants will attempt to modify their gaze behaviour to compensate for sensory conflict and thus facilitate acclimatization. Specifically, gaze and head position would be more erratic during a novel exposure as drivers try to compensate for the conflict. However, during a second day exposure gaze and head movements would show reduced variability compared to the first exposure due to adaptations becoming more pronounced. This hypothesis is based on the reasoning that when faced with the sensory conflict generated while driving a simulated curve participants will attempt to reduce the conflict by adapting their gaze and

head behaviours. Normally when people move through their environment their own

movements generate egocentric movement cues (visual, vestibular, and proprioceptive).

In a simulation however, the computer generates the visual experience, not actual

movement through the environment. As a result, the curved portions of the simulation

only include visual motion cues that the simulator creates. When faced with this

incongruity, participants must adapt from visual cues originating from their egocentric

movement (which match their other sensory inputs) to cues generated allocentricly by the

simulator (which do not match). One way to deal with conflict may be to “lock” the head

as a stationary point of reference. This would provide a stable platform for drivers to

interpret the motion without adding even more conflicting egocentric motion cues.

Holding the head and eyes relatively stationary would also reduce reliance on internal vestibular cues by reducing the need for the vestibulo-ocular reflex to be employed.

In addition to modifications to how they move their own body, previous research that has shown that trainees will modify their driving behaviour while in simulators to avoid high conflict situations, such as making a sharp high-speed turn (Kennedy,

Hettinger, and Lilienthal, 1990). Because of these findings, driving behaviours were monitored for any changes across successive drives that might affect sickness. Each of the following driving behaviours could have a specific link to reductions (or increases) in sensory conflict. The speed which a driver negotiates corners could affect the amount of steering control needed, in turn increasing the medial/lateral translational and roll rotational conflict. In addition, the speed a participant drives into or out of a curve can affect the necessary amount of brake and accelerator pedal pressure. By increasing the need for harder braking or acceleration, an increased anterior/posterior translational and pitch rotational conflict may arise. Based on this, I predicted that due to modification of driving behaviour to reduce conflict there will be reductions in average speed, steering variability, and the degree to which the accelerator and brake are depressed over successive driving days.

Summary of Hypotheses

1) Sickness will be lower on day two compared to day one.

2) Post-drive postural instability on day two will be higher than post-drive

postural instability on day one.

3) Both gaze and head movements will show reductions on day two compared to

day one.

4) All driving variables will show reductions on day two compared to day one

(average speed, percent of accelerator and brake press, and steering

variability).

Method

Participants

For this experiment, we recruited 32 participants from the undergraduate participant pool at the University of Guelph. Although there were 32 participants on the first day, only 23 returned for the second day of testing (17 females, 6 males; M age

=19.67; SD 2.42). Because the main analyses were based on successive driving days, we included only the participants who completed both sessions; Day One data were compared across participants who did and did not complete the experiment to ensure there were no systemic differences between the population groups that could have confounded the results. No significant differences in age, sickness pre-screening score,

Simulator Sickness Questionnaire total score, balance, or gaze behaviour was observed

(all p > 0.1). Of the participants who did not return, only one reported to us the reason they did not return for Day Two testing was that that they felt sick during the first session; the remainder offered no explanation. For a summary of the participant characteristics and the comparison of individuals who did and did not complete the experiment, see Table 1. All participants were given course credit for the portions of the experiment they attended.

Table 1 Participant Characteristics (Non-returning Participants Included for Comparison) Included Did Not Return Significance Age in Years (M) 19.66 19.38 p > 0.1 Age in Years (SD) 2.42 0.52 N/A Number of Females 6 1 N/A Number of Males 17 7 N/A Simulator Sickness Pre-Screening Score (M) 0.83 1.13 p > 0.1 Simulator Sickness Pre-Screening Score (SD) 1.15 1.13 N/A Simulator Sickness Total Score (% of max) 22.22 29.48 p > 0.1 Balance pre-exposure (sec) 26.29 28.66 p > 0.1 Balance post-exposure (sec) 24.12 24.47 p > 0.1 Gaze Toward Tangent During Curved Driving (%) 59.59 49.91 p > 0.1

Apparatus and Materials

Questionnaires. Three questionnaires were used in this experiment. To screen for

any medical conditions that may have been aggravated by simulator exposure (epilepsy,

claustrophobia, etc.) a customized questionnaire was used (see Appendix A). In order to

screen for persons who may have a higher risk of simulator sickness, the simulator

sickness pre-screening questionnaire was used (see Appendix B). This customized test

evaluated history of motion sickness and other factors that may have increased risk of

sickness in the participants (e.g. being “hung over”). To evaluate level of simulator

sickness the Simulator Sickness Questionnaire was used. Previous research has shown

that the Simulator Sickness Questionnaire is a highly valid and consistent measure of

sickness (Kennedy et al., 1993). In addition, the Simulator Sickness Questionnaire

provides a total sickness score and sub-scores for oculomotor discomfort, nausea, and

disorientation. The questionnaire consists of 16 self-report questions. Answers are given on a Likert type scale anchored at 0 and 3, with higher scores indicating increased sickness (see Appendix C).

Measurement tools for participant behaviours. In order to monitor gaze behaviour, an Applied Science Laboratories eye tracking system was used. This system

21 consists of a plastic headband that houses a camera and a monocle and a computer that records the approximate gaze location of the participant throughout the testing. To represent the point of regard visually, the computer overlaid the gaze position as a crosshairs on the recorded video of the participant’s field of view. To monitor for head movements, the drivers’ head position was videotaped. These videos were analyzed using the Peak MotisTM analysis suite. This program translated the video images into an X

(medial/lateral) and Y (superior/inferior) coordinate readout of head movements.

The Tandem Romberg test was used to evaluate balance (Graybiel & Fregly,

1966). Research on static posture tests for use in virtual environment research has shown that the Tandem Romberg is more sensitive than other comparable measures (Kennedy,

Fowlkes, & Lilienthal, 1993; Kennedy, Berbaum, & Lilienthal, 1997). There were other advantages to this test, insofar as it takes a short time to explain and administer and requires no complex equipment. During the test, participants closed their eyes and stood heel to toe (dominant foot behind). The participants held this position for 30 seconds or until they faltered, whichever came first. The time duration before they lost their balance was measured. This was calculated from the moment they began holding the position until either of their feet moved from the starting position. During the balance task, a computer videotaped the participants to permit more precise time measurement and to ensure reliability. Administering the test with the eyes closed removed vision, highlighting the vestibular and proprioceptive components of balance.

Figure 1. Drive Safety DS-600c fixed-base driving simulator.

Simulator and virtual environment. A Drive Safety DS-600c fixed-base driving simulator displayed the virtual world (see Figures 1 and 2). Average speed, accelerator pressure, brake pressure, and steering variability were all recorded directly by the simulator. This simulator consisted of image generation computers that project the simulation through LCD display systems onto six, seven-foot projection screens that provided a 300o wrap-around virtual environment (250o in front and 50o in the rear).

These screens were positioned around a Saturn four-door sedan. This car was equipped with all standard vehicle controls and augmented with audio and vibration transducers and force feedback to provide a reasonably realistic driving experience. System latency for this simulator, the time it takes for the system to respond to user input, was 48 msec.

The simulation consisted of a 20-minute drive through a rural environment. This duration was chosen for three reasons. First, it replicated the length of drives used in our previous work on simulator sickness. Second, research has shown that sickness increases by 23% per 15-minutes of exposure, therefore 20-minutes was enough time for sickness to arise without exposing participants to undue discomfort (Stanney & Kennedy, 2009).

Third, research has also shown that 15-minutes of exposure is sufficient to inducing a level of sickness at which some participants cannot continue (through an emetic response or overwhelming levels of sickness; Stanney & Kennedy, 2009). The simulated road

represented a paved surface with a single lane each way and no median. Each lane was

3.6 m wide with a 1.8 m hard shoulder transitioning into a 1.8 m dirt shoulder. In each drive, the participants negotiated eight gradual and eight sharp turns (four lefts and four rights of each – randomly distributed across the drive) separated by 1000 m of straight road. The sharp turns were at traffic light controlled intersections. The lights however always remained green for the participants. In addition, four 4000 m sections of winding hilly terrain were evenly distributed throughout the drives. The speed limit of the drive was posted at 80 km/h during all sections except the sharp turns, which were posted at 40 km/h.

Figure 2. Drive Safety DS-600c fixed-base driving simulator interior with view of virtual

environment.

Procedure

This experiment used two testing sessions on two separate days (one on each

day), two days apart to permit for the maximum day effect (Kennedy et al., 1993;

Watson, 1998). Both testing days involved one drive in the simulated environment. Each

test session had the same basic structure. Only session one however included the medical

and sickness screening tools. On the first day, participants filled out the medical and

24 simulator sickness pre-screening questionnaires. They were asked about any general medical, neurological, and/or vestibular disorders that might influence their performance or limit their ability to participate in the experiment (e.g. epilepsy). Participants were also screened to determine if they had any predispositions to simulator sickness that would make participating in the experiment unduly risky. Highly susceptible individuals were advised that they could be at higher risk of simulator sickness and were given the choice to not participate in the experiment but still receive credit. While at risk participants were advised of the risk, all participants choose to participate and the only exclusions were due to medical reasons (e.g. epilepsy).

Once the participants were cleared to participate, a pre-drive balance score was determined using the Tandem Romberg test. During this testing, a trained spotter was present to ensure participants did not fall. At this stage, participants were asked to enter the simulator. Once in, the eye tracker was fitted and calibrated. Participants were then verbally briefed on the operation of the simulator. Specifically they were told how to physically operate the car, were told to drive however they would normally in the real world, and were told to keep in mind the in-simulation rules of the road (i.e. traffic signals, speed signs, etc.). For a transcript of this briefing, see Appendix D. This briefing was conducted on both days as a reminder to the participants. Because the effect of repeated exposures to the simulation was a variable of interest, the participants only received this verbal briefing on simulator operation and not an actual practice drive.

The simulator began measuring speed, steering variability, and braking and acceleration behaviours as soon as the participants began driving. Gaze and head position data were captured in real-time and recorded throughout the drive. During the simulation,

real-time video and audio feeds of the participants were used to monitor for symptoms of

simulator sickness. If a participant exhibited observable symptoms of sickness, the

researcher asked them how they were doing and if they wanted to stop. In addition, if any

participant reported they wanted to stop, the simulation was ended immediately.

However, except in the extreme cases, the participants were still asked to complete the

balance testing and post-drive questionnaires.

Once the simulated drive was complete, participants repeated the balance test.

The participants were then asked to fill out a post-drive Simulator Sickness

Questionnaire. On day one, participants were debriefed and asked to confirm their

participation in the second session. On day two, participants were thanked for their time

and given their debriefing forms. On day one and two, the actual driving portion of the

test session took between 15 and 20 minutes. However, with all paperwork, testing, and

briefing/de-briefing the total time for each day was approximately 1.5 hours.

Data Manipulation and Results

Analyses examined the effect of repeated exposure to a virtual environment

driving simulation for any effects of day (first, second) on a number of variables. The

variables measured were level of sickness (Simulator Sickness Questionnaire), balance

(Tandem Romberg Score), eye movements, head movements (X and Y-axis), and driving

performance (Speed, Braking, Acceleration, and Steering).

The analyses conducted on each variable of interest followed a similar sequence.

To begin, the variable was analyzed for any data loss and was screened for outliers.

Second, if the variable needed pre-processing it was conducted (e.g. collapsing data across all turns of a drive). Third, the main analysis on the variable was performed.

Because of the multiple steps in the manipulation, validation, and analysis of each

variable, intermediary methodological steps are included along with the results of the

individual statistical analyses.

Did the Day Effect Occur in Terms of Sickness and Postural Stability?

The first two analyses performed ascertained if the day effect was present in this

experiment. Specifically, simulator sickness scores and balance data were analyzed. Any

changes between sessions would confirm whether the day effect was present across one

repeated exposure to a virtual environment and show support that increased

acclimatization was occurring.

Simulator sickness. Simulator Questionnaire scores were analyzed as a function

of the day of testing (overall scores, and three subscale scores). Normally the total and

subscales of the Simulator Sickness Questionnaire are scored out of different totals. This

made direct comparison of raw scores for the various subscales inappropriate. Therefore,

all subscale scores were converted to a percent of maximum score on that subscale to

allow comparisons between subscales. This resulted in a score range of 0-100 for each scale.

Data loss/screening. For this variable, the data were screened because the sample size was relatively small and with a small sample, a single outlier can have had a major effect. It was particularly important to screen for outliers on this variable because on ratings of sickness can be very notable. However, these outliers may be due to individual differences in how participants use the rating scales. If a participant had a Simulator

Sickness Score that was 2.5 or more standard deviations away from the mean of the group, the data for that participant were excluded from the analysis. Only one participant

was excluded based on this criterion. For this individual, the simulator Sickness

Questionnaire total score was 3.5 SD above the mean (more than any other participant,

sick or well) even though the participant did not report being “sick” and had no difficulty

completing the drive. The exclusion of this participant meant a loss of 4.3% of the total

sample for this analysis.

Analyses. To test the hypothesis that sickness would decrease as a function of repeated exposure, a one way repeated measures ANOVA was conducted. The independent variable (IV) was Day (first, second) and the dependent variable (DV) was

Sickness (Simulator Sickness Questionnaire total score percentage). As was hypothesized, there was a main effect of Day on Sickness, with a significant decrease on the second day: Day 1 M = 20.49, SEM = 3.38; Day 2 M = 14.07; SEM = 2.67, F (1, 21)

= 5.824, p = 0.025, partial η2 = 0.217. This result confirmed that with repeated exposures to a virtual environment something occurs resulting in a reduction of sickness (the day effect).

In order to examine if any particular component of simulator sickness was driving this reduction, a factorial ANOVA was conducted on the IVs Day (first, second), and

Scale (Nausea, Oculomotor, Disorientation). The DV for this analysis was Subscale score percentage (where percentage was the percentage of the maximal score for that specific scale). Again, as predicted, there was a significant main effect of Day on Score with an

overall reduction on the second day (F (1, 21) = 6.520, p = 0.019, partial η2 = 0.237).

However, there was no main effect of Subscale Type (p > 0.1) and no significant

interaction (p > 0.1). This result showed that the reduction in sickness observed with

repeated virtual environment exposure was not limited to one of the three areas of

28 simulator sickness (as identified by the Simulator Sickness Questionnaire). For a graph showing Total and Subscale scores across day, see Figure 3.

Figure 3. Mean Simulator Sickness Questionnaire Percentage Scores for Total and all

Subscales across day (+/- SEM).

Postural stability. Participant balance scores were analyzed as a function of day of testing. Balance was measured using the Tandem Romberg test (participants were tested with their eyes closed). The time in seconds that each participant could hold the

Tandem Romberg posture was recorded using video on a computer (maximum time = 30 seconds). This test was conducted pre and post-drive during day one and pre and post- drive during day two. This resulted in four measures of balance for the analysis.

Data loss. Four participants opened their eyes or laughed during the test and consequently their data were unusable. This represented a loss of 17% of the total data.

Analysis. In order to test the hypothesis that during session two post-drive balance would be more unstable, a factorial ANOVA was conducted. This ANOVA was conducted on the IVs Day (first, second), and Time (pre-test, post-test) and the DV

Balance (seconds). For this analysis, no significant effect of Day was observed (p > 0.1).

A marginally significant effect of Time was obtained with lower overall balance time

post-drive, F (1, 18) = 3.374, p = 0.083, partial η2 = 0.158. Contrary to prediction, no

significant interaction was obtained (p > 0.1). For a graph of all balance scores by day,

see Figure 4.

Figure 4. Average time to hold the Tandem Romberg test (seconds) pre and post-drive,

by day (+/- SEM).

Did Participant Eye and Head Movements Change Across Days?

In order to increase our understanding of the mechanism behind the day effect,

gaze and head movements were examined for any changes across experimental session.

The goal was to determine if changes in these behaviours could highlight a potential mechanism for adaptation to the sensory conflict inherent to fixed-base driving

simulators.

Gaze behaviour. This analysis was used to determine if there were any changes

in gaze behaviour across days during the gradual turns. If present, these changes in gaze

30 behaviour could be responsible for the reduction in sickness observed on day two. Gaze behaviour was defined as the percentage of time spent looking at the zone Tangent. This zone was identified based on the viewing locations for naturalistic driving as described by Land (2006). Specifically the Tangent zone was based on the tangent point of the bend, defined as “the moving point on the inside of each bend where the driver’s line of sight is tangential to the road edge; it is also the point that protrudes most into the road”

(pg. 306). Because of the resolution of the eye tracker signal the Tangent area was expanded around the actual tangent point to encompass the areas directly surrounding it

(see Figure 5).

Figure 5. Approximate representation of Tangent gaze zone.

This analysis was conducted on three of the gradual right hand turns from day one and three from day two: one turn from the beginning (700 m from the start of the drive), one from the middle (4300 m from the start of the drive), and one from the end of the drive (6400 m from the start of the drive). The same three turns were always used

regardless of day or participant. The decision to use these three turns gave a

representative sample from across the entire drive while reducing the overall amount of data manipulation and analysis needed. The analysis was conducted on the video of the participants’ point of regard, frame by frame. This analysis began from the moment the car passed the onset of the curve (as determined by the simulated road ceasing being straight and beginning to curve) and ended when the car passed the end of the curve

(determined to be the point where the simulated road ceased having a curve and became straight once again).

For each turn, the number of frames that the participant gazed toward the Tangent

zone was tallied. In addition, the number of frames data were lost due to error was

recorded. For example, frames where a participant blinked. Because the number of

frames for the turns would vary based on driving speed, the number of frames directed at

Tangent was converted into a percentage of the overall number of frames, minus error:

Tangent Percentage = 100 × [tangent frames ÷ (total frames – error frames)]

Thus, the measurement represented the percent of time during a turn that the participant

directed gaze at the Tangent zone.

Data loss/screening. The eye tracking equipment had two major limitations that

caused data to be lost for a number of participants. First, the tracking equipment

sometimes lost calibration when the apparatus moved during the testing. Because of the

nature of the simulation, participants may do something such as scratch their faces and

inadvertently move the tracking monocle. To correct this, the experiment would have had

to be stopped and re-started – potentially affecting the results. Consequently, the decision

was made to continue testing without recalibration if tracking was lost. Second, some

32 participants due to eye colour, eyelid shape, or the wearing of glasses could not have the tracker calibrated to their eyes. Due to these problems, only 12 of the participants tested had complete eye data for both days. This equates to a loss of 48% of the gaze data in this analysis.

In addition to total data loss, the tracker would occasionally loose tracking for a few frames during a trial (e.g. during blinks or when the participant made large eye movements). To determine if there were any differences between the experimental sessions, the percent of the total frames that were errors were compared:

Error Frame Percentage = 100 × (error frames ÷ total frames)

A paired samples t-test on the variable Error was conducted. The IV for this analysis was

Day (first, second), and the DV was Error (%). A marginally significant difference was observed with more error during the first session: Day 1 M = 14.73, SEM = 2.78; Day 2

M = 10.81, SEM = 2.49; t (11) = 1.849, p = 0.092. While one interpretation could be that there was a problem with the eye tracking equipment during the first session, an alternative explanation is that participants were making larger eye movements on day one. This would have increased error due to looking beyond the tracking area. Because of this, a decision was made to proceed with the analysis of these data even with this discrepancy.

Data pre-processing. The gaze data was obtained from six right turns during the drives, three on day one, and three on day two. These were collapsed by day to obtain one value for each zone on day one and one value for each zone for day two. Prior to averaging across the turns, data were analyzed for any potentially relevant changes in eye movements turn by turn (within each day). For session one, this analysis showed no

significant differences between the turns for percent of time gazing toward Tangent (p >

0.1). For session two however, between the first and last turn there was a significant

increase in percentage of time drivers spent gazing toward tangent: Turn 1 M = 63.66%,

SEM = 4.42; Turn 2 M = 70.94%, SEM = 2.99; Turn 3 M = 73.65%, SEM = 4.39; Turn 1

versus Turn 3 p = 0.030.

Analyses. To test the hypothesis that there would be fewer gaze movements on

day two compared to day one, an analysis was conducted on the gaze zone Tangent. A

paired samples t-test was conducted on the IV Day (first, second), and the DV Percent of

Gaze to zone Tangent. As was predicted, a marginally significant difference was obtained

between the first and second days: Day 1 M = 60.58, SEM = 6.11; Day 2 M = 69.42, SEM

= 3.55; t (11) = -2.09, p = 0.060. This result suggests that on the second day of testing, gaze was fixated more often at the tangent point. This suggests that drivers spent less time “looking around” on the second day. It must be noted however that when drivers traverse curves in real vehicles they spend 80% of their time looking at the tangent point

(Land, 2006). While gaze behaviour did change across days, on neither day did gaze behaviour conform to this expected norm for gaze behaviour (See Figure 6 for a graph of eye movement percentages.)

Figure 6. Percentage of time spent looking at zone Tangent during gradual turns, across day (+/- SEM). Expected real world gaze percentage included for comparison.

Head movements. This analysis examined head movements during the gradual turns, to determine whether reductions in movement on the X (medial/lateral) or Y

(superior/inferior) axis might be responsible for the reduction in sickness observed during a second day exposure to the virtual environment. Head movements were defined as the variability in head position as measured on the X and Y-axis, during the curved potions of the drive. In order to determine head position from the video data, the video display was divided into an area measuring 310 pixels in width and 230 pixels in height. This represented an area measuring 433 mm x 325 mm at a distance of ~400 mm. Each pixel represented an area of 1.4 mm x 1.4 mm.

The measurements were made on the same three gradual right hand turns from each day that the eye movement analysis was based on: 700 m from the start of the drive;

4300 m from the start of the drive; and 6400 m from the start of the drive. The same three turns were always used regardless of day or participant. For each participant, an initial

35 head position (X,Y) was established at the beginning of the turn in question. The X,Y head position was calculated using a feature common to every participant – the eye tracker’s calibration screw. Because every participant wore the eye tracker in the same position, this gave a consistent frame of reference for the head position to be based on.

The computer monitored the position of the screw throughout the turn reporting frame by frame its X and Y coordinate. Once the turn was complete, mean X and Y values were calculated.

Even though the tracked point was consistent across participants, each participant started at a different X,Y coordinate for each turn. This was because participants varied in height and because they could move around though the camera was fixed. Therefore, a comparison of mean X or Y scores between turn, day, or participant would be meaningless. Instead, the standard deviation of head position was calculated to measure the variability in head position during each turn. This approach allowed for comparisons of overall movement to be made between turns across days. A visualization of head movement data for one participant is provided in Figure 7.

Figure 7. Visualization of one participant’s mean head position over time, during one

gradual turn.

Data loss. Because the head movements were collected on both days, if collection

failed on one of the days no comparison could be made: Therefore, the data from the

other day was removed. During each collection, data were lost for the following reasons.

Sometimes the camera was knocked off position by the car door slamming; sometimes the computer failed to save the data; and sometimes participants shifted the position of the tracker (from where it was when it was calibrated) after the drive began. Because of these problems, six participants did not have complete data for both days of the analysis.

This represented a loss of 26% of the data.

Data pre-processing. The head movement data came from six gradual right hand

turns during the drives, three on day one, and three on day two. To determine whether

there were turn-by-turn changes in head movement variability across days, data from

individual turns were analyzed. This analysis showed no significant differences between

the turns (on either day) for the X or Y head movements (both p > 0.1). As a result, data

from the turns were averaged to produce average values for X and Y on day one and

average values for X and Y on day two.

Analyses. In order to test the hypothesis that participants locked their heads into

more stationary positions to reduce reliance on vestibular information and help reduce

conflict due to the presence of unnatural externally driven motion, a Factorial ANOVA

was conducted on the IVs Day (first, second) and Axis (X,Y) and the DV Variability (SD

of head movement). As predicted, a main effect of Day emerged (F (1, 16) = 15.34, p =

0.001, partial η2 = 0.489). A significant main effect of Axis (F (1, 16) = 38.724, p <

0.001, partial η2 = 0.708) and a significant interaction between Day and Axis were

observed (F (1, 16) = 15.142, p = 0.001, partial η2 = 0.486).

Due to the presence of the interaction, a simple effects analysis was conducted for

both the X and Y-axis variability. The simple effects analysis of X-axis variability by day

showed a significant effect of Day on Variability: Day 1 M = 6.86, SEM = 0.784; Day 2

M = 5.19, SEM = 0.584; F (1, 16) = 20.937, p < 0.001, partial η2 = 0.567. However, the

simple effects analysis for Y-axis variability showed no significant change in variability

between day one and two (p > 0.1). For a graph of all X and Y-axis movements, see

Figure 8. These results suggest that while people are stabilizing their heads more during second day exposure to the virtual environment they are only doing so for medial/lateral movements and not for superior/inferior movements. However, one point to note is that

Y-axis, or anterior/posterior, variability was very low during both exposures to the virtual environment.

Figure 8. Mean variability of head movement on the X and Y-axis for the gradual turns across day, measured in pixels (+/- SEM).

How Did Repeated Exposure Affect Driving Behaviours?

It is possible that specific driving behaviours may play a role in increasing or decreasing the level of sensory conflict (and thus sickness). Consequently, the effects of the day of testing were measured in terms of their influence on four aspects of driving performance: average speed, accelerator percent, brake percent, and steering percent. For all driving variables, three sections of the drive were analyzed. First, an overall measure was used that included all aspects of the drive (straight, gradual turns, sharp turns, and the winding sections). This measure gave an overall picture of driving behaviour. The second and third areas consisted of the data from the gradual and sharp turns. These analyses were conducted separately, as the speed limit of the turns and the overall structure of the turns was too dissimilar to combine into one analysis. The gradual turns were signed at

80 km/h and were designed to be negotiated with small amounts of braking, acceleration, and steering. The sharp turns however were signed at 40 km/h, and required heavier

braking, acceleration, and more steering control. Because the participants would

encounter each of the areas multiple times, they were collapsed into one score for each

area, per day. For all four variables, no data were removed or missing.

Average speed. The average speed, measured in km/h, was analyzed to test the hypothesis that participants would reduce their speed to reduce sensory conflict (in particular in high conflict areas such turns and slowing down/speeding up). In addition to the overall analysis, multiple areas of the gradual and sharp turns were examined as follows:

1) Gradual Turns

• Entering – the 70m prior to the turn

• During – the 314m of the turn

• Exiting – the 70m exiting the turn

2) Sharp Turns

• Entering - the 40m prior to the turn

• During - the 26.4m of the turn

• Exiting - the 40m exiting the turn

Average speed analyses. A repeated measures ANOVA with the IV Day (first, second) and the DV Average Speed was conducted. Contrary to the prediction that speed

would be slower during the second session, Day showed a significant effect on Average

Speed with faster average speeds during the second session: Day 1 M = 73.96, SEM =

1.17; Day 2 M = 78.605, SEM = 1.73; F (1, 22) = 33.49, p < 0.001, partial η2 = 0.604.

For the analyses of average speed during the gradual and sharp turns, two separate

factorial ANOVAs were conducted (factorial ANOVAs were used because each turn had

multiple areas of interest). Each ANOVA was conducted on the IVs Day (first, second),

and Turn Area (Enter, During, Exit) with the DV Average Speed. For the gradual turn

analysis, contrary to prediction, a significant effect of Day was observed with increased

speed on the second day (F (1, 22) = 16.380, p = 0.001, partial η2 = 0.427). Neither a

significant effect of Turn Area nor an interaction was obtained (both p > 0.1).

Also contrary to prediction, the analysis of average speed during the sharp turns

revealed a significant effect of Day, with higher overall speed on day two (F (1, 22) =

16.165, p = 0.001, partial η2 = 0.424). In addition, a marginally significant effect of Area

was observed (F (1, 44) = 2.706, p = 0.095, partial η2 = 0.110). Pairwise comparisons

showed this effect was driven by a marginally significant difference between speeds

during as opposed to exiting the turn (p = 0.069). Of the most importance was that this

analysis revealed a significant interaction (F (2, 44) = 11.152, p = 0.001, partial η2 =

0.336).

Because of the presence of an interaction, a simple effects analysis was

undertaken for each area of the turn across day. The simple effects analysis of entering

the turn showed a significant effect of Day on Average Speed: Day 1 M = 39.87, SEM =

1.36; Day 2 M = 44.41, SEM = 1.31; F (1, 22) = 27.721, p < 0.001, partial η2 = 0.558. In

addition, Day showed a significant effect on Average Speed during the turn: Day 1 M =

39.42, SEM = 0.661; Day 2 M = 44.67, SEM = 0.951; F (1, 22) = 66.403, p < 0.001,

partial η2 = 0.751. Day had no significant effect on Average Speed while exiting the turn

(p > 0.1). For a graph of average speeds by area for the sharp turns, see Figure 9.

Figure 9. Average speeds during each area of the sharp turns, across day (+/- SEM).

Accelerator percent. This analysis tested the hypothesis that participants reduced

conflict during the second session by reducing hard accelerations. Accelerator percent

was defined as the percent the accelerator was depressed (0-100%) with higher

accelerator percent scores indicating faster accelerations. Accelerator pressure was

measured across the entire drive, and in the exits from gradual and sharp turns. These

areas were chosen because acceleration within the simulation would be most prominent

when the participants needed to change their speed after negotiating a turn.

Accelerator percent analyses. Three repeated measures ANOVAs were

conducted on this set of data, one for each area of interest. Each ANOVA was conducted

on the IV Day (first, second) and the DV Accelerator percent. Contrary to prediction,

Day showed a significant effect on acceleration over the entire drive: Day 1 M = 15.65,

SEM = 0.60; Day 2 M = 17.48, SEM = 1.0; F (1, 22) = 6.82, p = 0.016, partial η2 = 0.237.

This was unexpected, as it appears that participants were pressing the accelerator harder during the second session. For the exit to the gradual turns, again unexpectedly, Day

42 showed a marginally significant effect: Day 1 M = 18.34, SEM = 1.1; Day 2 M = 20.91,

SEM = 1.5; F (1, 22) = 4.128, p = 0.054, partial η2 = 0.158. Again, the tendency appeared to be harder accelerations during the second session. For the exit to the sharp turns, no significant effect of Day was observed p > 0.1. For a graph of accelerator percent by day and area, see Figure 10.

Figure 10. Average percent of accelerator press during the overall drive and while exiting the gradual and sharp turns, across day (+/- SEM).

Brake percent. To test the hypothesis that participants used gentler braking in order to reduce conflict, braking behaviour was examined. Brake percent was defined as the percent the brake pedal was depressed (0-100%) with higher brake percent scores indicating harder braking. Brake pressure was measured across the entire drive and in specific areas (entrances to gradual and sharp turns). These areas were chosen because braking within the simulation would be most prominent when the participants needed to change their speed before negotiating a turn.

Brake percent analyses. An analysis of the percent of brake pedal press was conducted for each area of interest. Each analysis used a repeated measures ANOVA on the IV Day (first, second) and the DV Brake Percent. For Brake Percent over the whole drive, no effect of Day was observed p > 0.1. However, as predicted, for entering the gradual and sharp turns marginally significantly effects of Day were observed with percent of brake press reducing during the second day (Gradual F (1,22) = 3.89, p =

0.061, partial η2 = 0.150, Sharp F (1,22) = 3.95, p = 0.059, partial η2 = 0.152). For mean brake percent across day and area, see Figure 11.

Figure 11. Average percent of brake press during the overall drive and while entering the gradual and sharp turns, across day (+/- SEM).

Steering variability. In order to test the hypothesis that participants reduced conflict during turns by reducing variability in their steering behaviour, steering variability was analyzed. Steering variability was defined as the SD of wheel position.

Variability was determined based on an analysis of the degrees the wheel was turned

(right and left) from centre, for each frame during the curved areas of the drive (sharp and

44 gradual). These values were used to calculate a mean score for each area of interest and the SD was calculated. In addition to the drive overall, the actual portion of each turn

(gradual and sharp) where steering was needed to keep the car in the lane were examined to determine the variability in steering.

Steering variability analyses. Analyses were conducted for each area of interest.

For each analysis, a repeated measures ANOVA on the IV Day (first, second) and the DV

Variability (SD of wheel position) was used. For overall steering variability, contrary to prediction, no significant effect of day was observed (p > 0.1). For the analysis of the gradual turns, there was a significant effect of Day on Variability: Day 1 M = 5.57, SEM

= 0.243; Day 2 M = 6.65, SEM = 0.477; F (1, 22) = 5.61, p = 0.027, partial η2 = 0.203.

This result was in the opposite direction than predicted, with participants showing more variability in their steering on day two. The analysis of variability during the sharp turns and found no effect of Day (p > 0.1). For a graph of average variability during the turns, see Figure 12.

Figure 12. Average steering variability (degrees of wheel turn) during the overall drive and while negotiating the gradual and sharp turns, across day (+/- SEM). 45

Discussion

The main goal of this experiment was to isolate any specific changes in driver behaviour between an initial exposure to a simulation and a re-exposure two days later.

The intent was to help provide theoretical explanations for why simulator sickness reduces with multiple exposures over and above labelling it as general adaptation. See

Table 2 for a summary of all experimental results.

Table 2 Summary of Experimental Results Significant Effect of Condition? Direction or Effect as Predicted? Simulator Sickness Questionnaire Total Yes Yes Balance Post-Drive No No - No Difference Gaze Toward Tangent During Curved Driving Yes Yes

Head Variability X-Axis Yes Yes

Head Variability Y-Axis No No - No Difference Average Speed Overall Yes No - Opposite Average Speed Gradual Turn Yes No - Opposite Average Speed Sharp Turn Yes No - Opposite Accelerator Percent Overall Yes No - Opposite Accelerator Percent Gradual Turn Yes No - Opposite Accelerator Percent Sharp Turn No No - No Difference

Brake Percent Overall No No - No Difference Brake Percent Gradual Turn Yes (Marginal) Yes Brake Percent Sharp Turn Yes (Marginal) Yes Steering Variability Overall No No - No Difference Steering Variability Gradual Turn Yes No - Opposite Steering Variability Sharp Turn No No - No Difference

The analyses provided support for the hypothesis that sickness would reduce on a second day exposure. This was an important finding because it showed that the day effect could be replicated with one repeated exposure to a virtual environment. In addition, it showed that the Simulator Sickness Questionnaire is a reliable measure the day effect.

Unfortunately, the balance data did not support our initial hypothesis. This result was strange because studies of simulator sickness interventions have shown that decreased sickness usually accompanies decreases in post drive stability. However, limitations in this study could be responsible for this result. Specifically the balance test used, the Tandem Romberg, had some methodological issues. Many participants showed

46 scores at ceiling, cutting variability on the high end of the scores. In addition, because participants returned days later, even with reminders, it was hard to keep footwear constant across days and that may have influenced the results.

The analysis of the gaze behaviour and eye movements across day supported the hypotheses. Though the result was only marginally significant, there was evidence that participants directed their gaze more toward the tangent line during turns (they looked around less). The failure to achieve a significant effect in this analysis may be due to a variety of factors, some of which lend support to the hypothesis. First, there were more error frames on day one. The increased number of error frames may represent more eye movements on the first day insofar as the tracker becomes more unstable when gaze is directed toward the extremes of the tracking area (which would support the hypothesis).

However, the increased number of error trials meant that the analysis underestimates the number of frames in which the eyes were directed away from the tangent, thus making it difficult to achieve a significant effect in statistical analysis. Second, during day two there was a significant increase in the number of fixations from the start to the end of the drive. This could represent a strengthening of the gaze redirection response. However, changes in the number of fixations in different sections of the drive may have reduced the overall average score enough to obscure the result. Because of these factors, I believe that the marginally significant effect does represent a genuine change in gaze behaviour between the sessions.

For head movements, participants reduced their X-axis movements

(medial/lateral) during curved portions of the drive. When negotiating a curve in the real world, head movements are responses to the lateral acceleration produced by the turn. In

the simulator however, this type of head movement may have caused increased conflict in

the absence of proprioceptive and vestibular cues (particularly if the participant was

responding as if there was lateral acceleration). These head movements may have

increased sensory conflict by adding inappropriate egocentric motion cues and increasing the need for reliance on vestibular information to stabilize the eyes while the head moves

(vestibulo-ocular reflex). Therefore, the reduction on day two may represent a more appropriate response to the simulation and a reduction in what would “normally” happen in the real world.

The changes observed in the eye and head behaviours may be a response to the externally initiated motion cues. Because in the real world people would receive the egocentric vestibular and proprioceptive cues to their motion when traversing a curve, the absence of these cues in the simulation may cause conflict and disorientation. However, if people hold their head and eyes in a stable position, this may reduce internal cues to motion that do not match the visual stimuli. In essence, they create a stable platform, free from vestibular and proprioceptive cues that do not match, from which to interpret the

visual cues they are receiving. These results show that a major contributor to the day

effect could be the modification of gaze and head movements across days.

While the significant effect of day on average driving speed was contrary to

prediction, it may be that a change in overall average speed across an entire drive does

not affect conflict as much as might seem. This would be especially true in the straight

portions of the drive where speed changes are minor – a faster or slower overall speed

would be inconsequential. In the gradual turns, this may also be the case. The gradual

turns had the same speed limit as the straight portions of the drive. As such, driving

through them faster (as long as the speed matched the straightaway speed on either side) would have little effect on reducing or increasing conflict. During the sharp turns however, it seems like driving faster would increase conflict: a lateral visual shift would occur more rapidly, and the conflict between expected and actual vestibular input would be greater. One possible explanation why participants were driving these sections faster

(and running the risk of increasing this conflict) is that abrupt speed changes also cause conflict. In this situation, it might be the case that participants were learning to push the maximum speed possible to negotiate the sharp turns. The participants may have been doing this in order to alleviate the deceleration conflict by not slowing down as much when entering the sharp turn. It would not explain however why there was no change in speed when exiting the corner. This analysis does suggest that they day effect is probably not caused by changes in driving speed across days to reduce sensory conflict.

While participants reduced the percent they pressed the brake pedal, as predicted, the actual amount of difference was slight. The percent of pedal press in the gradual turns went from 1.1% on day one to 0.3% on day two and the percent in the sharp turns went from 12.3% to 10%. Although this result was statistically significant, it is unclear if a change of this magnitude would manifest in a noticeable change in how the simulation reacted. If the effect was only minor, it is unlikely that this had a great effect on reducing conflict and promoting the day effect.

Participants not only showed changes in their braking, they also showed significant changes in certain aspects of their acceleration. However, the change observed was not a reduction in pedal press, as predicted, but an increase. Increasing acceleration would seem detrimental to adaptation to the virtual environment, as it would increase

49 anterior/posterior conflict. However, as with the braking behaviour, the actual amount of difference was slight. Pedal press went from 15.65% to 17.48% for the entire drive and from 18.34% to 20.91% for the gradual turns. Despite the statistical significance of this effect, it is unknown whether a change this small would have a noticeable effect on conflict (and sickness) across days. What is clear is that because sickness was lower during the second session, this increase in acceleration did not result in a large enough increase in sickness for the day effect not to occur.

As with the average speed and acceleration analyses, the results of the steering variability analysis were also contrary to prediction. The observed increase in variability could have been a result of the gaze/head modification during the gradual turns. If participants were holding their head more rigidly, it could have been the case that they were compromising their ability to navigate the turn smoothly. Regardless, because increased variability should have increased conflict (and sickness) it appears that this level of a change was not sufficient to negate the day effect.

To summarize, the changes in driving behaviours did not seem to be directed at reducing sickness. Some, in fact, should have had the opposite effect. Instead, the changes seemed to be the result of the participants becoming accustomed to the simulation and thus more willing to drive aggressively. For example, the higher speed in the sharp turn could be a result of the participants improving their ability to traverse the curves. Another example is that the overall increase in average speed could reflect the participants “knowing what to expect” and therefore being more comfortable driving faster (speed increased from ~73 km/h on average to ~78 km/h.). Most areas of the drive were signed at 80 km/h, therefore if a participant was becoming more comfortable and

50 drove at or above the speed limit then this increase makes sense. Because the driving metrics appear more experience-based improvements, rather than adaptations to reduce sickness, it could be that the day effect is a by-product of becoming more comfortable with the simulation. However, because some of the driving behaviours should have created more conflict (rather than reducing it) it is difficult to see how this could be the case. Given these seemingly conflicting results, and the difficultly predicting how a small change in driving behaviour may manifest in conflict and sickness, later in the thesis I will describe a study that examines the impact of specific driving behaviours on sickness on day one testing (Experiment 4).

This experiment not only succeeded in showing that the day effect is a replicable, measurable effect but in addition, it identified potential behaviours that could be responsible for this effect. Specifically, it showed that the way participants modify their head and eye movements in response to the virtual environment may be a significant contributor to the reductions in sickness observed with the day effect.

Chapter 3

Experiment 2

In Experiment 1, I showed that participants made significant changes to their gaze and head movements while negotiating a simulated curve, between a first and second exposure to a virtual driving simulation (two days apart). Specifically, during the second exposure participants fixated their gaze more on the tangent line of the curve and reduced their head movements on the X-axis. I postulated that these behaviours were adaptations

to the sensory conflict experienced during the curved portions of the drive, and that they

may have been in part responsible for the reductions of simulator sickness observed

during the second session (the day effect). My theory was that because drivers

negotiating curves in a simulator held their eyes and head in a stable position, they

reduced the vestibular and proprioceptive cues to motion that did not match the visual

stimuli generated by the simulation and thereby reduced the conflict. This account is

consistent with previous research on motion sickness. In 2004, Flanagan and Dobie

presented participants with a video of a room filmed to make the participant feel as if

they were turning in place. They found that when participants fixated their gaze they

showed less motion sickness then participants who moved their eyes without restriction.

While this work relates to the experience in a simulator, it does not capture the

complexity inherent to a fully immersive simulation. In the Flanagan and Dobie study,

participants only watched a video. In a simulator however, the participant is part of the

environment and controlling the visual scene, not just reacting to it passively. In addition,

while they examined gaze fixation they did not control or measure head movements.

Therefore, while this previous research sets a starting point, this experiment will attempt

to replicate these findings in a more complex virtual environment, and with more

variables of interest.

One issue with Experiment 1 was that I did not directly manipulate gaze and head

movements. Therefore, I could not determine if changes in the way that drivers moved

their eyes and head caused the reduction in sickness. Experiment 2 addressed this

shortcoming by attempting to manipulate gaze and head movements directly. To that end,

I developed an instructional program to teach participants to fixate their gaze/head to

central portions of the display during curves in order to get them to stabilize their gaze

and head position rather than changing it.

A between subjects design (Control, Gaze Instruction Horizon, Gaze Instruction

Road) was used to evaluate the gaze instruction intervention on a number of dependent variables. In the control condition participants received no training over and above instruction on the simulator operation. For the gaze instruction conditions two sets of

instructions were used. The first instructed participants to focus on the horizon directly in

line with their vehicle and the other to focus on the road directly in front of their vehicle

(figures 13 and 14). The analyses tested these groups independently and together to

determine whether focusing on a specific area had more of an effect than the other did

and to see if focusing in general had an effect, regardless of the location.

In order to be effective, the instructional intervention had to be learnable. In this

case, it also had to be learnable before entering the simulation. Otherwise, participants

could have experienced simulator sickness while trying to learn how to reduce sickness!

In other fields of study, research has shown that gaze coaching, prior to action, is

effective at changing participant gaze behaviour and participant actions related to gaze

(Young & Hollands, 2010). In the Young and Hollands study, they trained older adults to

change their gaze pattern to negotiate an obstacle more effectively. They achieved this

with relatively little instruction before the activity (3-5 minutes). In order to test whether the gaze instruction intervention was effective at modifying gaze behaviour, gaze fixations were analyzed for each group. If the interventions were effective, then participants in the gaze instruction groups should have shown greater fixation towards their respective focal points. In addition, an analysis determined how often the control group gazed toward the tangent point of the curve. The tangent point was used as a point of reference for the control participants as it represents where, if it were a real driving situation, they should be looking to properly control the car around the curve. Thus, with no prior training, it is likely that they should have defaulted to looking at this location.

However, if participants behaved as in Experiment 1, they would move their gaze around more during their first experience in the simulator as would be depicted as an increase in gaze variability.

In order to determine if the use of the instructions to modify gaze behaviours would result in an artificial day effect, reducing sickness during an initial virtual environment exposure, sickness was evaluated across the three conditions. The hypothesis was that simulator sickness scores in the two gaze instruction conditions would be lower than the control condition scores. If focusing the eyes and head did play a role in reducing conflict during turns, then reductions in sickness should occur regardless of the focal point. I based this reasoning on my evaluation of the association observed during Experiment 1. Participants during the second exposure to the virtual environment

held their gaze and head in a more stable position and their sickness levels were lower

then during their first exposure.

As in Experiment 1, adaptation to the simulation was measured using a postural

instability test after the drive. I hypothesized that postural instability would be greater

post-drive in the gaze instruction conditions than the control condition. While the results

of Experiment 1 showed no indication that day had an effect on post-drive balance, it was

still included as a potential measure of adaptation. If the gaze modification program

helped participants adapt more quickly to the simulation then post-drive balance scores

should have reflected this improvement.

Head movements were also evaluated for possible links to the intervention.

Because gaze is the combination of head and eye movements, it was hypothesised that

participants in the gaze instruction conditions would show lower head movements than

those in the control condition. In addition, based on the results of Experiment 1, the X-

axis reduction should be more pronounced than any Y-axis reduction.

Modifications to how participants moved their eyes during the curved portions of

the simulation may have affected how they controlled their vehicles during the turns.

Directing gaze toward the tangent line is a behaviour adapted to steering around a curve,

therefore looking away from this point may have affected driving performance. Because

of this, driving behaviours were examined for any potential effects of gaze instruction

(average speed, accelerator percent, brake percent, and steering variability). It was

important to measure this because changes in driving behaviours could potentially affect

sickness. For example, if steering became more variable during the curves, then this may have increased medial/lateral conflict and potentially increased sickness.

Summary of Hypotheses

1) Gaze instruction participants will score lower on the Simulator Sickness

Questionnaire than those in the control group, regardless of where the

participants were instructed to direct their gaze.

2) Gaze instruction participants will have a greater reduction pre to post-drive in

postural stability than those in the control group, regardless of where the

participants were instructed to direct their gaze.

3) Head movements on the X-axis (medial lateral) will show less variability in

the both gaze instruction conditions as compared to control.

Method

Participants

For this experiment, we recruited 59 participants from the undergraduate participant pool at the University of Guelph. The mean age was 19.33 years (SD 1.95) and the sample included 44 females and 15 males. Course credit was given as payment for participation. Participants were assigned to one of three experimental conditions:

Control, Gaze Instruction – Horizon, Gaze Instruction – Road. This assignment was done in such a way that the proportion of males and females, propensity for simulator sickness, and age were matched across condition as much as possible (see Table 3 for a breakdown of participant characteristics).

Table 3 Participant Characteristics Control Horizon Road Age in Years (M) 19.59 19.69 18.71 Age in Years (SD) 2.06 2.21 1.59 Number of Females 24 11 9 Number of Males 7 3 5 Simulator Sickness Pre-Screening Score (M) 0.90 0.71 1.14 Simulator Sickness Pre-Screening Score (SD) 1.14 0.73 1.17

Apparatus and Materials

This study used the same apparatus as Experiment 1(eye track monitor and

driving simulator). In addition, it employed the same psychological tests (Medical

History Questionnaire, Simulator Sickness Pre-screening Questionnaire, Simulator

Sickness Questionnaire, and the Tandem Romberg test).

Procedure

As in Experiment 1, participants filled out medical and sickness pre-screening

forms. Those who were at risk of simulator sickness were advised of this risk and asked if

they wanted to continue. All participants chose to proceed with the study protocol. This

study involved having participants drive once through the same 20-minute simulated

drive used in Experiment 1. The control group drove the simulation with the standard

instructions (as outlined in Appendix D). Both the gaze instruction groups received

additional instructions prior to undertaking the drive. Specifically, before entering the

simulator, the participants were read a set of instructions that detailed how they should

direct their gaze to a restricted field of view during the curved sections of the road in the

drive. Two sets of instructions were used: one instructed the participants to look directly

ahead at the centre of the display (at the centre of the horizon, Figure 13); the second

instructed participants to look directly at the road in front of their vehicle (Figure 14). For

the specific instructions given to each group, see Appendix E.

Figure 13. Diagram of focal point shown to Gaze Instruction – Horizon participants.

Figure 14. Diagram of focal point shown to Gaze Instruction – Road participants.

Both sets of instructions included a point to note that reminder billboards would be present at the onset of every curve where gaze modification was required (see Figure

15 for a depiction). Once the verbal instruction was complete, the participants were shown a diagram of the appropriate gaze area and shown a brief video of appropriate eye movements (Figures 13 and 14). Each video consisted of two 20-second clips that

showed the point of view of a driver while negotiating a corner of the simulated drive.

Each clip included a crosshairs indicating the appropriate gaze fixation position.

Participants viewed the video twice, and any questions they had were answered.

Figure 15. Position of a billboard reminder at the onset of a curve. Billboard as shown is

blank, in the simulation the billboard depicted a text reminder.

Prior to entering the simulator, pre-drive balance was measured using the Tandem

Romberg test. During the simulation, eye and head movements and monitored driving variables were recorded. After the simulation concluded, balance was measured again. To conclude the experiment, participants filled out the Simulator Sickness Questionnaire and

were debriefed and thanked for their time. The total time to complete the experimental

protocol (including screening, the experimental drive, and debriefing) was approximately

1.5 hours.

Data Manipulation and Results

This experiment examined the effect of pre-exposure gaze instruction on gaze

modification, during the curved portions of a virtual drive. Eye movements, Simulator

Sickness Scores, balance (Tandem Romberg score), head movements (X and Y-axis), and

driving performance (average speed, acceleration, braking, and steering variability) were

measured.

To begin, a manipulation check was performed to determine if the gaze

modification instructions were successful in modifying gaze behaviour. After that,

analyses for each variable were performed in the following order. Each variable was

analyzed for data loss and screened for outliers. If pre-processing was needed it was conducted (e.g. collapsing data across all turns of a drive). Following these steps, the main analysis was performed.

Manipulation Check: Were the Participants Moving Their Eyes as Instructed?

To determine if participants followed the instructions, each condition was evaluated for compliance. Gaze behaviour was defined as the percentage of time spent looking at the three zones of interest: Control - Tangent (Figure 5); Gaze Instruction -

Horizon (Figure 13); and Gaze Instruction - Road (Figure 14). These data were collected exactly as outlined in Experiment 1. The only difference to note is that these were based on a single drive, while the analysis in Experiment 1 was based on two drives. The calculation of gaze percent was as follows:

Area of Interest Percent = 100 × [area of interest frames ÷ (total frames - error frames)]

The percent of time gaze was directed to each point of interest for each group was as follows: Control - Tangent (M = 57.75%, SD = 19.64); Gaze Instruction - Horizon (M

= 90.60%, SD = 6.53); and Gaze Instruction - Road (M = 89.09%, SD = 7.23). These values confirmed that the gaze instruction groups spent the vast majority of the time following the instructions. The control group however, showed a similar pattern to

Experiment 1 (i.e. participants “looking around”).

Was Gaze Modification Successful at Reducing Sickness and affecting Postural

Stability?

The first two analyses run ascertained if the instructional groups showed any difference in their level of simulator sickness (and postural acclimatization). Specifically, sickness scores and balance data were analyzed as a function of the gaze condition. Any differences between gaze condition groups would confirm that gaze fixation plays a part in reducing simulator sickness and promoting adaptation to a novel sensory arrangement.

Simulator sickness. This analysis examined whether the implementation of pre- exposure instructions on gaze direction could result a reduction of sickness during a novel virtual environment exposure (similar to the day effect). Scores on the Simulator

Sickness Questionnaire were used to evaluate level of sickness in the participants. Total score and scores for three subscales were evaluated (Nausea, Oculomotor Discomfort, and Disorientation). While normally all of these scores have different maximums, for ease of analysis all scores were converted to their percent of maximum resulting in a range of 0-100 for each scale.

Data loss/screening. The data were screened using the same procedure outlined in

Experiment 1. The exclusion criterion was a Simulator Sickness Questionnaire score above or below 2.5 standard deviations from the mean. Two participants in the Gaze

Instruction condition were excluded based on this criterion. The first participant had a SD

3.24 above the mean. This participant did not report being sick and had no difficulty completing the drive. The second participant had a SD 2.88 above the mean and became sick within the first two minutes of the drive. Although this intervention was designed to measure reductions in sickness, and therefore including participants who became sick

61 would be prudent, the decision was still made to remove her data. This decision was based on her abnormally high score and because she became sick prior to having a real opportunity to use the intervention technique. Because the intervention takes time to work, it is probable that it will not help individuals with extremely rapid sickness reactions. Dropping these two participants resulted in a loss of 8% of the data for the

Gaze Instruction - Road condition.

Analyses. To test the hypothesis that sickness scores would be lower in the Gaze

Instruction groups compared to Control, two sets of analyses were performed. The first analysis kept the two instructional groups separate to explore for possible effects of focusing gaze toward a specific area. A second analysis combined the two gaze instruction conditions to evaluate gaze fixation regardless of location. For each set of analyses a between subjects ANOVA was used for total sickness score and a factorial

ANOVA was used for the subscale scores.

Separated gaze instruction groups analyses. For the total score analysis the IV

Condition (Control, Horizon, Road), was examined for any effect on the DV Sickness

(Simulator Sickness Questionnaire total score percentage). There was a marginally significant effect of Condition on Sickness: F (2, 54) = 2.48, p = 0.093, η2 = 0.084.

Pairwise comparisons showed marginally significant differences between Control and

Horizon (p = 0.085) and Control and Road (p = 0.073), but no significant difference between the gaze intervention conditions (p > 0.1). For the subscale scores analysis, the effects of IVs Condition (Control, Horizon, Road), and Scale (Nausea, Oculomotor

Discomfort, Disorientation), were measured on the DV percentage sickness scores. For this analysis, as predicted, a significant effect of Condition was observed on Score: F (2,

162) = 5.92, p = 0.003, η2 = 0.068. There was no significant effect of Scale and no interaction (both p > 0.1). Pairwise comparisons of the overall scores, by condition, showed significant differences between Control and Horizon (p = 0.011), Control and

Road (p = 0.004), but not between Horizon and Road (p > 0.1). For a graph of the mean scores see Figure 16.

Figure 16. Mean Simulator Sickness Questionnaire total and subscale percentage scores, across all three experimental conditions (+/- SEM).

Combined gaze instruction groups analyses. Because there were no differences between the gaze road and horizon conditions, data from the two conditions were averaged. For the total score analysis, the IV Condition (Control, Gaze Instruction) was examined for any effect on the DV Sickness (Simulator Sickness Questionnaire total score percentage). As predicted, Condition had a significant effect on Sickness with scores being lower in the gaze instruction condition: Control, M = 24.1, SEM = 2.54;

Gaze Instruction, M = 15.66, SEM = 2.77; F (1, 55) = 5.03, p = 0.029, η2 = 0.084. For the subscale scores analysis, the effects of Condition (Control, Gaze Instruction), and Scale

(Nausea, Oculomotor Discomfort, Disorientation) were measured as they affected

percentage sickness scores. For this analysis, as predicted, a significant effect of

Condition was observed on Score: F (1, 165) = 11.86, p = 0.001, η2 = 0.067. No

significant effect of Scale and no interaction were present (both p > 0.1). For the mean

sickness scores see Figure 17.

Figure 17. Mean Simulator Sickness Questionnaire total and subscale percentage scores

for the Control and combined Gaze Instruction conditions (+/- SEM).

Given the results of the sickness analysis, it appears that the important factor is fixation per se rather than the specific locus of fixation. All remaining analyses were conducted on the combined Gaze Instruction condition.

Postural stability. This analysis examined participant balance for any changes due to instructional condition (Control, Gaze Instruction). Balance was measured using the Tandem Romberg test as outlined in Experiment 1. Balance was scored in seconds for this analysis.

Data loss. Data from four participants were excluded from the analysis. Two opened their eyes, laughed, etc. during the test, making their data unusable. Another two

became sick and consequently they could not complete their post-drive tests. Three were lost in the Control condition (two participants were lost due to technical issues and one due to sickness, 9% of the participants in that condition). In the Gaze Instruction condition, one was lost due to sickness, which represented 4% of the data.

Analysis. To test the hypothesis that participants in the Gaze Instruction condition would show a greater drop pre to post-drive in their balance scores as compared to controls, a factorial ANOVA was used. This analysis looked at the IVs Condition

(Control, Gaze Instruction) and Time (Pre-Drive, Post-Drive) for any effect on the DV

Balance (Seconds). The analysis revealed a significant effect of Condition (overall lower score for Gaze Instruction; F (1, 51) = 6.53, p = 0.014, partial η2 = 0.114) and Time

(overall lower score post-drive; F (1, 51) = 8.518, p = 0.005, partial η2 = 0.143).

However, contrary to prediction, no significant interaction was present (p > 0.1). For a

graph of all balance scores by condition, see Figure 18.

Figure 18. Average time to hold the Tandem Romberg test (seconds) pre and post-drive,

by condition (+/- SEM).

Did the Instructional Program Affect Head Movement?

Along with eye movements, head movements are an important component of gaze behaviour. In addition, head stabilization could aid in reducing reliance on the vestibulo- ocular reflex during high conflict areas of the drive (for example the turns). Therefore, the instructional condition was analyzed for any affect on head movements.

Head movements. Head movements were defined as the amount of variability on the X and Y-axis during the curved potions of the drive. These data were collected exactly as outlined in Experiment 1. For these analyses, one pixel represented an area measuring 1.4 mm x 1.4 mm.

Data loss. Because of technical issues (e.g. camera movement, computer failure, etc.) 12 participants did not have complete data. In addition, data from two more participants were dropped because they did not have complete datasets (they became sick before the end of the experiment). In the control condition data from 11participants s were lost, 10 due to technical issues and 1 due to sickness (34%). In the gaze Instruction, data from 1 participant was lost due sickness (4% f the data).

Data pre-processing. As in Experiment 1, the head movement data came from three gradual right hand turns. Average X and Y coordinates were obtained by averaging across turns for each participant. As in the previous study, averaging only occurred after an analysis was conducted to ensure there were no systematic changes in head movements turn by turn: p > 0.1 for both the X and Y-axis.

Analyses. To test the hypothesis that X-axis head movements would be lower in the Gaze Instruction condition versus control, a factorial ANOVA with the IVs Condition

(Control, Gaze Instruction), and Axis (X, Y) and the DV Variability (SD of head

movement) was conducted. As predicted, this analysis showed a significant effect of

Condition with lower overall variability in the Gaze Instruction condition (F (1, 46) =

4.14, p = 0.048, partial η2 = 0.083). A significant effect of Axis was also observed with

overall variability being lower for the Y-axis (F (1, 46) = 103.51, p < 0.001, partial η2 =

0.692). There was also a marginally significant interaction (F (1, 46) = 42, p = 0.063, partial η2 = 0.073) and this interaction was in the predicted direction.

Due to the presence of the interaction, a simple effects analysis was conducted for

both the X and Y-axis variability. For the X-axis, as predicted, Condition had a

significant effect on Variability: Control M = 7.38, SEM = 0.617; Gaze Instruction M =

5.60, SEM = 0.544; F (1, 47) = 4.66, p = 0.036, partial η2 = 0.092. For the Y-axis, there

was no significant effect of Condition (p > 0.1). For a graph of all X and Y-axis

movements, see Figure 19.

Figure 19. Mean variability of head movement on the X and Y-axis for the curved

portions of the drive across condition, measured in pixels (+/- SEM).

Did the Gaze Instruction Affect Driving Behaviours?

Changes to gaze behaviour may have affected how the participants drove, and

driving behaviours could affect the level of conflict experienced. Therefore, it was

important to determine if differences in driving behaviours could be the cause for the

effects of the gaze instructions. The following analyses examined the four measured

driving variables for any change across condition (average speed, accelerator percent,

brake percent, steering variability). These variables were all analyzed for the same areas

as outlined in Experiment 1: the drive overall; the gradual turns; and the sharp turns. In

addition, scores were reduced and/or calculated as outlined in Experiment 1. For each

analysis, ANOVAs with the IV Condition (Control, Gaze Instruction) and the DV Score

(on the measured variable) were conducted. The only exception was that for average

speed factorial ANOVAs were used to evaluate the gradual and sharp turns. For these

analyses, the IVs Condition (Control, Gaze Instruction) and Turn Area (Enter, During,

Exit) were examined for any effect on the DV Average Speed (km/h). This was because

average speed was evaluated for multiple parts of the turns, while the remainder of the

analyses only looked at one particular aspect of the turns.

Data loss. Two participants became sick and did not have full data sets. Because

they were missing a portion of the turns and straight-road sections, which went into the averaged scores, their data were not used. One participant was in the Control condition

(3%) and one was in the Gaze Instruction condition (4%).

Average speed. Average speed was measured in km/h. For the drive overall there was no significant effect of condition on speed (p > 0.1). For the gradual turns, the

analysis revealed no significant effect of Condition or Area (both p > 0.1) and no

interaction (p > 0.1). For the sharp turns, results were the same. No significant effects emerged: no main effects of Condition or Area (both p > 0.1) and no interaction (p > 0.1).

Accelerator percent. Accelerator percent was defined as the percent the

accelerator pedal was depressed (0-100%). No significant effects emerged for the drive overall or gradual turns (both p > 0.1). A significant effect of Condition was observed on acceleration during the exits to the sharp turns with the Gaze Instruction condition showing increased press: Control M = 23.36, SEM = 1.80; Gaze Instruction M = 28.52,

SEM = 1.90; F (1, 56) = 4.03, p = 0.049, partial η2 = 0.067.

Brake percent. Brake percent was defined as the percent the brake pedal was

depressed (0-100%). For the drive overall, there was a significant effect of Condition on

percentage of brake depression with the Gaze Instruction condition showing lower

amounts: Control M = 1.94, SEM = 0.10; Gaze Instruction M = 1.56, SEM = 0.12; F (1,

57) = 5.402, p = 0.024, partial η2 = 0.087. For both entrances to the gradual and sharp

turns, there were no significant effects of Condition (both p > 0.1).

Steering variability. Steering Variability was defined as the SD of wheel position

for the entire section of interest (measured in degrees). For the drive overall, Condition

had no effect on SD of wheel position (p > 0.1). For the gradual turns, no significant

effect was observed (p > 0.1). For the sharp turns however, there was a marginally

significant effect of Condition with more variability in the control condition: Control M =

35.11, SEM = 1.847; Gaze Instruction M = 30.47, SEM = 1.979; F (1, 56) = 2.94, p =

0.092, partial η2 = 0.050.

Discussion

The main aim of this experiment was to determine if giving instructions to participants on how to fixate gaze while driving around corners in a simulator, could be effective in reducing simulator sickness during a first exposure to a novel virtual environment. In addition, a secondary goal was to determine whether it was the act of fixation per se as opposed to fixation on a specific location that was important. See Table

4 for a summary of all experimental results.

Table 4 Summary of Experimental Results Significant Effect of Condition? Direction or Effect as Predicted? Simulator Sickness Questionnaire Total Yes Yes Balance Post-Drive No No - No Interaction Head Variability X-Axis Yes Yes Head Variability Y-Axis No Yes Average Speed Overall No No - No Difference Average Speed Gradual Turn No No - No Difference Average Speed Sharp Turn No No - No Difference Accelerator Percent Overall No No - No Difference Accelerator Percent Gradual Turn No No - No Difference Accelerator Percent Sharp Turn Yes No - Opposite Brake Percent Overall Yes Yes Brake Percent Gradual Turn No No - No Difference Brake Percent Sharp Turn No No - No Difference Steering Variability Overall No No - No Difference Steering Variability Gradual Turn No No - No Difference Steering Variability Sharp Turn Yes Yes

The first step in achieving these goals was to determine if a set of verbal instructions (and accompanying picture and video) could train participants to elicit the desired gaze behaviour. Therefore, gaze was the first variable analyzed. This analysis showed that participants followed the given instructions approximately 90% of the time.

This result showed that it was possible to train participants in abstraction of the task itself and that any differences between the groups could be attributed to the gaze modification.

After it was determined that the participants were in fact modifying their behaviour, the question remained as to whether this behaviour modification facilitated

70 adaptation to the simulation. The analysis of participant sickness supported the hypothesis that fixating gaze reduces sickness. The initial analysis of both gaze instruction conditions showed that regardless of the fixation point, sickness was lower than control (for both total sickness and all subscale scores). In addition, because the fixation conditions showed no differences in their effect on sickness, I postulated that the location of fixation was not the key to the reduction but simply the act of fixating. Based on this conclusion, the instructional conditions were combined.

The analysis based on the combined gaze instruction conditions again showed a reduction for all Simulator Sickness Questionnaire scores (Total, Nausea, Oculomotor

Discomfort, and Disorientation). While the reductions are strong support for gaze modification as a sickness intervention, I did not initially expect that the intervention would affect all three subscales. This is because each subscale potentially taps into different aspects of sickness and it is surprising that this intervention affects them all in the same way. For example, when Jaeger and Mourant (2001) examined subscale scores between a fixed and motion-platform simulator they found differences in the relative increases and decreases of individual scales. They attributed these differences to the addition of motion cues to the simulation. It is therefore surprising that fixating gaze lowered all three scales.

One point to note about the sickness analysis is that I removed the participant in the Gaze Instruction condition who became sick after only 2-3 minutes in the simulation.

Obviously, gaze instruction did not help this participant! However, the onset of this participant’s sickness and emetic response was so quick that it appears that her predisposition to sickness was disproportionately larger than is normal. What I feel this

shows is that for the majority of simulation users an instructional program like this could

be useful in lowering overall sickness scores. However, this program obviously does not

work in every instance. Therefore, it is clear that changes in gaze behaviour, while

negotiating turns, cannot fully account for why some people become sick. Because of

this, further research needs to determine what other factors go into full acclimatization to

a novel virtual environment.

Along with sickness, postural instability post-drive was used as an indicator of adaptation to the virtual environment. The hypothesis was that gaze modification would affect acclimatization and result in the gaze instruction group showing lower post-drive

stability. The data did not support this hypothesis. The gaze instruction group was

significantly lower in their pre and post-drive stability and both groups showed

reductions in stability post-drive. However, compared to control, there did appear to be a

larger reduction in stability pre to post drive in the gaze instruction condition (possibly

indicating increased adaptation in that group). Because of the large group differences in

pre-drive balance though, any interpretation of the magnitude of the pre-post drop is pure

speculation. While the hope was that the Tandem Romberg could be an indicator of

acclimatization, it appears that like Experiment 1 the data are too variable to be of use in

the precise monitoring of changes in balance.

Head movements are an important component to gaze and a potential mechanism

to reducing the vestibulo-ocular reflex in high conflict situations. Therefore, the effects of

gaze position were measured as they affected head movement. The analysis of the head

movements on the X-axis supported the hypothesis. Participants in the gaze instruction

condition did lock their head into a more stable position during curved driving. This

result showed that the instructional program did affect head movements as well as eye

movements. The potential result is that participants are able to reduce reliance on

vestibular input during high conflict areas.

I designed the gaze instruction program with the goal of reducing sickness

through gaze modification. However, by modifying where participants look during the

curved driving this could affect how they drive through the simulation. The problem is

that changes in driving behaviour can also affect sickness (increasing or decreasing it).

For the average speed data, no differences were observed and thus it seems the gaze

intervention did not have its effects on sickness by virtue of its effect on driving speed.

The analysis of braking pressure in the gradual and sharp corners showed no

significant difference between the groups. However when the overall braking behaviour

for the entire drive was examined, a significant difference was observed between the

groups (the gaze instruction group showed significantly lower overall brake pressure).

Because of the magnitude of this difference, (0.3%) it is unlikely that it could produce a

noticeable effect in the conflict experienced. However, in a later experiment an analysis

of the relationship between various driving behaviours and sickness is conducted to

verify this conclusion (Experiment 4).

For the drive overall, and for the gradual turns the analyses of acceleration

behaviour showed no differences between the gaze instruction and control conditions.

Nonetheless, there was an increase in acceleration pressure in the gaze instruction group

when exiting the sharp curves. This finding could relate to the lower steering variability

observed in the gaze instruction group. If participants are more stable while traveling through the sharp curve, it may be that they are in a better position to accelerate out of the

73 curve. This finding is problematic given that increased acceleration could be responsible for increased anterior/posterior and pitch sensory conflict. The magnitude of the difference was 5.1% of accelerator depression. While it is unknown how large an effect this would have on the conflict experienced, it would be worthwhile to examine this further. It could be that the increases in sickness caused by the acceleration cancel out any decreases in conflict due to the reduction in steering variability. In either case, to develop this intervention further, an examination needs to be conducted on of the effects of acceleration on sickness.

For the steering variability, the analyses showed that for the drive overall and the gradual turns, no group differences were observed. However, steering variability in the sharp turns showed a marginal decrease in the gaze instruction group (SD 35.11 to SD

30.47; measured in degrees). The intervention did have the potential to change participant behaviour and in turn affect level of sickness. If participants are locking their head, it may allow them to navigate the sharp turns more precisely. However, it is unclear why this may be as the normal behaviour while negotiating corners is to look fully into the turn (as much as 50° relative to the car in a sharp 90° turn; Land, 2008). While the explanation for this behaviour is unclear, it is possible that reducing steering variability during sharp turns reduces medial/lateral and roll sensory conflict in these areas. At this point, it is unclear how strong of an effect steering variability has on sickness. In this experiment, on average participants turned the steering wheel 150° during sharp curves.

(This measurement encompasses the turning the wheel into the curve and straightening it after.) The gaze condition groups had approximately 5° less variability in steering than the control. It is unknown if a reduction in variability that small (compared to how much

the wheel is turned overall) could affect the visual scene in a significant way. Therefore,

for this intervention to be developed further, the effect on sharp turn steering and the

ramifications of this effect would need to be studied further (see Experiment 4).

This experiment showed that pre-exposure gaze instruction could modify behaviour, thus reducing sickness during the first exposure to a virtual environment driving simulation. The overall conclusion is that the observed reduction in sickness is the result of gaze and head stabilization during the curved portions of the road. This stabilization could provide a reduction in incongruent vestibular and proprioceptive egocentric motion cues during the processing of visual information in conflicting sensory situations. In addition, reducing head movements could reduce the activation of the vestibulo-ocular reflex; in turn, this could suppress conflicting vestibular information.

However, certain analyses indicate that in order to develop this intervention fully, some of the effects on driver behaviours and any subsequent change in sickness need further evaluation. One main issue with this intervention is that it does not appear to work for everyone. Nonetheless, if developed fully this intervention may be useful in reducing sickness in a large percentage of simulation users.

Chapter 4

Experiment 3

In Experiment 1, I showed that specific changes in participant gaze and head movements during the curved portions of two virtual driving simulations, two days apart, could be responsible for the reductions in simulator sickness observed between the

exposures. Following this, in Experiment 2 I showed that teaching participants to fixate

their gaze during the curved portions of a simulated drive reduced sickness during a first

exposure (regardless of where they fixated their gaze). The results of these two

experiments have shown that there is a clear link between level of sickness and

behavioural changes in a fixed-base driving simulator. To build on this further,

Experiment 3 examined the effects a simulator sickness intervention designed to reduce

sensory conflict by artificially presenting missing sensory input. To determine position in

space, the brain integrates visual, vestibular, and proprioceptive inputs. However, in a

fixed-base driving simulator the arrangement of these inputs does not match “normal”

experience. Specifically, the visual input gives the illusion of motion (vection) while the

vestibular and proprioceptive inputs signal the body is at rest. Under the predominant

theories of motion sickness, because of this mismatch sickness can arise. Therefore, an

intervention that artificially replicates the “normal” sensory arrangement should prevent

sickness from occurring.

One possible way to replicate these inputs artificially is through the application of

electrical stimulation. While in a fixed-base driving simulation, the vestibular or proprioceptive system could be stimulated in order to replicate the sensory inputs felt in the real world. For example, when driving around a simulated curve the vestibular or

proprioceptive system could be stimulated so that the user feels like they are experiencing

the centripetal acceleration “pulling” them. In our previous work, we have shown that

both of these types of stimulation (vestibular or proprioceptive) reduce simulator sickness

when applied during the turns and curves of a simulated drive in a fixed-base driving

simulator (Reed-Jones, Reed-Jones, Trick, & Vallis, 2007; Reed-Jones, Vallis, Reed-

Jones, & Trick, 2008; Reed-Jones, Reed-Jones, Trick, Toxopeus, & Vallis, 2009). In this previous work, we used Galvanic Vestibular Stimulation to provide vestibular

perturbations during driving simulations. To achieve this, electrodes were placed

bilaterally over participants’ mastoid processes and a current was applied to the

electrodes to directly stimulate the eighth cranial nerve afferents. We applied this

stimulation during the turns and curves in order to represent the real world vestibular

response to driving around the corners. The result of those studies was that participants

who received the stimulation showed significantly lower Simulator Sickness

Questionnaire scores (compared to no-stimulation controls). In addition to using

vestibular stimulation, one study used Galvanic Cutaneous Stimulation of the neck to

stimulate the stretch receptors and give the proprioceptive impression of being pulled to

one side (Reed-Jones et al., 2009). In this study, we applied the stimulation during the

turns and curves of a drive in a fixed-base simulator. The results of that study showed

that participants who received this proprioceptive stimulation had Simulator Sickness

Questionnaire scores that were marginally lower than control participants’ scores.

One problem with these studies however, was that they did not control for an

alternative explanation of the results. If the stimulation distracted attention, then it is

entirely possible that this served to make participants less aware of their own sickness.

This could have reduced sickness scores in two ways. First, it could have distracted them

from being conscious of their sickness, even if it was present. Second, it could have made

them less likely to recall how they felt accurately when asked later because they were not

paying attention during the simulation (the Simulator Sickness Questionnaire was

administered after the drive was over). There is evidence to show that electrical stimulation of a variety of types can lower a range of types of sickness. There is data to support the allegation that electrical stimulation of the wrists or neck alleviates postoperative nausea and vomiting (Lee & Fan, 2009; Cekmen, Salman, Keles, Aslan, &

Akcabay, 2007, respectively). However, there have been no conclusive findings as it relates to its use in preventing motion sickness. Some experimenters have found that wrist stimulation reduces motion sickness (Hu, Stritzel, Chandler, & Stern, 1995) while others have found it does not (Miller, & Muth, 2004). The problem with most of these sickness studies is that they postulate the mechanism behind the reductions in sickness to be the stimulation of acupressure points. These studies have no point of reference to

theories for why motion sickness occurs.

This experiment evaluated a single between subjects factor: Intervention Type

(Control, Assistive Vestibular Stimulation, Opposing Vestibular Stimulation). Assistive

Vestibular Stimulation provided a vestibular experience that reflected what would be felt

when driving around a corner in the real world. Opposing Vestibular Stimulation (as a

control condition) provided the opposite vestibular experience than both Assistive

Vestibular Stimulation and what would be felt in the real world while driving around a

corner. If electrical stimulation distracted attention, then simulator sickness should have

decreased regardless of the type of stimulation (assistive or opposing). Conversely, if

Assistive Vestibular Stimulation reduced sickness because it reduced conflict, then

Opposing Vestibular Stimulation should have increased sickness because it increased the

amount of conflict (stimulating the vestibular system in exactly the opposite way as

would normally occur during a curve).

As in Experiment 1 and 2, the experimental conditions were evaluated for their

effects on a number of dependent variables (sickness, balance, head movements, and

driving behaviour). To determine if the intervention was effective at reducing sickness,

Simulator Sickness Questionnaire total and subscales scores were analysed. I

hypothesised that sickness would be lower in the Assistive Vestibular Stimulation

condition compared to control, whereas sickness would be greater in the Opposing

Vestibular Stimulation condition. This is because the Assistive Vestibular Stimulation should reduce the sensory conflict whereas the Opposing Vestibular Stimulation should increase it. If however both types of stimulation decreased sickness, this would have supported the alternative theories.

Along with the inclusion of the Opposing Vestibular Stimulation condition for comparison, postural stability was used to measure acclimatization. If Assistive

Vestibular Stimulation removed sensory conflict, then adaptation to the simulation would not have been needed. This is because the sensory input would have been the same as the real world. Therefore, I hypothesised that for the Assistive Vestibular Stimulation condition there would be no difference in postural stability pre-drive to post-drive. For the Control and Opposing Vestibular Stimulation conditions however, stability should have dropped pre to post-drive as people began to acclimatize to the simulation. If however distraction is the key component to reducing sickness, then both of the

stimulation conditions should have shown no reduction in balance pre to post- drive as

both would be removing the conflict.

As an exploratory analysis, head movements were assessed. This analysis

highlighted possible alternate explanations for the mechanism through which Assistive

Vestibular Stimulation or possibly stimulation in general reduces sickness. For example if

the Assistive Vestibular Stimulation intervention changed head movements (as observed

in Experiments 1 and 2) this could be an alternative explanation for the effectiveness of

the intervention(s).

As with experiment 2, subjecting participants to an intervention could have

changed the way they drove within the simulation. To monitor this and identify any changes that could potentially affect sickness, driving behaviours were assessed.

Summary of Hypotheses

1) Simulator Sickness Questionnaire scores will be lower in the Assistive

Vestibular Stimulation condition compared to Control; Simulator Sickness

Questionnaire scores will be higher in the Opposing Vestibular Stimulation

condition than the Control condition.

2) In the Assistive Vestibular Stimulation condition, postural stability will

remain constant pre-drive to post-drive whereas in the other two conditions it

will not.

Method

Participants

Participants were recruited from the undergraduate participant pool at the

University of Guelph. Seventy participants were tested (48 females and 22 males; Age M

= 18.81, SD = 2.88). Participants were assigned to one of three experimental conditions

(Control, Assistive Vestibular Stimulation, or Opposing Vestibular Stimulation) in such a

way that propensity for simulator sickness was matched across conditions as much as

possible (age, sex, and simulator sickness pre-screening score). For a summary of participant characteristics by group, see Table 5. All participants received course credit for their participation in the experiment.

Table 5 Participant Characteristics by Experimental Condition Control Assistive Opposing Age in Years (M) 19.6 18.88 18.5 Age in Years (SD) 2.01 1.62 0.62 Number of Females 25 11 12 Number of Males 7 7 8 Simulator Sickness Pre-Screening Score (M) 0.88 0.61 1.05 Simulator Sickness Pre-Screening Score (SD) 1.13 0.78 1.00

Apparatus and Materials

In addition to the psychological tests (Medical History Questionnaire, Simulator

Sickness Pre-screening Questionnaire, Simulator Sickness Questionnaire, and the

Tandem Romberg test) and apparatus (eye track monitor and driving simulator ) used in

Experiment 1, Experiment 3 employed a stimulation device. Specifically, an A395 Linear

Stimulus Isolator provided stimulation to the participants in the Assistive Vestibular

Stimulation and Opposing Vestibular Stimulation conditions.

Procedure

Overall procedure. The overall procedure for this study followed the basic structure as outlined in Experiment 1. However, only one experimental session was conducted totalling approximately 1.5 hours. It included the following steps: administration of measures of medical history and risk of simulator sickness; screening

out of those at the highest level of risk (none were); a pre-drive balance test; a briefing

(Appendix D); a 20-minute drive in the simulator; a post-drive balance test; and an

administration of the Simulator Sickness Questionnaire. However, the manipulation in

this study involved exposing the participants to one of the three interventions (Control,

Assistive Vestibular Stimulation, or Opposing Vestibular Stimulation).

Stimulator setup and calibration. The setup and calibration of the stimulator

was conducted prior to participants entering the simulator. Preparation for the electrode

application involved swabbing the area behind each ear with alcohol to remove the

presence of dead skin. Self-adhering electromyography electrodes were then applied to

the skin over the right and left mastoid processes (the bony prominences behind each

ear). This placement allowed for direct stimulation of the eighth cranial nerve afferents.

The threshold testing for the Assistive Vestibular Stimulation and Opposing Vestibular

Stimulation conditions consisted of determining the level of stimulation that was

associated with a "sense of movement" perceived by the participant, and a very small

head movement towards the anode (visually determined by the investigators). The

threshold for each participant was assessed; the average threshold was approximately 0.6

mA.

Stimulation intervention details. The control group drove the simulated route

with no intervention and only instructions on how to drive the simulator (Appendix D).

The Assistive Vestibular Stimulation and Opposing Vestibular Stimulation participants also received the driving instructions. In addition, the intervention groups received vestibular stimulation at various points in the drive. For each participant, the stimulation applied was two times his or her threshold. This calibration resulted in average

stimulation strength of 1.2 mA. This procedure was used because it resulted in a

stimulation strength that produced an effect, but at the same time was comfortable for

most participants (Bent, McFadyen, Merkley, Kennedy, & Inglis, 2000). In addition,

afferent stimulation of ~1 mA signals a rotational acceleration of ~2°/s2 (Fitzpatrick &

Day, 2004), thus using this level of stimulation reflected dynamic inertial cues that were

either similar (assistive) or opposite (opposed) to those experienced during curved driving in the real world.

During the experimental drive, a researcher sat in the back seat of the simulator

behind the driver (no researcher was in the back seat for the control participants). While

the presence of the researcher could have provided a distracting stimulus in and of itself,

a previous study shows significant effects of stimulation even when there was an

individual in the back seat in the control condition (Reed-Jones et al., 2009).

The same researcher was responsible for conducting all of the stimulations to

ensure consistency. During the curved portions of the drive, the researcher manually

turned on the stimulation whenever the participants began a turn, and turned it off when

the turn was completed. The stimulation had an immediate onset and termination and was

constant throughout each turn. The exact moment the stimulation began was when the

researcher saw the participant move the steering wheel from straight ahead. It ended

when the wheel returned to straight ahead. In the Assistive Vestibular Stimulation

condition, participants received stimulation in the direction appropriate to real world

driving. For example, in the real world when driving around a right hand curve the lateral

acceleration pulls you to the left. The natural compensation for this pull is to act against

it, leaning right. The stimulation applied in this situation activated the participants’

83 vestibular systems in the same way. It gave the impression that they were tilted to the left and their bodies naturally compensated by tilting right. In the Opposing Vestibular

Stimulation condition, the participants received stimulation in the opposite direction as would be felt in the real world. For example, when traveling around a right hand curve the stimulation gave the impression of being pulled to the right (the opposite of what is felt in the real world). This in turn elicited a response of tilting to the left; again, the opposite reaction to the real world response. If any participant was uncomfortable with the stimulation, either during the threshold testing or during the drive, the stimulation was stopped and not used on that participant again.

Data Manipulation and Results

This experiment examined the effects of a simulator sickness intervention designed to give approximated real world sensory input during simulated driving. The variables measured were level of sickness (Simulator Sickness Questionnaire), balance

(Tandem Romberg), head movements (X and Y-axis), and driving performance (average speed, acceleration, braking, and steering variability). Where appropriate, the analyses followed a progression similar to the previous experiments. Variables were analyzed for data loss and screened. Pre-processing was conducted as needed (e.g. collapsing data across all turns of a drive). Then the main analysis on each variable was performed.

Did Assistive Vestibular Stimulation and Opposing Vestibular Stimulation affect

Sickness and Postural Stability?

Simulator sickness. This analysis examined whether the interventions (Assistive

Vestibular Stimulation and Opposing Vestibular Stimulation) resulted in an increase or decrease in the level of simulator sickness. Level of sickness was determined based on

scores on the Simulator Sickness Questionnaire. Total score and all subscale scores were

evaluated (Nausea, Oculomotor Discomfort, and Disorientation). As in the previous

experiments, the scores for each scale were converted to their percent of maximum for

ease of analysis (range of 0-100).

Data loss/screening. As with the previous experiments, the screening procedure

used the criterion of a Simulator Sickness Questionnaire score above or below 2.5

standard deviations from the mean. No participants were excluded based on this criterion.

Analysis. To test the hypotheses that the Assistive Vestibular Stimulation

condition would show lower Simulator Sickness Questionnaire scores than control, and

that the Opposing Vestibular Stimulation condition would show higher scores than

control, a between subjects ANOVA was run. This analysis tested the IV Condition

(Control, Assistive Vestibular Stimulation, Opposing Vestibular Stimulation) for any

effect on the DV Sickness (Simulator Sickness Questionnaire Total score). The test

revealed a main effect of Condition: F (2, 67) = 3.8, p = 0.027, partial η2 = 0.102; Control

M = 23.67, SEM = 2.59; Assistive Vestibular Stimulation M = 12.35, SEM = 3.45;

Opposing Vestibular Stimulation M = 16.31, SEM = 3.27. As was predicted, Assistive

Vestibular Stimulation showed significantly lower scores than Control (p = 0.029).

Contrary to prediction, Opposing Vestibular Stimulation showed marginally lower scores

than Control (p = 0.083), and in fact, the average for the Opposing Vestibular Stimulation condition was not significantly different from Assistive Vestibular Stimulation (p > 0.1).

For the Simulator Sickness Questionnaire subscales analysis, a factorial ANOVA

explored the effects of the IVs Condition (Control, Assistive Vestibular Stimulation,

Opposing Vestibular Stimulation) and Scale (Nausea, Oculomotor, Disorientation), on

85 the DV Score. A significant effect of Condition was observed: F (2, 67) = 3.81, p =

0.027, partial η2 = 0.102. As predicted, Assistive Vestibular Stimulation was lower than

Control (p = .010). Again, contrary to prediction, Opposing Vestibular Stimulation was marginally lower than Control (p = 0.092) and showed no significant difference from

Assistive Vestibular Stimulation (p > 0.1). For this analysis there was no effect of Scale, and no interaction was observed (both p >. 1). For a graph of all Simulator Sickness

Questionnaire scores, see Figure 20.

Figure 20. Mean Simulator Sickness Questionnaire total and subscale percentage scores, across all three experimental conditions (+/- SEM).

Postural instability. This analysis examined participant balance for any changes due to instructional condition (Control, Assistive Vestibular Stimulation, and Opposing

Vestibular Stimulation). As in Experiments 1 and 2 balance was measured using the

Tandem Romberg test and scored in seconds.

Data loss. As in the previous studies, some data were lost because participants laughed, opened their eyes etc. during the test. Data from one participant was lost in this

way. Data from another two were lost because the participants became ill. For the control

condition, one participant was lost due to technical issues and one due to sickness (9% of the data in that condition). For the Opposing Vestibular Stimulation condition, one

participant was lost due to sickness (5% of the data in that condition).

Analysis. To test the hypothesis that in the postural stability in the Assistive

Vestibular Stimulation condition would be the similar pre and post-drive, a factorial

ANOVA was conducted. This analysis tested Condition (Control, Assistive Vestibular

Stimulation, Opposing Vestibular Stimulation), and Time (pre-test, post-test) for any effects on Balance (seconds). Contrary to prediction, no significant effect of Condition and no significant interaction were observed (both p > 0.1). However, a significant effect of Time on balance was observed with overall lower balance scores post-drive: F (1, 63)

= 8.792, p = 0.004, partial η2 = 0.122. For a graph of all balance scores, see Figure 21.

Figure 21. Average time to hold the Tandem Romberg test (seconds) pre and post-drive, by condition (+/- SEM).

Did Assistive Vestibular Stimulation and Opposing Vestibular Stimulation Affect

Head Movements?

The explanation for why Assistive Vestibular Stimulation lowers sickness was

that it negates sensory conflict and thus sickness. However, if the application of the

stimulation affected head movements, this could offer a possible alternate explanation for any reductions in sickness. Therefore, this analysis examined head movements for any

effect of intervention condition (Control, Assistive Vestibular Stimulation, and Opposing

Vestibular Stimulation) on both the X and Y-axis. Head movements were defined as the

head variability on the X and Y-axis during the curved potions of the drive (measured in pixels; 1.4 mm x 1.4 mm). These data were collected as outlined in Experiments 1 and 2.

Data loss. Head movement data could be lost through multiple technical issues

(camera shift, computer failure, etc.). Because of this, 12 participants did not have complete data. In addition, three participants became sick and could not complete the drive and post-drive tests. In the Control condition, 10 participants were lost due to technical issues and one became sick (34%). For the Assistive Vestibular Stimulation condition, one was lost due to technical issues (5.5%). For the Opposing Vestibular

Stimulation condition, one was lost due to technical issues and one was sick (10%).

Data pre-processing. The head movement data were collected during three right hand turns during the drive (as outlined in Experiment 1). These turns data were collapsed to obtain one value for X and Y per participant. (Averaging across turns was justified given a preliminary analyses that showed there were no systematic changes in head movements turn by turn in the X or Y-axis: both p > 0.1.)

Analysis. To determine if any significant differences in head movements were

present, a Factorial ANOVA was conducted. This analysis examined the IVs Condition

(Control, Assistive Vestibular Stimulation, and Opposing Vestibular Stimulation) and

Axis (X, Y) for any effect on the DV Variability (SD of head movement). Interestingly, while no significant effect of Condition was present (p > 0.1) there was a significant effect of Axis (F (1, 53) = 120.04, p < 0.001, partial η2 = 0.694) and a significant

interaction (F (1, 53) = 20.998, p = 0.003, partial η2 = 0.195). Because of the presence of

the interaction a simple effects analysis was conducted for both the X and Y-axis movements.

For the X-axis, a significant main effect of Condition was observed on Variability with the Opposing Vestibular Stimulation condition showing lower variability than both the Assistive Vestibular Stimulation and Control conditions: F (1, 53) = 3.347, p = 0.043, partial η2 = 0.11; Control M = 7.38, SEM = 0.666; Assistive Vestibular Stimulation M =

7.65, SEM = 0.740; Opposing Vestibular Stimulation M = 5.25, SEM = 0.720. Pairwise

comparisons revealed that the variability in the Opposing Vestibular Stimulation

condition was significantly lower than both the Control (p = 0.035) and Assistive

Vestibular Stimulation (p = 0.024) conditions. The Y-axis analysis showed no significant

effect of Condition on Variability (p > 0.1). For a graph of mean head movement

variability, see Figure 22.

Figure 22. Mean variability of head movement on the X and Y-axis for the curved portions of the drive across condition, measured in pixels (+/- SEM).

Did Assistive Vestibular Stimulation, and Opposing Vestibular Stimulation Affect

Driving Behaviours?

Any intervention that could affect how the participants drove could affect their

level of sickness. Therefore, it was important to rule out changes to driving behaviour as

being responsible for changes observed. The following analyses examined the four

measured driving variables (average speed, accelerator percent, brake percent, and

steering variability) for any change across condition. All driving variables were analyzed

for the same areas as outlined in Experiment 1: the drive overall; the gradual turns; and

the sharp turns. In addition, data were reduced and/or calculated as outlined in

Experiment 1. Each analysis consisted of an ANOVA with the IV Condition (Control,

Assistive Vestibular Stimulation, and Opposing Vestibular Stimulation) and the DV

Score (on the measured variable). The exception was average speed where two factorial

ANOVAs were used, one each for the gradual and sharp turns. For these analyses, the

IVs Condition (Control, Assistive Vestibular Stimulation, and Opposing Vestibular

Stimulation) and Turn Area (Enter, During, Exit) were examined for any effect on the

DV Average Speed (km/h).

Data loss. Three participants became sick and did not have full data sets. Because

they were missing some of the turns and straights that went into the averaged scores their

data were not used. Control 1 lost (3%), Opposing Vestibular Stimulation 2 lost (20%).

Average speed. Average speed was measured in km/h. The first analysis showed

that for the drive overall, no effect of Condition was present (p > 0.1). For the gradual

turn analysis, no effect of Condition was observed (p > 0.1). However, Turn Area had a

significant effect on Speed: Exit > Enter > During; F (1.4, 88.23) = 6.423, p = 0.007,

partial η2 = 0.093 (Greenhouse-Geisser correction). No significant interaction was present

(p > 0.1). For a graph of mean speeds in the gradual turns, see Figure 23.

Figure 23. Mean average speeds during the gradual turns, by area (+/- SEM).

For the sharp turn analysis, no significant effect of Condition was observed (p >

0.1). As with the gradual turns, a significant effect of Turn Area was present: Enter >

During > Exit; F (1.8, 113.16) = 3.519, p = 0.038, partial η2 = 0.053 (Greenhouse-Geisser

correction). In addition, a marginally significant interaction was present (F (3.59, 113.16)

= 2.195, p = 0.081, partial η2 = 0.065, Greenhouse-Geisser correction). Due to the

presence of an interaction, post hoc tests for average speed during sharp turns were

conducted. However, none of these analyses revealed a significant effect of Condition on

Speed for any of the three areas (all p > 0.1). For a graph of average speeds during the

sharp turns, see Figure 24.

Figure 24. Mean average speeds during the sharp turns, by area (+/- SEM).

Accelerator percent. Accelerator percent was defined as the percent the

accelerator pedal was depressed (0-100%). For the overall drive, no effect of Condition was observed (p > 0.1). For the analysis of the gradual turns, a marginally significant effect of Condition was revealed with the Assistive Vestibular Stimulation and Opposing

Vestibular Stimulation conditions, which had higher accelerator percentage than Control:

F (2, 64) = 3.119, p = 0.051, partial η2 = 0.089; Control M = 18.66, SEM = 1.08; Assistive

Vestibular Stimulation M = 23.29, SEM = 1.95; Opposing Vestibular Stimulation M =

21.56, SEM = 1.23. Pairwise comparisons showed that while the Assistive Vestibular

Stimulation condition was significantly higher than Control (p = 0.019) the Opposing

Vestibular Stimulation condition was not (p > 0.1). In addition, the Assistive Vestibular

Stimulation and Opposing Vestibular Stimulation conditions were not significantly different (p > 0.1). For the analysis of the sharp turns no significant effect of condition was present (p > 0.1).

Brake percent. Brake percent was defined as the percent the brake pedal was depressed (0-100%). For the analysis of the overall drive, no significant effect of

Condition was observed (p > 0.1). There was a significant effect when entering gradual turns with the Assistive Vestibular Stimulation and Opposing Vestibular Stimulation conditions showing lower braking percentage than Control: F (2, 64) = 3.921, p = 0.025, partial η2 = 0.109; Control M = 0.95, SEM = 0.28; Assistive Vestibular Stimulation M =

0.26, SEM = 0.15; Opposing Vestibular Stimulation M = 0.10, SEM = 0.06. Pairwise comparisons showed that the brake percent was significantly lower than Control for both the Assistive Vestibular Stimulation (p = 0.043) and Opposing Vestibular Stimulation (p

= 0.014) conditions. The Assistive Vestibular Stimulation and Opposing Vestibular

Stimulation conditions showed no significant differences (p > 0.1). The analysis of the sharp turns revealed no significant effect of condition (p > 0.1).

Steering variability. Steering Variability was defined as the SD of wheel position for the entire section of interest (measured in degrees). The analyses conducted revealed no effect of Condition on Variability for any of the three areas of interest (all p > 0.1).

Discussion

This experiment had two goals. First, it examined the effectiveness of Galvanic

Vestibular Stimulation as an intervention to reduce simulator sickness. Second, it investigated the underlying mechanism responsible for Galvanic Vestibular Stimulation reducing sickness. In order to achieve these goals, three experimental conditions

(Control, Assistive Vestibular Stimulation, and Opposing Vestibular Stimulation) were evaluated for their effects on sickness and acclimatization, head movements, and driving behaviour. See Table 6 for a summary of all experimental results.

Table 6 Summary of Experimental Results Significant Effect of Assistive Significant Effect of Opposing Direction of Effect as Predicted? / Vestibular Stimulation? Vestibular Stimulation Other Observations Simulator Sickness Questionnaire Total Yes Yes (Marginal) No - Both Reduced Balance Post-Drive N/A N/A No - All Conditions Reduced Head Variability X-axis No Yes - Lower Than Both N/A Head Variability Y-axis No No N/A Average Speed Overall No No N/A Average Speed Gradual Turn No No Effect of Area on Speed Average Speed Sharp Turn Yes- Within Areas of the Turn Yes- Within Areas of the Turn Effect Not Present in Control Condition Accelerator Percent Overall No No N/A Accelerator Percent Gradual Turn Yes No N/A Accelerator Percent Sharp Turn No No N/A Brake Percent Overall No No N/A Brake Percent Gradual Turn Yes Yes N/A Brake Percent Sharp Turn No No N/A Steering Variability Overall No No N/A Steering Variability Gradual Turn No No N/A Steering Variability Sharp Turn No No N/A

As was hypothesised, an application of Assistive Vestibular Stimulation during the curved portions of a simulated drive in a fixed-base simulator did reduce sickness compared to the no-intervention controls. This result replicated previous research showing the same effect. The surprising result however was that Opposing Vestibular

Stimulation also reduced sickness (albeit only marginally so). The application of

Opposing Vestibular Stimulation during the curves should have increased conflict in those situations, and in turn increased sickness. This result suggests that the stimulation’s effect on the vestibular system may not be the reason sickness reduces, and that the

94 presence of stimulation in general may be responsible for reducing sickness through some other mechanism.

These are only the results of a single experiment, and results should be replicated before firm conclusions can be made. However, if both Assistive Vestibular Stimulation and Opposing Vestibular Stimulation reduce sickness, there are a variety of explanations for this effect. First, the physical response to the opposing stimulation is for the head to tilt in the direction that it would be if real world lateral force were pulling it. For example, when travelling around a right hand curve the lateral force pulls the head left

(and then compensation occurs). The response to the opposing stimulation is that the body automatically “corrects” by tilting the head left in response to the vestibular cues.

This physical response could create a situation where the vestibular system does not match vision, but the proprioceptive system matches both vision and the “experience” normally associated with that situation. This could reduce conflict in one way (through correct proprioception), while simultaneously increasing it in another (through incorrect vestibular).

A second set of explanations for this effect are related to electric stimulation and attention. If electrical stimulation diverts attention (this assumption needs to be tested), it could have several different effects. First, given that perceptions of discomfort are prominent in conscious experience, electric shock could simply make participants less aware of their feelings of stomach discomfort, eyestrain, etc. Second, the electric shock might interfere with participants’ memory for how they felt during the drive (the

Simulator Sickness Questionnaire was administered post-drive).

However, a third explanation may be better. It is possible that attention moderates

the strength of the induced self-motion illusion produced by visual stimulation (vection).

In two studies Seno, Ito, and Sunaga (2009, 2011) showed that there could be a direct link between attention and vection. In Experiment 1, Seno, Ito, and Sunaga (2009) presented participants with a display that showed two sets of moving luminance-defined gratings. One grating was in the centre of the display and one surrounded it. Each set of gratings moved in opposition to one another, one up, and one down. When participants attended the surrounding gratings, they reported stronger feelings of vection then when they did not directly attend them. This experiment defined the “strength” of vection as the amount of time during a single trial the participants reported the feeling of motion

(recoded by a button press). To follow this up, Seno, Ito, and Sunaga (2011) presented participants with a moving, full field, luminance defined grating. In addition to a grating only condition, the researchers had participants complete one of two cognitive tasks superimposed onto the grating. The first task was the RSVP (Rapid Serial Visual

Presentation), it required the participants to fixate on a rapid serial visual sequence of letters displayed the centre of the display and count the number of times an X appeared.

While completing this task, participants also pressed a button whenever they felt vection.

The second task was a multiple-object tracking task. In this condition participants were

required to fixate on the centre of the display and track a number of circles (in a field of

identical circles) while they moved about the display. At the end of the trial, the

participant had to identify which of the circles were the original ones assigned to be

tracked. In addition, during the trial the participants were required to indicate through

button press when they felt vection. Even though both cognitive tasks were superimposed

onto the gratings, they only obscured a small portion of the display. What this study

showed was that in the cognitive task conditions, the self-report of vection was lower than the control condition. From the results of these studies, Seno, Ito, and Sunaga postulated that because attention directed at motion cues (moving gratings) increased vection and attention directed away from motion cues lowered vection (through a cognitive task) then attention must be a required component of vection.

If electrical stimulation does divert attention, then the result could be a reduction in the induced self-motion illusion. If participants did not have a visually induced

“feeling of motion”, that would mean there would be less conflict between vision and the other senses. This would in turn reduce conflict and therefore sickness. Specifically, if electrical stimulation removed or reduced the illusion of motion in a fixed-base simulator then the visual experience would match the vestibular and proprioceptive experience

(therefore, no adaptation would be needed). This interpretation would explain why different types of stimulation (not just vestibular) reduce simulator sickness.

The postural instability measure was included in order to help highlight the mechanism of action for Assistive Vestibular Stimulation; however, as with Experiments

1 and 2 the data were contrary to prediction. Because Assistive Vestibular Stimulation should have removed conflict during the turns, people should not have needed to adapt to those portions of the drive (they should have been the same as in the “real” world). What the data showed was that in all conditions postural stability post-drive was lower than pre-drive. This created a problem for both theories. It conflicted with the theory that

Assistive Vestibular Stimulation reduces conflict and in turn reduces the need for adaptation to occur. In addition, it conflicted with the attention-related theory. If

stimulation in general reduced vection (and in turn conflict) then both stimulation

conditions should have shown no reduction pre to post-drive. To address these problems two important points must be noted. First, under either theory, the stimulation may not have been 100 percent effective in either alleviating conflict or reducing vection (the visually induced illusion of self-motion). Therefore, stimulation may only be partially effective, leaving the need for some acclimatization to occur. Second, as was pointed out in Experiment 1, the Tandem Romberg test may yield inconclusive results, either from ceiling effects or from participant inconsistencies due to footwear.

While the findings related to sickness showed support for an attention-related account of the results, the analysis of the head movement variability possibly offers an alternative explanation. In the previous two experiments, I have shown a clear link between reducing head movements on the X-axis and reducing sickness. In this experiment, the Opposing Vestibular Stimulation condition showed significantly lower

X-axis head movements compared to Assistive Vestibular Stimulation and Control. If

Opposing Vestibular Stimulation reduces X-axis movements then it is entirely possible that this behaviour was effective in reducing sickness. Opposing Vestibular Stimulation was increasing conflict while at the same time inducing a counteracting behaviour

(bracing the head). The question remains though, why did Opposing Vestibular

Stimulation reduce X-axis movements? One speculation is that because the head movement was “correct” in terms of being pulled by the lateral force, then participants could have been responding by correcting back to centre (as if they were really driving).

In contrast, in the Assistive Vestibular Stimulation condition the stimulation would reflect normal forces acting on the person (and the correct head response), and as such

98 would not elicit this kind of corrective response. Because this is speculation, if an

Assistive Vestibular Stimulation intervention to be fully understood/developed, future work needs to clarify its exact mechanism of action.

One very interesting set of findings was how Assistive Vestibular Stimulation and

Opposing Vestibular Stimulation affected driving behaviours. When driving in the real world the medial/lateral forces exerted during driving around a curve help the driver determine how stable the vehicle is, and people use this information to modify their driving accordingly. What this experiment showed is that Control participants showed little modulation in speed when driving around the gradual or sharp turns – consistent with no feedback. The Assistive Vestibular Stimulation and Opposing Vestibular

Stimulation participants however appeared to modulate their speed during the turns in a way that more closely resembles driving in the real world – slowing down in the curve and accelerating out. While this was one set of results, it was an interesting finding that both Assistive Vestibular Stimulation and Opposing Vestibular Stimulation participants showed these behaviours. It is not entirely clear how participants would use feelings of opposite medial/lateral acceleration to help modify speed. Perhaps the mere presence of the artificial force resulted in a reaction based on recovering an unstable vehicle back to straight regardless of the direction of the force. Even more simply, perhaps Assistive

Vestibular Stimulation and Opposing Vestibular Stimulation participants were reacting to the stimulation by letting up on the accelerator during the stimulation. Regardless of the mechanism, this result shows that participants in the intervention conditions negotiate turns similar to how they would in the real world. This is an important effect if clinical

99 and research settings use this intervention. Future research should however fully explore why this effect occurs.

This experiment achieved its goals. It replicated previous research showing that

Assistive Vestibular Stimulation was a useful intervention in reducing simulator sickness in a fixed-base simulator (during a first exposure/drive). In addition, the use of Opposing

Vestibular Stimulation as a control condition helped to identify attention-related factors as a potential explanation of how electrical stimulation may reduce simulator sickness.

This finding is important for two reasons. First, even though a link needs to be shown between stimulation and attention, it does show a potential relationship between attention, sensory conflict, and sickness. Second, in fixed-base simulators the self-motion illusion (vection) is the key to giving people the feeling as if they are part of the simulation. Therefore, these findings could potentially show an aspect of simulator sickness that is unique among the family of motion related sicknesses.

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Chapter 5

Experiment 4

Experiments 1 through 3 were designed to highlight any changes in participant behaviours (natural or through interventions) that could reduce sickness in a fixed-base

driving simulator. While learning what behaviours can contribute to sickness is

paramount in helping alleviate sickness, these studies did not address the question of why

there are notable individual differences in propensity to illness. To study the role of

individual differences in sickness, Experiment 4 combined all of the information about

individual differences in participant characteristics and driver behaviours from the

previous experiments. First, I examined individual difference measures for any

correlations to level of sickness. These individual difference measures were presence,

immersive tendencies, risk factors (present feelings of illness, or a history of motion

sickness), video game use, and driving history. The goal of this part of the study was to

provide information to researchers or trainers that they could use to identify high-risk

individuals prior to them entering a virtual environment.

Presence and immersive tendencies were the first variables of interest. Witmer

and Singer (1988, p. 225) define presence as “the subjective experience of being in one

place or environment, even when one is physically situated in another”. Witmer and

Singer have shown that presence and simulator sickness correlate negatively. Because

presence directly relates to the quality of a simulation (low quality simulations can reduce

feeling present) this correlation could reflect reactions to less than optimal sensory input.

In order to measure presence the Presence Questionnaire was used (Witmer & Singer,

1988). Witmer and Singer based the Presence Questionnaire on four factors thought to

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tap into the underlying mechanisms of presence. The first factor was Control, measuring

the level of control a person felt within an environment (e.g. “How natural did your

interactions with the environment seem?”). The second factor was Sensory, measuring a

person’s perceived ability to explore an environment through their senses (e.g. “How

compelling was your sense of moving around inside the virtual environment?”). The third

factor was Distraction, measuring a person’s awareness of the real world and/or the

simulator interface mechanism (e.g. “How much did the visual display quality interfere or

distract you from performing assigned tasks or required activities?”). The final factor was

Realism, measuring the perceived connectedness and continuity of the stimuli (e.g. “How

inconsistent or disconnected was the information coming from your various senses?”).

When put together these four factors have been proposed to capture a person’s feeling of

being present within any given virtual environment.

Witmer and Singer (1988, p. 226) define immersion “the perception of being

enveloped by, included in, and interacting with an environment that provides a

continuous stream of stimuli”. This is different from presence in one key way. Presence is

a feeling of being within a virtual environment yourself (such as in a simulator), whereas

immersion is the feeling of taking a predetermined role in an environment (such as

becoming “lost” in a novel). Even though there is a difference, immersion is a key

variable in establishing presence. Therefore, by measuring predisposition to immersion

we attempted to predict the level of presence they felt within a simulation. This could be

a useful tool to evaluate people pre-exposure for simulator sickness risk as presence and sickness negatively correlate (Witmer & Singer, 1988).

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In order to test for immersive tendencies, the Immersive Tendency Questionnaire was used (Witmer & Singer, 1988). This questionnaire has been correlated with the

Presence Questionnaire (r = 0.24, p < 0.01). Therefore, it could make an excellent predictor of potential sickness. The Immersive Tendency Questionnaire measured three factors to determine immersive tendency. The first was Involvement, measuring how engrossed a person felt when taking part in third person interactions (such as reading a book, or watching television). An example question is “Do you ever become so involved in a movie that you are not aware of things happening around you?” The second factor was Focus, measuring how much a person could ignore external events while undertaking a task (e.g. “How good are you at blocking out external distractions when you are involved in something?”). The third factor was Games, measuring previous self-report of immersion in video games (e.g. “Do you ever become so involved in a video game that it is as if you are inside the game rather than moving a joystick and watching the screen?”).

When combined, these factors predict a person’s tendency to become immersed in a virtual environment.

Based on this information, the hypotheses made were that the presence score would correlate negatively with sickness scores, and that the immersive tendencies score would correlate positively with the presence score (both replications of previous work;

Witmer & Singer, 1988). However, I also hypothesised that because immersive tendencies are a component of establishing presence, and presence relates to sickness, that immersive tendency score would also negatively correlate with sickness. If this is the case then immersive tendencies could be used as a potential pre-screening for simulator sickness.

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Other individual difference variables of interest in this part of the experiment were risk factors such as feeling unwell or having a history of motion sickness (as outlined in the Simulator Sickness Pre-Screening Questionnaire, Appendix B). Two factors are included in the pre-screening: current sickness (cold, hangover, etc.), and history of motion sickness. Because previous research has shown that both these factors relate to simulator sickness then this questionnaire should correlate with sickness, validating its use (Stanney et al., 2002; Stanney & Kennedy, 2009). The hypothesis was that a positive correlation would be present between score on the Simulator Sickness Pre-

Screening Questionnaire and the measures of sickness.

The final two factors examined in this part of the experiment were related to past behaviours of the participant (video game use and driving history). The inclusion of these variables was an exploratory analysis examining any possible links between partaking in behaviours similar to those involved in a driving simulation and sickness. Video game use was chosen as it involves interacting with a simulation of an environment, albeit not a fully immersive one. Driving history was chosen as it directly involves the activity simulated in this research. Because this analysis was only exploratory, no specific hypotheses were made.

The second set of participant characteristics examined was how participants actually drove and behaved in the simulator. Specifically, seven participant variables were examined for any relationships to level of sickness: head movement variability during the gradual turns; balance as measured by the Tandem Romberg Test (seconds); and four driving variables (speed, acceleration percent, brake percent, steering variability).

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Head movement variability was examined because all three of the previous

experiments showed that it changed along with session and intervention. The prediction

was that reductions in head movement variability (on the X-axis) reduced conflict and sickness. Therefore, the hypothesis was that a positive correlation should be present for

X-axis head movement variability and measures of sickness. Even though balance

measures did not support any of the hypotheses in the previous experiments, an

exploratory analysis looked for any trends were present that could relate balance to

sickness. Finally, four driving behaviours were examined during the whole drive and

during the sharp and gradual turns (average speed, accelerator percent, brake percent, and

steering variability). Each of the driving variables measured could have affected the

medial/lateral or anterior/posterior sensory conflict within a simulation. For example,

increased pressure on the accelerator as participants accelerated out of a turn could have

created a situation where participants experienced a rapid change in optic flow (signalling

movement) combined with no sense of motion from the vestibular or proprioceptive

systems. This could have increased the anterior/posterior conflict and in turn increased

sickness. I hypothesized that because each of the variables could have increased conflict

there would be positive correlations between each of these variables and the measures of

sickness.

Summary of Hypotheses

1) Presence score will correlate negatively with sickness scores.

2) Immersive tendencies score will correlate positively with presence score.

3) Immersive tendency score will correlate negatively with sickness.

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4) Simulator Sickness Pre-Screening Questionnaire score will correlate

positively with sickness scores.

5) X-axis head movement variability will correlate positively with sickness

scores.

6) All measured driving variables will correlate positively with sickness scores:

(faster average speed; stronger accelerator and brake press; and increased

steering variability).

Method

Participants

For this experiment, I pooled all the participants from the previous experiments.

For Experiment 1 participants, only their Day 1 data were included. This pooling resulted

in the inclusion of 98 participants in these analyses. The composition was 68 females and

30 males with a mean age of 19.2 years (SD = 1.75).

Apparatus and Materials

Questionnaires. For rating immersive tendencies and presence, the Immersive

Tendency Questionnaire and the Presence Questionnaire were used (Appendix G & H).

Both of these tests have been shown to be highly internally consistent, and each has a

high reliability (Cronbach’s Alpha 0.75 - Immersive Tendency Questionnaire and 0.81 -

Presence Questionnaire). The Immersive Tendency Questionnaire consisted of 27

Questions (self-report, 7-point, Likert type scale). The Presence Questionnaire consisted

of 32 Questions (self-report, 7-point, Likert type scale). To capture participant

demographic information, an-in house questionnaire was used (age, sex, driving history

(hrs/month), video game use (hrs/week), etc.; see Appendix F). For risk of sickness, the

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Simulator Sickness Pre-Screening Questionnaire was used (six yes or no questions; see

Appendix B) and for sickness, the Simulator Sickness Questionnaire was used (percent of maximum used for all scales; see Appendix C).

Procedure

The data collection procedure for each set of participants followed the details as outlined in each previous experiment. However, to add to those descriptions, in each experiment the demographic questionnaire was given prior to each experimental session, as was the Immersive Tendency Questionnaire. The Presence Questionnaire was given at the conclusion of each experimental session.

Data Manipulation and Results

Did Immersion, Presence, or Participant Characteristics Correlate with Sickness?

The first analysis was an attempt to identify any variables that may be useful in predicting sickness pre-exposure. Immersive tendencies, video-game use, driving history, and the simulator sickness pre-screening measures were analyzed. The descriptive statistics for these variables indicate that there were no ceiling or floor effects (see Table

7). For the Immersive Tendency and Presence Questionnaires, it appears that the obtained scores may have clustered around the midpoint. However, for both questionnaires the range observed still included approximately half of the possible scores.

Table 7 Descriptive Statistics for Participant Characteristics and Measures of Immersion, Presence, and Sick ness Min/Max Possible Min/Max Observed M SEM SD Age in Years N/A 18-30 19.12 0.18 1.75 Immersive Tendencies Questionnaire 27-189 59-127 91.04 1.33 13.06 Presence Questionnaire 32-224 59-153 106.40 1.95 19.22 Video Game Use (hrs/week) N/A 0-12 1.54 0.25 2.43 Driving History (hrs/month) N/A 0-45 6.60 0.94 9.31 Simulator Sickness Pre-Screening 0-6 0-4 0.89 0.10 0.99 Simulator Sickness Questionnaire Total 0-100 0-74.80 18.77 1.56 15.42 Nausea Subscale 0-100 0-76.32 18.67 1.71 16.92 Oculomotor Discomfort Subscale 0-100 0-66.74 21.06 1.67 16.53 Disorientation Subscale 0-100 0-81.04 20.09 1.85 18.36 107

To identify potential relationships a bivariate correlation was carried out (2-

tailed). For the measures of presence, immersive tendencies, and sickness, the analysis

only partially supported the hypotheses. Presence scores did correlate negatively with

sickness scores (Total, r (97) = -0.25, p = 0.014; Nausea, r (97) = -0.227, p = 0.025;

Oculomotor, r (97) = -0.285, p = 0.005; and Disorientation, r (97) = -0.204, p = 0.045). In

addition, presence scores positively correlated with immersive tendency scores (r (96) =

0.331, p = 0.002). However, contrary to prediction, immersive tendency score did not

correlate with any sickness scores (all p > 0.1). For a summary of these correlations, see

Table 8.

Table 8 Correlations for Participant Characteristics and Measures of Immersion, Presence, and Sickness Simulator Immersive Presence Sicknes Tendencies Nausea Oculomotor Disorientation Questionnaire Questionnaire Questionnaire Total Immersive Tendencies Questionnaire Pearson Correlation 1 0.311(**) 0.058 0.037 0.036 0.039 Sig. (2-tailed) 0.002 0.575 0.722 0.728 0.707 N 97 96 97 97 97 97 Presence Questionnaire Pearson Correlation 0.311(**) 1 -0.25(*) -0.227(*) -0.285(**) -0.204(*) Sig. (2-tailed) 0.002 0.014 0.025 0.005 0.045 N 96 97 97 97 97 97 Simulator Sicknes Questionnaire Total Pearson Correlation 0.058 -0.25(*) 1 0.918(**) 0.884(**) 0.947(**) Sig. (2-tailed) 0.575 0.014 0 0 0 N 97 97 98 98 98 98 Nausea Pearson Correlation 0.037 -0.227(*) 0.918(**) 1 0.76(**) 0.811(**) Sig. (2-tailed) 0.722 0.025 0 0 0 N 97 97 98 98 98 98 Oculomotor Pearson Correlation 0.036 -0.285(**) 0.884(**) 0.76(**) 1 0.782(**) Sig. (2-tailed) 0.728 0.005 0 0 0 N 97 97 98 98 98 98 Disorientation Pearson Correlation 0.039 -0.204(*) 0.947(**) 0.811(**) 0.782(**) 1 Sig. (2-tailed) 0.707 0.045 0 0 0 N 97 97 98 98 98 98 (**) Correlation is significant at the 0.01 level (2-tailed). (*) Correlation is significant at the 0.05 level (2-tailed).

The validation of the Simulator Sickness Pre-Screening questionnaire revealed

correlations as predicted. Total pre-screening score correlated positively with the total simulator sickness total score and all subscale scores (Total, r (98) = 0.322, p = 0.001;

Nausea, r (98) = 0.310, p = 0.002; Oculomotor, r (98) = 0.286, p = 0.004; and

Disorientation, r (98) = 0.297, p = 0.003).

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The exploratory analysis of video game use and driving history showed no correlations between them and level of sickness (all p > 0.1). However, the analysis did reveal two correlations of interest. First, presence score correlated negatively with pre- screening score (r (97) = -0.267, p = 0.008). This is not surprising if one considers that people more prone to sickness would also be those that became sick, thus resulting in lower feelings of presence. The second interesting (negative) correlation was between pre-screening score and video game use (r (98) = -0.205, p = 0.043). A possible explanation for this correlation is that people who have a history of motion sickness avoid activities that may aggravate it, such as video games.

Did Any Participant Behaviours, During Simulated Driving, Correlate with

Sickness?

The second analysis evaluated participant variables taken during the experimental session for potential links to sickness. Measures of balance and head movement variability were included in this analysis. In addition, for this analysis, the driving variables were defined and collected as outlined in the previous experiments (average speed, accelerator percent, brake percent, and steering variability). Furthermore, each of the areas of interest used in the previous experiments was examined for correlations

(drive overall, gradual turns, and sharp turns). In the case of average speed, all areas of each of the gradual and sharp turns were also included. Simulator Sickness Questionnaire total and subscale scores were all examined. Because participants who became sick part way through the session did not have complete driving data, their driving data was excluded from the analysis (4 participants). In addition (as was specified in previous experiments), participants with missing head position or balance data were also not

109 included in those portions of the analysis (head, 15; Balance pre-drive, 3; Balance post- drive, 5). Table 9 summarizes the descriptive statistics for the variables included in this analysis. From that table it is clear that no floor or ceiling effects or a restriction of range is present for any of the variables (although balance scores tended to cluster near the top end of the range).

Table 9 Descriptive Statistics for Balance, Participant Behaviours During Simulated Driving, and Measures of Sickness Mix/Max Possible Min/Max Observed M SEM SD Head Movements X-Axis N/A 1.90-17.57 6.39 0.33 3.02 Head Movements Y-Axis N/A .57-8.53 2.88 0.16 1.44 Balance Score Pre-Drive 0-30 1.81-30 24.79 0.90 8.82 Balance Score Post-Drive 0-30 2.06-30 20.97 1.11 10.72 Average Speed for the Entire Drive N/A 16.81-93.28 53.28 2.71 26.23 Average Speed Entering Gradual Turns N/A 17.92-33.11 23.55 0.29 2.83 Average Speed During Gradual Turns N/A 16.14-31.52 23.17 0.30 2.94 Average Speed Exiting Gradual Turns N/A 16.36-31.52 23.66 0.32 3.14 Average Speed Entering Sharp Turns N/A 6.51-16.92 11.29 0.19 1.84 Average Speed During Sharp Turns N/A 7.72-17.45 11.07 0.16 1.54 Average Speed Exiting Sharp Turns N/A 6.64-20.59 10.91 0.20 1.96 Acceleration for the Entire Drive N/A 9-29 0.16 0.00 0.04 Acceleration Exiting Gradual Turns N/A 4.88-46.25 0.21 0.01 0.07 Acceleration Exiting Sharp Turns N/A 3.43-52.63 0.26 0.01 0.09 Braking for the Entire Drive N/A 0-3 0.02 0.00 0.01 Braking Entering Gradual Turns N/A 0-7.75 0.01 0.00 0.01 Braking Entering Sharp Turns N/A 0-26.50 0.11 0.01 0.06 Steering Variability for the Entire Drive N/A 27.26-76.39 36.96 0.75 7.28 Steering Variability During Gradual Turns N/A 1.84-20 6.17 0.25 2.43 Steering Variability During Sharp Turns N/A 16.46-61.93 31.95 0.95 9.19 Simulator Sickness Questionnaire Total 0-100 0-74.80 18.77 1.56 15.42 Nausea Subscale 0-100 0-76.32 18.67 1.71 16.92 Oculomotor Discomfort Subscale 0-100 0-66.74 21.06 1.67 16.53 Disorientation Subscale 0-100 0-81.04 20.09 1.85 18.36

As in the first part of the analysis, a bivariate correlation was carried out (2- tailed). To begin, the predicted correlation between head movements and sickness did not emerge (p > 0.1 for both X and Y-axis variability). This result was extremely surprising given the results of Experiments 1 through 3, which showed less X-axis variability during days and conditions that also showed lower levels of sickness. As with experiments 1 through 3, pre and post-drive balance showed no correlations of interest (all p > 0.1).

Also surprising were the correlations between sickness and driving behaviours.

The initial hypothesis was that all the variables would show positive correlations with

110 sickness; this however, was not the case. The overall average speed for the entire drive was the only variable to show a significant positive correlation with sickness (Total, r

(94) = 0.241, p = 0.019; Nausea, r (94) = 0.24, p = 0.02; Oculomotor, marginal, r (94) =

0.193, p = 0.063; and Disorientation, r (94) = 0.215, p = 0.037). As observed in Table 10, all remaining significant correlations between the driving variables and sickness scores were opposite to what was predicted.

Table 10 Significant Correlations for Participant Behaviours During Simulated Driving, and Measures of Sickness SSQ Total Nausea Oculomotor Disorientation Speed Overall Pearson Correlation 0.241(*) 0.24(*) 0.193 0.215(*) Sig. (2-tailed) 0.019 0.02 0.063 0.037 N 94 94 94 94 Speed Enter Gradual Pearson Correlation -0.225(*) -0.173 -0.247(*) -0.212(*) Sig. (2-tailed) 0.029 0.096 0.016 0.04 N 94 94 94 94 Speed During Gradual Pearson Correlation -0.206(*) -0.165 -0.243(*) -0.187 Sig. (2-tailed) 0.046 0.111 0.018 0.071 N 94 94 94 94 Speed Exit Gradual Pearson Correlation -0.167 -0.131 -0.252(*) -0.129 Sig. (2-tailed) 0.107 0.208 0.014 0.217 N 94 94 94 94 Acceleration Gradual Pearson Correlation -0.16 -0.174 -0.209(*) -0.107 Sig. (2-tailed) 0.123 0.094 0.043 0.305 N 94 94 94 94 Brake Overall Pearson Correlation -0.172 -0.134 -0.167 -0.179 Sig. (2-tailed) 0.097 0.197 0.108 0.084 N 94 94 94 94 Brake Sharp Pearson Correlation -0.207(*) -0.128 -0.195 -0.218(*) Sig. (2-tailed) 0.045 0.22 0.06 0.035 N 94 94 94 94 (**) Correlation is significant at the 0.01 level (2-tailed). (*) Correlation is significant at the 0.05 level (2-tailed).

Discussion

This experiment had two main goals. The first was to identify any participant characteristics that could be used as a pre-screening for sickness. The second was to identify any relationships between driving/participant behaviours that could be influencing sickness. This experiment partially accomplished these goals. See Table 11 for a summary of hypotheses made and the relevant correlations.

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Table 11 Summary of Hypotheses Made and Correlations Correlation Found? Direction as Predicted? Presence and Simulator Sickness Questionnaire Total Yes Negative Yes Immersive Tendencies and Presence Yes Positive Yes Immersive Tendencies and Sickness No No - Predicted Negative Head Variability X-Axis and Simulator Sickness Questionnaire No No - Predicted Positive Average Speed Overall and Simulator Sickness Questionnaire Yes Positive Yes Average Speed Gradual Turn (Enter, During, and Exit) and Simulator Sickness Questionnaire Yes Negative No Average Speed Sharp Turn (Enter, During, and Exit) and Simulator Sickness Questionnaire Yes Negative No Accelerator Percent Overall and Simulator Sickness Questionnaire No No - Predicted Positive Accelerator Percent Gradual Turn and Simulator Sickness Questionnaire Yes Negative No Accelerator Percent Sharp Turn and Simulator Sickness Questionnaire No No - Predicted Positive Brake Percent Overall and Simulator Sickness Questionnaire Yes Negative (Marginal) No Brake Percent Gradual Turn and Simulator Sickness Questionnaire No No - Predicted Positive Brake Percent Sharp Turn and Simulator Sickness Questionnaire Yes Negative No Steering Variability Overall and Simulator Sickness Questionnaire No No - Predicted Positive Steering Variability Gradual Turn and Simulator Sickness Questionnaire No No - Predicted Positive Steering Variability Sharp Turn and Simulator Sickness Questionnaire No No - Predicted Positive

The results replicated previous findings that significant relationships existed between sickness and presence and presence and immersion. However, it did not reveal a significant relationship between immersive tendencies and sickness. Initially, the prediction was that immersive tendencies would facilitate feeling present and in turn lower sickness (thus the correlation between presence and sickness). However, it is more likely that sickness in fact reduces the sense of presence and this was the reason for the correlation. Based on these results, a pre-exposure test of immersive tendencies does not appear to be a viable screening measure.

Our in-house screening questionnaire was the most promising predictor of sickness. Because the total score for this questionnaire was based on the combined ratings of current feelings of sickness (a cold, etc.) and previous motion sickness experience, the correlation between the total score and the scores on the Simulator Sickness

Questionnaire was not surprising. However, it did appear that the combination and phrasing of the questions used was robust enough to capture the key elements of these links.

The analysis of potential variables, measured during driving, that had correlations with sickness revealed results inconsistent with the hypotheses. To begin, a major

112 variable proposed to lower sickness (X-axis movement) showed no correlation with sickness. Because head movements were a variable in the previous studies that changed when sickness level changed, this result was surprising. However, because none of the experiments manipulated head movements, a causal link was never confirmed. Therefore, to explore this result further, an experiment directly manipulating head movements needs to be conducted.

The analysis of the driving variables showed some interesting results. To begin, it appeared that the key variable linked to sickness was average speed. However, the results were not consistently in the same direction. For the overall drive, average speed showed a positive correlation to sickness, while average speed during the gradual turns showed a negative correlation with sickness. This result is hard to interpret as the explanation could go two ways. It could be that participants who were feeling sick drove the simulation faster to escape it, or it could be that participates driving faster increased conflict and in turn sickness. The gradual turn correlation shares the same problem of interpretation.

Perhaps sick participants drove slower around turns to try to avoid becoming sicker (but drove faster in the straights because conflict would not change). Alternatively, perhaps driving faster around the gradual turns reduced the time that participants were exposed to the conflict. To help sort out these issues, a study controlling for average speed needs to be conducted.

For the remainder of the variables the links to sickness were variable. To begin, steering variability showed no significant correlations to any measure of sickness.

Tentatively, this result rules out the differences observed in previous experiments as being linked to increasing or decreasing sickness. Unlike steering variability, other

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variables did show connections to sickness. Acceleration out of the gradual turns showed

a marginal link to nausea score and a significant link to oculomotor discomfort score.

Braking during the overall drive also showed a marginal correlation to total score and

disorientation. Braking when entering the sharp turns was found to have a significant

correlation to total sickness and disorientation and a marginal correlation to oculomotor

discomfort. Because of these results, two things are clear. First, changes in participant

driving behaviours may potentially affect sickness. Second, more work needs to be done

to identify whether it is the behaviour influencing sickness or the sickness influencing the

behaviour.

Overall, this experiment achieved its goals. It was shown that individual

differences could be used for as a basis for pre-screening (Simulator Sickness Pre-

Screening Questionnaire). In addition, links between average speed and sickness were identified for future consideration in their role in reducing (or increasing) simulator sickness.

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Chapter 6

General Discussion

There are a number of different theories of motion and simulator sickness. While each differs in the exact mechanism proposed, they all share a common feature: conflict.

For the Postural Instability theory, Ricco and Stoffregen (1991) proposed that sickness arises when a person needs to integrate a novel set of sensory inputs in order to maintain postural stability. Reason and Brand (1975) proposed the sensory conflict theory, stating that when a person encounters a new arrangement of sensory inputs, this conflicts with their pre-established norms for that situation (for example, driving a car versus driving a fixed-base driving simulator). A result of this conflict is that until a person adapts, they will experience sickness. Treisman’s (1977) evolutionary hypothesis is similar to the sensory conflict model, however he proposed that the conflict arises because the senses are providing incongruent sensory information (between each other) and therefore sickness occurs. The common theme between all of these theories is that a novel arrangement of sensory inputs needs to be adapted to, in order to register it as “normal” and remove the conflict (and sickness). Previous work on motion and simulator sickness has labelled this adaptation as “nervous system adaptation” an unobservable phenomenon that occurs when a person experiences and overcomes sensory conflict.

The overall goal of this dissertation was to identify any observable behaviour that may accompany or be part of the adaptation to a virtual environment. To achieve this goal, I tested potential mechanisms of adaptation for casual links to sickness. Once I identified potential casual mechanisms of adaptation, I examined the underlying theories of conflict by attempting to remove the conflict and thus the need for adaptation. Finally,

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I attempted to identify individual differences that could predict sickness, including

behaviours that could not be manipulated in previous studies (for example, gaze

behaviour was manipulated while average driving speed was only measured).

Under the three predominant theories of motion/simulator sickness, adaptation

must occur to a novel sensory environment if sickness is to be avoided. While

learning/encoding a new sensory arrangement as “normal” is a part of this adaptation,

modification of behaviours could also play a role. At a minimum, by modifying their

behaviours people could potentially reduce the conflicting inputs. In experiment 1, I

showed this was the case. Over two exposures to a virtual environment, participants

showed both changes in their behaviours and reductions in their levels of sickness.

Specifically, participants changed the way they directed their gaze between a first and

second drive in a fixed-base simulator.

When I analyzed how participants looked around during the curved portions of a

simulated drive, I found that during a second exposure (two days later) they restricted

their gaze toward the tangent line and reduced their X-axis head movement variability.

Based on the three theories of sickness, I proposed two explanations for why participants

may have been changing their eye and head movements in this way. First, it is possible

that participants were learning that behaviours appropriate in the real world were actually

counterproductive to acclimatization in the simulator. Specifically, when driving around a

corner in the real world the head (and body) is subject to lateral accelerations. In the

simulator however, there is no such acceleration to accompany turning (hence the sensory conflict). One way people could have facilitated acclimatization would have been to reduce reliance on vestibular information (which conflicts with the visual stimulus). If

116 however people, through a muscle memory response, moved their heads as if they were in the real world then this would have activated the vestibulo-ocular reflex (to stabilize gaze) in turn increasing reliance on vestibular information. Therefore, it could be that the reductions in head and eye movements observed across session represented an appropriate response to the simulation.

The second possible explanation for the decreases in head and eye movements is that it was a response to the motion cues switching from being internally driven (as in the real world) to being externally driven (as in the simulation). In the real world, people generate their own motion and receive the corresponding vestibular and proprioceptive cues to their motion. However, the absence of these cues in the simulation may have caused conflict and disorientation. Therefore, in the second session people could have been holding their head and eyes in a stable position to reduce internal cues to motion that did not match the visual stimuli. This would have allowed them to concentrate on acclimatizing to the simulation (an environment that only had visual cues to motion).

Even though I showed in Experiment 1 that behaviours changed along with sickness, I could not show that this relationship was causal. Therefore, in Experiment 2 I implemented two instructional programs that taught participants to control their gaze behaviour during a first-time exposure to the simulator (each instructional program used a different fixation point). These gaze fixation interventions were both successful at modifying behaviour and did not show differences between each other in their effects.

Participants in both groups fixated their gaze ~90 percent of the time during high conflict areas of the drive (turns and corners). The outcome of the participants fixating their gaze was that sickness, as measured by the Simulator Sickness Questionnaire, was

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significantly lower than it was for the control participants. In addition, even though the

instructions only taught gaze-fixation, participants also reduced their X-axis head

movements. This result mirrors the result of Experiment 1, showing head movements

could play a part in reducing sickness. Through this experimental manipulation, I was

successful in showing a potentially causal link between gaze fixation and level of

sickness. In addition, because the interventions were not different from one another in

their effect on sickness, I showed that gaze fixation per se, and not gaze fixation location

was critical to lowering sickness. Finally, I showed that pre-immersion interventions could be useful in reducing simulator sickness.

My major focus in Experiments 1 and 2 was to show links between behaviour and sickness. The results of these experiments allowed me to highlight the observable changes related to acclimatization and sickness. However, I did not go into great depth about how these behaviours related to the theories of sickness. Therefore, to explore the theoretical basis of simulator sickness more directly, I tested an intervention designed to remove sensory conflict and thus the need for adaptation. The results of this experiment showed that for simulator sickness specifically, attentional reallocation could be the driving mechanism behind reducing sickness.

Previous research has shown that Assistive Vestibular Stimulation reduces

simulator sickness (Reed-Jones, Reed-Jones, Trick, & Vallis, 2007; Reed-Jones et al.,

2008; Reed-Jones, Reed-Jones, Trick, Toxopeus, & Vallis, 2009). The original

explanation for this result was that stimulation replaced the missing vestibular input and

in turn removed the conflicting situation. To test this, Experiment 3 used both Assistive

Vestibular Stimulation and Opposing Vestibular Stimulation as interventions during

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simulated driving. If the opposing condition increased sickness then the conclusion would have been that it increased sensory conflict and therefore more sickness. If however, they both reduced sickness that would have pointed to an alternative mechanism. What I showed was that both conditions reduced sickness during a first time experience in a fixed-base driving simulator (and their effects were not significantly different from one another). This result called into question the theory that the Assistive Vestibular

Stimulation replaces the missing vestibular cues and as such reduces conflict and thus sickness.

Based on current work on attention and vection, I proposed an alternative explanation for why the electrical stimulation, in general, reduced sickness. Seno, Ito, and

Sunaga (2009, 2011) showed that attentional allocation to a stimulus increased the feeling of vection (the induced self-motion illusion produced by vision), and that attentional reallocation through a cognitive task reduced the feeling of vection. I proposed that electrical stimulation could also capture attention and therefore reduce vection. The result would be reduced conflict and therefore sickness. Because simulator sickness is unique in that the “motion” component is visually induced, this finding could be important in distinguishing how simulator sickness is different from other forms of motion related sickness.

While this explanation is a good fit with the existing theories of motion sickness, research needs to address three problems before it can be supported more fully. First, the connection between electrical stimulation and attention is hypothetical, and more evidence is needed before firm conclusions can be made. Second, even if a link can be shown between electrical stimulation and attention, other explanations for the result could

119 be made. The electrical stimulation could be diverting conscious attention from perceiving the sickness, but not actually reducing it. In addition, the Simulator Sickness

Questionnaire is given after the simulation is complete. If the stimulation diverted attention from the simulator sickness during the task, then it may have compromised the memory of the magnitude of sickness experienced. The third problem is methodological.

Because the opposing stimulation moved the head in a way that mimicked real-world behaviour (tilting opposite to the direction of the curve), the question remains as to how much conflict was experienced due to incorrect vestibular input versus the correct proprioceptive input that the head tilt may have caused.

I undertook Experiments 1 and 2 with the sensory conflict model in mind.

Therefore, in light of the findings of Experiment 3, I will revisit the results of experiment

1 and 2 for a moment. While the original explanations of the relationship between gaze- behaviour (and head movement variability) and sickness may be valid, there are other alternatives. In Experiment 1, on day one the experience in the simulator was novel.

Because of this, participants may have been paying more attention to the mechanism of the simulation rather than the driving task. This could have resulted in more attention being allocated to processing the visual cues to motion. If this occurred, it could increase vection and thus sensory conflict. Given that there was less novelty on the second day of testing, participants could have been concentrating harder on driving. This could explain why participants looked more toward the tangent while negotiating corners. Increased concentration on the task could cause participants direct their visual attention more to the task of driving and less to toward the visual cues that induced illusory self-motion

(reducing vection and in turn reducing conflict). For Experiment 2, by giving participants

120 a secondary task (modulating their gaze to a specific point), this could have taken attentional resources from vection cues. This in turn could have reduced vection, removed the conflict and thus sickness.

It is important to note that there were important changes in driving behaviour as a result of the manipulations in Experiments 1-3. While for the most part the observed changes were small, it is unknown how much of a change in these behaviours could have affected the visual representation of medial/lateral or anterior/posterior motion. These changes had the potential to increase or decrease sensory conflict and in turn sickness.

Because of this, I undertook an analysis of potential correlations between driving behaviours and sickness.

The analyses of the driving variables had mixed results. The variable that correlated best with sickness was average speed. However, this result was hard to interpret. For the drive overall the correlation was positive, while the correlation with the gradual turns was negative. From this result, it is clear that average speed is an important factor in sickness, however without further study it is impossible to tell exactly how the two variables relate. Does average speed affect sickness or does sickness affect average speed? A question only future research can answer.

For the remainder of the variables the links to sickness were also inconsistent.

Steering variability showed no correlations to sickness, even though it was a variable that changed session to session in Experiment 1. Acceleration and braking behaviours also showed correlations with various portions of the drive (on various subscales of sickness).

However, neither variable correlated consistently with all parts of the drive or all sickness scales. What these results do show is that there are identifiable links between driving and

121 sickness. This finding is important because it tells me that any program of study that wants to either identify adaptive behaviours or develop a behavioural intervention needs to take into account that the task itself may affect sickness. This is especially important for the study of driving behaviours in a simulator or training people in a simulator. If people change the way they drive to overcome sickness, this could result in inaccurate findings in research or the transfer of potentially incorrect behaviours to real world performance.

In addition, because previous research has shown that individual differences can play a large role in simulator sickness, my analysis also looked at participant characteristics for any links to sickness. To begin, the analysis of presence, immersion, and sickness replicated previous work that showed presence and sickness had a positive correlation. What the analysis did not support was the hypothesis that immersive tendencies would correlate with sickness. This was disappointing, as immersive tendencies could have been a useful predictor of simulator sickness. However, there are two interesting implications of these results relating to the attention-vection account of simulator sickness. First, a subset of questions on the Presence Questionnaire asks participants to rate their feelings of motion within the simulation. These questions could have been assessing the strength of the vection participants felt. If this was the case, then the results observed could refute the attention-vection account of sickness. If people felt more vection, they should have scored higher on the presence scale and felt more sickness. The results did not show this though and in fact, the correlation was in the opposite direction (higher presence correlated with lower sickness). This result could be an argument against the attention-vection account of sickness. However, the questions

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directly relating to sickness are only a small part of the entire questionnaire and therefore,

future research should explore vection in isolation before any conclusions are made.

The second implication is related to the Immersive Tendency Questionnaire.

Because it measures the ability to focus attention on a given task and therefore, the scores, under the attention-vection account, should have correlated with sickness. This could be thought of as either people becoming focused on the task (driving) and therefore lowering their feeling of vection and sickness, or it could be thought of as people focusing on the mechanism (the simulator) and thus increasing their vection and sickness.

The fact that the analysis showed no correlation at all could mean that attention, regardless of whether it is on the task or the motion cues, does not relate to sickness.

However, this account also has problems. The Immersive Tendency Questionnaire only predicts tendencies, so there is no way to know how much people actually attended once in the simulation. In addition, if some people attended the mechanism and some the task, then the correlation could have been washed out. Therefore, as with presence, future research could focus on measuring attention to the task or mechanism in isolation, in order to get at the specific effects of attention on vection and sickness.

This individual differences analysis did reveal one promising result. The

Simulator Sickness Pre-Screening questionnaire was highly correlated with sickness. This finding validated this questionnaire as a useful tool in identifying people who might be higher risk for simulator sickness.

Limitations

Four main limitations were present in this work. First, even though Experiment 1 identified multiple variables that changed along with level of sickness, it was unclear

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whether causal relationships existed. The problem was that within the scope of this

project, I was only able to concentrate on the manipulation of a limited number of these

variables. Specifically, I manipulated gaze fixation with the hope of changing eye and

potentially head movements (the only experimental manipulation of participant

behaviours). This manipulation was a novel attempt at identifying a potential mechanism

for sensory adaptation. However, because I did not directly manipulate any other

variables any causal relationships between them and sickness could not be determined.

To correct for this, future research could manipulate variables such as the average speed

at which participants drove the simulation or impose limitations on the extent of

accelerations or decelerations. These types of manipulations could determine any causal

relationships between them and sickness.

The second limitation was the collection and analysis of the eye movement data.

Due to technical limitations in the resolution of the tracking data, the only reliable data was specific to gross eye movements and fixations. If the tracking equipment was more precise, then the analysis could have included measures of smooth pursuit and gaze variability. This would have given a robust picture of exactly what components of gaze modulation go into adaptation. To follow up on this work, the employment of a more precise gaze tracking (and head tracking) apparatus would be integral in identifying the contribution of gaze behaviour on reducing conflict.

Third, as was pointed out in the previous experiments, the Tandem Romberg test may have not been the best choice for measuring postural instability. Due to ceiling effects and inconsistencies in participant footwear, the results of these tests may have been too inconsistent (or restricted) to make reliable conclusions. Any future work

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including postural instability as a measure of acclimatization would benefit from a more

precise test of balance such as the use of a force plate or refining the Tandem Romberg

procedure to increase accuracy. Some examples of refinements include the following:

testing participants in bare feet to control for footwear variability; increasing the time

from 30-seconds to avoid ceiling effects; or repeating the test multiple times and

averaging the scores to avoid inconsistencies due to bad trials (laughing, etc.).

Finally, because of the large number of analyses carried out, it is possible that

some of the significant effects came about due to Type I error. Therefore, it is important

that these results be replicated. The inclusion of a large number of dependent variables

was due in part to this work being exploratory. There are a number of variables that could

potentially affect sickness; therefore many variables were included (eye and head

movements, balance, driving behaviours, etc.).

Future Directions

Based on the results of this dissertation, three areas for future research stand out.

First, research could examine ways to improve pre-immersion behavioural modification interventions. Second, research could continue to look at what happens when sensory inputs are re-introduced artificially to replace the ones missing in simulation. This could make fixed-base simulators more immersive, potentially contributing to participant

behaviours that are more realistic. Third, research could examine the potential link

between attention and vection.

To begin, future research on intervention and sickness prevention could build on

the results of Experiment 2 in order to develop fully a pre-immersion sickness

intervention. Because this experiment showed that minimal instruction in gaze

125 modification was significant in reducing sickness (though it did not remove it completely), adding instruction on other behavioural modifications could make this type of intervention even more effective. To do this, future studies need to identify other behaviours key to reducing sickness. For example, Experiment 4 showed a link between average speed and sickness. If a future study could show a causal link between these two variables, then instruction on how fast (or slow) one should drive in various parts of a simulation could be added to the intervention. As research identifies more and more of these behavioural links, these could form the basis of a complete behavioural intervention strategy to reduce sickness during a first time exposure to a simulator.

A good place to begin this type of research would be with the correlations found in Experiment 4. By identifying casual relationships between driving behaviours and sickness, these could prove to be valuable aspects of sickness-reducing interventions.

Depending on the results of this line of research, especially if other behaviours abstract of the task could be identified (e.g. head movements), it could branch into interventions for any of the other forms of motion related sickness (sea, train, space, etc.).

The second direction this work could take is to continue examining how adding artificial vestibular or proprioceptive stimulation to simulations affects simulation users.

Both past work (Reed-Jones et al., 2007) and this work showed that participants modulate their driving behaviour when given stimulation (vestibular and opposing vestibular). This response could be useful when designing simulations that need to evoke behaviours more closely resembling the ones people exhibit in the real world. For example, participants in previous work and Experiment 3 showed that they modulated their speed in corners as if they were feeling lateral accelerations (slowing down into the curves and speeding up

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during the exits; Reed-Jones et al., 2007). This was contrary to control participants who

maintained a constant speed throughout the curves. This result points to vestibular

stimulation being a potential tool for increasing realistic behaviours. However, because

this work showed that giving inappropriate stimulation also had this effect, further research needs to pinpoint why it is participants are reacting in this way. Could it be that they are reacting to the stimulation in general? Alternatively, could it be that the combination of sensory and physical effects of the stimulation was responsible (e.g. head movements)? Future work needs to explore the range of effects various types of stimulation have on participants (vestibular, proprioceptive, etc.). These results could help us to understand exactly what combination of sensory and physical changes occur, and how these relate to conflict and behaviour modification. The results of this work could help in determining if stimulation could be tailored for use in fixed-base simulators to increase naturalistic behaviours or even as a surrogate for motion platforms.

The third area future research could explore is the potential relationship between attention and vection. The first step in this type of research would be to determine if electrical stimulation diverts attention in the same way as a cognitive task. This would support or refute the attention-vection explanation for why electrical stimulation in general reduced sickness. If it is found that electrical stimulation distracts attention, then any of the three theories proposed could be responsible. However if it is found to not affect attention, then research needs to explore other explanations. A logical starting place for this work would be to build on the findings of Seno, Ito, and Sunaga (2011). If a relationship between strength of vection and electrical stimulation could be found, for example by measuring perception of vection while administering vestibular stimulation

127 combined with viewing a moving luminance-defined grating, then this would support the attention theories for the effect of stimulation on sickness. However if stimulation were found not to affect the strength of vection, then alternative theories would have to be explored. One additional point is that button press may not be the best way to measure strength of vection (used by Seno, Ito, and Sunaga). Attending to pressing the button may itself be a cognitive task, potentially influencing the results. Therefore, finding a better measure of the strength of vection could be a part of this line of research.

If this link were established, research would still need to rule out other attention related explanations for the effect. Specifically, these were that diverting attention from the sickness allows you to ignore it or that diverting attention interferes with recall of the magnitude of the sickness. One possible line of research that could rule out both alternative explanations would be to use a real-time physiological measure of sickness during simulated driving. For example, Min, Chung, Min, and Sakamoto (2004) and

Bertin, et al. (2005) both showed that both heart rate and skin temperature correlated with sickness. If research could shown that actual physiological sickness is reduced when stimulation is applied (not just the self-report of sickness) then it would rule out the perception or memory arguments for why diverting attention may work.

In addition, if stimulation were found to being effective due to its distracting effects then its use a simulator sickness aid would be called into question. Why subject people to electrical shocks if alternative attention diverting mechanisms could be employed? Therefore, one aspect of this work could be to identify other attention diverting tasks/mechanisms that could decrease conflict. In addition, research on how best to employ these tasks/mechanisms within a simulation without distracting from the

128 task itself, would need to be done. One of the issues however, would be that if sickness is lowered in this way (by reducing vection) then this would reduce the overall feeling of being part of the simulation. Because of this, research would also need to identify the optimal use of distraction to lower overall sickness without taking away the overall feeling of immersion due to vection.

Conclusions

Adaptation to a simulated environment is a complex process. Through this program of research, I have examined adaptation to a fixed-base driving simulator. What

I found was that adaptation to this type of simulation involved measurable, observable, behaviours. Behaviours such as changes in eye movements, head movements, and changes in driving behaviour all accompany adaptation to this type of virtual environment. Not only did I identify these behaviours, but I also showed that by training people to modify their gaze behaviour (influencing eye and head movements) they could reduce feelings of sickness during a first-time experience in a simulator. I hypothesized that these behavioural modifications helped reduce sensory conflict and facilitated acclimatization, which resulted in reduced sickness.

In addition to directly manipulating behaviours, I was able to show correlations between various simulated driving behaviours and sickness. These findings will be critical to any research aimed at developing simulator sickness interventions. Future studies could use the correlations found between sickness and average speed, braking pressure, and acceleration to formulate a clearer picture of how to overcome simulator sickness.

129

Finally, I tried to confirm that conflict between various senses plays a role in adaptation and sickness. I showed that by applying Assistive Vestibular Stimulation

appropriate and opposite to the virtual motion during high conflict areas of a simulated

drive, I could reduce participant sickness. This result provided support for the theory that

distracting attention could lower vection and therefore reduce conflict. This result was a

novel finding related to simulator sickness. Under the previous theory, Opposing

Vestibular Stimulation should have increased conflict and sickness, which was not the case. Therefore, this result points to a completely new way of thinking about the use of electrical stimulation in virtual environments.

130

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Appendix A

Medical screening questionnaire

1. Do you have heart problems or have you had a heart attack?

Yes No

2. Do you experience lingering effects from stroke, tumour, or head trauma?

Yes No

3. Do you suffer from epileptic seizures?

Yes No

3. Do you have any inner ear problems (vertigo)?

Yes No

4. Do you have diabetes for which insulin is required?

Yes No

5. Do you have problems with low blood sugar (hypoglycemia)?

Yes No

6. Are currently taking medications that make you feel extremely nauseated or dizzy?

Yes No

[If a participant answers yes to any of these questions, indicate that they may be at higher risk for problems resulting to simulator exposure and ask them if they want to continue.

If participants answer yes to two of these questions, do not permit them to go on into the simulator phase of the study.]

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Appendix B

Simulator sickness pre-screening questionnaire

1. Are you currently feeling ill? Y/N

If yes, please describe. ______

2. Are you currently feeling “hung-over”? Y/N

3. Do you have a history of motion sickness? Y/N

If yes, please describe (where: car, boat, train, airplane) and when (recently vs. when a child):______

4. Have you ever experienced dizziness or nausea while watching a movie in a wide- screen (e.g. Silver City or Omnimax Theatre)? Y/N

If yes, please describe ______

5. Do you experience dizziness or nausea while reading in a moving car? Y/N

6. Do you prefer to be the driver, compared to the passenger, because otherwise you experience dizziness or nausea? Y/N

[If a participant answers yes to any of these questions, tell them that they may be at

higher risk for problems resulting to simulator exposure. In particular, people who have

had experiences of dizziness or nausea as a result of motion (especially if these are recent

experiences) or viewing wide screen movies may experience similar symptoms in a

simulator. However, the motion sickness experienced on a boat is much more typical in

the population. We are especially worried about people who get carsick or train sick.]

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Appendix C

Simulator sickness questionnaire

Please circle the degree to which you experienced any of the below symptoms

SSQ Symptom NONE SLIGHT MODERATE SEVERE

General discomfort 0 1 2 3

Fatigue 0 1 2 3

Headache 0 1 2 3

Eyestrain 0 1 2 3

Difficulty focusing 0 1 2 3

Increased salivation 0 1 2 3

Sweating 0 1 2 3

Nausea 0 1 2 3

Difficulty concentrating 0 1 2 3

Fullness of head 0 1 2 3

Blurred vision 0 1 2 3

Dizzy (eyes open) 0 1 2 3

Dizzy (eyes closed) 0 1 2 3

Vertigo 0 1 2 3

Stomach awareness 0 1 2 3

Burping 0 1 2 3

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Appendix D

Participant briefing

As you can see our simulator consists of a real car, and you control it exactly as you would in the real world. For example, you will need to put it in gear and use the pedals and steering wheel as you would normally. One the simulation begins we would like you to drive a simulated route through the country. Please drive as you normally would, while keeping in mind the rules of the road.

You will encounter four main road types during this drive: straight sections; gradual turns; winding sections; and T-intersections. All areas except the T-intersections are signed at 80 km/h. The T-intersections are signed at 40 km/h and you will need to slow down to navigate those turns safely. For the T-intersections, you will see that there are traffic lights. These will always be green (so you will not have to come to a complete stop). In addition, before each of the gradual or T-intersections turns there will be a sign indicating which way you are supposed to turn – please follow these signs.

Once you finish the drive there will be a barricade across the road indicating the drive is finished. Please come to a stop when you approach the barricade.

If at any time you feel uncomfortable during the drive and you want to stop immediately close your eyes and notify me (do not worry about stopping the car!). I will be monitoring you through video and audio in the control room. I will stop the simulation and come down to assist you.

Any questions?

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Appendix E

Instructions used for visual training

Horizon Focus Instructions

As a driver, you know that while driving you need to be aware of your surroundings. You have probably, and perhaps unconsciously, developed your own techniques to scan your environment while you drive. While in the simulator however we would like you to direct your gaze in a particular way. During the curved portions of the drive (identifiable by reminder signs) we would like you to do the following:

We would like you to concentrate on the area on the horizon car as shown in this picture.

As much as possible avoid looking too far down (towards the road directly in front of your car).

Try not to move your eyes off the screen (to the left or right).

Lane Focus Instructions

We would like you to concentrate on the area just in front of your car as shown in this picture.

As much as possible avoid looking too far up (towards the horizon).

Try not to move your eyes off the screen (to the left or right).

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Appendix F

Data capture questionnaire

Part A. General Information

1. Name ______

2. Sex ______

3. Birthday ______

4. Have you ever been diagnosed with problems of your eyes/vision? Y/N

If yes, what type? ______

If yes, is it corrected with glasses/contacts/etc? ______

5. Have you ever been diagnosed with problems of your ears/hearing? Y/N

If yes, what type? ______

If yes, is it corrected? ______

6. Have you been diagnosed with Attention Deficit Disorder? Y/N

If yes, are you currently taking medication for it? Y/N

If yes, what type? ______

7. Please indicate any sports you play or have played in the past.

(Please indicate the type of sport, how recently you played it, and how often you play/played per week)

______

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8. Please indicate any types of video games you play or have played in the past (e.g. first

person shooters, role playing, racing, etc.)

(Please indicate the type, how recently you played on a regular basis, and how often you

play or played, in hours, per week)

______

9. Please indicate how many hours you spend on a computer in a week (non-gaming).

______

10. Do you use a cell phone? Y/N

If yes, how much time per day do you spend talking on your phone? ______

11. Do you use a Smartphone (e.g. iphone)? Y/N

If yes, how much time per day do you spend texting, browsing, etc. on your

phone? ____

12. Do you watch TV? Y/N

13. Please indicate what type of shows and how many hours you spend each per week.

______

14. Is there anything else that you think may be useful for us to know about you? If so,

please tell us about it.

144

______

Part B. Driving Experience Questions

1. When did you first start driving? (Age of ______)

2. When is the last time you drove? (Choose one). Today

Yesterday

Within the last week

Within the last 2 weeks

Within the last month

Within the last 6 months

Within the last year

Within the last 2 years

Other ______

3. How many times a month do you drive on average? (0- every day of the month)

______

4. How far do you drive on an average day? ______(KM and hours/amount of time)

5. What kind of driving do you do most often? City Country Highway

6. Any other things you would like to add ______

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Appendix G

Immersive tendency questionnaire

(All items are scored on a seven-point scale)

1. Do you ever get extremely involved in projects that are assigned to you by your boss or your instructor, to the exclusion of other tasks?

2. How easily can you switch your attention from the task in which you are currently involved to a new task?

3. How frequently do you get emotionally involved (angry, sad, or happy) in the news stories that you read or hear?

4. How well do you feel today?

5. Do you easily become deeply involved in movies or TV dramas?

6. Do you ever become so involved in a television program or book that people have problems getting your attention?

7. How mentally alert do you feel at the present time?

8. Do you ever become so involved in a movie that you are not aware of things happening around you?

9. How frequently do you find yourself closely identifying with the characters in a story line?

10. Do you ever become so involved in a video game that it is as if you are inside the game rather than moving a joystick and watching the screen?

11. How physically fit do you feel today?

12. How good are you at blocking out external distractions when you are involved in something?

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13. When watching sports, do you ever become so involved in the game that you react as

if you were one of the players?

14. Do you ever become so involved in a daydream that you are not aware of things

happening around you?

15. Do you ever have dreams that are so real that you feel disoriented when you awake?

16. When playing sports, do you become so involved in the game that you lose track of

time?

17. Are you easily disturbed when working on a task?

18. How well do you concentrate on enjoyable activities?

19. How often do you play arcade or video games?

(OFTEN should be taken to mean every day or every two days, on average.)

20. How well do you concentrate on disagreeable tasks?

21. Have you ever gotten excited during a chase or fight scene on TV or in the movies?

22. To what extent have you dwelled on personal problems in the last 48 hours?

23. Have you ever gotten scared by something happening on a TV show or in a movie?

24. Have you ever remained apprehensive or fearful long after watching a scary movie?

25. Do you ever avoid carnival or fairground rides because they are too scary?

26. How frequently do you watch TV soap operas or docu-dramas?

27. Do you ever become so involved in doing something that you lose all track of time?

147

Appendix H

Presence questionnaire

(All items are scored on a seven-point scale)

1. How much were you able to control events?

2. How responsive was the environment to actions that you initiated (or performed)?

3. How natural did your interactions with the environment seem?

4. How completely were all of your senses engaged?

5. How much did the visual aspects of the environment involve you?

6. How much did the auditory aspects of the environment involve you?

7. How natural was the mechanism which controlled movement through the environment?

8. How aware were you of events occurring in the real world around you?

9. How aware were you of your display and control devices?

10. How compelling was your sense of objects moving through space?

11. How inconsistent or disconnected was the information coming from your various senses?

12. How much did your experiences in the virtual environment seem consistent with your real-world experiences?

13. Were you able to anticipate what would happen next in response to the actions that you performed?

14. How completely were you able to actively survey or search the environment using vision?

15. How well could you identify sounds?

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16. How well could you localize sounds?

17. How well could you actively survey or search the virtual environment using touch?

18. How compelling was your sense of moving around inside the virtual environment?

19. How closely were you able to examine objects?

20. How well could you examine objects from multiple viewpoints?

21. How well could you move or manipulate objects in the virtual environment?

22. To what degree did you feel confused or disoriented at the beginning of breaks or at the end of the experimental session?

23. How involved were you in the virtual environment experience?

24. How distracting was the control mechanism?

25. How much delay did you experience between your actions and expected outcomes?

26. How quickly did you adjust to the virtual environment experience?

27. How proficient in moving and interacting with the virtual environment did you feel at the end of the experience?

28. How much did the visual display quality interfere or distract you from performing assigned tasks or required activities?

29. How much did the control devices interfere with the performance of assigned tasks or with other activities?

30. How well could you concentrate on the assigned tasks or required activities rather than on the mechanisms used to perform those tasks or activities?

31. Did you learn new techniques that enabled you to improve your performance?

32. Were you involved in the experimental task to the extent that you lost track of time?

149