February 2015 

NASA/TM–2015-218681 

Comparison of Flight Simulators Based on 

Human Motion Perception Metrics 

Ana R. Valente Pais and Bruno J. Correia Grάcio 

Delft University of Technology, Delft, The Netherlands 

Lon C. Kelly 

Unisys Corporation, Hampton, Virginia 

Jacob A. Houck 

Langley Research Center, Hampton, Virginia 
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February 2015 

NASA/TM–2015-218681 

Comparison of Flight Simulators Based on 

Human Motion Perception Metrics 

Ana R. Valente Pais and Bruno J. Correia Grάcio 

Delft University of Technology, Delft, The Netherlands 

Lon C. Kelly 

Unisys Corporation, Hampton, Virginia 

Jacob A. Houck 

Langley Research Center, Hampton, Virginia 
Available from: 

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NASA Langley Research Center 

Hampton, VA 23681-2199 

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official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics and 

Space Administration. 
Abstract

In flight simulation, motion filters are used to transform aircraft motion

into simulator motion. When looking for the best match between visual

and inertial amplitude in a simulator, researchers have found that there

is a range of inertial amplitudes, rather than a single inertial value, that

is perceived by subjects as optimal. This zone, hereafter referred to as

the optimal zone, seems to correlate to the perceptual coherence zones

measured in flight simulators. However, no studies were found in which

these two zones were compared. This study investigates the relation

between the optimal and the coherence zone measurements within and

between different simulators. Results show that for the sway axis, the

optimal zone lies within the lower part of the coherence zone. In addition,

it was found that, whereas the width of the coherence zone depends

on the visual amplitude and frequency, the width of the optimal zone

remains constant.

Contents

1 Introduction 3

2 Background 5

2.1 Coherence Zone . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Optimal Zone . . . . . . . . . . . . . . . . . . . . . . . . . 7

3 Method 9

3.1 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1.1 VMS . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1.2 GFD . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1.3 IFD . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2 Experimental design . . . . . . . . . . . . . . . . . . . . . 11

3.3 Motion and visual signals . . . . . . . . . . . . . . . . . . 12

3.4 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.5 Subjects and subjects’ instructions . . . . . . . . . . . . . 14

3.6 Motion base performance . . . . . . . . . . . . . . . . . . 15

3.7 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Results 20

4.1 Coherence zones . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 Optimal zone . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.3 Coherence zone versus optimal zone . . . . . . . . . . . . 32

5 Discussion 36

5.1 Coherence zone versus optimal zone . . . . . . . . . . . . 36

5.2 Comparing simulators . . . . . . . . . . . . . . . . . . . . 39

5.2.1 VMS and CMF: comparing motion base performance 39

1
5.2.2 GFD and IFD: comparing different visual systems 40

5.2.3 VMS , GFD and IFD . . . . . . . . . . . . . . . . 41

5.3 Perception metrics and motion simulation . . . . . . . . . 42

6 Conclusions 43

7 Recommendations 44

2
1 Introduction

Ideally, flight simulators would expose pilots to visual and inertial cues

indistinguishable from those experienced during real flight. However,

the visual and inertial cues in the simulator have limitations not encountered during real flight. For example, visual cues are limited by

the projection system (e.g., resolution, luminance, contrast), transport

delay, low spatial frequency, and no stereoscopic cues. Nonetheless, the

visual amplitude can be reproduced one-to-one in most flight simulators.

The inertial cues, however, have to be scaled down to guarantee that the

simulator is kept within its physical limits.

The use of motion filters ensures that the mechanical limits of the simulator are not reached while providing inertial cues to the pilots. These

motion filters introduce amplitude and phase distortions with respect to

the real aircraft motion and to the displayed visual cues.

To guarantee a high fidelity of the simulation, the impact of these

differences on the pilot has to be understood. Throughout the years,

many studies have, therefore, focused on understanding pilot perception

in simulator environments [1–13] and others have investigated the effect of motion cues and motion filter settings on pilot behavior [14–19].

There also has been some effort in categorizing different filter settings in

equivalent fidelity regions [20–23].

Some studies have focused specifically on the combined perception of

visual and inertial stimuli [24–26]. It has been shown in various driving

simulation [27, 28] and flight simulation studies [6, 29] that simulations

where the visual and inertial cues had equal magnitudes were not preferred by subjects. In these studies, the amplitude of the inertial cues was

lowered because subjects perceived the inertial motion as “too strong”.

This overestimation of inertial motion was hypothesized to occur due

to the differences between the simulator and real world visual properties [29], and due to motion distortions imposed by the motion filters and

the vehicle model [30]. Regardless of the reason for the overestimation,

it is clear that using current simulation technology, a one-to-one match

between inertial and visual cues may not be the best choice. Inertial

motion amplitudes different from the visual motion amplitudes may still

be acceptable or even better than the one-to-one case.

For a better understanding of which amplitudes of inertial motion are

perceived as acceptable, or even optimal, in a simulation environment,

two approaches have been developed: the measurement of perception

coherence zones (CZ) [31–34], and the measurement of optimal zones

(OZ) [35].

The CZ is a concept first introduced by Van der Steen [31, 32] when

studying amplitude differences between visual and inertial cues. He defined the CZ as a zone where inertial and visual amplitudes are perceived

to be coherent although their amplitudes are different. To obtain a CZ,

3
the maximum and minimum inertial amplitudes that are still perceived

as coherent with a certain visual amplitude are measured. These maximum and minimum coherent amplitudes are referred to as the upper

and lower thresholds, respectively.

More recently the work of Van der Steen has been extended to include

different types of stimuli, such as higher amplitudes [34] and different

frequencies [33], to investigate the effect of workload on CZ [36] and to

measure not only amplitude coherence zones but also phase coherence

zones [37].

Amplitude coherence zones are influenced by a number of factors such

as the amplitude and frequency [33, 34, 38] of the visual stimuli. Coherence zones seem to also be significantly affected by the type of visual

and motion system used. In ValentePais et al. [33], data from two different simulators were compared and it was observed that the measured

coherence zones were different between simulators. Such a result indicates that when transforming coherence zone knowledge to motion filter

design and tuning, attention should be given to the simulator specific

configuration in terms of motion base and visual system.

The OZ was introduced in a study by Correia Gr ́acio et al. [35]. Using

sinusoidal signals as stimuli, they asked their subjects to tune the inertial

motion amplitude such that it would optimally match the amplitude of

the presented visual scene motion. Although researchers expected to

find a single inertial amplitude matching the visual amplitude, results

showed that the optimal inertial amplitude depended on the initial value

of the inertial amplitude. When the amplitude of the inertial motion

of their first run was well above the amplitude of the visual stimulus,

subjects tended to choose higher optimal gains than when having a first

run with a lower inertial amplitude. As a consequence, the optimal gain

value became an optimal gain zone, delimited by the higher and lower

values found.

From this result, the question arose to whether the measured optimal

zone was not in fact a coherence zone. Moreover, it was thought that

whether or not these zones are equivalent, they might be useful in the

tuning of motion filters for specific simulator configurations. For this

purpose it is necessary to test how much of the differences among simulators can be captured by coherence zones, optimal zones, or both.

To investigate what was the relationship between optimal zone and

coherence zone, and whether these two metrics could indeed differentiate

between simulator configurations, a study was designed where optimal

gain measurements and coherence zone measurements in sway were made

using the same subjects, stimuli and identical measurement methods in

three different simulators [39].

An experiment was conducted at the NASA Langley Research Center

in Hampton, Virginia, where both the Cockpit Motion Facility (CMF)

and the Visual Motion Simulator (VMS) were used. In the CMF two

4
different cockpits were used: the Generic Flight Deck (GFD) and the

Integration Flight Deck (IFD). In all three simulators, the OZ and the

CZ were measured. Both zones were measured for two different stimulus amplitudes and two different frequencies. The measurements were

performed in sway, so that the results can be compared to previous studies [27, 28, 35].

This report will discuss the results of the experiment and is structured

as follows. In Section 2 we will briefly discuss the concepts and earlier

results obtained regarding the coherence and optimal zone experiments.

Section 3 describes the experimental method used, the results are given

in Section 4 and discussed in detail in Section 5. The report ends with

conclusions and recommendations for future work.

2 Background

2.1 Coherence Zone

When humans walk or control a vehicle, the visual information is coherent with the inertial information. To detect self-motion, the human

body integrates information from the visual, vestibular, and somatosensory systems, and combines it with the expectation derived from bodily

actions. In the real world, the visual information is always matched to

the vestibular information one-to-one. However, this might not be the

case in a simulation environment, where the visual information can be

completely different from the inertial information. For example, a simulator visual could be displaying a 10 meter movement while the motion

base only moved 1 meter.

If the difference between the visual and inertial information is too

large, humans will detect that the perceived inertial movement is incongruent with the visual scene. To study these types of visual-inertial

interactions, Van der Steen [31, 32] introduced the concept of coherence

zone (CZ). The CZ defines a perceptual zone where the visual and inertial cues are perceived as “coherent”. In this paper, the coherence zones

are studied in terms of amplitude differences between visual and inertial

cues. However, the concept of CZ can also be extended to include other

stimulus properties such as phase differences [26, 37].

To define a CZ, one needs to measure the maximum and minimum

inertial amplitudes that are still considered by subjects to be coherent

with a particular visual amplitude. The maximum coherent amplitude

is defined as the upper threshold (thup), whereas the minimum coherent

amplitude is defined as the lower threshold (thlo). To capture the CZ, the

coherence zone width (CZW) and the point of mean coherence (PMC)

metrics can be defined using Equations (1) and (2), respectively.

CZW = thup − thlo (1)

5
PMC = thlo +

CZW

2

(2)

In Ref. [34], three experiments are described that aimed at extending

the knowledge on the CZs first measured by Van der Steen [32]. In the

first experiment, the CZ measured by Van der Steen [32] was extended

to higher amplitudes. The CZs were measured for visual amplitudes of

0, 4, 12, 18, 22, 26, and 30 deg/s2. The motion profile used for the visual

and inertial motion was based on smoothed steps in acceleration. An updown staircase procedure was used to obtain both the upper and lower

thresholds. Subjects had to answer affirmatively or negatively the following question: “Did the amplitude of the visual movement correspond

with the magnitude of the motion?”. Then, using a staircase algorithm,

the inertial motion of the next run would change, while the visual motion would remain constant. Based on the subjects’ successive answers,

the inertial motion would then converge to a certain value, later used to

calculate the upper and lower thresholds.

Results showed that, up to a visual amplitude of 12 deg/s2, the obtained CZs were very similar to the ones measured by Van der Steen [32].

The PMC showed values close to the corresponding visual amplitudes

while the CZW increased with the visual amplitude. However, for higher

visual amplitudes the PMCs became smaller than the corresponding visual amplitudes; the CZW remained approximately constant with the

increase of the visual amplitude.

In a second experiment, the staircase measurement method was compared with a self-tuning method where subjects could decide the amount

of inertial motion they would like to experience in the next run. This

was done because the staircase method was time consuming and the task

proved to be difficult for the subjects. Details on the latter self-tuning

method are discussed in Subsection 3.4 of this report. Using the two

methods, the CZ was measured for the 12 and 30 deg/s2 amplitudes.

The motion profiles were again based on smoothed steps in acceleration. For both the PMC and CZW measurements, the results obtained

with the two measurement methods were very similar, and the small differences in the data obtained with both methods were not statistically

significant. This does not mean, however, that both methods are equal

and that their use would always yield the same results. Since the experiments presented in this paper are very similar to those with which the

measurement methods yielded similar data, we were confident however

that in this particular case the self-tuning method would lead to the same

trends as the more task-demanding staircase method. It is recommended

to study the wider validity of this claim in later experiments.

In a third experiment, the effect of the frequency of the motion stimulus on the CZ was tested. The chosen visual amplitudes were again

12 and 30 deg/s2. In this experiment there were three different motion

profiles: a sinusoid with a frequency of 2 rad/s, a sinusoid with a fre6
quency of 10 rad/s, and the smoothed step in acceleration used in the

previous experiments. The self-tuning method was used to measure the

CZs. Results showed that the PMC as well as the CZW increase with the

stimulus amplitude (similar to the first experiment) but decreased with

the stimulus frequency. The frequency dependency of the results was hypothesized to be related to the dynamics of the semicircular-canals [34].

2.2 Optimal Zone

In theory, one-to-one simulation (i.e., when inertial cues are equal to visual cues) should result in the best perceived match between visual and

inertial information. However, recent studies have shown that one-to-one

motion in a simulation can be perceived as too strong [6,27–29]. In driving simulation, Feenstra et al. [27] studied the effect of providing drivers

with different motion conditions during a slalom maneuver. They tested

motion gains, the ratio between inertial and visual motion, of 0, 0.4,

0.7, and 1. A motion gain of 0 means that only visual information was

displayed while a motion gain of 1 means that the visual information

corresponded to the inertial information. Surprisingly, results showed

that 0.7 was the preferred motion gain. The Motion Cueing Algorithm

(MCA) used in this experiment cued the lateral road position one-to-one.

However, longitudinal specific force was not cued because the car travelled at constant speed. All the other degrees-of-freedom (DoF) where

cued one-to-one. In all DoFs a limiter block was used to prevent damage of the simulator whenever the actuators were close to their limits.

Pretto et al. [28] conducted a similar study in a different simulator. The

authors stated that in this experiment, the vehicle dynamics from the

simulated car were directly mapped to the simulator motion. The motion system had a transport delay of 41ms. They tested motion gains of

0.5, 0.75, 1, and 1.25. Results showed that the value 0.5 and 0.75 were

most preferred by subjects. Similar trends in the motion gain were also

found in flight simulation. Groen et al. [6] tested different motion filter

configurations to simulate a take off maneuver. The preferred motion

filter conditions involved a motion gain of 0.2 for the surge filter and a

motion gain of 0.6 for the pitch filter. Groen et al. [6] stated in the study

that, “Remarkably, unity gains were rejected as [being] too powerful.”

However, in this study the MCA cued high-frequency longitudinal cues

with simulator surge and low-frequency longitudinal cues with simulator

pitch (tilt-coordination). This classical way of cueing might have introduced distortions in the motion profiles [30]. In another study, pilots

reported that the lateral motion experienced during a decrab maneuver

was too strong, even though the motion gain was 0.7 [29]. According to

the authors, in this condition the motion from the aircraft model was

sent directly to the motion platform without involving any MCA in the

process. The visual delay was about 50 ms.

To determine if these reported motion gains were being influenced

7
by the vehicle model, an experiment where subjects were asked to find

the “best match” between the visual and inertial amplitude was conducted [35]. This best match is hereafter referred to as Optimal Zone

(OZ). The OZ was measured in sway for sinusoidal motion profiles in acceleration with amplitudes of 1 and 2.5 m/s2 and frequencies of 0.2, 0.4

and 0.8 Hz. Subjects were given a visual stimulus with constant amplitude and were asked to change the inertial amplitude until they decided

that the ‘best match’ between the visual and inertial acceleration was

obtained. A side-stick was used to change the inertial amplitude while

experiencing the constant visual amplitude. A left-deflection reduced the

inertial amplitude while a right deflection increased it. At the start of a

measurement, an initial inertial amplitude was provided that was either

higher or lower than the corresponding visual amplitude. Results showed

that this initial inertial amplitude influenced the optimal zone. An initial inertial amplitude set higher than a certain visual amplitude led to

inertial amplitudes that were higher than those obtained when the initial

inertial amplitude was set lower than the same visual amplitude. As a

result, each visual amplitude had two values where the inertial motion

was perceived to be ‘optimal’, thus resulting in an optimal zone. The

upper boundary of the optimal zone is referred to as the upper optimal

amplitude; the lower boundary of the optimal zone is referred to as the

lower optimal amplitude.

In the same study it was found that the inertial amplitude chosen by

subjects decreased for stimuli with higher frequency, suggesting a sensitivity to the acceleration derivative (i.e., jerk) as reported in previous

research [30, 40, 41]. There was also an effect of the visual signal amplitude. The visual signal with an amplitude of 2.5 m/s2

led to lower

motion gains than the conditions using a visual signal with an amplitude

of 1 m/s2.

When comparing the OZ with the CZ, it is convenient to use similar

metrics. Therefore, new metrics for the OZ are hereby introduced which

characterize the width and the mid point of the OZ. The width of the

OZ is defined by the optimal zone width (OZW) while the mid point

is defined by the point of mean optimal (PMO) zone. The OZW and

PMO are given by Equations (3) and (4), respectively, where upOZ is

the upper optimal amplitude and loOZ is the lower optimal amplitude:

OZW = upOZ − loOZ (3)

PMO = loOZ +

OZW

2

(4)

Summarizing, for both zones we defined metrics to characterize the

mid point of the zone, the PMC and PMO, for the CZ and the OZ

respectively. In the following, the PMC and the PMO will be referred to

as “point of mean zone” (PMZ) measures. Similarly, the CZW and the

8
OZW will be referred to as “zone width” (ZW) measures. Although the

PMC may be different from the PMO, we consider the concept behind

these measures to be the same, thus allowing us to define both measures

as PMZs. The same applies to the concept behind the CZW and the

OZW.

3 Method

3.1 Apparatus

The experiment was conducted in the Visual Motion Simulator (VMS)

and the Cockpit Motion Facility (CMF), located at NASA Langley Research Center (LaRC) in Hampton, Virginia, USA. The CMF consists of

one motion base and three interchangeable simulator cockpits. For this

experiment, two different cockpits were used on the CMF, the Generic

Flight Deck (GFD) and the Integration Flight Deck (IFD). Figure 1

shows the VMS and CMF motion bases.

(a) VMS.

(b) CMF.

Figure 1: The VMS and CMF motion bases. (Courtesy of

NASA.)

9
3.1.1 VMS

The VMS has a six degree-of-freedom (DOF), hexapod type, motion

base with an actuator stroke of 1.5 m. The maximum displacement,

velocity and acceleration in the lateral axis are ±1.2 m, ±0.6 m/s and

±5.9 m/s2, respectively. For motion control, commands were sent to the

motion base at 50 Hz.

The visual system consists of four Wide Angle Collimated (WAC)

windows of 1024 by 944 pixels with an update rate of 60 Hz. The front

windows provide a field-of-view (FoV) of 65.93 deg horizontal by 45.23

deg vertical. The lateral window on the left-hand side has a FoV of 48.50

deg horizontal by 35.50 deg vertical.

The lateral acceleration of the motion base was measured using Sundstrand QA-900 accelerometers (serial number 1271). These have been

tested and calibrated to ±2g at up to 100 Hz, and have deviations of less

than 1000 μg.

3.1.2 GFD

The motion base of the CMF is also a 6 DOF, hexapod motion base,

although it is larger and newer than the VMS. The actuator stroke is 1.9

m and the lateral motion limits are ±1.4 m, ±1.0 m/s, and ±6.9 m/s2

in position, velocity and acceleration, respectively. The motion base was

controlled at 50 Hz.

The GFD visual system consists of four WAC windows with an update rate of 60 Hz. The front windows have a horizontal FoV of 46 deg

and a vertical FoV of 34 deg. The lateral windows have a FoV of 49

deg horizontal by 37.5 deg vertical. Although the FoV is smaller in this

cockpit than in the VMS, the resolution of the screens is higher, with

each window having 1280 by 1024 pixels.

The lateral acceleration was recorded with a Honeywell Q-Flex(R)

accelerometer (Model QA-700) which was placed under the dynamic

platform at the centroid position.

3.1.3 IFD

The IFD cockpit is also part of the CMF, so the motion base description

given for the GFD also applies to this simulator.

The visual system is quite different from the GFD and VMS. It consists of a collimated panoramic display, with a horizontal FoV of 200 deg

and a vertical FoV of 40 deg. Five projectors are used, each with 1440

by 1024 pixels, and an update rate of 60 Hz.

The interiors of the three simulators are shown in Figure 2.

10
(a) VMS. (b) GFD.

(c) IFD.

Figure 2: View of the interiors of the three cockpits. (Courtesy

of NASA.)

3.2 Experimental design

The experiment had a four way repeated measures design. The independent factors considered were the three simulators described above,

the two types of instructions given to subjects (either optimal or coherence zone tuning), two visual stimulus amplitudes, and two stimulus

frequencies.

The visual stimulus amplitudes used were 0.5 and 1 m/s2. These

amplitudes were chosen such that the results could be directly compared

to previous studies on optimal gain tuning performed in other simulators.

The choice of frequencies was less straightforward. Initially three

frequencies of 2, 3, and 5 rad/s were chosen for the GFD and IFD part of

the experiment. The lowest frequency of 2 rad/s was the lowest possible

frequency to be tested while still remaining within the motion base limits.

For the VMS the minimum frequency was 3 rad/s, however, so only two

frequencies would be tested in this simulator.

During preliminary tests it became clear that because both coherence zones and optimal gain measurements were being performed, the

experimental sessions were too long and there was the risk that subjects

would become too tired. For this reason, one of the frequencies was eliminated from the tests in the GFD and IFD. To maintain symmetry with

respect to the tests in the VMS, it would have been better to eliminate

the 2 rad/s condition. However, it was thought that maintaining this low

frequency would allow a more direct comparison to results from other

studies that used the same stimulus frequency. Moreover, the larger the

11
differences between tested frequencies the easier it would be to observe

the effect of frequency on the coherence zones. It was then decided to

maintain the 2 and 5 rad/s conditions for the GFD and IFD and test

the 3 and 5 rad/s conditions in the VMS. With this design, comparison

among the three simulators can be done only at the frequency of 5 rad/s.

In each simulator and for each of the conditions, two measurements

were taken: during the optimal tuning, one measurement for each of two

initial amplitudes of the inertial motion cue; and during the coherence

zone measurements, one for the upper thresholds and one for the lower

threshold. For each of these measurements three repetitions were made,

resulting in a total of 48 experimental trials in each simulator.

3.3 Motion and visual signals

The visual and inertial motion stimuli consisted of sinusoidal signals

with amplitude and frequency defined by the experimental conditions

described above. The signals were designed such that experimental runs

of different frequencies would have the same duration. The length of the

motion signals were 2, 4 and 8 periods for the conditions with frequencies

of 2 rad/s, 3 rad/s and 5 rad/s, respectively.

These sinusoidal signals were faded in and out to guarantee that

the acceleration, velocity and position signals always started and ended

at zero. The fade in and fade out parts of the signal are described by

Equation (5), where A is the amplitude in m/s2, ω is the signal frequency

in rad/s, and ωs and ωc are the smoothing and compensation frequencies,

respectively, also in rad/s. Both the smoothing and the compensation

frequencies equaled half of the signal frequency.

f(t) = 1

2

A sin(ωt) −

1

2

A sin(ωt) cos(ωst) + Ac sin(ωct) (5)

The complete motion signal is given by Equation (6) where T is the

period of the signal and N is the number of periods in one run. The

number of periods does not include the two periods that are necessary

to perform the fade in and fade out. Including the fade in and the fade

out, the total length of one run was 12.57 seconds.

a(t) =







f(t), 0 < t ≤ T

A sin(ωt), T < t ≤ (N + 1)T

f(t − T), (N + 1)T < t ≤ (N + 2)T

(6)

Figure 3 shows examples of complete runs for all three frequencies

and an amplitude of 1 m/s2.

3.4 Procedure

The experiment was divided in three parts. The first part was conducted

in the GFD, the second part in the VMS and the third part in the IFD.

12
Lateral motion, m/s

2, m/s, m

Time, s

acceleration velocity

position

0 2 4 6 8 10 12

-1.5

-1

-0.5

0

0.5

1

1.5

2

(a) 2 rad/s

Lateral motion, m/s

2, m/s, m

Time, s

0 2 4 6 8 10 12

-1.5

-1

-0.5

0

0.5

1

1.5

2

(b) 3 rad/s

Lateral motion, m/s

2, m/s, m

Time, s

0 2 4 6 8 10 12

-1.5

-1

-0.5

0

0.5

1

1.5

2

(c) 5 rad/s

Figure 3: Example of the motion signals during one run for the

three different frequencies and an amplitude of 1 m/s2.

Due to simulator scheduling it was not possible to have all simulators

available at the same time, so randomization between simulators was

not possible. There was one month separating the tests in the GFD

and in the VMS and four months between the VMS and the IFD parts.

The same subjects were used in all three simulators. The order of the

48 experimental trials performed in each simulator was randomized for

every subject. For each subject, the trial order was the same on all three

simulators.

In both simulators, the optimal zone experimental block was performed before the coherence zone experimental block for all subjects.

This was done to ensure that knowledge regarding a “coherence zone”,

which is a necessary part of the coherence zone instructions, did not

negatively influence subjects’ strategy during the optimal zone measurements. After being told that a range of amplitudes exist where motion

and visual cues are perceived as coherent although they are not a physical match, subjects could reject the concept of an optimal zone, and the

idea that it could be found by further tuning the inertial amplitude of a

simulator. This could hinder their motivation to find the “best match”

during the optimal zone measurements. Obviously, for the experimental

trial in the second and third simulators, subjects were already familiar

with both concepts. However, in this case, they have already experienced

that indeed both tasks, optimal and coherence zone measurements, are

possible to accomplish using the same tuning method.

Subjects were seated in the left-hand seat of the simulator cockpit.

The subject wore a headset with active noise cancellation which allowed

communication with the experiment supervisor. Three buttons located

in the sidestick, which was located on the left side of the participants,

were used to record their answers throughout the experimental runs.

For each experimental trial, the visual motion amplitude was kept

constant and the inertial motion amplitude was varied throughout a set

of runs. For each trial of the optimal zone measurements, the ampli13
tude of the first run was randomly selected at an amplitude between 1.4

and 1.6 times the visual amplitude for runs that approach the optimal

zone from above or a random value between 0.4 and 0.6 times the visual amplitude for approaching the optimal zone from below. For the

coherence zone measurements, the initial inertial amplitude was a random value between 1.1 and 0.9 times the visual amplitude. Before each

coherence zone trial started, subjects were informed whether that trial

corresponded to a lower or an upper threshold measurement.

At the end of each run within a trial subjects could change the motion

of the next run. They did this by pushing a switch button multiple

times up or down until they reached a certain number of increments

or decrements. The chosen number was shown on a head-down display

placed directly in front of the subjects. A positive number meant the

next run would have a higher amplitude motion, and a negative number

meant a lower amplitude motion. After giving their answer, subjects

pressed a second button to signal that they were ready for the next run.

The trial ended when subjects’ answers had two consecutive reversals

of one increment or decrement, i.e., a sequence of 1, -1, 1, or -1, 1, -1.

This indicated that subjects converged to a certain amplitude of motion

that could not be increased or decreased anymore. To avoid fatigue, trials

were also stopped if subjects reached 30 runs. The size of the increment

or decrement was 0.025 times the visual amplitude, which corresponded

to 0.0125 m/s2for the lowest amplitude condition and 0.050 m/s2for

the highest amplitude condition.

Before starting the experiment, subjects performed three randomly

chosen experimental trials for training purposes.

3.5 Subjects and subjects’ instructions

Eight subjects were selected from the employees of the LaRC Flight

Simulation Facility. There were seven male participants and one female

participant. The subjects’ average age was 49 years, ranging between 31

and 64 years old. All eight subjects were able to complete the experiment,

and there were no complaints of motion sickness.

The participants were instructed to sit upright and refrain from making head movements throughout the experiment. They were, however,

allowed to gaze over the visual scene at will.

Participants were told they were to perform a series of experimental

trials which consisted of several runs. In each trial the visual scene would

move the same way but the amplitude of the simulator inertial motion

would vary between runs depending on their input.

For the optimal zone measurements, subjects were not informed whether

the trial was an upper or a lower optimal amplitude measurement. Participants were instructed to find the inertial motion amplitude that, in

their opinion, matched the visual amplitude cue the best.

14
For the coherence zone measurements, subjects were told at the beginning of the trial whether an upper or a lower threshold measurement

was being performed. For an upper threshold measurement, subjects

were asked to find the strongest inertial motion amplitude that was still

perceived as coherent with the visual cue. For a lower threshold measurement they were asked to find the weakest inertial motion amplitude

that was still coherent with the visual cue.

For both optimal and coherence zone measurements subjects were instructed to decrease and increase the inertial motion amplitude as many

times as needed until they were satisfied with their choice. Subjects were

advised to start with increments of 10 or more and decrease the number of increments or decrements at every direction reversal. They were

informed of the stopping criteria of the trials.

3.6 Motion base performance

To observe the differences in motion base performance between the three

simulators, the commanded lateral acceleration was compared to the

measured signals for each of the three simulators. Figure 4 and Figure 5 show time histories of the commanded and measured signals for all

frequencies at high and low amplitudes. The amplitudes chosen to be

plotted are around 0.2 m/s2 and 1.5 m/s2for the commanded signal, and

are representative of the limits of the search interval for most subjects.

As can be seen, there were quite some differences in performance

between the VMS and the CMF motion bases. The VMS showed a more

pronounced “turn around bump” at the low amplitude and low frequency

conditions and a clear overshoot at the high amplitude conditions. The

low amplitude conditions at a frequency of 5 rad/s seemed to be equally

demanding for both motion bases. In these conditions the time histories

clearly showed an additional oscillation around 15 rad/s for the VMS and

20 rad/s for the CMF. Although these oscillations were more pronounced

at the low amplitude and high frequency conditions, a spectral analysis

of the time histories showed that they were present in all other conditions

as well.

The data from the CMF motion base also showed oscillations around

180 rad/s, which were stronger with the GFD than with the IFD cabin.

The oscillations can clearly be seen in the time histories in Figure 4c

and Figure 5c. Although both cockpits were mounted on the same motion base, differences in the mass of the cockpits, if not fully taken into

account by the motion base controller, may have affected the overall

performance.

Since there were considerable differences between the commanded

and the measured motion, the thresholds should be based not on the

amplitudes of the commanded signals, but on the amplitudes of the

measured signals. However, some care should be taken, since some of

the peak amplitudes of the measured signal might have been caused by

15
Inertial acceleration, m/s

2

Time, s

commanded

measured

67 68 69 70 71 72

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(a) VMS, 3 rad/s, low amplitude.

Inertial acceleration, m/s

2

Time, s

66 67 68 69 70 71

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

(b) VMS, 3 rad/s, high amplitude.

Inertial acceleration, m/s

2

Time, s

64 65 66 67 68 69

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(c) GFD, 2 rad/s, low amplitude.

Inertial acceleration, m/s

2

Time, s

21 22 23 24 25 26

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

(d) GFD, 2 rad/s, high amplitude.

Inertial acceleration, m/s

2

Time, s

34 35 36 37 38 39

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(e) IFD, 2 rad/s, low amplitude.

Inertial acceleration, m/s

2

Time, s

34 35 36 37 38 39

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

(f) IFD, 2 rad/s, high amplitude.

Figure 4: Time histories of the commanded and the measured

lateral acceleration for frequencies of 3 and 2 rad/s.

16
Inertial acceleration, m/s

2

Time, s

commanded

measured

34 34.5 35 35.5 36

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(a) VMS, 5 rad/s, low amplitude.

Inertial acceleration, m/s

2

Time, s

52.5 53 53.5 54 54.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

(b) VMS, 5 rad/s, high amplitude.

Inertial acceleration, m/s

2

Time, s

40.5 41 41.5 42 42.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(c) GFD, 5 rad/s, low amplitude.

Inertial acceleration, m/s

2

Time, s

54 54.5 55 55.5 56

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

(d) GFD, 5 rad/s, high amplitude.

Inertial acceleration, m/s

2

Time, s

33 33.5 34 34.5 35

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(e) IFD, 5 rad/s, low amplitude.

Inertial acceleration, m/s

2

Time, s

48.5 49 49.5 50 50.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

(f) IFD, 5 rad/s, high amplitude.

Figure 5: Time histories of the commanded and the measured

lateral acceleration for the 5 rad/s frequency.

17
noise inherent to the accelerometer and not motion of the platform.

To preserve the measured accelerations and eliminate high frequency

noise that is unlikely to have resulted from the inertial motion of the

platform, the measured signals were filtered with a second order filter

with a cutoff frequency of 50 rad/s. Examples of the measured signals

before and after the filtering are shown in Figure 6. Only examples of

the low amplitude runs are plotted, since the differences between the

measured and filtered signals are more noticeable in these.

For the coherence zones, the threshold values were determined by

averaging the peak amplitude of the last two runs in each trial. For

the optimal zone, thresholds were determined by the peak amplitude of

the last run. The so determined thresholds were then averaged across

the three repetitions of each condition. Especially for the VMS, the

differences in peak amplitudes between the commanded and the filtered

signals were quite large, so a large difference can be expected between

the thresholds if they are determined from the commanded signals or

from the filtered signals.

Regarding the IFD and the GFD, the differences in weight of the two

cockpits, with the IFD weighting 10% more than the GFD, was expected

to slightly influence the motion base performance. However, apart from

a high frequency oscillation that is stronger in the GFD than in the IFD,

the lateral motion performance of the motion platform was very similar.

It is not clear what caused the high frequency oscillation. One hypothesis

would be that it is not actually an oscillation of the motion base, but of

the measurement unit. The frequency of this oscillation was around 180

rad/s or 28 Hz, which may be too low for electrical noise, but it could be

explained by some type of mechanical vibration at the attachment point

between the measurement unit and the platform.

3.7 Data analysis

From the coherence zone measurements we obtained inertial amplitudes

for the upper (thup) and lower (thlo) thresholds. From these thresholds

we obtained the coherence zone width (CZW) and the point of mean

coherence (PMC), as defined by Equations (1) and (2), respectively. For

the data analysis of the coherence zone measurements, the dependent

measures were the PMC and the CZW; the independent variables were

the visual amplitude and frequency.

From the optimal zone measurements we obtained inertial amplitudes

for the upper (upOZ) and lower (loOZ) optimal amplitudes. With these,

we calculated the point of mean optimal (PMO) zone and the optimal

zone width (OZW) as defined by Equations (3) and (4), respectively. For

the data analysis of the optimal zone, the dependent measures were the

PMO and the OZW; the independent variables were the visual amplitude

and frequency.

18
Inertial acceleration, m/s

2

Time, s

commanded

measured

filtered

67 68 69 70 71 72

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

(a) VMS, 3 rad/s.

Inertial acceleration, m/s

2

Time, s

34 34.5 35 35.5 36

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(b) VMS, 5 rad/s.

Inertial acceleration, m/s

2

Time, s

64 65 66 67 68 69

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

(c) GFD, 2 rad/s.

Inertial acceleration, m/s

2

Time, s

40.5 41 41.5 42 42.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(d) GFD, 5 rad/s.

Inertial acceleration, m/s

2

Time, s

34 35 36 37 38 39

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

(e) IFD, 2 rad/s.

Inertial acceleration, m/s

2

Time, s

33 33.5 34 34.5 35

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

(f) IFD, 5 rad/s.

Figure 6: Time histories of the measured and the filtered lateral acceleration signals for all frequencies and low amplitude

inertial motion.

19
To compare the OZ with the CZ we calculated their respective ‘point

of mean zone’ (PMZ) and ‘zone width’ (ZW) metrics. The PMZ for

the OZ is given by the PMO while for the CZ it is given by the PMC.

Similarly, the ZW for the OZ is given by the OZW while for the CZ it is

given by the CZW. The PMZs and the ZWs were then used as the ‘dependent measures’ when comparing the coherence zone with the optimal

zone. The independent variables were then the visual amplitude, the

frequency, and the instructions that the subjects got. That is, the difference between the OZ and CZ measurements is examined as a function

of the different instructions that the subjects received before conducting the experiment. With the CZ instructions, subjects are expected to

converge to inertial amplitudes that are higher or lower than the corresponding visual amplitude. Conversely, for the OZ instructions, subjects

are expected to converge to inertial values closer to the visual amplitude.

Therefore, it is expected that the inertial amplitudes obtained with the

OZ instructions lie within the inertial amplitudes obtained with the CZ

instructions.

When comparing both simulators we again used the PMZs and ZWs

as dependent measures and the simulator, instructions and visual amplitude as independent variables.

For the statistical analysis we conducted repeated measures analysis

of variance (ANOVAs). We considered as highly significant the main

effects with a p value lower or equal to 0.01, and as significant the main

effects with a p value between 0.01 and 0.05. The statistical analysis

were performed with SPSS PASWS 19.

4 Results

4.1 Coherence zones

For the tests in the VMS, the thresholds determined from the commanded signals are shown together with the thresholds determined from

the filtered signals in Figure 7.

As can be seen, there was indeed a considerable difference between

the commanded signal thresholds and the filtered signals thresholds, especially for the upper thresholds of the high amplitude conditions. Although the difference between commanded and filtered thresholds for the

other simulators was negligible, for the sake of consistency, all thresholds were determined from the filtered signals. Therefore, except where

explicitly mentioned otherwise, all threshold values presented here were

determined from the filtered signals.

Figure 8 shows the determined thresholds for all conditions in the

VMS. In this and all following figures, the error bars indicate the 95%

confidence interval of the mean.

20
Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

one-to-one

3 rad/s comm

3 rad/s filt

5 rad/s comm

5 rad/s filt

0.5 1

0

0.5

1

1.5

2

(a) 3 rad/s.

Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

0.5 1

0

0.5

1

1.5

2

(b) 5 rad/s.

Figure 7: Upper and lower thresholds based on commanded

(comm) and filtered (filt) signals in the VMS. Error bars indicate the 95% confidence interval of the mean. Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

one-to-one 3 rad/s 5 rad/s

0.5 1

0

0.5

1

1.5

2

Figure 8: Upper and lower thresholds for the VMS.

21
For both frequencies and amplitudes of the visual stimulus, the upper

and lower thresholds defined a coherence zone that included the one-toone line. The variation of the lower threshold values across conditions

was small, whereas the upper thresholds clearly increased with the visual

stimulus amplitude and decreased with frequency.

A repeated measures Analysis of Variance (ANOVA) was performed

to investigate whether the observed differences were significant. The

results of the analysis are shown in Table 1. All data were normally

distributed. Both the frequency and the amplitude had a significant

effect on the upper thresholds, but not on the lower thresholds. There

were no significant interaction effects.

Table 1: Results of the ANOVA for the measured thresholds in the VMS, where ** is highly significant (p≤ 0.01),

* is marginally significant (p≤ 0.05), and - is not significant

(p> 0.05).

Dependent measures

Independent

variables

Lower threshold Upper threshold

df F sig. df F sig.

Amplitude 1,7 2.47 - 1,7 48.36 **

Frequency 1,7 1.60 - 1,7 5.59 *

Amplitude × Frequency 1,7 3.07 - 1,7 0.02 -

The effect of frequency, with higher frequencies leading to lower upper

thresholds, is similar to what was found for yaw motion in Valente Pais

et al. [33,34,36] and it is in agreement with previous studies that showed

that subjects judge motion strength not only on the basis of acceleration

but also of jerk [42].

In Valente Pais et al. [33] it was argued that the influence of frequency

on the yaw coherence zone might be related to the dynamics of the

semicircular canals (SCC). The gains applied to the inertial stimulus by

the SCC might not be taken into account during the internal comparison

of visual and inertial stimuli resulting in a deviation of the Point of Mean

Coherence (PMC) from the one-to-one line. If the same rationalization

were to be applied to the lateral motion, one would expect a similar but

weaker effect, since the gain of the otoliths also increases with frequency

[10], but only slightly, for a frequency range between 1 and 10 rad/s.

This may help explain the variation in the upper threshold values with

increasing frequency, but it is not in agreement with what was found for

the lower threshold values.

Surprisingly, the lower thresholds were not affected by the stimulus

frequency. One explanation for this fact might be the performance of

22
the motion platform at the low amplitudes. The crosstalk might have

masked the lateral motion to such an extent that it hindered subjects in

their task. One other explanation could be the relatively lower resolution

of the measurement method at the lower amplitudes. In the experimental setup, the minimum amplitude intervals that are tested depend on

the amplitude of the visual stimulus, so they are the same for the lower

and the upper threshold trials. A small enough amplitude interval at the

higher amplitude runs may be too large to capture the slight differences

in lower threshold measurements. Of course, it may happen that for

these specific combinations of lateral motion amplitudes and frequencies

the lower thresholds of the perceived coherence zone are indeed very similar.

The amplitude of the visual stimulus also had a significant effect

on the upper thresholds but not on the lower thresholds. Again, the

lower threshold runs might have been affected by the resolution of the

experimental method. Although not immediately observable from the

upper and lower threshold plots, very similar to what happens for yaw

coherence zones, the coherence zone bends down with respect to the oneto-one line. This effect is easier to see by looking at the PMC values.

For the higher amplitude the PMC value is still higher than 1 m/s2, but

it is much closer to the one-to-one line than for the lower amplitude.

The thresholds determined in the GFD and IFD are shown in Figure 9. Similarly to the results from the VMS, the visual stimulus amplitude and frequency had a more noticeable effect on the upper thresholds

than on the lower thresholds. For both simulators, there was a very small

increase in the lower thresholds for the higher amplitude and a slight decrease for the higher frequency conditions. The same trend can be seen

for the upper thresholds, but here the differences are much larger. The

upper thresholds measured in the GFD were slightly lower than the ones

measured in the IFD, and the lower thresholds were slightly higher.

To investigate whether these effects were significant, an ANOVA was

performed on the data. Table 2 shows the results of this analysis. Only

the main effects and significant interactions are shown. The effect of

amplitude and frequency on the lower threshold values was not significant. Moreover, the lower thresholds from the two simulators were not

significantly different. The upper threshold values were significantly affected by the simulator, amplitude and frequency factors. In addition,

the effect of frequency on the upper thresholds was larger on the IFD

than on the GFD, as is confirmed by the significant interaction effect

found between simulator and frequency.

The influence of frequency and amplitude on the thresholds followed

the same trends as the ones seen in the VMS. The higher frequency conditions resulted in lower values, although that effect was only statistically

significant for the upper thresholds. The fact that also in these two

23
Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

one-to-one 3 rad/s 5 rad/s

0.5 1

0

0.5

1

1.5

2

(a) GFD.

Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

0.5 1

0

0.5

1

1.5

2

(b) IFD.

Figure 9: Upper and lower thresholds for the GFD and the

IFD.

Table 2: Results of the ANOVA for the measured thresholds

in the GFD and IFD, where ** is highly significant (p≤ 0.01),

* is marginally significant (p≤ 0.05), and - is not significant

(p> 0.05).

Dependent measures

Independent

variables

Lower threshold Upper threshold

df F sig. df F sig.

Simulator 1,7 2.48 - 1,7 24.84 **

Amplitude 1,7 2.56 - 1,7 156.07 **

Frequency 1,7 2.96 - 1,7 21.77 **

Simulator × Frequency 1,7 0.05 - 1,7 7.37 *

24
simulators the different conditions did not significantly affect the lower

thresholds indicates that either the amplitudes and frequencies chosen

were not different enough, or indeed that the measuring method lacks

resolution at the lower amplitudes. Since this is a result that is constant

across simulators, the probability that it is related to a specific simulator

configuration, is small.

The measured thresholds from all three simulators can be compared

only for the highest frequency, since in the VMS the lower frequency was

3 rad/s and not 2 rad/s as in the GFD and IFD. The upper and lower

thresholds at 5 rad/s and at the two amplitudes for all simulators are

shown in Figure 10. For comparison purposes both the thresholds calculated from the commanded and from the measured signals are shown. Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

one-to-one

VMS

GFD

IFD

0.5 1

0

0.5

1

1.5

2

(a) Commanded signals.

Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

one-to-one

VMS

GFD

IFD

0.5 1

0

0.5

1

1.5

2

(b) Measured signals.

Figure 10: Upper and lower thresholds, for the 5 rad/s frequency conditions, in the VMS, GFD and IFD, obtained from

commanded and measured signals.

The lower thresholds did not vary much from one simulator to the

other. The largest differences were seen in the upper threshold values.

The VMS showed the highest values, especially for the conditions with

the 1 m/s2 amplitude. The GFD showed the lowest upper thresholds. An

ANOVA was performed on the data. The results are shown in Table 3.

The lower thresholds were not significantly different across conditions

and simulators. The simulator and the amplitude had a significant effect on the upper thresholds. Although the larger difference seemed to be

between the VMS and the other two simulators, a post-hoc pairwise comparison, using Bonferroni adjustments for multiple comparisons, showed

that the VMS and the IFD were not significantly different, but the GFD

was significantly different from the VMS and the IFD.

The coherence zones limited by the upper and lower thresholds can

also be represented in terms of a PMC and a Coherence Zone Width

25
Table 3: Results of the repeated measures ANOVA for the

measured thresholds in all three simulators for a stimulus frequency of 5 rad/s, where ** is highly significant (p≤ 0.01),

* is marginally significant (p≤ 0.05), and - is not significant

(p> 0.05).

Dependent measures

Independent

variables

Lower threshold Upper threshold

df F sig. df F sig.

Simulator 2,14 1.32 - 1.19,10.18a 7.96 *

Amplitude 1,7 3.15 - 1,7 72.64 **

Simulator × Amplitude 2,14 1.03 - 12,14 2.03 -

a Greenhouse-Geisser sphericity correction applied.

(CZW). Figure 11 shows the results in the three simulators in terms of

PMC and CZW. Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

one-to-one

VMS

GFD

IFD

0.5 1

0.5

1

1.5

(a) Point of mean coherence.

Inertial stimulus amplitude, m/s

2

Visual stimulus amplitude, m/s2

0.5 1

0.5

1

1.5

(b) Coherence zone width.

Figure 11: Measured coherence zones.

The PMCs increased with amplitude, approximately following the

one-to-one line, although for the higher amplitude they were slightly

lower with respect to this line than for the lower amplitude conditions.

The VMS had the highest PMCs, which is to be expected, given that the

upper thresholds were also much higher in this simulator. The CZWs

were also larger for the higher amplitude conditions. The VMS had the

wider coherence zones, followed by the IFD. The GFD presented the

narrowest zones.

The ANOVA results, shown in Table 4, indicate that the effect of

26
amplitude was significant on both the PMC and the CZW. The simulator

factor was also significant on both metrics, although it was not possible

to statistically differentiate the CZWs in the three simulators using a

post-hoc pairwise comparison (again using Bonferroni adjustment). For

the PMC the post-hoc tests showed a significant difference between the

VMS and the GFD, but not between the VMS and the IFD and between

the GFD and the IFD.

Table 4: Results of the repeated measures ANOVA for the

PMC and CZW in all three simulators for a stimulus frequency of 5 rad/s, where ** is highly significant (p≤ 0.01),

* is marginally significant (p≤ 0.05), and − is not significant

(p> 0.05).

Dependent measures

Independent

variables

PMC CZW

df F sig. df F sig.

Simulator 1.20,8.41a 5.39 * 2,14 7.44 **

Amplitude 1,7 108.49 ** 1,7 24.82 **

Simulator × Amplitude 1.95,8.37a 1.67 − 2,14 1.51 −

a Greenhouse-Geisser sphericity correction applied.

4.2 Optimal zone

Figure 12 shows the average upper and lower optimal amplitudes obtained for the VMS simulator based on the commanded and filtered

signals. Similar to what was observed for the coherence zone measurements, the filtered amplitudes were higher than the commanded amplitudes. This amplitude difference is more noticeable for the upper optimal

amplitude because the VMS turnaround peaks increase with the increase

of the commanded amplitude. To be consistent with the coherence zone

analysis, the optimal zone results were obtained using the filtered amplitudes.

Figure 13 shows the mean amplitudes defining the optimal zone for

the VMS simulator. These mean amplitudes increased with the increase

of the visual stimulus amplitude. On the other hand, the mean amplitudes of the optimal zone decreased with the increase of the visual

stimulus frequency. This frequency difference is also observed in the optimal zone intersection with the one-to-one line. The 3 rad/s optimal

zone contained the one-to-one line at the 0.5 m/s2 amplitude, with a

lower optimal gain close to 0.5 m/s2, but not at the 1 m/s2 amplitude,

where both the upper and lower optimal amplitudes were below the one27
Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

3 rad/s comm

3 rad/s filt

5 rad/s comm

5 rad/s filt

0.5 1

0

0.5

1

1.5

2

(a) 3 rad/s.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) 5 rad/s.

Figure 12: Upper and lower optimal amplitudes based on commanded (comm) and filtered (filt) signals in the VMS. Error

bars indicate the 95% confidence interval of the mean.

to-one line. The 5 rad/s optimal zone also contained the one-to-one line

at the 0.5 m/s2 amplitude but the upper optimal gain was close to 0.5

m/s2. The upper and lower optimal amplitudes were again lower than

the one-to-one line at the 1 m/s2 amplitude.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

3 rad/s

5 rad/s

0.5 1 0

0.5

1

1.5

2

Figure 13: Upper and lower optimal amplitudes for the VMS.

Error bars indicate the 95% confidence interval of the mean.

Figure 14 shows the optimal zone obtained for the GFD and IFD

simulators. Similarly to the VMS, the mean amplitudes of the optimal

zone increased with the increase of the visual stimulus amplitude, both

for the GFD and IFD. Again, for both simulators the mean amplitudes

of the optimal zone decreased with the increase of the visual stimulus

frequency. Additionally, the IFD optimal zone had higher amplitudes

than the GFD optimal zone both for the stimulus with 2 and 5 rad/s.

The GFD 2 rad/s optimal zone contained the one-to-one line at the 0.5

28
Table 5: Results of the repeated measures ANOVA for the

measured optimal gain amplitudes in the VMS, where ** is

highly significant (p≤ 0.01), * is marginally significant (p≤ 0.05),

and − is not significant (p> 0.05).

Dependent measures

Independent

variables

Lower optimal

amplitude

Upper optimal

amplitude

df F sig. df F sig.

Amplitude 1,7 14.89 ** 1,7 10.07 *

Frequency 1,7 34.50 ** 1,7 13.41 **

Amplitude × Frequency 1,7 0.98 − 1,7 1.24 −

m/s2 amplitude but was lower than it at the 1 m/s2 amplitude. The 5

rad/s optimal zone of this simulator was always below the one-to-one line.

The IFD, on the other hand, had an optimal zone at 2 rad/s that was

higher than the one-to-one line at the 0.5 m/s2 amplitude and contained

the one-to-one line at the 1 m/s2 amplitude. The IFD 5 rad/s optimal

zone contained the one-to-one line at the 0.5 m/s2 amplitude but was

lower than it at the 1 m/s2 amplitude.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

2 rad/s

5 rad/s

0.5 1

0

0.5

1

1.5

2

(a) GFD.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) IFD.

Figure 14: Upper and lower optimal amplitudes for the GFD

and the IFD. Error bars indicate the 95% confidence interval

of the mean.

In this section we compared the three different simulators using the

optimal zone measurements. This comparison was performed for the

stimuli with a frequency of 5 rad/s since these were shared by the three

motion-based simulators used in this study. Figure 15 shows that, except for the VMS simulator, the filtered and commanded signals were

29
Table 6: Results of the repeated measures ANOVA for the

measured optimal gain amplitudes in the GFD and IFD, where

** is highly significant (p≤ 0.01), * is marginally significant

(p≤ 0.05), and − is not significant (p> 0.05).

Dependent measures

Independent

variables

Lower optimal

amplitude

Upper optimal

amplitude

df F sig. df F sig.

Simulator 1,7 5.32 − 1,7 5.72 *

Amplitude 1,7 19.53 ** 1,7 14.78 **

Frequency 1,7 8.23 * 1,7 15.53 **

very similar. As seen before (Figure 12), the VMS simulator commanded

signals have smaller amplitudes than the filtered signals. For the commanded signals, we observe that the VMS optimal zone has the lowest

amplitudes whereas the IFD optimal zone has the highest amplitudes.

For the filtered signals, while the lower optimal amplitudes follow a similar trend to the one observed for the commanded signals, with the VMS

having the lowest lower optimal amplitude and the IFD the highest, the

amplitude increase observed on the VMS filtered signals generated an

upper optimal amplitude for the VMS similar to the one observed for

the IFD.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

VMS

GFD

IFD

0.5 1

0

0.5

1

1.5

2

(a) Commanded signals

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) Measured signals

Figure 15: Upper and lower optimal amplitudes based on commanded (comm) and filtered (filt) signals in the VMS, GFD,

and IFD. Error bars indicate the 95% confidence interval of the

mean.

Figure 16 shows the point of mean optimal zone (PMO) and the

30
Table 7: Results of the repeated measures ANOVA for the measured optimal amplitudes in all three simulators for a stimuli

frequency of 5 rad/s, where ** is highly significant (p≤ 0.01),

* is marginally significant (p≤ 0.05), and − is not significant

(p> 0.05).

Dependent measures

Independent

variables

Lower optimal

amplitude

Upper optimal

amplitude

df F sig. df F sig.

Simulator 1.18,8.23a 2.10 − 1.09,7.65a 0.282 −

Amplitude 1,7 12.91 ** 1,7 8.51 *

Simulator × Amplitude 2,14 0.06 − 2,14 0.61 −

a Greenhouse-Geisser sphericity correction applied.

optimal zone width (OZW) for the VMS, GFD, and IFD. The filtered

signals were used in this figure. Overall, we observe that the PMOs for

the 0.5 m/s2stimulus were closer to the one-to-one line than the 1 m/s2

PMOs. The IFD PMO is higher than the VMS and GFD PMOs for

the 0.5 m/s2stimulus. However, for the 1 m/s2stimulus the IFD PMO

was approximately the same as the VMS PMO but it was still higher

than the GFD PMO. The OZW from all simulators was approximately

constant when the stimulus amplitude increased from 0.5 to 1 m/s2.

The VMS OZWs where larger than the GFD and IFD OZWs, which

were approximately the same. The larger OZW on the VMS is related

with the increase of the upper optimal amplitude observed in the filtered

data (Figure 15b).

Table 8: Results of the repeated measures ANOVA for the

PMO and OZW in all three simulators for a stimuli frequency of

5 rad/s, where ** is highly significant (p≤ 0.01), * is marginally

significant (p≤ 0.05), and − is not significant (p> 0.05).

Dependent measures

Independent

variables

PMO OZW

df F sig. df F sig.

Simulator 1.08,7.53a 0.61 − 2,14 1.67 −

Amplitude 1,7 12.84 ** 1,7 0.07 −

Simulator × Amplitude 2,14 0.41 − 2,14 0.28 −

a Greenhouse-Geisser sphericity correction applied.

31
Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

VMS

GFD

IFD

0.5 1

0

0.5

1

1.5

2

(a) Point of mean optimal gain

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) Optimal zone width

Figure 16: Optimal zone measurements for the VMS, GFD,

and IFD. Error bars indicate the 95% confidence interval of

the mean.

4.3 Coherence zone versus optimal zone

To compare the two instructions given to the subjects (coherence and

optimal zone), we compared the lower threshold with the lower optimal

amplitude and the upper threshold with the upper optimal amplitude.

Therefore, statistical differences in these boundaries would identify differences between the two instructions. For clarity, the dependent measure “lower boundaries” is used for the comparison between the lower

threshold and the lower optimal amplitude whereas the dependent measure “upper boundaries” is used for the comparison between the upper

threshold and the upper optimal amplitude.

The comparison between the two instructions was done for the three

simulators used in this study. Figures 17, 18, and 19 show the comparison between the coherence and optimal zone for the VMS, GFD, and

IFD, respectively. The lower optimal amplitude and the lower thresholds

reveal similar lower boundaries for the VMS and GFD simulators.

In fact, in these simulators (see Table 9 and Table 10) we found no

statistical differences between the two lower boundaries. For the IFD,

however, Figure 19 shows that the lower optimal amplitude is higher

than the lower threshold, a significant result as shown in Table 11.

The upper boundaries showed the same trend for the three simulators, where the upper optimal amplitude was higher than the upper

threshold. This result, as shown by the respective statistical analysis

in Table 9, Table 10, and Table 11, was highly significant for all the

simulators in this study.

The consequence of both instructions having similar lower boundaries

but different upper boundaries is shown in Figure 20, Figure 21, and

Figure 22. Here, we observe that the CZW increases with the visual

32
PSfrag

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

CZ

OZ

0.5 1 0

0.5

1

1.5

2

(a) 3 rad/s.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1 0

0.5

1

1.5

2

(b) 5 rad/s.

Figure 17: Coherence zone and optimal zone in the VMS. Error

bars indicate the standard error of the mean.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

CZ

OZ

0.5 1

0

0.5

1

1.5

2

(a) 2 rad/s.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) 5 rad/s.

Figure 18: Coherence zone and optimal zone in the GFD. Error

bars indicate the standard error of the mean.

33
Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

CZ

OZ

0.5 1

0

0.5

1

1.5

2

(a) 2 rad/s.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) 5 rad/s.

Figure 19: Coherence zone and optimal zone in the IFD. Error

bars indicate the standard error of the mean.

Table 9: Results of the repeated measures ANOVA for the measured thresholds and optimal amplitudes in the VMS, where

** is highly significant (p≤ 0.01), * is marginally significant

(p≤ 0.05), and - is not significant (p> 0.05).

Dependent measures

Independent

variables

Lower boundaries Upper boundaries

df F sig. df F sig.

Instruction 1,7 2.16 − 1,7 14.76 **

Frequency 1,7 17.57 ** 1,7 11.30 *

Amplitude 1,7 9.39 * 1,7 46.72 **

Instruction × Frequency 1,7 33.13 ** 1,7 0.10 −

Instruction × Amplitude 1,7 8.75 * 1,7 21.50 **

34
Table 10: Results of the repeated measures ANOVA for the

measured thresholds and optimal amplitudes in the GFD,

where ** is highly significant (p≤ 0.01), * is marginally significant (p≤ 0.05), and - is not significant (p> 0.05).

Dependent measures

Independent

variables

Lower boundaries Upper boundaries

df F sig. df F sig.

Instruction 1,7 1.25 − 1,7 24.03 **

Frequency 1,7 4.41 − 1,7 18.49 **

Amplitude 1,7 7.53 * 1,7 101.36 **

Instruction × Amplitude 1,7 0.89 − 1,7 20.13 **

Table 11: Results of the repeated measures ANOVA for the

measured thresholds and optimal amplitudes in the IFD, where

** is highly significant (p≤ 0.01), * is marginally significant

(p≤ 0.05), and - is not significant (p> 0.05).

Dependent measures

Independent

variables

Lower boundaries Upper boundaries

df F sig. df F sig.

Instruction 1,7 11.01 * 1,7 29.19 **

Frequency 1,7 7.26 * 1,7 21.76 **

Amplitude 1,7 14.60 ** 1,7 104.19 **

Instruction × Amplitude 1,7 20.45 ** 1,7 16.00 **

35
stimulus amplitude whereas the OZW remains approximately the same,

a result caused by having an upper threshold that is higher and has a

steeper slope when increasing with the visual amplitude than the upper

optimal gain.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

CZ 2 rad/s

OZ 2 rad/s

CZ 5 rad/s

OZ 5 rad/s

0.5 1 0

0.5

1

1.5

2

(a) Point of mean zone

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1 0

0.5

1

1.5

2

(b) Zone width

Figure 20: Zone measurements for the VMS. Error bars indicate the standard error of the mean.

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

CZ 2 rad/s

OZ 2 rad/s

CZ 5 rad/s

OZ 5 rad/s

0.5 1 0

0.5

1

1.5

2

(a) Point of mean zone

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1 0

0.5

1

1.5

2

(b) Zone width

Figure 21: Zone measurements for the GFD. Error bars indicate the standard error of the mean.

5 Discussion

5.1 Coherence zone versus optimal zone

In this experiment we measured perception coherence zones and optimal

zones. As was also found in a previous study [35], the optimal zone

36
Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

one-to-one

CZ 2 rad/s

OZ 2 rad/s

CZ 5 rad/s

OZ 5 rad/s

0.5 1

0

0.5

1

1.5

2

(a) Point of mean zone

Visual stimulus amplitude, m/s2

Inertial stimulus amplitude, m/s

2

0.5 1

0

0.5

1

1.5

2

(b) Zone width

Figure 22: Zone measurements for the IFD. Error bars indicate

the standard error of the mean.

measurements did not result in one single value, but in a zone bounded by

an upper and a lower limit. For comparison purposes, this optimal zone

was defined in terms of point of mean optimal zone (PMO) and optimal

zone width (OZW), similar to what was done in other studies [33,34] for

coherence zones, which were defined in terms of point of mean coherence

(PMC) and coherence zone width (CZW).

The PMO and PMC both increased with the visual stimulus amplitude and decreased with the visual stimulus frequency. These trends

agree well with those reported for optimal zone measurements in sway [35]

and coherence zone measurements in yaw [33, 34].

The PMO and PMC both increased with visual stimulus amplitude.

For the lowest amplitude the PMCs and PMOs were close to the one-toone line and for the highest amplitude they were both below this line.

This decrease with respect to the one-to-one line was more evident for

the PMO.

The PMC was generally closer to the one-to-one line than the PMO.

The coherence zone upper threshold was always above the one-to-one

line, whereas the upper optimal amplitude crosses the one-to-one line

for the 2 rad/s conditions and is below the one-to-one line for the 5

rad/s conditions. Careful observation of the limits for both zones shows

that this difference was caused mainly by the upper threshold and upper

optimal amplitude since the lower bounds of both zones were similar.

The OZW was significantly lower than the CZW. The CZW was affected by the visual amplitude and frequency, with higher amplitudes

leading to wider zones and higher frequencies leading to narrower zones.

The OZW was unaffected by these two independent variables. The OZW

remained approximately the same (around 0.23 m/s2) for all the experimental conditions. No reason could be found as to why the OZW was

37
unaffected by frequency and amplitude whereas the CZW was affected

by these variables.

As explained above, the lower limits of the CZ and OZ where similar

whereas the OZ upper limits were considerably smaller than the CZ

upper limits. This resulted in an OZ that covered the lower amplitude

values of the CZ. The OZ was thus contained within the CZ, as if there

was a subset of coherent inertial and visual cues that was perceived as a

“better match”.

Together, the coherence and optimal zones defined three regions:

within the optimal zone, outside the optimal zone and within the coherence zone, and outside the coherence zone. These three regions can

be considered as a gradient of accepted motion ranging from best (within

the optimal zone) to worst (outside the coherence zone) motion. These

could be compared to the three fidelity regions (low, medium and high) of

a Sinacori plot [43], which are commonly used to classify motion cueing

algorithms.

It is not clear what perception mechanism allows subjects to distinguish between a coherent and an optimal region. Two different hypotheses can be posed to explain the differences.

First, the different initial inertial amplitudes could have created different paradigms when measuring the two zones. For the coherence zone

measurements, subjects started with similar inertial and visual amplitudes and their objective was to increase or decrease the inertial amplitude until a difference between the inertial and visual amplitude was

noticed. Conversely, for the optimal zone measurements, subjects started

from different inertial and visual amplitudes and their objective was to

change the inertial amplitude until the two stimuli were perceived as

equal. Although both tasks sound similar, asking for differences between

two stimuli appears to create a different result than asking whether two

stimuli are equal.

Different results for similar tasks were also found when measuring

thresholds for linear motion [5, 10, 44]. Here, subjects were asked to indicate when they perceived motion and when they stopped perceiving

motion. In the first case, the motion profile was a sinusoid with amplitude increasing from zero to a supra-threshold value whereas in the

second case the motion profile was a sinusoid with its amplitude decreasing from a supra-threshold value to zero. The amplitude at which motion

was detected was significantly higher than the value at which subjects

stopped perceiving motion.

Second, what could also have led to the differences between the two

zones was what may be referred to as “tuning for comfort”. Higher inertial amplitudes can be more uncomfortable for the subjects than lower

inertial amplitudes. For the optimal zone, subjects were asked to find

the best match between visual and inertial information. It could happen that this best match was not where the visual amplitude was equal

38
to the inertial amplitude but rather the one that felt most comfortable,

that is, ‘less arousing’ inertial amplitude which was still perceived as

coherent with the visual motion. Such an approach would lead to a

PMO lower than the PMC, since for the coherence zone measurements

subjects were looking for the boundary of the coherence and not for the

most comfortable motion.

This second explanation agrees with the results: PMOs were significantly lower than the PMCs and the optimal zone excluded only the

higher inertial amplitudes within the coherence zone.

5.2 Comparing simulators

The three motion simulators used in this experiment combine two types

of visual systems, WAC windows and collimated panoramic display, and

two different motion bases. Both motion bases are hexapods, but the

VMS is a considerably older platform than the motion base of the GFD

and IFD (CMF). The performance of the motion bases were monitored

by looking at the commanded and measured motion signal.

5.2.1 VMS and CMF: comparing motion base performance

As was to be expected, the older motion platform, the VMS, showed

larger turnaround “bumps”. The intensity of these bumps increased

with the amplitude of the commanded signal. However, in relative terms,

the bump was worse at lower amplitudes. This situation was known a

priori and the worse performance of the motion base was thought to

have an influence especially on OZ and CZ lower limits, since here the

amplitudes of the inertial motion were lower. It was thought that the

large turnaround bump could increase the overall perception of strength

of the inertial motion, leading the subjects to tune down the motion for

both high and low amplitudes.

However, at the very low amplitudes, possible crosstalk (motion in

other DOFs) might mask the inertial lateral motion, which could lead

subjects to tune the motion up, such that motion in the lateral DOF

could be distinguished from the crosstalk. The fact that both the coherence zone lower thresholds and the lower optimal amplitudes were not

significantly different across simulators shows that if any of these issues

played a role, they either canceled each other out, or the effect was too

small to be measurable.

The turnaround bump might also have influenced the OZ and CZ

upper limits in the VMS. The values obtained from the commanded

signals were clearly lower than the ones computed from the measured

and filtered signals. The upper thresholds from the commanded signals

were in fact lower than the ones obtained in the other two simulators,

but when using the measured signals, the peak amplitudes due to the

39
turnaround bump resulted in higher threshold values. For the upper

optimal amplitudes the same trend could be observed, but the difference

between the commanded and filtered values was smaller.

On the one hand, one might say that including the peak amplitude of

the turnaround bump into the calculation is wrong because it artificially

raises the threshold or optimal amplitude value. On the other hand, it is

also incorrect to base the values on the peak amplitude of the commanded

signals instead of the measured signals when the difference is so large. It

was considered that presenting the results in terms of actual measured

amplitudes was the most correct.

When comparing the CZ commanded upper threshold values in the

VMS to the ones in the GFD, which has the same type of visual system,

one might argue that if the turnaround bump was the cause of lower

values, then considering the peak amplitude of the turnaround bump in

the threshold computation should result in similar thresholds. The fact

that the measured thresholds were in fact higher than the ones in the

GFD, indicate that although the turnaround bump influenced subjects’

perceived motion strength, it did not account for the whole difference

between the VMS and the GFD.

The turnaround bump represents a high-frequency component of the

motion signal. Subjects perhaps based their CZ judgment on the lower

frequency component of the signal and took the turnaround bump only

partially into consideration.

The upper optimal amplitudes were not significantly different across

simulators indicating that perhaps here the effect of the turnaround

bump was negligible. One reason for this might be the fact that during

the measurement of the upper limits of the OZ, the amplitude of the

inertial motion was generally lower than during the measurements of the

CZ. At lower amplitudes, the absolute value of the turnaround bump is

also smaller, rendering it perhaps negligible.

5.2.2 GFD and IFD: comparing different visual systems

Unlike the VMS, the difference between the commanded and measured

signals peak amplitudes in the IFD and GFD simulators was small, which

resulted in very similar commanded and measured threshold values.

Contrary to the lower limits, the upper limits were different between

the two simulators for both the CZ and the OZ. The IFD presented

higher upper limits than the GFD. When looking at the motion platform

performance, the larger differences between simulators were observed at

the lower amplitudes. Any differences in the results that would derive

from the motion base would then be expected to be more accentuated

at the lower limits. However, it is precisely in the upper limits that

significant differences are seen. This indicates that the differences found

between the limit values were probably due to the visual system.

Chung et al. [45] showed that collimation greatly influenced subjects’

40
perception of lateral velocity and a larger field-of-view improved position

control during a hover task. Both the IFD and the GFD have collimated

displays, but the IFD has a panoramic display whereas the GFD has four

WAC windows. For a subject sitting in the left-hand seat of the simulator only two of those windows provide visual stimulation. This greatly

decreases the field-of-view which might lead to an underestimation of

the visual cue amplitude in the GFD relative to the IFD. The effect of

this underestimation is not observed on the lower limits, which, as mentioned before, might be related to a lack of resolution of the measurement

method at low amplitudes.

The CZ thresholds measured in the IFD resulted in a wider coherence

zone, although the point of mean coherence was not significantly different

between simulators. The larger panoramic display of the IFD favors the

onset and strength of vection [46–49] which can lead to a stronger sense

of self motion, which in turn allows larger inertial amplitudes before

subjects detect some mismatch between the visual and the inertial cue.

One can even say that the IFD has a more convincing display, that allows

the inertial cue amplitude to divert further from the visual cue before it

is noticed by subjects.

However, for the OZ measurements, the IFD did not result in a wider

zone. Although the lower optimal amplitudes did not change significantly

across simulators, they did change enough to maintain the width of the

OZ similar for both simulators. Both lower and upper limits varied with

the IFD presenting higher values. This shows that although a more

convincing display might allow for a larger mismatch between the visual

and the inertial cues, the zone of optimal values remains the same width.

The PMO does seem to increase for a better visual system.

5.2.3 VMS , GFD and IFD

When comparing all three simulators for the conditions with a stimulus

frequency of 5 rad/s, only one metric was significantly different across

simulators: the CZ upper thresholds. Based on the thresholds calculated

from the measured signals, the post-hoc tests showed that statistically,

the upper thresholds in the VMS and the IFD were not different from

each other but were significantly different from the values in the GFD.

The differences found between the upper thresholds in the VMS and

IFD as compared to the GFD are somewhat non intuitive. Similarities

could be expected between simulators that have similar visual systems,

such as the VMS and the GFD, or simulators with the same motion base,

such as the GFD and IFD. In this respect, the GFD could be expected

to be in between the VMS and the IFD. The GFD has a newer motion

platform than the VMS, with improved performance, but it has a visual

system that is not as sophisticated as the one in the IFD.

When comparing the upper thresholds based on the commanded signals, indeed the results fit the expectations, with the VMS showing the

41
lowest thresholds and the IFD the highest. As noted before, calculating

the thresholds from commanded signals might not be entirely correct

but does help in understanding how the turnaround bump might influence the subjects’ judgment of motion. The lower values for the upper

thresholds in the VMS are in agreement with the hypothesis that subjects tuned the motion down to compensate for the turnaround bump.

The comparison of the three simulators might be summarized in a

very generic way by saying that better simulators allow for larger differences between the inertial and visual cues. That is, for higher quality

systems, subjects are more lenient in their judgment of matching visual and inertial cues. Moving from the VMS to the IFD, the quality

of the inertial motion feedback and the visual systems could be said to

improve. This improvement led to higher upper thresholds, with the

exception of the thresholds in the VMS that include the peaks caused

by the turnaround bump. The values obtained for the OZ were not statistically different across simulators but they do show a trend of higher

PMO values with increasing simulator quality.

It should be noted that since all subjects performed the experiments

in each of the simulators in the same order, an effect of learning should

not be disregarded. However, there is some evidence that in this type

of task, the effect of learning is negligible. In this experiment, as well

as in all experiments described in Valente Pais [38], each condition was

always repeated by the same subject two or three times. In Chapter 7 of

this reference it is mentioned that the effect of the repetition has never

been found. Moreover, in Valente Pais et al. [34], three experiments are

described where two of these experiments were performed by the same

subjects with a few weeks of time in between. Also here, no effect of

learning or habituation was found.

5.3 Perception metrics and motion simulation

This study showed that the inertial amplitudes inside the coherence zone

are not perceived equally. In fact, the lower inertial amplitudes of the

coherence zone seemed to be perceived as the best match for the tested

visual amplitudes. This might indicate not only that the use of lower

inertial amplitudes is acceptable in flight simulation but that they are in

fact, optimal.

If these results were to be used in motion filter tuning, the motion

gain (i.e., the ratio between the inertial and visual motion), should generally be less than one, especially for the highest amplitude. This supports

the findings of other studies [6,27–29,35] where a motion gain of one was

judged as too strong. One reason why the one-to-one motion may be

judged too strong in a simulation environment may be the quality of the

visual display [6, 29]. The increasing PMO values for increasing quality

of the visual system, as obtained in the comparison between the GFD

and the IFD, seems to support this hypothesis.

42
Visual amplitude in a simulator environment is perceived differently

than in the real world. For example, Kemeny and Panerai [50] showed

that in driving simulators, observers underestimate driving speed when

image contrast, luminance or texture are reduced. They also observed

that drivers underestimate distances to a lead vehicle when compared to

a similar situation in a real road.

Therefore, instead of matching the inertial amplitude with the expected displacement in the real world, the inertial amplitude should be

matched to the displacement perceived from the virtual world. Current

self-motion perception models do not make a distinction whether the

motion was felt in the aircraft or in the simulator. Therefore, when minimizing the error between the motion perceived in the aircraft from that

perceived in the simulator, both perception models have the same structure and parameters. However in this experiment, the preferred inertial

amplitude was lower than the visual amplitude, which might indicate

that one-to-one motion was perceived as too strong.

By acknowledging this inertial overestimation and measuring its limits, using either a coherence or an optimal zone or both, one could calculate perception model parameters specific for simulation scenarios.

6 Conclusions

Perception coherence zones (CZ) and optimal zones (OZ) for lateral acceleration were measured in three different simulators for two different

visual stimulus amplitudes and two stimulus frequencies.

The OZ was contained within the CZ. The points of mean optimal

zone (PMOs) were significantly lower than the points of mean coherence

(PMCs) and the OZ excluded only the higher inertial amplitudes within

the CZ. Apparently, even though all amplitudes within the CZ are perceived as coherent, some motion amplitudes are preferred over others

within that zone. The point of mean coherence (PMC) was significantly

higher than the point of mean optimal (PMO) zone. This could be an

indication that the optimal amplitude zone is a result of tuning for comfort.

The PMO and the PMC both increased with the visual stimulus amplitude and decreased with the visual stimulus frequency. The coherence

zone width (CZW) was affected by the visual amplitude and frequency,

with higher amplitudes leading to wider zones and higher frequencies

leading to narrower zones. The optimal zone width (OZW) was unaffected by these two independent variables.

Differences in simulator configuration resulted in differences in the

measured upper thresholds of the CZ. When considering thresholds determined from the commanded signals, the upper thresholds increased

as going from the VMS, to the GFD and then to the IFD. When the

thresholds were determined from the measured signals, the VMS showed

43
the highest upper thresholds because these include large amplitude peaks

caused by a “turnaround bump”.

The increase in upper thresholds was thought to be related to an

increase in overall simulator quality. The values obtained for the OZ

were not statistically different across simulators but they do show a trend

of higher PMO values with increasing simulator quality.

7 Recommendations

To further investigate the potential of coherence zones (CZ) and optimal

zones (OZ) as perceptual metrics to quantify differences in simulator and

motion cueing performances, more degrees of freedom, amplitudes and

frequencies should be tested across different simulators. One practical

way of bypassing the complicated logistics of running multiple simulator

studies is to use one simulator and artificially degrade both the motion

base performance and the visual system quality (see Nieuwenhuizen [51]).

Further research should also be done to investigate if the constant

optimal zone width (OZW) results found for the tested amplitudes also

occur at higher amplitudes, and whether comfort has in fact an effect on

the optimal zone measurements.

For a better understanding of the differences between the OZ and the

CZ the effect of these initial conditions on the zones should be further investigated. In this study, the OZ measurement always started with initial

inertial amplitudes that were higher or lower than the visual amplitudes

whereas the CZ measurements had an initial inertial amplitude that was

close to the amplitude of the visual stimulus. As stated previously, these

different initial conditions could have biased the differences found between the two zones. To test this hypothesis the coherence and optimal

zones could be measured in the same experimental trial. Subjects could

then indicate optimal amplitude first and thereafter the boundaries of

the coherence zone, or the other way around.

For applications in motion simulation, it is also important to determine what the impact on behavior is when moving out of the optimal

zone while remaining within the coherence zone.

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Comparison of Flight Simulators Based on Human Motion Perception 

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14. ABSTRACT

In flight simulation, motion filters are used to transform aircraft motion into simulator motion. When looking for the best 

match between visual and inertial amplitude in a simulator, researchers have found that there is a range of inertial amplitudes, 

rather than a single inertial value, that is perceived by subjects as optimal. This zone, hereafter referred to as the optimal zone, 

seems to correlate to the perceptual coherence zones measured in flight simulators. However, no studies were found in which 

these two zones were compared. This study investigates the relation between the optimal and the coherence zone 

measurements within and between different simulators. Results show that for the sway axis, the optimal zone lies within the 

lower part of the coherence zone. In addition, it was found that, whereas the width of the coherence zone depends on the

visual amplitude and frequency, the width of the optimal zone remains constant. 

15. SUBJECT TERMS

Coherence zone; Flight simulators; Motion; Motion perception; Optimal zone; Simulators; Visual

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