ARL-TR-8094 AUG 2017

US Army Research Laboratory

Measured Visual Motion Sensitivity at Fixed 

Contrast in the Periphery and Far Periphery

by Paul D Fedele, Joel T Kalb, Barry D Vaughan, and

Kathy L Kehring

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ARL-TR-8094 ● AUG 2017

US Army Research Laboratory

Measured Visual Motion Sensitivity at Fixed 

Contrast in the Periphery and Far Periphery

by Paul D Fedele, Joel T Kalb, Barry D Vaughan, and

Kathy L Kehring

Human Research and Engineering Directorate, ARL

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4. TITLE AND SUBTITLE

Measured Visual Motion Sensitivity at Fixed Contrast in the Periphery and Far 

Periphery

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Paul D Fedele, Joel T Kalb, Barry D Vaughan, and Kathy L Kehring

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US Army Research Laboratory

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ARL-TR-8094

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13. SUPPLEMENTARY NOTES

14. ABSTRACT

The US Army Materiel Analysis Activity (AMSAA) Infantry Warrior Simulation (IWARS) is a constructive wargame for 

studying individual and small-group Soldier performance. Soldier performance depends on visual detection of enemy 

personnel and materiel. Vision modeling in IWARS is neither physiologically accurate nor behaviorally realistic. Movement 

in peripheral vision significantly influences target detection, but the IWARS visual model does not include target movement 

in visual detection. Monaco et al. report that most of the research in peripheral motion was conducted with limited fields of 

view and thus does not apply to target acquisition by dismounted Soldiers. To measure motion detection thresholds, we used 

targets consisting of bivariate Gaussian contrast distribution, with a Michelson contrast of –0.2 recommended by AMSAA, 

shaped like upright humans at ranges between 50 and 200 m and eccentricities between 40° and 80°. Measured movement 

thresholds were between 0.72 and 18°·s-1

, corresponding to speeds between 0.71 and 37 m·s-1. Responses indicate that some 

targets could not be seen at all. These measured peripheral visual motion detection thresholds can be applied in developing a

motion detection criterion supporting improved Soldier vision modeling and simulation. 

15. SUBJECT TERMS

vision, motion, detection, threshold, periphery

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Paul D Fedele

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Contents

List of Figures iv

List of Tables iv

1. Introduction 1

2. Background 1

3. Methods 4

3.1 Participants 7

3.2 Instrumentation 7

3.3 Test Stimuli Presentation 8

3.4 Procedures 13

4. Overall Results 22

5. Conclusion 28

6. References 29

Appendix. Angular Displacement Rates and Equivalent Speeds for Target 

Eccentricities and Ranges 31

List of Symbols, Abbreviations, and Acronyms 35

Distribution List 36
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List of Figures

Fig. 1 Gabor patches........................................................................................ 5

Fig. 2 Target images used in this study........................................................... 5

Fig. 3 Schematic representation of screens, central fixation, and participant 

locations................................................................................................ 7

Fig. 4 Illustration of test control stimuli (not to scale).................................... 9

Fig. 5 Initial target array on the full left or right screen for a typical motionvisibility trial (not to scale)................................................................. 10

Fig. 6 Mean and standard deviation of the PD for the MVT in successive 

QUEST trials in which the actual 50% threshold is –5 dB................. 16

Fig. 7 MVT PD mean values from 250 simulated experimental trials ......... 17

Fig. 8 MVT PD means with an out-of-range actual threshold value of 

25 dB................................................................................................... 18

Fig. 9 Example of MVT progression for TP 10: a specific color represents a 

specific eccentricity and a specific line thickness represents a humanform target viewed at a specific range. In the legend, eccentricities are 

given in degrees following the letter “E”, and range is given in meters 

following the letter “R”....................................................................... 20

Fig. 10 MVTs for TP 10 vs. eccentricity and range........................................ 22

Fig. 11 Histogram of all threshold values obtained in the study..................... 23

Fig. 12 Histogram of MVT values greater than –20 dB and less than 4 dB ... 24

Fig. 13 Average MVT surface as a function of eccentricity and range .......... 25

Fig. 14 MVT vs. eccentricity at ranges of 200, 100, and 50 m with error bars 

showing the standard deviation estimated for the MVT distribution at 

each eccentricity-range combination .................................................. 26

Fig. 15 MVT vs. range at eccentricities of 80°, 70°, 60°, 50°, and 40° at each 

eccentricity with error bars showing the standard deviation estimated 

for the MVT distribution at each eccentricity-range combination...... 27

List of Tables

Table 1 Average and standard deviation MVTs in dB across all participants. 25

Table A-1 Angular displacement rates (°•s-1) for target eccentricities ................ 32

Table A-2 Velocity (mph) at ranges (m) for 60° eccentricity.............................. 34
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1. Introduction

This research supports the development of a more complete model of observer 

response to peripheral motion stimuli in military contexts. The objective of this 

project is to measure detection thresholds for visual stimuli that briefly move in the 

far periphery to support the development of subsequent improvements in visual 

target detection models. The stimuli were designed to simulate the appearance of 

dismounted enemy combatants moving on foot at ranges of 50–200 m. Specifically,

for a set of visual eccentricities ranging from 40° to 80°, we determined the 

minimum speed required for a stimulus to be judged reliably as moving among a 

set of similar stimuli that are stationary. These data are provided in the form of 50% 

detection thresholds.

2. Background

The US Army Natick Soldier Research, Development and Engineering Center 

(NSRDEC) and the Army Materiel Systems Analysis Activity (AMSAA) have 

collaborated to develop the Infantry Warrior Simulation (IWARS) to model 

lethality, survivability, mobility, sustainability, and command, control, 

communications, computers, intelligence, surveillance, and reconnaissance during 

military operations. IWARS makes use of so-called intelligent agents that simulate 

the behavior and performance of individuals. These agents can sense changes in 

their environment and act based on anticipated human perception of those changes 

to perform tasks specified by the computer program user in a mission plan.

One such IWARS task is visual target acquisition, which includes a Soldier visually 

detecting, recognizing, and identifying a potential target. The Behavior Model 

within IWARS defines how visual search proceeds over time by means of a simple 

“spotlight” metaphor: A Soldier’s central vision is modeled as a spatially 

contiguous region (a spotlight) that gathers visual information from the portion of 

the scene within that spotlighted region. The Behavior Model determines visual 

detection, recognition, and identification by spotlighting a portion of the scene and 

applying the process used in the ACQUIRE target acquisition model, which was 

first developed and applied to target acquisition using IR imaging systems (Ratches

et al. 2001). The ACQUIRE model predicts the probability of detecting, 

recognizing, and identifying a target if one is present in the region. If a target is 

detected, the Behavior Model triggers the appropriate task response to the target 

(inspect, engage, etc.). If a target is not detected, the Behavior Model moves the
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2

region to the next sequential portion of the scene and calls ACQUIRE again. If no 

targets continue to be detected, this process is continued until the entire scene has 

been searched, after which a new scene is selected. 

The agent-based approach adopted by IWARS requires that the simulated agents 

perceive and behave in realistic ways for task simulation to be accurate and 

meaningful; the agent perception must accurately reproduce human perception over 

a range of perceptual conditions. In the case of human visual search, a spotlight 

metaphor and the application of ACQUIRE is a reasonable representation of human 

vision during search because the parts of the visual system primarily responsible 

for the human ability to perceive spatial detail, color, and stereoscopic depth are 

those associated with central vision. 

Unfortunately, although the spotlight metaphor may be reasonable, the way the 

IWARS Behavior Model both defines the size of the spotlight and determines how 

the spotlight is directed about the scene are both physiologically inaccurate and 

behaviorally unrealistic. These problematic assumptions result in slow IWARS 

execution and unrealistic search behavior (Venezia 2009). Since battlefield target 

engagement is a highly time-critical and order-dependent activity, these unrealistic 

characterizations of target detection time and order severely limit the realism of 

modeled Soldier performance in IWARS. 

Specifically, 2 critical aspects of human vision are poorly defined in IWARS. First, 

the size of the region corresponding to central vision has been fixed as a circle with 

a visual angle diameter of 20°. Applying a visual detection threshold over a 20°

visual region is unrealistic because the high-resolution region that establishes the 

visual detection threshold is the highest-resolution region of the visual field: the 

macula, which is much smaller than 20°, is approximately 2° (Anstis et al. 1998; 

Brooks and Cooper 2006). The macula is the visual region required to achieve the 

visual threshold detection specified by the ability to resolve 3 Johnson criteria bars 

(Johnson 1985) over the smallest visual angle presented by the target (Brooks and

Cooper 2006). While it is possible to detect a supra-detection-threshold target in a 

visual field larger than the macula, the detection threshold still requires use of the 

highest-resolution portion of the visual field, which is much smaller than the region 

applied in the IWARS Behavior Model. 

Second, the IWARS Behavior Model fails to account for peripheral (noncentral) 

vision. That is, visual events outside the 20° region being assessed by ACQUIRE 

are invisible to the model. Events outside the 20° region have no effect on the 

subsequent redirection of the visual field. Humans show a highly sensitive ability 

to detect visual events, such as flicker, movement, and onset, in the visual periphery 

during visual search (Yantis and Hillstrom 1994; Yantis 1996; Rauschenberger 
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2003). In real-world visual search, humans use peripheral visual information to 

reorient their gaze toward the visual periphery, where flicker, movement, or onset 

has occurred during visual search (Jonides 1981). In contrast, IWARS ignores such 

events and always defines the progression of simulated eye movements along a 

fixed path from top left of the scene to bottom right, moving the way one’s eyes 

move when reading a page of text. 

Improvements in the visual characteristics of the IWARS Behavior Model could be 

achieved by applying a more realistic size for the central vision region where the 

ACQUIRE model reasonably applies the Johnson threshold detection criteria. 

Previous work by Monaco et al. (2010) measured degradation of visual resolution 

outside a central vision field approximately 8° in diameter. Targets in the Monaco 

et al.study were Gabor patches (sinusoidal gratings inside a bivariate, 2-D Gaussian 

envelope) of varying spatial frequencies. Beyond this range of eccentricity, Monaco 

et al. noted a decrease in perception of high-spatial-frequency information 

corresponding to degradation in visual acuity below 20/40, which is the prevalent 

minimum visual acuity standard required for driving without visual correction 

(TransAnalytics LLC 2003). 

The improved realism afforded by the restriction of central vision to a somewhat 

different diameter, such as 8°, may improve model performance. The region of 

application of the detection threshold will be more realistic, and the number of scan 

regions in a field of view may be more accurately represented, but this improvement 

would still apply only if the Soldier actually scanned a field of view as if reading a 

page of text. 

A more significant improvement in the visual IWARS Behavior Model could be 

achieved by introducing detection processes that reorient the central vision toward 

events, such as flicker, movement, and onset, in the visual periphery during visual 

search. Movement in peripheral vision has been shown to play a major part in 

predator detection of prey (Fouts and Nelson 1999; Ioannou and Krause 2009). 

Even though movement is known to attract visual attention (e.g., Yantis and

Hilstrom 1994), the human visual performance model used in IWARS does not 

include target movement as a factor in visual detection. This change may improve 

the sequence of visual target detection and thus better represent real-world Soldier 

performance.

Monaco et al. (2007) performed an initial study to identify experimental methods 

for measuring the motion visibility threshold (MVT) in the far periphery. They 

performed initial trials using Bayesian threshold estimation procedures and new 

virtual reality display technology. They applied a Parameter Estimation by 

Sequential Testing (PEST) (Taylor and Creelman 1967) threshold estimation 
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procedure and measured the MVT using high-contrast black-dot targets on a bright 

white screen. Targets were presented at 53.4°, 72.6°, and 90° eccentricity in the 

temporal visual field. Measurements were reported for 2 subjects with normal 

visual function. Monaco et al. (2007) demonstrated that the PEST procedure could 

converge on MVTs within 10 yes/no stimulus trials. For the 2 subjects, the MVTs 

demonstrated a systematic increase with increasing eccentricity within the visual 

field. The average angular velocity motion thresholds MVT (°⋅s-1) were the 

following: at 53.4°, 0.5; at 72.6°, 1.35; and at 90°, 2.1 (Monaco et al. 2007).

These findings show that it is possible to obtain MVTs in the far peripheral visual 

field using Bayesian threshold estimation procedures that use a relatively small 

number of trials such as PEST. 

However, sharply edged dark-black targets on a bright white screen, as used in the 

Monaco et al. (2007) study, are not representative of targets on the battlefield. 

Based on measured screen brightness used by Monaco et al., the Michelson contrast 

of a black target on a white screen is –0.97. Battlefield targets are intentionally 

made to be less visibly conspicuous. Camouflage patterns are used to break up 

sharp edges, and low-intensity colors are used to reduce contrast with surroundings. 

To better address combat-relevant visual performance, we considered different 

targets. 

Since we wanted to isolate the influence of motion in visual detection, we chose 

not to use sharp-edged targets such as those traditionally used to define the Johnson 

visual target criteria (Johnson 1985). With such targets, a video screen 

representation of motion would necessarily require turning on and off specific 

pixels, and this process could also be detected as flash or illumination onset rather 

than movement. Sharp-edged targets also are subject to aliasing in digital video

screen representations, and again, aliasing would create sudden pixilated 

incremental movements that would poorly represent smoothly moving target 

objects. For these reasons, we chose not to 1) use photorealistic stimuli (images of 

scenes, cutouts of enemy combatants and noncombatants, etc.), 2) incorporate 

naturalistic background scenes, or 3) represent target objects by sharply edged 

images. 

3. Methods

Images often used in visual testing are designed to eliminate sharp contrast lines 

and aliasing in digital representations. One such type of image is the Gabor patch, 

which are 2-D Gaussian-shaped visibility envelopes placed over uniform sinusoidal 

contrast variations. Examples of Gabor patches are shown in Fig. 1.
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Fig. 1 Gabor patches

In Fig. 1, the Gabor patch on the left uses a circular Gaussian envelope in which 

the vertical and horizontal dispersions are equal, while the patch on the right uses 

an elliptical envelope in which the vertical dispersion is greater than the horizontal 

dispersion. Also, in the left image the envelope is centered on a point of maximum 

luminance, while on the right the envelope is centered on a point of average 

luminance. 

To investigate motion detection for the human-form, we chose to use targets 

consisting of 2-D Gaussian-shaped contrast variations over a uniformly luminous 

background. Our targets used no sinusoidal luminance variations in the Gaussian 

window. We used only a Gaussian-shaped reduction of luminance over a 

background with an otherwise uniform luminance level. We approximated the form 

of a standing human body by using a height-to-width ratio of 4:1. Figure 2

illustrates the appearance of target images used in this study.

Fig. 2 Target images used in this study

The images in Fig. 2 are approximations of a human form at ranges of 50 (left), 100 

(center), and 200 (right) m. The size of the Gaussian patch is changed to 
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6

approximate the appearance of a human form at each range. Using a Gaussianshaped contrast variation as a target image allowed us to better isolate the detection 

of motion of a target of specific contrast without introducing aliasing or onset 

effects due to finite pixilation of the image presented on a video screen. Lateral 

movement of targets like those illustrated was used to measure MVTs. 

Using probability functions based on successful and unsuccessful motion 

observation, we can empirically determine the probability that a movement in the 

visual periphery would be detected as a function of image angular size and 

eccentricity. In particular, we are interested in determining motion parameters for 

stimuli in the far visual periphery (greater than 45° eccentricity), as this region of 

the visual field has not been as well characterized historically as the near periphery 

(<20° eccentricity). For example, McKee and Nakayama (1984) measured 

peripheral motion sensitivity in the lower vision field. McKee and Nakayama 

demonstrated that, when normalized to units of resolvable distance per unit time, 

motion sensitivity remains constant for movement in the lower visual field. Since 

resolvable distance increases with eccentricity, their conclusion indicates that 

movement detection thresholds increase with eccentricity. No comparable 

measurements are available for the far peripheral visual field. 

The following vignette defines the problem that we addressed and guided us in 

methodology development. A dismounted Soldier is viewing a complex visual 

scene in which dismounted enemy combatants may be present at ranges from 50 to 

200 m. The Soldier is viewing the scene unaided by electro-optical devices or 

binoculars. When viewing the scene, a key indicator that an enemy is present is 

movement of an enemy combatant from one location to another. If movement is 

detected, the Soldier will reorient his gaze to the location of the movement and 

inspect that location more deliberately to determine if an enemy is present. 

Measurements were conducted in the Tactical Environment Simulation Facility 

(TESF) of the US Army Research Laboratory’s (ARL’s) Human Research and 

Engineering Directorate. Targets were presented on a flat uniform gray screen. 

We measured visual motion detection sensitivity at lateral eccentricities from 40°, 

50°, 60°, 70°, and 80° using 3 targets with angular sizes corresponding to human 

targets at 50, 100, and 200 m. 

The target profiles were simulated by bivariate Gaussian-shaped dark patches with 

standard deviations forming a height-to-width ratio of 4:1. Target images were 

illustrated previously in Fig. 2.
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3.1 Participants

Sixteen individuals were recruited to participate in this study from ARL (Aberdeen 

Proving Ground, Maryland) between the ages of 18 and 40 with normal or 

corrected-to-normal vision in both eyes. Because we were investigating vision in 

the far periphery, participants with corrected vision were required to wear contacts 

rather than eyeglasses. Individuals with eyeglasses were not permitted to 

participate. Normal vision was defined as 20/40 or better Snellen acuity in both 

eyes as measured by a Titmus Model i500 test apparatus (Chester, Virginia). All 

subjects voluntarily provided their informed consent prior to participation, and all 

experimental procedures were in compliance with federal and Army regulations for 

the protection of human subjects (US Department of the Army 1990), as verified 

by ARL’s Institutional Review Board. The investigator adhered to Army policies 

for the protection of human subjects.

3.2 Instrumentation

All stimuli were displayed in the TESF on an immersive 3-screen rear-projection 

system manufactured by Fakespace Systems (Marshalltown, Iowa; currently 

Mechdyne Inc., Kitchner, Canada). The 3 screens were positioned symmetrically: 

one screen in the center, one screen on the left, and one screen on the right, in the 

form of 3 sides of an equilateral hexagon. The interior angle between screens was

120°. Participants were seated 9 ft from the center of all 3 screens. An overhead 

view of the participant and the projection screens is shown in Fig. 3.

Fig. 3 Schematic representation of screens, central fixation, and participant locations
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System images on each screen were projected by a Christie Digital 6000-lumen 

projector (Cypress, California) onto a 10-ft-high by 12.5-foot-wide screen. The 

display resolution for each screen was 1600 × 1200 pixels. At the center of each 

screen (i.e., at –60° and +60° eccentricities), one pixel subtended 4.3 min of arc. 

This value changes with eccentricity about the screen center, as we discuss when 

addressing movement rates in Section 3.3. 

To ensure that both left and right screens presented stimuli at the same level of 

brightness and contrast, we performed a gamma correction procedure to linearize 

the luminance of the projectors. Using a Photo Research Litemate PR-525 

photometer (Chatsworth, California), we measured display luminance from the 

observer’s vantage point at the center and extreme left and right locations on each 

screen for every 5 grayscale levels from 0 to 254. These data were combined to 

produce an illuminance function for each screen. These illuminance functions 

provided the red-green-blue (RGB) values to be sent to the display projector to 

create the desired luminous contrast intensity on each projection screen. 

Each screen was controlled by a separate PC running the Microsoft Windows 2000 

operating system. The PCs were networked together via a Transmission Control 

Protocol/IP local area network isolated from all other network traffic. Custom 

software written in Delphi (Borland Corporation; Cupertino, California) was used 

to generate displays, collect participant responses, save data in Microsoft Excel 

format files, and ensure timing accuracy of networked PC events.

3.3 Test Stimuli Presentation

All test screens were filled with a uniform gray background. Experiments were 

conducted with a background luminance of 16 candelas per square meter (cd/m2). 

This value is well within the photopic range of luminance (Hood and Finklestein 

1986). The 3 screens produced an illuminance of 0.4 foot-candle in the test 

participant’s facial plane. All test control stimuli and targets were presented in 

grayscale. 

Test control stimuli consisted of a fixation cross, a trial-underway fixation cross, 

and text instructions including “Make your response now” and “Click the mouse 

for next trial”. Figure 4 illustrates the appearance of the presented test control 

stimuli. As shown in Fig. 4, the fixation cross was a plus-sign subtending 1° of 

visual angle displayed on the middle screen at eye level of the seated participant. 

The fixation cross was displayed in black (RGB values 0,0,0). The trial-underway 

fixation cross was the fixation cross with a black box around it. Dimensions of the 

box were 1.5° × 1.5° visual angle, and it was centered at the fixation point. Text 

instructions were displayed on the middle screen, directly above the fixation cross, 
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9

in black Times New Roman font. The text was displayed in a size sufficient to be 

easily read by the observers. When the left or right choice was made, the text above 

the central fixation stimuli changed to read “Click the mouse for next trial”. At any 

time after the target movement began, or after the target movement had ended, the 

participant responded to the movement stimuli by using a mouse to select 1 of 2 

buttons that appeared on the center screen after each trial: the left button for motion 

on the “left” and the right button for motion on the “right”. Participants used the 

mouse to begin each trial and to respond to the 2-alternative forced choice.

Fig. 4 Illustration of test control stimuli (not to scale)

Target motion-detection stimuli were arrays of images approximating the human

form at various ranges and eccentricities, as shown in Fig. 2. The selection of 

stimuli characteristics was designed to be applicable to IWARS development 

needs. We selected stimuli to resemble dismounted Soldiers in both shape and 

luminance contrast. Discussions with personnel from AMSAA indicated that the 

way Soldiers are represented in the ACQUIRE target acquisition model is to have 

vertically oriented figures with a mean luminance approximately 50% lower 

(darker) than their immediate background. We chose to represent potential target 

stimuli as narrow, vertically oriented, bivariate Gaussian-shaped shadows. 

Symmetric stimuli scenes were displayed on the left and right screens. Scenes 

consisted of 2 sets of 15 potential-target stimuli semi-randomly distributed across 

eccentricities from 40° to 80°. One set of 15 was displayed on the left screen and 

the other set of 15 was displayed on the right screen. Three targets were presented 

vertically at each eccentricity angle, one target for each simulated target range: 50 

and 200 m. For each eccentricity angle, the small, medium, and large targets were 

randomly placed on the screen: one target centered at 5° above the horizontal plane, 
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10

one centered directly on the horizontal plane, and one centered 5° below the 

horizontal plane. No movement stimuli were displayed on the center screen. For all 

nonmoving targets, the left and right scenes were symmetric about the central 

fixation. Nonmoving targets were dithered horizontally at a random angle selected 

between ±5° about each eccentricity angle. The target to be moved was placed 

randomly at +5° or –5° with respect to its central eccentricity (i.e., horizontally) so 

it could move through a 10° arc straddling the target’s central eccentricity. 

Dithering the nonmoving targets made it impossible to determine which target 

would be moved during the trial. 

Figure 5 shows an illustration of one side of the target matrix for a typical motion 

visibility trial. 

Fig. 5 Initial target array on the full left or right screen for a typical motion-visibility trial 

(not to scale)

The initial target array was mirrored on the left and right screens, symmetrically 

about the center of the middle screen, giving a total of 30 stimuli. During each trial, 

one of the 30 stimuli moved through an angle of 10°. 

Although visual acuity may vary between the left and right sides, for these initial 

studies, results from trials when the moving target was on the left side were 

combined with results when the moving target was on the right side, producing a 

symmetric approximation of peripheral motion detection thresholds. Right side and 

left side responses were combined to reduce the data collection burden, and 

symmetry was assumed as a first approximation for vision modeling. 
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Each stimulus was assigned a simulated range of 50, 100, or 200 m. The height of 

the vertical dispersion of the target stimulus Gaussian window was a function of 

range as follows:

R

H D H c = , (1)

where H = height of the vertical dispersion

 D = distance from participant to center of screen

 Hc = actual height of combatant (assumed to be 2 m)

 R = simulated range from the observer to the combatant

We recognize that MVTs should depend on target contrast, so we selected a target 

design different from that used in the Monaco et al. (2007) study. Based on 

recommended military target spotting conditions recommended by AMSAA 

personnel, we determined the background illuminance for the screen and the 

minimum illuminance presented at the center of each Gabor patch target. Though 

there is some disagreement and a good deal of recent research examining how best 

to define contrast for complex objects such as Gabor patches (Peli 1997), we used 

a modified Michelson contrast (Peli and Arend 1993) to empirically set minimum 

and maximum luminance values of our Gaussian-shaped target stimuli. We 

specified contrast using a modified Michelson contrast given by 

CCmm = IITT− IIBB

IITT+ IIBB

. (2)

In Eq. 2, IT is the target luminance and IB is the background luminance. Guidance 

indicated that relevant target contrast should be –0.2, so we applied a background 

luminance of 16 cd/m-2 and a target luminance of 10 cd/m-2, giving a Michelson 

contrast of –0.2. By comparison, a fully black target on a fully white screen, as used 

by Monaco et al. (2007), gives a Michelson contrast of –0.97. Using the luminance 

functions established for each screen, we determined the RGB values to be sent to 

each screen to produce uniform background luminance over each screen and 

uniform contrast across all targets.

Movements were produced by displacing the Gaussian pattern by a number of 

pixels over a number of screen-refreshes. A movement rate of one pixel for each 

screen-refresh was used as the basis for the range of movement rates. This pixel

displacement rate corresponded to an angular target movement rate of 3.6°⋅s-1

. 

Angular movement rates were varied from a minimum of 1/10 of this amount to 10 

times this amount over 40 intervals. 
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Movement rates were varied in decibel increments. Consistent with other sensory 

perception processes, we defined the movement decibel level based on the square 

of the ratio of angular movement rate to the rate of 1 pixel per screen-refresh. 

Decibel movement levels, LdB, were determined as

LLdddd = 10llllgg10 ��

nnpppppppppppp/ ssss ss− rr h

1pppppppppppp/ ssss ss− rr h

�

2

� = 20llll 10 ��

nnpppppppppppp/ ssss ss− rr h

1pppppppppppp/ ssss ss− rr h

�

1

�. (3)

Applied angular displacement rates and equivalent speeds for all target 

eccentricities and ranges are given in the Appendix. 

In Eq. 3, the number of pixels per screen-refresh, n, ranged from 0.1 to 10 in decibel 

steps uniform in the square of the number of pixels moved per each screen-refresh. 

The smallest and largest movement rates used in this study were 0.36°⋅s-1 and 

36°⋅s-1

, respectively. 

The targets simulated upright, dismounted humans. The range of target movement 

rates was selected to extend below and well above the range of possible movement 

rates of people on foot so that we could ensure that measured movement visibility 

thresholds could apply to dismounted, moving human combatants. The smallest 

target speed simulated by the our angular movement rate occured at the smallest 

simulated target range (50 m) and equals a speed of 0.31 m⋅s-1. The largest speed 

used in this study was 126 m⋅s-1 and corresponds with a target range of 200 m. For 

the shortest-range targets (50 m), the fastest target speed simulated was 32 m⋅s-1. A 

reasonable upper bound for the maximum rate at which a person can move on foot 

is approximately 20 m⋅s-1 (Weyand et al. 2000). Thus, the maximum speed used in 

our study was higher than a reasonable upper limit for human running speed under 

all of our eccentricities and simulated target ranges. 

Target movement occurred over a time period not more than 750 ms and took place 

in the horizontal plane, either toward or away from the central fixation point. A 

potential confound in the study was the fact that as a stimulus moves along the 

horizontal line, its eccentricity necessarily changes. This confound was mitigated 

by limiting the duration of the movement. Also, because we modeled downrange 

combatants as Gabor patches, for which edges are defined by smoothly varying 

luminance values rather than abrupt high-low luminance changes from pixel to 

pixel, we could display motion at subpixel levels to simulate slowly moving targets 

at larger ranges. 

The movement rates of target images were determined at the center of the screen 

and were applied, as a constant, across the entire screen. Because our target images 

were projected onto a flat screen, the actual angular velocity of the stimulus about 

the participant’s view point was not constant across all eccentricities. Angular 
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13

motion rates decreased symmetrically, as eccentricities either increased or 

decreased, about 60°. Angular motion changed by less than 11.7% from the center 

of the screen to the edge of the screen, so we report motion visibility thresholds as 

on-screen movement rates and angular rates corresponding to the center of the side 

screen. 

3.4 Procedures

For each of the 15 conditions (5 eccentricities and 3 ranges, since left and right 

results were combined) in the experiment, the participant performed a QUEST*

(Watson and Pelli 1983; King-Smith et al. 1994) adaptive procedure to find the 

50% speed threshold required to detect the moving stimuli at the ranges and 

eccentricities in this study. QUEST was chosen because it reduces bias and reliably 

converges on the 50% threshold values with only a small number of trials required 

(Klein 2001). For each condition (eccentricity and target range), 20 trials were 

conducted to allow for convergence of the QUEST procedure. Thus, each 

participant performed 300 total motion detection trials.

Each trial in the QUEST procedure began with the presentation of the “Click the 

mouse for the next trial” instruction above the fixation cross. The fixation marker 

remained on the screen throughout the experiment. When clicked, this instruction 

disappeared and the trial-underway fixation cross appeared with the elements of the 

simulated scene. At a random interval between 1000 and 2000 ms later, one 

stimulus is moved by an angle of 10°. At the start of the movement, the response 

prompt was displayed immediately under the boxed cross (trial-underway) fixation 

display, allowing the observer to respond to the movement as soon as movement 

could be observed. This also may have helped the subject remain focused on the 

central fixation point by adding change-saliency to the fixation point as the 

movement began. After the movement interval of 750 ms, the “Make your response 

now” instruction appeared over the fixation cross and the response buttons. This 

instruction remained on the screen until the participant made a response by clicking 

the left or the right response box. The participant could respond by clicking the left 

or right button with the mouse at any time after the stimulus movement interval had 

started. Responses were not rushed and response times were not collected. 

Participants clicked the mouse pointer on either the right or the left response button, 

indicating that they felt the stimulus that moved (the target) was one of the targets

displayed on the left or one of the targets displayed on the right. Participants were 

instructed to guess if they failed to observe motion on either side. 

 *Watson and Pelli (1983) dubbed as QUEST their maximum likelihood procedure for parameter 

estimation by sequential testing. 
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14

When the left or the right response button was clicked, the left and right screens 

returned to their uniform gray background, the “Click the mouse for next trial”

instruction again appeared, and the next trial began upon the next mouse click. No 

feedback was given to participants regarding whether their response was correct.

Participants were permitted to take breaks as needed between trials by delaying 

pressing the mouse button when prompted to do so to begin a new trial. In addition, 

after 100 and 200 trials the participants were interrupted and offered a chance to 

take a break to relax their eyes. 

Participants were told that because half of the time the movement was on the left 

and half on the right, their best strategy would be to keep their eyes directed toward 

the fixation cross. Participants also were told not to look continuously at one side. 

By this strategy, individuals could look only at one side and, failing to see motion 

on that one side, they could guess motion on the other side. This strategy would 

have resulted in participants not using their peripheral vision at the intended 

eccentricity, and the obtained data would not have represented motion detection at 

precise angles in the periphery and far periphery. To ensure that participants did 

not look exclusively at one side, participants were told they would be watched from 

the side. No participant appeared to be observing only a single side of the overall 

display, and all participants appeared to continue to look toward the central fixation 

throughout their trials. Participants also were told that this experiment would 

resolve and measure motion detection thresholds, and therefore during many trials 

the participant could expect to be unable to detect motion on either the left of the 

right side, but that they should nonetheless guess the particular side where 

movement may have occurred. 

The adaptive QUEST procedure was used to determine the probability distribution

(PD) for the MVT for each test participant at each target range and eccentricity. 

The QUEST process starts with an a priori assumed MVT PD. In this study, the 

initially assumed PD was a uniform distribution over the domain from +20 to 

–20 dB, with 0 dB corresponding to an angular movement rate of 3.6°⋅s-1

(0.063 rad⋅s-1

). This single angular movement rate corresponds to a target velocity 

that depends on target range. At target ranges of 50, 100, and 200 m, target 

velocities corresponding to 0 dB are 3.2, 6.3, and 12.6 m/s, respectively. The final 

determined MVT value is the average over the final MVT PD. This average is the 

threshold movement rate where a person is expected to see, or detect, movement in 

half of all equal movement presentations. 

QUEST trials were started by presenting a stimulus at a movement rate at the center 

of the domain of the initially assumed MVT PD. If the initial trial response is 

correct, the PD is modified by multiplying it, at each point, by the probability of a
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15

correct response if the actual MVT equaled that point in the domain. Alternately, if 

the response is not correct, the PD is modified by multiplying it by the probability 

of an incorrect response if the actual MVT equaled the value at that point in the 

domain. Subsequent trials are conducted by presenting a stimulus at the expectation 

value (average) of the PD resulting from the previous modification. After each trial 

response, the threshold PD is again adjusted, based on whether the response was 

correct or incurrent. If the response is correct, the threshold PD is again modified 

by the probability of getting a correct response for each possible threshold value in 

the domain. If the response is incorrect, the threshold PD is again modified by the 

probability of getting an incorrect response for each possible threshold value in the 

domain. The trial-response-modification process is repeated a total of 20 times for 

each combination of range and eccentricity. The expectation value of the MVT PD 

after the 20th trial is taken as the resulting threshold. 

With 20 trials, the final response is expected to be within 5 dB or less of the actual 

threshold (Klein 2001). We verified Klein’s finding by using Monte Carlo 

simulations of experimental results. We also used the simulations to illustrate 

expected QUEST results to obtain greater understanding of the results of our 

experimental trials.

An example of a Monte Carlo simulation showing convergence of the QUEST 

procedure is shown in Fig. 6.
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Fig. 6 Mean and standard deviation of the PD for the MVT in successive QUEST trials in 

which the actual 50% threshold is –5 dB

Starting with a uniform rectangular PD for the MVT, we simulated responses using 

a psychometric function 50% threshold of –5 dB and a slope of 0.2. When the 

response was correct, the next trial was conducted with a slower movement rate, 

and when the response was not correct, the next trial was conducted with a faster 

movement rate. As the example in the Fig. 6 shows, the mean of the MVT PD 

converged to within 5 dB of the actual value (–5 dB) within 20 trials. Figure 6 also 

shows that the standard deviation of the MVT PD converged to a stable value after 

20 trials. 

To demonstrate the reliability of this convergence, we simulated 250 experimental 

trials using an actual threshold value of –5 dB, a psychometric function slope of 

0.2, and an initial stimuli guess of 15 dB. A total of 250 MVT PD means were 

calculated and are plotted in the histogram in Fig. 7.
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17

Fig. 7 MVT PD mean values from 250 simulated experimental trials

All histograms in this report were plotted with Microsoft Excel 2013. The bin width 

used in this histogram is 0.5 dB. The first bin on the left (the bin above –15 dB) 

contains the number of results less than –15.0 dB. There are no results less than 

–15.0 dB. The second bin from the left contains the number of results equal to or 

greater than –15.0 dB and less than –14.5 dB, which again contained no results. The 

third bin contains values equal-to-or-greater than –14.5 and less than 10.4 dB. One 

result value is in this bin, and so on. 

The average value of the means in Fig. 7 is –4.79 dB. The standard deviation of the 

means is 1.92 dB. This demonstrates that the applied QUEST procedure reliably 

converges on the actual MVT to within 5 dB in 20 trials. Of the 250 trials, 7 

produced results outside of the –10.0- to 0-dB range. Thus, the probability, P, of 

convergence within the ±5-dB range is P = (250–7/250) = 0.972, or better than 97%. 
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18

Watson and Pelli (1983) indicate that this QUEST procedure will converge toward 

the highest value in the domain if the actual threshold is out-of-range higher than 

the upper domain limit, and that the QUEST procedure will converge toward the 

lowest value in the domain if the actual threshold value is out-of-range below the 

lower domain limit. 

To verify this out-of-range behavior in our QUEST implementation, we simulated 

trial results with an actual threshold value of +25 dB, which is above the range 

between –20 and +20 dB. This simulates the situation when, for example, a test 

participant cannot see any motion within the entire domain of movement rates or 

cannot at all see the target at a given eccentricity. In such cases, the QUEST 

procedure converged to the maximum movement rate in the experiment, verifying 

Watson and Pelli’s finding in our QUEST implementation. 

Simulating 100 experimental trials with an actual threshold value of 25 dB, a 

psychometric function slope of 0.2 and an initial stimuli guess of 5 dB, the 

simulation produced the MVT PD mean values shown in Fig. 8.

Fig. 8 MVT PD means with an out-of-range actual threshold value of 25 dB
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19

The bin width in the histogram in Fig. 8 is 1 dB, and the first bin (the bin above 

–20) contains the number of results less than –20 dB; the second bin contains the 

number of results equal to or greater than –20 dB and less than –19 dB. Figure 8 

shows that the QUEST procedure converges on the maximum motion rate used in 

the study when the actual MVT is above the experimental domain. The average 

value of the means in Fig. 8 is 16.0 dB. The standard deviation of the means is 

7.4 dB. The distribution in Fig. 8 is neither normal nor symmetric. Over 50% of the 

values are greater than 18 dB. Figure 8 demonstrates that large numbers of MVT 

means concentrated at the endpoints of the experimental motion domain indicate 

that actual MVT values may be outside of the experimental motion domain or that 

the participant could not see the target at all, regardless of the target’s movement. 

When the target cannot be seen at all, the actual MVT may be undefined because 

the target cannot be seen regardless of its movement.

Our simulation trials demonstrate that the 20-trial QUEST procedure converges 

closely to the actual MVT when the MVT is within the experimental motion 

domain, and it converges on the extreme value of the motion domain when the 

MVT is out of range, beyond the limits of the experimental domain. Therefore, in 

interpreting our experimental results, experimental values greater than 15 dB, and 

by symmetry less than –15 dB, may not represent actual MVT values. 

To illustrate the out-of-range behavior in test participant results, we show the results 

produced by human subject test participant (TP) number 10. Significant variability 

was observed between different individuals. The results achieved by TP 10 are used 

to clearly illustrate the out-of-range behavior. 

The results from TP 10 are shown in Fig. 9. The data format is summarized in the 

figure legend.
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Fig. 9 Example of MVT progression for TP 10: a specific color represents a specific 

eccentricity and a specific line thickness represents a human-form target viewed at a specific 

range. In the legend, eccentricities are given in degrees following the letter “E”, and range is 

given in meters following the letter “R”. 

In Fig. 9, lines of one color represent measurements at a particular eccentricity, 

while lines of the same thickness represent a particular range. The thickest line 

represents targets simulating a range of 50 m, while the thinnest line represents 

targets simulating a range of 200 m. The 5 eccentricity values are represented by 

different colors: red, 40°; orange, 50°; yellow, 60°; green, 70°; and blue, 80°. In the 

legend, the letter E denotes eccentricity, and the number following the letter E is 

the eccentricity in degrees. The letter R denotes range, and the number following 

the R is the simulated range of the target in meters. 

We refer to experimental conditions as (E#, R#) in the legend of Fig. 9 and in the 

text. For example, we label experimental condition as E40R50, indicating an 

eccentricity of 40° and a simulated range of 50 m. 

A collection of MVTs in Fig. 9 is above 15 dB. We consider these values to 

represent conditions where the actual MVT values may be above our experimental 

domain or may be nonexistent. Also, for (E40, R50) the test participant never made 
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21

an incorrect choice of the side where the motion occurred. We consider that the 

actual MVT for (E40, R50) may be below the lowest movement rate used in this 

study. 

Our simulations show that trial results generally progress to steady values;

however, such behavior is not always observed. Final values can continue to change 

if the observer experiences increasing fatigue or eye strain as the iterations 

continue. Although participants were allowed and encouraged to take breaks during 

the trials, we cannot ensure that fatigue was never a factor in our data. 

In addition, the QUEST procedure does not produce a limiting value equal to the 

actual MVT as the number of iterations increases toward infinity. When a stimuli 

is presented at the actual MVT value, the probability of correctly observing the 

stimuli (P = 0.5) equals the probability of incorrectly missing the stimuli (P = 0.5). 

As we have shown, the QUEST procedure, however, gives an estimate of the MVT 

to within 5 dB after 20 iterations. The QUEST result continues to randomly 

fluctuate about the MVT, as trials continue, regardless of how many iterations are 

conducted beyond 20 iterations. 

MVT results have been graphed for TP 10 as a function of both eccentricity and 

range, as shown in Fig. 10, which shows the MVT surface for all eccentricity and 

range values, illustrating the shape of the threshold surface as a function of 

eccentricity and simulated target range. 
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Fig. 10 MVTs for TP 10 vs. eccentricity and range

4. Overall Results

All individual MVT values—all eccentricities and ranges—are shown in the 

histogram illustrated in Fig. 11.
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Fig. 11 Histogram of all threshold values obtained in the study

Figure 11 shows behaviors consistent with the simulation results shown in 

Fig. 8. A large number of trials resulted in values at the upper limit of our 

experimental range. The simulated results of Fig. 8 indicate that the values in 

Fig. 11— in the range between 18 and 20 dB plotted above the 18-dB value—are 

due to MVT values above 20 dB or beyond visible limits altogether. 

At the lower limit of our experimental range, Fig. 11 shows a decrease in the 

number of measured thresholds as the movement rate approaches –20 dB. This 

indicates that, unlike the number of MVT values above domain limit or possibly 

undefined, only a few participants had MVT values that might be below the 

experimental domain. This shows that we adequately captured the lower limit of 

motion detection at 40° eccentricity; we presented movement so slow that even if 

people could see the targets, most participants could not detect target movement at 

the slowest rates presented. Thus, we generally presented motion slow enough to 

not be seen by test participants. 

Figure 11 also shows a local minimum number of MVT measurements between 

–2 and 8 dB. MVT values are more prevalent at movement rates from –20 to 

–2 dB than at movement rates from –2 to 8 dB. For movement rates above 

–2 dB, our measurements indicate that people general can no longer see movement

at any eccentricities or ranges.

Figure 12 shows the distribution of MVT values greater than –20 dB and less than 

4 dB.
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Fig. 12 Histogram of MVT values greater than –20 dB and less than 4 dB

At the upper range of our experimental domain, the similarity in MVT value 

distributions in Figs. 8 and 11 indicate that MVT measurements above a value of 

approximately 10 dB may be due to out-of-range MVT values or targets that could 

not be seen at all, regardless of movement rate. In the range below –2dB, the 

average of the MVT values is approximately –10 dB. 

There is no precise separation point between actually observed MVT values and 

MVT values resulting from the QUEST procedure when the actual MVT value was 

above our experimental domain or when the target could not be seen regardless of 

motion. We can only estimate an average separation point across all subjects. Our 

data have a local minimum at 8 dB, and a separation estimate may be as low as 

–2 dB or as high as 8 dB. Neural imaging may present an experimental procedure 

to further refine this estimate for individual participants. 

Overall, our combined data indicate that the distributions of MVTs for human 

peripheral vision extend from a minimum value near –20 dB to an upper limit 

possibly between –2 and 8 dB, and the distribution has an average value of 

approximately –11 dB for eccentricities between 40° and 80° for targets 

representing human form shadows with a target contrast of –0.2. 

We calculated the average and standard deviation of the human threshold 

distribution for motion detection as a function of eccentricity and range for the 

targets and conditions of our study.
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25

Average and standard deviation MVTs across all participants are shown in Table 2.

In Table 1, eccentricity values are in the red region, range values are in the yellow 

region, and average and standard deviation MVT values are in the green region

Table 1 Average and standard deviation MVTs across all participants

Average and ± standard deviation 

MVT results (dB)

Range (m)

50 100 200

Eccentricity (°) 40 –13.0 ± 2.5 –5.4 ±3.0 9.4 ± 4.7

50 –10.9 ± 4.1 –0.8 ±5.6 16.0 ± 3.4

60 –9.4 ± 4.1 10.4 ±5.8 17.8 ± 1.3

70 4.5 ± 5.6 17.7 ±2.0 18.0 ± 1.0

80 17.8 ± 2.5 18.2 ±1.3 17.4 ± 1.7

Table 1 shows a decrease in standard deviation for MVT distributions with averages 

below –11 dB and above 15 dB. As noted earlier, measurements above a value of 

approximately 10 dB may be due to out-of-range MVT values or targets that could 

not be seen at all regardless of movement rate. 

Average MVTs across all participants are shown as a function of range and 

eccentricity by the MVT surface in Fig. 13.

Fig. 13 Average MVT surface as a function of eccentricity and range
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26

The lowest average MVT value is at 40° eccentricity (the lowest eccentricity value) 

and 50-m range (the lowest value in the range variable). The upper limit of real 

MVT values remains imprecise. Figure 13 indicates the shape of a real movement 

visibility threshold surface between –15 and +10 dB. Above about 10 dB, the 

QUEST procedure was more than 5% likely to have simply been converging on the 

maximum threshold in our study.

To help illustrate the dependence of the MVT on range and eccentricity, we plot 

MVT as a function of one independent variable for each separate value of the other 

independent variable in Figs. 14 and 15.

Fig. 14 MVT vs. eccentricity at ranges of 200, 100, and 50 m with error bars showing the 

standard deviation estimated for the MVT distribution at each eccentricity-range combination
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Fig. 15 MVT vs. range at eccentricities of 80°, 70°, 60°, 50°, and 40° at each eccentricity with 

error bars showing the standard deviation estimated for the MVT distribution at each 

eccentricity-range combination

Figure 14 shows the dependence of thresholds on eccentricity for various ranges. 

Measurements of rates above 10 dB may be biased by individual thresholds that 

were beyond the 20-dB maximum range of our study or by MVT values that 

unbounded because targets could not be seen regardless of motion. This bias is also 

indicated by the 95% confidence intervals for mean MVTs at each range and 

eccentricity. The confidence intervals decrease as the threshold values plateau 

toward the maximum motion rate. Since we have no reason to expect confidence 

intervals to decrease, this decrease is an indication that the QUEST procedure was 

converging at the upper experimental limit. 

Again, as the MVT averages rise, the upper experimental limit appears to restrict 

the QUEST values. The values of MVT above 10 dB remain uncertain. As the 

dashed line for an eccentricity of 80° shows, MVT thresholds for all ranges at that 

eccentricity are either above the limit of our experimental range or the thresholds 

are nonexistent because the targets could not be seen regardless of movement. 

As an example of the application of these measurements, we consider a person 

running with a speed of 4 m·s-1

. Table A-2 in the Appendix shows that this speed 

corresponds to 2 dB at 50 m, –4 dB at 100 m, and –10 dB at 200 m. Figure 14 shows 

a range of 50 m with a movement equivalent to 2 dB. A target of human size and 
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28

form moving with this running speed could be visually detected by this movement 

at eccentricities less than 60°–70°. If movement were at 100 m with a movement 

equivalent to –4 dB, detection of this movement would likely occur only at 

peripheral angles less than 45°. At 200 m, with a movement equivalent to –10 dB, 

this motion would go undetected at all peripheries greater than 40°. 

5. Conclusion

This research provides initial estimates of motion sensitivity in the far periphery. 

Target size and target contrast were selected from recommendations for 

representing humans as they may appear in some battle environments. Targets 

approximate the human form at ranges from 50 to 200 m, and lighting conditions 

produced a Michelson contrast of –0.2 darkened with respect to the background.

It is useful to compare our results with those obtained by Monaco et al. (2007), who 

reported MVTs of 1.35°·s-1 at 72.5° eccentricity and 2.3°·s-1 at 90° eccentricity. 

Our threshold visibilities values are much larger: approximately 6°·s-1 at 70° 

eccentricity and above 36°·s-1, if not nonexistent, at 80° eccentricities. We attribute 

the difference to the difference between the contrasts of the stimuli used in the 

studies. We applied a recommended Michelson contrast of –0.2, while Monaco 

et al. applied a contrast estimated to be –0.97, which is much greater. We also used 

Gabor patch targets, which lack sharply defined edges, while Monaco et al. applied 

solid black targets on white backgrounds. 

Measured MVT values can be used to develop a Soldier behavioral target-searching 

strategy that includes deviations from a rigid rasterized target-search process. The 

deviations can be based on target movement cues that direct foveal attention toward 

an object when the object moves at angular rates above the threshold at the 

appropriate visual eccentricity and target range within the Soldier’s field of view. 

Further research is required to better refine the upper limits of movement visibility 

thresholds at larger values of eccentricity. Further measurements also may help 

resolve absolute limits of movement detection at increasing eccentricities, where 

the target cannot be seen regardless of its rate of movement. Also, further 

measurements are needed to determine how MVTs depend on contrast and target 

edge thickness. Regardless, incorporating motion-cued target detection in Solder 

visual performance modeling will provide a model of the human performance based 

on behavioral characteristics as Soldiers search for enemy combatants, and the 

motion visibility estimates provided in this report can form visibility hypotheses 

that can be tested in the field. 
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Appendix. Angular Displacement Rates and Equivalent Speeds

for Target Eccentricities and Ranges
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Table A-1 gives angular displacements in degrees per second at each eccentricity 

angle.

Table A-1 Angular displacement rates (°•s-1) for target eccentricities

Target 

speed

Angle

(°)

Angle

(°)

Angle

(°)

Angle

(°)

Angle

(°)

Level 40 50 60 70 80

dB °•s-1 °•s-1 °•s-1 °•s-1 °•s-1

20 3.19E+01 3.51E+01 3.62E+01 3.51E+01 3.19E+01

19 2.85E+01 3.13E+01 3.22E+01 3.13E+01 2.85E+01

18 2.54E+01 2.79E+01 2.87E+01 2.79E+01 2.54E+01

17 2.26E+01 2.48E+01 2.56E+01 2.48E+01 2.26E+01

16 2.02E+01 2.21E+01 2.28E+01 2.21E+01 2.02E+01

15 1.80E+01 1.97E+01 2.03E+01 1.97E+01 1.80E+01

14 1.60E+01 1.76E+01 1.81E+01 1.76E+01 1.60E+01

13 1.43E+01 1.57E+01 1.62E+01 1.57E+01 1.43E+01

12 1.27E+01 1.40E+01 1.44E+01 1.40E+01 1.27E+01

11 1.13E+01 1.25E+01 1.28E+01 1.25E+01 1.13E+01

10 1.01E+01 1.11E+01 1.14E+01 1.11E+01 1.01E+01

9 9.00E+00 9.89E+00 1.02E+01 9.89E+00 9.00E+00

8 8.03E+00 8.81E+00 9.09E+00 8.81E+00 8.03E+00

7 7.15E+00 7.86E+00 8.10E+00 7.86E+00 7.15E+00

6 6.37E+00 7.00E+00 7.22E+00 7.00E+00 6.37E+00

5 5.68E+00 6.24E+00 6.43E+00 6.24E+00 5.68E+00

4 5.06E+00 5.56E+00 5.73E+00 5.56E+00 5.06E+00

3 4.51E+00 4.96E+00 5.11E+00 4.96E+00 4.51E+00

2 4.02E+00 4.42E+00 4.55E+00 4.42E+00 4.02E+00

1 3.58E+00 3.94E+00 4.06E+00 3.94E+00 3.58E+00

0 3.19E+00 3.51E+00 3.62E+00 3.51E+00 3.19E+00

–1 2.85E+00 3.13E+00 3.22E+00 3.13E+00 2.85E+00

–2 2.54E+00 2.79E+00 2.87E+00 2.79E+00 2.54E+00

–3 2.26E+00 2.48E+00 2.56E+00 2.48E+00 2.26E+00

–4 2.02E+00 2.21E+00 2.28E+00 2.21E+00 2.02E+00

–5 1.80E+00 1.97E+00 2.03E+00 1.97E+00 1.80E+00

–6 1.60E+00 1.76E+00 1.81E+00 1.76E+00 1.60E+00

–7 1.43E+00 1.57E+00 1.62E+00 1.57E+00 1.43E+00

–8 1.27E+00 1.40E+00 1.44E+00 1.40E+00 1.27E+00

–9 1.13E+00 1.25E+00 1.28E+00 1.25E+00 1.13E+00

–10 1.01E+00 1.11E+00 1.14E+00 1.11E+00 1.01E+00

–11 9.00E-01 9.89E-01 1.02E+00 9.89E-01 9.00E-01

–12 8.03E-01 8.81E-01 9.09E-01 8.81E-01 8.03E-01

–13 7.15E-01 7.86E-01 8.10E-01 7.86E-01 7.15E-01

–14 6.37E-01 7.00E-01 7.22E-01 7.00E-01 6.37E-01

–15 5.68E-01 6.24E-01 6.43E-01 6.24E-01 5.68E-01

–16 5.06E-01 5.56E-01 5.73E-01 5.56E-01 5.06E-01

–17 4.51E-01 4.96E-01 5.11E-01 4.96E-01 4.51E-01

–18 4.02E-01 4.42E-01 4.55E-01 4.42E-01 4.02E-01

–19 3.58E-01 3.94E-01 4.06E-01 3.94E-01 3.58E-01

–20 3.19E-01 3.51E-01 3.62E-01 3.51E-01 3.19E-01
Approved for public release; distribution is unlimited.

33

Table A-2 gives the velocity of movement at each range. Velocities are shown for 

the 60° eccentricity. Velocities at other eccentricities are approximately equal to 

the velocity at 60° eccentricity. 

To add perspective to the velocities in the tables, the velocity values are colorshaded to represent the following approximate ranges:

• Green: human walking speeds; velocity <2.24 m•s-1 (5 mph)

• Yellow: human running speeds; 2.24 m•s-1 (5 mph) < velocity < 8.94 m•s-1

(20 mph)

• Red: vehicle driving speeds; velocity >8.94 m•s-1 (20 mph)
Approved for public release; distribution is unlimited.

34

Table A-2 Velocity at ranges for 60° eccentricity

Target speed

(mph)

Range

(m)

Range

(m)

Range

(m)

Level 50 100 200

dB m/s m/s m/s

20 31.57 63.15 126.30

19 28.14 56.28 112.56

18 25.08 50.16 100.32

17 22.35 44.71 89.41

16 19.92 39.84 79.69

15 17.76 35.51 71.02

14 15.82 31.65 63.30

13 14.10 28.21 56.41

12 12.57 25.14 50.28

11 11.20 22.41 44.81

10 9.98 19.97 39.94

9 8.90 17.80 35.59

8 7.93 15.86 31.72

7 7.07 14.14 28.27

6 6.30 12.60 25.20

5 5.61 11.23 22.46

4 5.00 10.01 20.02

3 4.46 8.92 17.84

2 3.97 7.95 15.90

1 3.54 7.09 14.17

0 3.16 6.31 12.63

–1 2.81 5.63 11.26

–2 2.51 5.02 10.03

–3 2.24 4.47 8.94

–4 1.99 3.98 7.97

–5 1.78 3.55 7.10

–6 1.58 3.16 6.33

–7 1.41 2.82 5.64

–8 1.26 2.51 5.03

–9 1.12 2.24 4.48

–10 1.00 2.00 3.99

–11 0.89 1.78 3.56

–12 0.79 1.59 3.17

–13 0.71 1.41 2.83

–14 0.63 1.26 2.52

–15 0.56 1.12 2.25

–16 0.50 1.00 2.00

–17 0.45 0.89 1.78

–18 0.40 0.79 1.59

–19 0.35 0.71 1.42

–20 0.32 0.63 1.26
Approved for public release; distribution is unlimited.

35

List of Symbols, Abbreviations, and Acronyms

2-D 2-dimensional

AMSAA US Army Materiel Systems Analysis Activity

ARL US Army Research Laboratory

cd/m2 candelas per square meter

IP Internet Protocol

IR infrared

IWARS Infantry Warrior Simulation

MVT motion visibility threshold

NSRDEC US Army Natick Soldier Research, Development and Engineering 

Center

PC personal computer

PD probability distribution

PEST Parameter Estimation by Sequential Testing

RGB red-green-blue

TESF Tactical Environment Simulation Facility

TP test participant
Approved for public release; distribution is unlimited.

36

1 DEFENSE TECHNICAL

(PDF) INFORMATION CTR

DTIC OCA

2 DIRECTOR

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RDRL CIO L

IMAL HRA MAIL & RECORDS

MGMT

1 GOVT PRINTG OFC

(PDF) A MALHOTRA

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(PDF) RDRL HRB B

T DAVIS

BLDG 5400 RM C242

REDSTONE ARSENAL AL 

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R SPENCER

BLDG E2929

DESERT STORM DR

FORT BRAGG NC 

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8 ARMY RSCH LAB – HRED

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300 ARMY PENTAGON 

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1 USAF 711 HPW

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2698 G ST BLDG 190 

WRIGHT PATTERSON AFB OH 

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1 USN ONR

(PDF) ONR CODE 341 J TANGNEY

875 N RANDOLPH STREET

BLDG 87 

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(PDF) RDNS D D TAMILIO

10 GENERAL GREENE AVE 

NATICK MA 01760-2642

1 OSD OUSD ATL

(PDF) HPT&B B PETRO

4800 MARK CENTER DRIVE

SUITE 17E08

ALEXANDRIA VA 22350

ABERDEEN PROVING GROUND

15 DIR USARL

(PDF) RDRL DPT

K KEHRING

RDRL HR

J LOCKETT

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B VAUGHN

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J GRYNOVICKI

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C PAULILLO

RDRL HRF A

A DECOSTANZA

RDRL HRF B

A EVANS

RDRL HRF C

J GASTON

P FEDELE

RDRL HRF D

A MARATHE

RDRL LOC I

A KALB