Technical Report 427

EVALUATION OF A GUNNERY SIMULATOR'S

VISUAL DISPLAY AND SEVERAL STRATEGIES

FOR LEADING MOVING TARGETS

Brian L. Kottas

and

David W. Bessemer

ARI FIELD UNIT AT FORT KNOX, KENTUCKY

I

U. S. Army

* Research Institute for the Behavioral and Social Sciences

October 1979

4 Approved for public release; distribution unlimited.

80 7 10 016
MIIU

I U. S. ARMY RESEARCH INSTITUTE

? FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

WILLIAM L. HAUSER

JOSEPH ZEIDNER Colonel, U S Army

Technical Director Commander

NOTICES

DISTRIBUTION, Primary distribution of this report has been made by AR I. Please address correspondoncf concerning distribution of reports to: U. S. Army Research Institute for the Behavioral and Social Sciences,

ATTN PERI.P, 5001 Eisenhooer Avenue, Alexandria, Virginia 22333.

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the U. S. Army Research Institute for the Behavioral and Social Sciences.

PJIM The findings in this report are not to be construed as an official Deartment of the Army position. unless so designated by other authorized documents.
Unclassified

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REPORT DOCUMENTATION PAGE BEFORE READ COMPLETING INSTRUCTIONSFORM

REPORT NUMBER 2. GOVT ACCESSION NO. 3./'EPIENT'S CATALOG NUMBER

Technical Report 427 -9

A . -, a. , OVERED

EVALUATION OF A YUNNERY -SUAL ETS FIMULATOR'S TSPLA¥ -ERE

"AND SEVERAL STRATEGIES IR LEADING MOVING TGETS : Oct 077-Sep 078 -~o -Iv T ER

7. AUTHORa) B. CONTRACT OR GRANT NUMBER(e) ' Brian. LKottas --"& David W: Bessemer

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. Po , PRg EC. TASK

US Army Research Institute for the Behavioral and AREA &WUNIT NUMBERS

Social Sciences, 5001 Eisenhower Avenue, Program 17 A ,

Alexandria, VA 22333 (PERI-IK) Projec 2Q762722A777

11. CONTROLLING OFFICE NAME AND ADDRESS - .

Directorate of Training Developments _ct_ _ __ _ _79

US Army Armor School

Fort Knox, KY 40121 52

14. MONITORING AGENCY NAME & ADDRESS(it dilferent from Controlilng Office) IS. SECURITY CLASS. (of this report)

Unclassified

ISa. DECL ASSI FICATION/DOWNGRADING SCHEDULE

16. DISTRIBUTION STATEMENT (of thls Report)

19. KEY WOR A (Continue on reverae aide It necaaary a.d Identify by block number)

Tanks (Combat Vehicles) Estimates Instructional Systems Development

Moving Targets Velocity

Simulators Distance

Training Slant

AS.MTRACT (OrVts an ,mvwa ef if neav amd Identify by block nuabar)

The presence of high-speed armor targets on the modern battlefield necessitates training gunners to judge target speeds in order to apply appropriate

leads. The current research investigated trainees' speed judgments of a moving

target and spatial estimates of a gunnery simulator's visual display. Distance,

range, and slant estimates in the simulator revealed systematic underestimation

of distance and range, overestimation of slant, and large inter-observer variability, a pattern of results typical of research on space perception. Applica

tion of speed Judgment data to a model of tank gunnery indicated that asking ll/

DD I JAN 73 EDITION OF INOV65ISOSOLETE Unclassified

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Item 20 (Continued)

gunners to classify target speed as either fast, medium, or slow and to apply

one of three corresponding leads should prove an effective lead strategy.

Application of the data to the gunnery model demonstrates that selection of

a cognitive strategy for training may have an impact on operational performance.

The current research will be of value to instructional system developers,

because it demonstrates the importance of unobservable processes, such as

cognitive strategies, for training.

*11

Unclassified

j SECURITY CLASSIFICATION OF THIS PAGE(ften Dota Entered)

" ., I
Techaical Report 427

EVALUATION OF A GUNNERY SIMULATOR'S

VISUAL DISPLAY AND SEVERAL STRATEGIES

FOR LEADING MOVING TARGETS

Brian L. Kottas

and

David W. Bessemer

Submitted by:

Donald F. Haggard, Chief

ARI FIELD UNIT AT FORT KNOX, KENTUCKY

Approved by:

E. Ralph Dusek, Director

PERSONNEL AND TRAINING

RESEARCH LABORATORY

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria, Virginia 22333

Office, Deputy Chief of Staff for Personnel

Department of thArmy

October 1979

Army Project Nu mber Training Simulators

20762722A777 t

Apoproved for public release; distribution unlimited.

iii I, 2
ARI Research Reports and Technical Reports are intended for sponsors of

R&D tasks and for other research and military agencies. Any findings ready

for implementation at the time of publication are presented in the last part

of the Brief. Upon completion of a major phase of the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.

!:
FOREWORD

The Fort Knox Field Unit of the U.S. Army Research Institute for the

Behavioral and Social Sciences (ARI) has conducted this research as part

of an in-house investigation of simulators for training. This research

is responsive to the requirements of the Directorate of Training Develop- ments, USAARMS, Ft Knox, Kentucky under Human Research Needs 78-140,

"Conduct of fire maintenance of gunnery proficiency," and 78-161,

"Feedback characteristics of training devices and simulators" and the

objectives of RDTE Project 2Q762722A777, FY 78.

HEIDNER

Technical Director

r J

tV I ________

i4
*EVALUATION OF A GUNNERY SIMULATOR'S VISUAL DISPLAY AND SEVERAL STRATEGIES

FOR LEADING MOVING TARGETS

Brief

Requirement:

The Ft Knox Field Unit of ARI evaluated the gunnery training provided

by a conduct of fire trainer, the Chrysler Fire Control Combat Simulator

(FCCS). The present research investigated whether a simple visual

display, such as that of the FCCS, contains the cues necessary for

training moving-target gunnery. It further investigated the kind of

lead strategy best suited for moving target gunnery in the simulator.

This report discusses the potential of various lead strategies for use

in the field.

Procedure:

Armor trainees viewed FCCS displays containing a moving target and

judged (a) various distances between points in the display, (b) target

ranges, and (c) the slant of the ground upon which the target moved. One

group of trainees also judged the target's speed to the nearest 5 mph,

while another group simply classified target speed as either slow,

medium, or fast.

Findings:

Both groups of trainees systematically underestimated distances in

the display and also underestimated target ranges. They systematically

overestimated the slant of the ground from the horizontal. Although

judgments based on the simple FCCS display were systematically in error

and revealed large individual differences, the judgments were qualitatively

similar to those reported in several previous field studies and in other

research on space perception.

In addition to errors in judging distances, ranges, and slant,

trainees were very inaccurate in judging the speed of a moving target to

the nearest 5 mph in the simulator; speed judgments also showed large

individual differences. However, trainees categorized the three target

speeds as fast, medium, or slow with relative accuracy. Parameters

reflecting speed judgment performance were derived for both of the

speed judgment conditions, and were applied to a tank gunnery model.

vii

__ __ _ _ __ _ __ __ I
This analysis revealed a large difference in expected hit probabilities

for the two different approaches to judging target speed. Requiring

gunners to categorize target speeds and to apply one lead for each

category restricts the huge variability that occurs when observers judge

target speed in miles per hour, and also covers the speed range of the

1980's battlefield more effectively than a single standard lead. Hence,

a categorization strategy could potentially produce a dramatic superiority

in hit probabilities over a strategy requiring gunners to calculate lead

based on judgment of target speed, or one requiring gunners to use a

single standard lead.

Utilization of Findings:

This research has several implications for training moving target

gunnery:

(a) Since the pattern of spatial judgments in the simplified display

of the FCCS did not differ markedly from patterns of spatial judgments

obtained in past research on space perception, it may be unnecessary to

incorporate high-fidelity displays in simulators intended to train basic

principles in leading moving targets. The very simple displays of the

FCCS allowed gunners to categorize target speeds quite well; accurate

categorization of target speeds should produce effective gunnery

performance, provided appropriate training is given. However, gunnery

training with evasive targets, training of battlesight techniques, or

training of adjustment of fire techniques such as Burst-on-Target (BOT)

may require a much richer display than that of the FCCS. The fidelity

necessary for a general-purpose conduct-of-fire-trainer should be

determined.

(b) Given the extreme variability of observers' speed judgments (in

the FCCS and in the field) when they are asked to estimate target speed

in miles per hour, it is unreasonable to attempt to teach a lead strategy

that requires gunners to calculate lead based on exact target speed.

The variability of perceived speed indicates that simply allowing

observers to practice leading moving targets and providing them with

knowledge of results will probably be ineffective. Even if a target

moves at a constant actual speed, its perceived speed will vary over

time, and a trainee will not experience a constant relationship of

perceived speed to required lead. This psychophysical variability

necessitates the use of a strategy that will restrict gunners' response

variability, such as a categorization strategy. Furthermore, the

difficulty of complex mental calculations for the average gunner plus

the difficulty of remembering formulas for calculating lead and carrying

out the required lead calculations under the stress of combat discourages

attempts to train lead calculation based on judgment of target speed.

viii

j.1
(c) Further research also must be conducted to determine the optimal

number of speed categories for training when targets can move at any

speed within the range anticipated for ground targets on the 1980's

battlefield. Effective training on engaging moving targets may require

presenting targets across the entire range of possible speeds for

categorization.

The current research also has implications for the development of

instructional systems:

(a) It emphasizes the importance of unobservable processes, such as

cognitive strategies, for training.

(b) It provides an example of a methodology for assessing the expected

impact of training alternative strategies on operational performance.

This research illustrates that the amount of fidelity required in

a simulator depends on the task to be trained. While subjective impressions may lead one to ask for high fidelity, efficient task performance

may often be trained on simple simulators if the task demands are

brought into agreement with trainees' cognitive and perceptual abilities.

ix

__,.______
EVALUATION OF A GUNNERY SIMULATOR'S VISUAL DISPLAY AND SEVERAL STRATEGIES

FOR LEADING MOVING TARGETS

INTRODUCTION

Problem. Simulation will play an increasingly important role in gunnery

training in the future. It is important to determine the amount of fidelity

and detail required in a simulator's visual display for training various

gunnery tasks. Too little fidelity will produce ineffective training, while

too much fidelity incurs needless expense. One area of increasing importance

for training, and hence for simulation, is moving target gunnery.

Moving targets present a complex problem in tank gunnery. In order to

hit a moving target, the gunner must apply the correct amount of lead; for

a given kind of ammunition, the correct lead depends primarily on the target's

speed. Training gunners to estimate the speed of a moving target in order

to apply the correct amount of lead therefore presents a major training

problem. This training problem exists not only for tank fire control systems

without automatic lead, but also for "degraded mode" gunnery in systems with

automatic lead when the lead function is inoperative. Gunners must learn an

efficient and effective strategy for applying different leads to different

speed targets in either case.

Background. ARI recently evaluated several facets of the gunnery training provided by a conduct-of-fire simulator, the Chrysler Fire Control Combat

Simulator (FCCS). With the FCCS, trainees use realistic gunner's controls to

move a computer-generated reticle upon a CRT display and "fire" a simulated

round at the target. Although the computer-generated visual display of the

FCCS is quite simple (as Figure I shows), it can present a moving target.

Unlike the single-speed moving targets usually presented to trainees, the

FCCS can present a target moving at one of three different, pre-programmed

1J
Figure 1. Visual Display of the Chrysler FCCS.

2
speeds; the instructor simply selects the target speed prior to each engagement. The flexibility of the FCCS potentially allows valuable training on

engaging moving targets, but the simplicity of its display raises the question of whether it contains enough information to allow effective training.

Several requirements for the visual display of a conduct of fire trainer

come to mind immediately. First, the display must appear three-dimensional

to lend realism to the simulation. Second, it must represent distances and

ranges adequately. Third, for the trainer to be useful for training moving

target gunnery, it must represent target speed adequately. To accurately

represent a three-dimensional space and the speed of a target in the space,

one would suspect that the display of a conduct of fire trainer must incorporate the major monocular cues to depth. These are generally considered to be:

(1) the familiar size of objects (one can infer the distance of a familiar

object such as a car by its projected size in the visual field), (2) linear

perspective (the apparent size and spacing of lines reduces with increasing

distance from the observer -- for example, railroad tracks appear to converge

in the distance), (3) texture gradients (the apparent size of objects decreases regularly and their apparent density increases regularly with increased

distance from the observer -- for example, clods in a plowed field appear

smaller and denser in the distance than they do nearby), (4) height in the

visual field (farther objects tend to be higher in the visual field than

nearer objects), (5) monocular motion parallax (objects at different distances from the observer appear to move across the visual field at different

speeds as the observer moves), (6) inter-position (farther objects are

blocked from view Ly nearer objects), and (7) aerial perspective (objects in

3

r!4
the distance appear somewhat hazy and have a bluish color relative to

nearer objects). The FCCS display clearly does not incorporate all these

cues, and incorporates some only partially (refer again to Figure 1).

Rather than several familiar objects, the display contains a single, rectangular target. While the lines of the grid comprising the ground converge into

the distance, their thickness does not decrease as it would in a real,

three-dimensional scene. The display fails to incorporate either a texture

gradient or aerial perspective. The current research investigated the effect

of these shortcomings.

Since no simulator can be evaluated independently of the task it will

be used to train, questions about whether the FCCS display contains sufficient

information for training gunners to engage moving targets cannot be asked

without reference to the kind of lead strategy that is trained. Therefore,

this research not only addressed the adequacy of the FCCS display, but investigated its potential in training different strategies for leading moving

targets.

Several kinds of lead strategies have been proposed for engaging moving

targets. These strategies, discussed below, differ primarily in the demands

placed on the gunner to judge the target's speed accurately.

The first, and least demanding lead strategy, requires gunners to apply

a single standard lead to all moving targets, regardless of speed. This

strategy will be referred to as the Standard Lead strategy. Current Armor

doctrine indorses this strategy in FM 17-12-2. If a simple display, such as

that of the FCCS, fails to provide sufficient information for observers to

determine anything about target speed, then the single Standard Lead strategy

4
is the only feasible alternative for training with the simulator. The

obvious strengths of a single Standard Lead strategy are simplicity in

training, and speed of firing the first round against a moving target.

However, one must consider that a single lead covers oniy one small part

of the speed range expected from targets on the modern battlefield.

Furthermore, the lead specified in FM 17-12-2 is optimal only for targets

moving at approximately 10-12 mph (when firing APDS ammunition); one can

reasonably assume that vehicles on the modern battlefield will move at

much higher speeds than this.

The second, and slightly more demanding kind of strategy, requires

gunners to categorize target speed into one of a number of possible speed

ranges and apply a different lead for each speed range. This strategy will

be referred to as the Speed Categorization strategy. TRADOC Bulletin

No. 5, Training with LAW, recommends this kind of strategy in using the

U.S. Light Antitank Weapon. Specifically, the TRADOC bulletin recommends

using only two target leads, one lead for fast targets and one for slow

targets. Jones and Jehan (1978) also recommended categorizing target

speeds. They recommended that gunners classify a target's speed as either

slow or fast, and use a different lead for each speed category. If a simple

display, such as that of the FCCS, allows gunners to place targets into one

of two or more speed categories, training a Speed Categorization strategy

should prove superior to training a single-lead strategy; a categorization

strategy would cover a range of speeds much more effectively than a single

lead.

A major empirical question with a Speed Categorization strategy concerns

the number of categories into which the speed range should be divided.

5
Optimal performance requires that the number of lead categories equals the

number of categories into which gunners can divide the speed range.

Considering this, the recommendation of only two categories by both TRADOC

Bulletin No. 5 and the report by Jones and Jehan (1978) seems puzzling.

A massive amount of psychological and human factors research demonstrates

that observers should be able to divide a sensory continuum into at least

three categories, if not more (see Garner, 1962). In suggesting only two

speed categories, the TRADOC bulletin references a U.S. Army Infantry

Board report (1975) indicating that bracketing speed judgments into three

categories is too difficult. However, the Infantry Board report considered

targets moving about 15 mph or less. The restricted speed range considered

may have been at least partially responsible for the conclusions.

Observers may have little difficulty dividing the broader speed range of

the 1980's battlefield into at least three categories. As for Jones and

Jehan, their recommendation of using two speed categories was based on subjective impressions of the difficulty of placing speed judgments into three

categories, rather than on objective data. The current research was designed

to empirically determine whether or not observers can accurately place

moving targets into more than two categories.

The third, and most complex kind of lead strategy, involves calculation

of the amount of lead needed based on the estimated speed of a moving target.

Bessemer and Kraemer (1979) recommended such a strategy, which will be

referred to as the Speed Magnitude Estimation strategy. Specifically, for

APDS ammunition they recommended that gunners determine a target's speed to

the nearest five miles per hour, divide this speed by ten miles per hour,

6

4'- a.
and multiply the result by 2.5 mils. For HEAT ammunition, a multiplier of

5 mils is required. Performance with this strategy would depend, of course,

on how accurately observers could judge target speed to the nearest five

miles per hour. If observers can accurately determine a target's speed to

the nearest five mph in a simple display such as the FCCS, this strategy

may be preferable for training; it would produce a high proportion of hits

at any target speed. On the other hand, the complexity of the mental arithmetic required would severely tax the abilities of the average gunner, and

the time required for the mental calculations may prohibit any such calculation strategy from being used by gunners in combat. If gunners can

accurately judge target speed to the nearest 5 mph in the simulator, further

research will be necessary to determine whether the complexity of this

strategy makes it prohibitive for use in combat.

The kind of strategy most appropriate for training with simple displays,

such as that of the FCCS, depends on observers' accuracy in judging target

speed in the simulator. All three strategies require some kind of speed

discrimination, but differ in the demands each places on the gunner's perceptual system. A Standard Lead strategy demands only that gunners be able

to discriminate moving from stationary targets, a Speed Categorization

strategy involving a small number of categories demands only that gunners

make a few discriminations among broad categories, and a Speed Magnitude

Estimation strategy demands that gunners be able to estimate target speed

fairly accurately along a continuum. While the complexity of the discrimination increases from a Standard Lead to a Speed Magnitude Estimation strategy,

the potential payoff in terms of target hits also increases, provided that

gurners can make the perceptual discriminations each kind of strategy demands.

7

-I
Unfortunately, psychological research has failed to yield pat answers

to questions about observers' ability to judge the speed of targets. This

failure is not due to poor quality or insufficient research, but reflects

the relativistic operation of the human visual system. Brown (1931) demonstrated that the perceived speed of a target depends not only on its physical

speed, but on the structure of the background against which the target is

moving. To state the problem somewhat differently, several factors influence

judgments of a target's speed; the apparent distance of the target, for

example, exerts a major influence on its perceived speed (see Gogel and Tietz,

1974; Gogel, 1977; Epstein, 1978). The apparent distance of a target in

turn, depends on certain cues, or characteristics of the scene in which the

target appears. The cues to distance incorporated into a simulator's

display will therefore influence judgments of target speed, and could be

expected to reduce transfer of lead training if target speed is systematically

misperceived.

As can be seen from the above discussion, the current research investigated the adequacy of the perceptual information provided by the FCCS display.

That is, the current research assessed observers' ability to judge target

ranges, distances along the ground, and the slant of the ground as represented

by the display. The purpose of collecting these data was to determine the

kind of impression of three-dimensional space provided by the display, and to

determine whether the pattern of errors in spatial judgments differed

markedly from errors in spatial judgments in the field.

Since no trainer can be assessed independently of the way it is to be

used, a different facet of the question about the adequacy of the FCCS

8owj
display involves its adequacy with respect to the kind of gunnery technique

trained with it. In the current research, three different strategies for

leading moving targets were assessed by examining the ability of observers

to determine target speed when different kinds of speed judgments must

be made. Because of the minimal demands of a single Standard Lead strategy,

the experimenters did not collect empirical data on how well observers

could discriminate stationary from moving targets, but concentrated on the

kind of speed discriminations demanded by the other two strategies. The

kind of speed judgments observers are able to make will determine the kind

of lead strategy that is most appropriate for training using a simulator

with a simplified display, such as the FCCS. The current report concludes

with a logical analysis of the use of lead strategies in the field, and

makes recommendations for further research, to determine whether a lead

strategy that can be taught in the simulator is effective in the field.

9
METHOD

Subjects. 28 trainees (25 gunners and 3 drivers) in the One Station

Unit Training (OSUT) course at Ft Knox served as observers. Troops were

assigned to two groups of 14 subjects each. One group consisted of 13 gunners and one driver; the other consisted of 12 gunners and two drivers.

Group assignment was counter-balanced based on the order in which observers

came to the experiment. On the first day the first observer was assigned

to the Speed Categorization group, and the second was assigned to the Speed

Magnitude Estimation group; on the second day the first observer was assigned

to the Speed Magnitude Estimation group, and the second was assigned to the

Speed Categorization group, etc.

Apparatus. The Chrysler FCCS (Fire Control Combat Simulator), shown

in Figure 2, consists of an instructor's console and a gunner's station.

Chrysler Corporation describes the instructor's console and gunner's station, respectively, as follows:

"This console provides power control, self-test, program direction and

engagement start/stop commands. Two visual displays are provided by the

console. A display monitor . . . provides a visual assessment of the

gunner's proficiency in . . . tracking and firing. A printer provides

a permanent record of the gunner's performance .

"An eyepiece allows the gunner to view the action area as if viewing

through the tank. The gunner can observe terrain, target and aiming reticle.

Through handle inputs, the gunner can move the field of view (FOV) to acquire, . . . track, lead, and fire on a target."

10

. .... .. ....
IO ,

Figure 2. The Chrysler FCCS
The FCCS presented trainees with visual displays of a moving, rectangular target. The simulator was programmed to present the moving target at

a simulated speed of either 10, 15, or 25 mph. Observers viewed displays

(see Figure 1) through an eyepiece like that of the primary sight in an

M60Al tank. The experimenter timed the duration of the visual displays

with a hand-held stopwatch. Observers matched the perceived slant of the

display's "ground" from the horizontal with a slant board approximately

8 inches long, hinged to a large horizontal base. The adjustable angle

between the slant board and the base was measured by a protractor.

Procedure. Each observer was tested individually. Observers sat in

front of the simulator's gunner controls and adjusted the sight's focus

while viewing tie FCCS display of a stationary head-on target at a range

of 1500 meters. The observer then received the instructions provided

under heading 2 of the data sheet in Appendix A. The experimenter centered

the reticle cross on the target and instructed the observer to operate

the controls and scan the display until he had seen the entire grid pattern

forming the display "ground." The experimenter then centered the crosshairs on the target and asked the observer to report the width of the

grid representing the ground (to the nearest 50 yards or 50 meters, whichever the observer preferred) at: (1) the edge nearest the observer

(2) the edge farthest from the observer, and (3) an intermediate distance.

The observer was then asked to estimate the length of the grid (again, to

the nearest 50 yards or 50 meters). Before the observer responded, the

experimenter moved the crosshairs over the distance to be judged in order

to avoid confusion and refresh the observer's memory. Each observer was

12
then asked to adjust the angle of the slant board until it matched the

slant of the ground in the display. Finally, each observer judged the

range of the target (again, to the nearest 50 yards or 50 meters) at

1000 meters, 1500 meters, and 2500 meters.

The experimenter told each observer that he would see some displays

of moving, tank-sized targets and that his task was to judge each target's speed. Each of the two experimental groups received different

specific instructions about judging target speed. Page 2 of the sample

data sheet in Appendix A contains the specific instructions. The experimenter informed the Speed Categorization group that the targets would

move across their field of view at either a slow, medium, or fast speed

at various distances from them, and that they were simply to say on each

trial whether the speed of the target was slow, medium, or fast. The

experimenter told the Speed Magnitude Estimation group that the targets

would move across the screen at different speeds and at different distances

from them, and that on each trial they were to report to the nearest 5 mph

how fast the target was moving.

At the beginning of each trial, the experimenter instructed the

observers in both groups to look away from the eyepiece, and not to look

into it until the experimenter said "go." The experimenter initiated the

display and adjusted the reticle crosshairs so the horizontal line of the

crosshairs was even with the bottom of the target, and the vertical line

of the crosshairs was centered horizontally on the grid. When the target

moved to the center of the grid, the experimenter said "go" and began

timing the display, as the observer looked into the sight. After approximately

13
five seconds, the display went off and the observer reported the target

speed according to the instructions for his group. The experimenter

recorded the observer's response and initiated the next trial. Because

the target moved at different speeds, and therefore took different times

to reach the center of the grid, the inter-trial interval was varied

independently of the target speed. This prevented differences in intertrial interval from serving as a cue to target speed.

Each observer judged target speed in four blocks of 18 trials each.

Each block of 18 trials consisted of targets at one of three different

ranges (1000, 1500, or 2500 meters), moving at one of three different

simulated speeds (10, 15, or 25 miles per hour), and moving one of two

different directions (left or right). Each possible combination of

these variables occurred only once in a random sequence during each block.

During the first two blocks of 18 trials, observers received no

information about the target range. During the second two blocks, the

experimenter told observers the target's range before each trial to

determine whether range information produced a sharp improvement in

velocity judgments beyond practice effects accruing over successive blocks.

After all four blocks of trials, observers repeated the distance and

slant judgments of the display just as at the beginning of the session.

Page 5 of Appendix A indicates the exact procedure followed.

14

...... i a
RESULTS AND DISCUSSION

Distance and Slant Judgments. Figure 3A shows + one standard deviation about the mean width judgment of the display grid, calculated over

all observers' data. The dotted line indicates the correct width of the

display (1500 m). On the average, observers slightly underestimated the

display's width before being told the ranges at which the target appeared,

and slightly overestimated the width after receiving range information.

However, because of the extreme inter-observer variability, as reflected

by the standard deviations in Figure 3A, little emphasis should be placed

on these averages.

Figure 3B shows observers' estimates of the slant of the grid.

Large inter-observer variability also characterized slant responses, consistent with past research on slant perception (Stavrianos, 1945).

In addition to being highly variable, observers overestimated the 50 slant

of the ground over which the target moved by almost 300% on the average!

That is, they estimated that the grid representing the ground in the display

deviated much more from the horizontal plane than it actually did. This

result also agrees with past research on slant perception (see Gibson,

Gibson, Smith, and Flock, 1957).

Observers' estimates of the length of the grid (shown in Figure 3C)

and range estimates (shown in Figure 4) also reveal large intersubject

variability. The large amount of variability in these data is consistent

with that found in a good deal of past research on space perception (see,

for example, Stark, Wolff, and Haggard, 1961; Johansson, 1973; Eriksson,

1974). Observers' Judgments initially showed large deviations from the
A

-Before Range Information After kangt- Information

2000

1000

Near Mid Far Near Mi Fa

C

B 3500

S3000

S2S '500

20 ~.2000

is 1 1500

10 1 000

Before Range After Range Bfr ag fe ag

~~~Information Informatoniof~ tion

Figure 3. Width, Slant, and Length Estimates ofT

FCCS Grid. ISD

16
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go LI)n

CD CD 0

0 0 2 t

LLT.

17-
values represented in the display. Before observers received any range

information, the length of the grid was grossly underestimated; average

estimates were somewhat less than 50% of the actual length. The accuracy

of observer's length estimates increased after they received range information, as can be seen in Figure 3C. The tendency to systematically underestimate distance in the display persisted for judgments of target range,

and continued even after observers were told the ranges at which the target

appeared, as can be seen in Figure 4.

This tendency to underestimate distance (that is, for observers to

say that the target was closer than it should have appeared in the simulator) agrees with results of research on distance estimates in the field.

For example, Gibson, Bergman, and Purdy's (1955) untrained observers significantly underestimated target range. Examination of Stark, Wolff, and

Haggard's (1961) data shows that, over all groups, their observers tended

to underestimate target range, and that this tendency was marked at ranges

beyond 1,000 meters. Range underestimation also has been demonstrated for

aerial targets; Wright (1966) found that observers using unaided vision

consistently underestimated target ranges of less than 10,000 meters.

If the systematic range and distance underestimates in the FCCS had

not agreed with results from the field experiments cited above, one might

have attributed the errors and the large variability obtained in the present

research entirely to insufficient depth cues contained in the FCCS display.

Since the pattern of results obtained does not differ markedly from that of

some field research, it is not clear that adding depth cues to the FCCS

display would improve observers' distance or slant judgments. Adding

18
further depth cues to displays intended for moving target gunne training

may not be cost effective.

The present study, however, does not justify any decision on the display complexity required in a conduct-of-fire training simulator. The

adequacy of a simplified display for transfer of lead strategies remains

to be investigated. Furthermore, other aspects of gunnery performance,

such as battlesight technique, firing on-the-move, or fire adjustment may

require much more realistic depth cues or a higher level of detail.

Decisions on display complexity should be based on an assessment of requirements for effective training and transfer of all gunnery skill components.

Speed Judgments. Speed judgments by the Speed Magnitude Estimation

group showed large inter-observer variability, and remained variable over

all four blocks of trials. Figures 5 and 6 show the speed judgment data.

The dotted diagonals in these figures indicate perfect performance. Before

observers received range information, they consistently underestimated

target speed, but improved after they received range information. However,

the extreme inter-observer variability discourages much discussion based

on average performance. The large amount of variability in speed judgments

agrees with that found at Haglund and Torre (1978).

Observers in the Speed Categorization group identified target speed

as either fast, medium, or slow quite well. Table 1 shows the proportion

of correct identifications for each speed. It is clear from Table 1 that

the greatest proportions of confusions occurred between the slow and medium

speed groups. Since the difference between the slow and medium target

speeds was so small (5 mph), this was to be expected.

19
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TABLE 1

Proportion of Correct Identifications in Each Speed Categorization Group

ACTUAL PROPORTION OP RESPONSES IN EACH CATEGORY OVERALL

TARGET SLOW MEDIUM FAST PROPORTION

SPEED (10 mph) (15 mph) (25 mph) CORRECT

Block 1

(No Range

In formation)

10 mph *.714 .238 .048

15 mph .250 *.619 .131 .710

25 mph .012 .190 *.798

Block 2

(No Range

Information)

10 mph *.750 .250 .000

15 mph .167 *.798 .035 .817

25 mph .000 .095 *.905

Block 3

(Range

Information)

10 mph *.702 .274 .024

15 mph .155 *.738 .107 .774

25 mph .012 .107 *.881

Block 4

(Range

Information)

10 mph *.798 .202 .000

15 mph .131 *.833 .036 .849

25 mph .000 .083 *.917

*Indicates correct identifications.

a

22

'-wo *I
One cannot directly compare performance of the two groups, since the

kind of judgments made by observers in the two groups were different.

However, one can compare the performance of the two groups indirectly by

using their speed judgments as input parameters to a model of tank gunnery.

Inputting different speed judgment parameters allows calculation of predicted hit probabilities for different kinds of lead strategies.

Description of the Tank Gunnery Model. Converting speed judgment data

to expected hit probabilities first required the calculation of hit probabilities for various amounts of lead when applied to different speed

targets. These calculations were made for a model of tank gunnery incorporating the characteristics of the FCCS and making the following

assumptions:

1) A normal distribution of rounds about the lay point, having

a standard deviation of .32 mil horizontal and .28 mil vertical (from

Pfleger and Bibbero, 1969).

2) Normally distri,uted vertical lay errors, having a standard deviation of .1 mil (also from Pfleger and Bibbero, 1969).

3) Normally distributed horizontal tracking errors, having a standard

deviation of 1.0 mil (estimated from errors on a tracking task in research

1

by Obermayer, Swartz, and Muckler, 1961).

1The tracking errors reported were converted to mils and the standard deviation of that distribution was doubled since Obermayer, Swartz, and Muckler

used a well-defined tracking point in contrast to the absence of such a

point when tracking a moving target and applying lead with the M6OAI reticle.

Although 1 mil was used as an error of tracking performance with the device,

some evidence suggests that tracking error, and even vertical lay error,

would be much larger for camouflaged targets actively using cover and concealment (see Garry, 1974).

23

eWi
4) Error factors such as zeroing errors, wind, temperature, coriolis

effects, etc., were negligible.

Speed Judgment Data Applied to a Model of Tank Gunnery in the FCCS.

Predicted hit probabilities for the Chrysler FCCS firing APDS were calculated for four different hypothetical lead strategies: (1) a strategy in

which the gunner applies a single standard lead of 2.5 mils regardless of

target speed, (2) a lead calculation strategy in which the gunner estimates

target speed to the nearest 5 mph, and leads the target by 2.5 mils for

every 10 mph of target speed, as suggested by Bessemer and Kraemer (1979),

(3) a lead calculation strategy in which the gunner estimates target speed

to the nearest 5 mph, and leads the target by 3.5 mils for every 10 mph

of target speed, to optimally compensate for the tendency to underestimate

target speed, and (4) a 3-lead strategy in which the gunner judges the

target's speed to be either slow, medium, or fast and applies a 2.5, 5.0,

or 7.5 mil lead, respectively. The method for calculating estimated hit

probabilities is described in detail in Appendix B. In examining the estimated hit probabilities, two points should be kept in mind. First, the

hit probabilities predicted using the model apply only to the device and

would be much lower overall in any field tests of lead strategies; live

firing would involve error factors that were not incorporated into the

model for the sake of simplicity. Second, no data were actually collected

on the application of leads to moving targets. Hit probabilities were

determined by applying speed judgment parameters to the model while assuming

a relationship between judged speed and applied lead that would be appropriate for a given lead strategy. That is, the predicted hit probabilities

24
were derived under the assumption that a given lead strategy would be

exactly and accurately adhered to.

Predictions of hit probabilities were not made for a strategy requiring

gunners to classify target speed into one of two categories. The data

collected in this research do not allow statements about how accurately

observers can segregate target speeds into two broad categories. Furthermore,

if gunners accurately segregate target speeds into three categories, a twocategory strategy does not take full advantage of their perceptual capabilities.

Figure 7 shows the calculated hit probabilities for the four hypothetical lead strategies as a function of target range, combined over the three

target speeds used in this research. Figure 7 also shows optimal performance as limited by the error factors considered in the model, and optimal

performance using the strategy suggested by Bessemer and Kraemer (1979),

given speed judgments that are always correct to the nearest 5 mph.

According to the predictions of the model, the 3-lead categorization

strategy will produce approximately 20% more hits than the next best alternative over all three ranges. Furthermore, the model predicts that pelformance using the 3-lead strategy will be roughly equivalent to the best

performance possible when using the strategy of estimating target speed

to the nearest 5 mph and calculating lead using the formula suggested by

Bessemer and Kraemer. As Figure 7 shows, predicted performance using the

3-lead strategy approaches optimal performance within the limits imposed

by the error terms incorporated into the model.

25

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S Lfn Un (A

CLfn - U CD~4

E r= C

Q ~ Cd 0' Sf 0 LI) -4 Ln 0

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26
Figure 8 conveys a different aspect of performance; it shows predicted

hit probabilities as a function of target speed, with predictions collapsed

over all three target ranges. The prediction for the standard 2.5 mil lead

is superior to the other strategies for targets moving approximately 10 mph,

since there would be almost no variance and little error in applying the

standard lead at this speed. However, the rapid drop in predicted performance for this strategy with increasing target speed, and the low probability that targets will move only 10 mph on the modern battlefield make

it an undesirable strategy. Over all speeds, the model predicts that the

3-lead strategy will produce over 20% more hits than the next best alternative. Again, the prediction for the 3-lead strategy is close to optimal

performance.

Figure 9 illustrates an additional advantage of the 3-lead strategy;

the model predicts higher hit probabilities for the 3-lead strategy

initially, and shows that this superiority should continue over practice.

Observers in this research did not improve substantially in estimating

speed over the course of the experiment. It might take extremely long for

gunners to learn to estimate speeds well enough so that other lead strategies would even approach the performance expected from using the 3-lead

strategy, if they could even do so. Recall that nearly perfect judgments

of speed would be required using a lead calculation strategy such as that

proposed by Bessemer and Kraemer (1979) in order to match performance with

the 3-lead strategy.

27
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Some readers might object to the conclusion that performance using the

3-lead strategy would prove superior to performance using any of the other

lead strategies, since the parameters input to the model are based on data

from judgments on only three target speeds. One might be concerned about

the predicted hit probabilities for targets moving at speeds other than 10,

15, and 25 mph. To address this concern, the predicted hit probabilities

were calculated for a target moving at 5 and at 20 mph for all three

strategies.

Hit probabilities for the lead calculation strategy proposed by Bessemer

and Kraemer were calculated by first obtaining estimated means and standard

deviations of the distribution of speed magnitude judgments at 5 and 20 mph.

The means and standard deviations at these speeds were estimated from regression lines fit to the means and standard deviations of speed judgments

for 10, 15, and 25 mph target speeds. The regression equations were calculated separately for means and for standard deviations at each of the three

target ranges--resulting in six separate regression equations. Since there

were only three points on which to base each regression equation, the

regression li.es were forced through the origin based on the reasonable

assumption that stationary targets would always be perceived as stationary

so that resulting speed judgments would have both a mean and standard deviation of zero. Even with this restriction, the regression equations for the

mean speed judgments accounted for approximately 98% of the variance at all

three ranges. The regression equations for the standard deviations fared

almost as well; each accounted for at least 94% of the variance. Predicted

30
hit probabilities were then calculated for target leads based on the estimated distributions of speed judgments, as described earlier.

Hit probabilities for the 3-lead strategy were calculated by first

estimating the proportion of slow, medium, and fast categorizations of targets at each of the two speeds. The proportion of "slow" (i.e., 10 mph)

categorizations of a 20 mph target was estimated by the average proportion

of categorizations of fast speed targets (25 mph) as medium speed targets

(15 mph) observed over all four blocks of trials.2 The remaining proportion

of responses was assumed to be equally divided between "medium" and "fast"

categorizations. The proportion of categorizations of the 5 mph target as

"fast" was conservatively estimated by the proportion of categorizations

of slow speed (10 mph) targets as "fast" observed over all four blocks of

trials. The proportion of categorizations of 5 mph targets as "medium"

(i.e., 15 mph) was estimated from the average proportion of observed

categorization of medium speed targets as "fast" -- also a difference of

3

10 mph. The remaining proportion of categorizations was "slow."

The results of the estimated hit probabilities for targets moving from

5 to 25 mph are shown in Figure 10. One can see that although the hit probability for the 3-lead strategy drops at target speeds of 20 mph, it still

2This figure was used to estimate misidentification of a 20 mph target

as slow (10 mph) since it reflects confusions of two target speeds that

were also 10 mph apart. It should be a conservative estimate, since Weber's

law predicts that a given difference between two stimuli that have low

values on a scale should be more detectable than the same difference between

two stimuli that have high values on an equal interval scale. 3This estimate should be very conservative, for the same reason as that

given in footnote 2.

31
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32
stays well above that predicted for the strategy of calculating lead based

on an estimate of target speed in miles per hour. The strategy proposed

by Bessemer and Kraemer proves to be superior to a 3-lead strategy only for

very slow target speeds, as can be seen by the estimated hit probabilities

on a 5 mph target. The poor hit probability using the standard 2.5 mil

lead at target speeds of 15 mph and faster make it unacceptable.

Comparison of the lead calculation and 3-lead strategies on a 5 mph

target clearly brings home the point that overall, estimating target speed

in miler per hour produces poor performance because of observers' variability in estimating target speed. At extremely slow speeds, this variability in speed judgments is reduced, partly because the lower limit on

responses (0 mph) is so close to the actual speed (i.e., there is a basement

effect) and partly because in the current research the variability of speed

judgments was directly related to the mean.

The reason for the difference in performance between the two speed

estimation groups probably reflects the combined operation of two different

phenomena. First, the difference almost certainly reflects the operation

of an uncertainty effect. Recall that the Speed Categorization group could

make only one of three responses -- slow, medium, or fast. The Speed

Magnitude Estimation group, on the other hand, could make any one of 11

different responses between 0 and 50 mph inclusive; they were more uncertain

about the stimulus that would occur (and hence which response they should

make) and tended to use a broader range of the responses available than was

warranted by the stimuli. This tendency is reflected by the fact that the

33

!W
Speed Magnitude Estimation group assigned an average of slightly over 4.1

different speeds to targets in each block of trials, even though only three

different target speeds were presented. The uncertainty effect operated

by producing highly variable responses within the responses of individuals

in the Speed Magnitude Estimation group. Limiting the number of allowable

responses to three for the Speed Categorization group avoided the large

variability by restricting the fineness with which observers attempted to

make the speed discriminations.

Second, the difference in performance of the two groups reflects the

perceptual system's facility in processing relative information and inaccuracy in processing absolute information (see Gogel, 1977; Kottas, 1978).

A major problem in making absolute judgments seems to be one of calibrating

responses correctly, provided that the cues for ordering stimuli are

available. Expressing speed judgment performance in terms of hit probabilities makes it clear that a categorization strategy would be the most

effective for training gunners using simplified displays, as in the FCCS.

While the results indicate that training gunners to discriminate target speeds within three broad categories will provide more effective performance than attempting to train them to estimate target speed to the

nearest 5 mph in the simulator, the results are not definitive for application in the field. The simulated targets in this research only moved at

one of three different speeds; different results may be obtained when targets are allowed to move at speeds other than 10, 15, and 25 mph, or are

allowed to move at any speed. Increasing or decreasing the number of

34
categories into which observers must classify target speeds may influence

the accuracy of their judgments, and affect transfer to field conditions.

While fewer discrimination categories should lead to fewer confusions among

categories, reducing the number of categories would reduce the number of

leads used and would lead to less efficient coverage of the entire continuum

of target speed. Using more leads would cover the speed continuum more

effectively, but increasing the number of speed judgment categories should

produce more confusions among categories (see Lappin and Uttal, 1976).

Despite these questions, a categorization strategy has certain intuitive advantages over either a single-lead, or lead calculation strategy,

all of which required empirical test. First, requiring trainees to learn

one lead for each of several target speed categories seems more reasonable

than attempting to teach trainees formulas that they must use under extreme

stress to calculate the amount of lead needed. Second, because of the

stress and time constraints during combat, speed judgments overall are

likely to be worse than those obtained during research, and may render a

Speed Magnitude Estimation strategy totally ineffective. Third, the

reticle markings on the M60Al provide easy references for a few lead

categories (2-1/2, 5, and 7-1/2 mils for APDS, 5, 10, and 15 mils for

HEAT). Trainees will certainly experience difficulty in attempting to

lead targets at other than these well-defined marks; more tracking error

will be introduced when intermediate reference points are used, as would

occur often for a lead calculation strategy. Fourth, allowing gunners to

use only a few memorized leads should be much faster than requiring them

35

,i ~ - ';.. -~,- - - ~ -
to calculate leads. Fifth, a speed categorization strategy has a much

higher first round hit probability than a single-lead strategy. A singlelead strategy rests heavily on the ability of gunners to adjust fire using

techniques such as Burst-on-Target (BOT) to achieve a second round hit.

In order for BOT against a moving target to be optimally effective, the

first round must be sensed accurately, and the target must not change speed

or direction, stop momentarily or become obscured after the first round

impacts. On the modern battlefield, these conditions may not always be

met. Furthermore, most sources indicate that applying BOT results in a

25% increase in second round hit probability, at best (see, for example,

Hannig, 1979). If one refers to Figures 7 and 8 again, it is clear that

averaged over all target speeds and ranges, the hit probability obtained

using the standard lead on the first round and BOT on the second still

fails to reach the first round hit probability predicted for a categorization strategy.

Although a categorization strategy of some kind should be very effective, the number of speed categories that will provide optimal performance

against targets moving at speeds representative of the 1980's battlefield

remains to be determined through empirical research. Four speed categories

(i.e., 10, 20, 30, and 40 mph) may be optimal for use against modern threat

armored vehicles, but this remains to be tested empirically. It is also

important to stress that the optimal number of speed categories for applying

lead is not simply the number of categories that observers can discriminate

perfectly. Discriminating two categories perfectly, for example, would

36

| _
probably not be as effective as discriminating among more categories with

some small amount of error. On the other hand, the time required to fire

the first round is also a factor to be considered in determining the number

of categories to be used. As the number of categories increases, one

would expect longer times to elapse before firing the first round. An overwhelming amount of psychological literature demonstrates a tradeoff between

reaction time and the number of alternatives to be discriminated (for

example, see Sternberg, 1966). Both the value of various lead strategies

with respect to speed-accuracy tradeoff and the utility of Speed Categorization strategy demand careful empirical research and field validation.

Although a categorization strategy has certain intuitive appeals, it

cannot be overemphasized that empirical research must be done in the field

before suggestions are made to install it in the Army's gunnery training.

A complete test would require pitting a categorization strategy against

other strategies in a live firing exercise with variable-speed targets.

Finally, we must stress that this research does not demonstrate training effectiveness of the FCCS. Gunnery training with evasive targets,

training of battlesight techniques, training of fire adjustment techniques,

and other important gunnery skills have not been addressed in this research.

Validation of the training capability required by a general purpose conduct

of fire trainer demands further research, both in the laboratory and in the

field.

37
REFERENCES

Bessemer, D. W., and Kraemer, R. E. Development of Tank Gunnery

Performance Prescriptions for M6OAI Main Gun Target Engagements.

(Draft Research Report). Alexandria, VA: US Army Research Institute

(Ft Knox Field Unit), 1979.

Brown, J. F. The visual perception of vclocity. Reprinted from

Psychologiche Forschung, 1931, 14, 199-232, In Spigel, I. W. (Ed.),

Readings in the Study of Visually Perceived Movement. New York:

Harper & Row, 1965.

Eriksson, E. S. Movement parallax during locomotion. Perception and

Psychophysics, 1974, 16, 197-200.

Epstein, W. Two factors in the perception of velocity at a distance.

Perception and Psychophysics, 1978, 24(2), 103-114.

Garner, W. R. Uncertainty and Structure as Psychological Concepts.

New York: John Wiley and Sons, Inc., 1962.

Garry, T. A. Tank Gunners' Aiming Performance Against Clear Versus

Indistinct Targets (IHEL Technical Memorandum 13-74). Aberdeen Proving

Ground, MD: US Army Human Engineering Laboratory, July 1974.

Gibson, E. J., Bergman, R., and Purdy, J. The effect of prior training

with a scale of distance on absolute and relative judgments of

distance over ground. Journal of Experimental Psychology, 1955, 50,

97-105.

Gibson, E. J., Gibson, J. J., Smith, W. W., and Flock, H. Motion

parallax as a determinant of perceived depth. Journal of Experimental

Psychology, 58(l), 1959, 40-51.

Gibson, J. J. The Perception of the Visual World. Boston: Houghton

Mifflin, 1950.

Giordano, D. J. Simplified Procedures for Engaging hving Targets

with the M72A2 LAW (HEL Technical Memorandum 28-76). Aberdeen

Proving Ground, MD: US Army Human Engineering Laborato-y, November

1976.

Gogel, W. C. The metric of visual space. In W. Epstein (ed.),

Stability and Constancy in Visual Perception: Mechanisms and Processes.

New York: John Wiley and Sons, Inc., 1977.

39

-:i
Gogel, W. C. and Tietz, J. D. The effect of perceived distance on

perceived movement. Perception and Psychophysics, 1974, 16, 70-78.

Haglund, N. and Torre, J. P. Prediction of Target Travel during

Missile Time of Flight: A Computer Simulation (HEL Technical

Memorandum 17-78). Aberdeen Proving Ground, MD: US Army Human

Engineering Laboratory, June 1978.

Hannig, N. Can western Europe be defended by conventional means?

International Defense Review, 1979, 1, 27-34.

Johansson, G. Monocular movement parallax and near-space perception.

Perception, 1973, 2, 135-146.

Jones, M. J. and Jehan, H. I. A Procedure to Improve Gunner Skills

Analysis Based on HIMAG IIA Extended Report to Tank Gunner Training.

Ft Knox, KY: US Army Armor and Engineer Board, January 1978.

Kottas, B. L. The Visual Three-Dimensional Information from Motion

Parallax. Unpublished Doctoral Dissertation, Vanderbilt University,

Nashville, Tennessee, August 1978.

Lappin, J. S. and Uttal, W. R. Does prior knowledge facilitate the

detection of visual targets in random noise? Perception and

Psychophsics, 1976, 20, 367-374.

Obermayer, R. W., Swartz, W. F., and Muckler, F. A. The interaction

of information displays with control system dynamics in continuous

tracking. Journal of Applied Psychology, 1961, 45, 369-375.

Pfleger, K. R. and Bibbero, R. J. The Evaluation of Combat Vehicle

Fire Control/Gunnery Systems. Frankfort Arsenal: Department of the

Army, September 1969. AD 866-471

Stark, E. A., Wolff, P. C., and Haggard, D. F. Target Detection:

Study I, A Preliminary Investigation of the Trainability of Target

Detection and Distance Estimation Skills (HumRRO Research Memorandum).

Alexandria, VA: Human Resources Research Organization, July 1961.

Stavrianos, B. K. The relation of shape perception to explicit

judgments of inclination. Archives of Psychology, New York, 1945,

No. 296. Cited in Woodworth, R. S., and Schlosberg, H. Experimental

Psychology, New York: Holt, Rinehart, and Winston, 1954.

Sternberg, S. High-speed scanning in human memory. Science, 1966,

153, 652-654.

40

£
US Army Infantry oard. Force Development Test and Experimentation of

M72A2 LAW Training Effectiveness Analysis. TECO! Project No. 8-MU014-072-046, Ft B'nning, GA, December 1975.

US Army. Tank ' .nery for M60, M60AI, M6OAl(AOS), and M48A5 Tanks.

Field Manual P .7-12-2. Washington, DC: Headquarters, Department of

the Army, March 1977.

US Army. TRADOC Bulletin 5. Training with LAW. Fort Monroe, VA,

June 1976.

Wright, A. D. The Performance of Ground Observers in Detecting,

Recognizing, and Estimating Range to Low-Altitude Aircraft (HumRRO

Technical Report 66-19). Alexandria, VA: Human Resources Research

Organization, July 1961.

41

II . . . , . * .. 't --= -_ " . . . .. " ' ' :, , i/'¢ ' ' ' ''

'-° ' ' " ' '" V
APPENDIX A

INSTRUCTIONS FOR VELOCITY JUDGMENT STUDY CHRYSLER FCCS

1. Conditions for the Firing Tank remain the same for all engagements:

SPEED, stationary; TERRAIN, Smooth; AMMO, SABOT, Initially set the

target RANGE to 1500 meters, the target SPEED to 0, and the target

DIRECTION to HEAD ON.

2. Tell each subject:

I'm going to ask you to tell me some things about how the picture

looks in this gunnery simulator. Don't fire the main gun, juast move it

around and get a feel for the size and slant of that grid in front of

you. That box represents a tank-sized object.

First, tell me approximately how wide the closest part of that grid

is (to the nearest 50 yards or 50 meters, whichever you prefer).

About how wide is the farthest part of that grid?

About how wide is the middle of that grid?

About how long, to the nearest 50 yards or 50 meters (whichever you

prefer) is the grid?

How much is the grid slanted, if 00 is flat against the ground and

900 is straight up and down (your answer can be anywhere between 0 and

900)?

About how far, to the nearest 50 yards or 50 meters, does that tanksized object appear to be?

The experimenter should then readjust the RANGE to 2500 meters and ask:

About how far, to the nearest 50 yards or 50 meters, does that tanksized object appear to be now?

The experimenter should then readjust the RANGE to 1000 meters and

ask:

About how far, to the nearest 50 yards or 50 meters, does the tanksized object appear to be now?

Now I'm going to show you some displays of tank-sized objects moving

either to the right or left. I want you to simply tell me how fast it is

moving after each display goes off.

43

- - -- 1
Instructions for Group A:

The tank-sized objects can move at one of three different speeds:

a slow speed (10 mph), a medium speed (15 mph), and a fast speed (25 mph).

It will move across your field of view for approximately 5 seconds, at

different distances from you. Tell me how fast it is going after each

trial. Do you have any questions?

Instructions for Group B:

The tank-sized object can move at any speed between 0 and 50 mph. It

will move across your field of view for approximately 5 seconds, at

different distances from you. Tell me to the nearest 5 mph how fast it

is going after each trial. Do you have any questions?

44

...................
DATA SHEET

VELOCITY JUDGMENT STUDY CHRYSLER COFT

NO RANGE INFORMATION

ENGAGEMENT SPEED DIRECTION RANGE JUDGED VELOCITY

________ 10 L 1000 _ _____

________ 10 L 1500 _ _____

________ 10 L 2500 ____

________ 10 R 1000 ________

_______ 10 R 1500 _ _____

_______ 10 R 2500 _ ______

______is 1 L 1000 _ _______

______is 1 L 1500 ________

______is 1 L 2500 ________

______is 1 R 1000 ________

________ 15 R 1500 _ _______

______is1 R 250 S_______

________ 25 L 1000 ________

________ 25 L 1500 ________

_____2__F_ L 2500 _ _______

________ 2:;) R 1000 _ _ _ _ _ _ _ _

__ __ __ 25 R 150_________

________ 25 R 2500 ________

__ __ __ _10 L 1000 _ ______

________ 10 L 1500 ________

__ __ __ _ 10 L 2500 _ _ _ _ _ _ _

________ 10 R 1000________ _______ 10 R 1500 _______

__ __ __ 10 R 2500 _ _____]

___ __ _is1 L 1000 _ _ _ _ _ _ _

_____is1 L 1500 ______

______is 1 L 250 S________

__ __ __ _ 15 R 1000 _ _ _ _ _ _ _

______is 1 R 1500 ________

______is1 R 250 S_______

__ __ __ _ 25 L 1000 _ ______

________ 25 L 1500 ______

________ 25 11 2500 _ _______

_______ 25 R 1000 _ ______

________ 25 R 1500 ________

________ 25 R 2500

45
GIVE RANGI- INFORHATION!

ENGAGEMIEINT SPII) 1) I RECTI ON RANGE IUIDGII ) VELOCITY

10 1500

1 1. 2500

i 0 P, 1000

10 R 1500

10 R 2500

15 L 1000

15 L 1500

15 L 2500

15 R 1000

15 R 1500

15 R 2500

25 L 1000

25 L 1500

25 L 2500

25 R 1000

25 R 1500

25 R 2500

10 L 1000

10 L 150

10 L 25O0

1 10 R 1000

10 R 1500

10 R 2500

15 L 1000

15 L 1500

15 L 2500

15 R 1000

15 R 1500

15 R 2500

25 L 1000

25 L 1500

25 L 2500

25 R 1000

25 R 1500

25 R 2500

46
Reset RA4Ui. to 15oom, SPEED to 0, and DIRECTION to HEAD ON.

Again, how wide is the closest part of that grid?

How wide is the farthest part?

How wide is the middle?

How long, in yards, or meters is th ' grid?

How much is it slanted (again, between 0 and 90 )?

How far is that tank-sized object?

Reset RANGE to 2500m and ask:

How far is it now?

Reset RANGE to lO00m and ask:

How far is it now?

47

i.

--.. C .
APPENDIX B

CALCULATION uF FCCS EXPECTED HIT PROBABILITIES

Hit probabilities were calculated for the strategy of using a single

standard lead. This straightforward calculation yielded expected hit

probabilities with a 2.5 mil lead for each target speed, and at each of

the three ranges at which speed judgments were made (1000, 1500, and 2500

meters).

Hit probabilities were also determined for the lead calculation

strategy suggested by Bessemer and Kraemer (1979) using speed estimation

data from the Speed Magnitude Estimation group. Since estimates of target

speed varied, there was actually a distribution of estimated target speeds

for each physical target speed. These distributions of speeds converted

to hypothetical distributions of lead when estimated target speeds were

divided by 10 mph and the result multiplied by 2.5 mils, as suggested by

Bessemer and Kraemer. This yielded a distribution of applied leads, and

hence a distribution of hit probabilities for each physical target speed.

For the sake of convenience in calculating overall hit probabilities, the

experimenters assumed a normally distributed population of speed estimates,

and hence a normally distributed population of hit probabilities for each

target speed. Summing hit probabilities weighted by their probability of

occurrence'across small discrete intervals of the distribution of hit

probabilities produced a single estimated hit probability for each target

speed at each of the three target ranges.

This process was repeated with the data from the Speed Magnitude

Estimation group, but using a multiplicative factor of 3.5 mils when converting speed estimates to lead distributions. The factor of 3.5 mils

compensated optimally for observers' tendency to slightly underestimate

target speed overall.

Finally, estimated hit probabilities were calculated for a 3-lead

strategy in which a gunner judges whether the target's speed is either

slow, medium, or fast and applies a 2.5, 5.0, or 7.5 mil lead, respectively.

Hit probabilities for this strategy were calculated from the data of the

Speed Categorization group by simply multiplying the proportions of slow,

medium, and fast responses to a given physical target speed by hit probabilities for respective leads of 2.5, 5.0, and 7.5 mils given that target

speed. Summing these products for each physical target speed yielded an

expected hit probability for targets moving 10, 15, and 25 mph at each of

the three target ranges.

49

I
DISTRIBUTION

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51
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52j

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