ARL-TR-2885 FEBRUARY 2003

The Human Dimension of Battlespace

Visualization: Research and Design Issues

Michael J. Barnes

Approved for public release; distribution is unlimited.

ARMY RESEARCH LABORATORY
NOTICES

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Department of the Army position unless so designated by other

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DESTRUCTION NOTICE Destroy this report when it is no longer

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Army Research Laboratory

Aberdeen Proving Ground, MD 21005-5425

____________________________________________________________________________

ARL-TR-2885 February 2003

____________________________________________________________________________

The Human Dimension of Battlespace Visualization:

Research and Design Issues

Michael J. Barnes

Human Research & Engineering Directorate

____________________________________________________________________________

Approved for public release; distribution is unlimited.

____________________________________________________________________________
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Contents

List of Figures iii

1. Background 1

2. Overview and General Model of Visualization 2

3. Current Situational Understanding 3

3.1 Terrain ............................................... 3

3.2 Displaying Tactical Information............................... 7

3.3 Abstract Processes ........................................ 11

4. Prediction 13

4.1 Visualization and Decision Making During Uncertainty ................ 13

4.2 Naturalistic Decision Making and Mental Simulation.................. 15

4.3 Visualization Issues for Supervisory Control of Automated Battlefields ...... 17

4.4 Visualization and Prediction in Complex Military Environments .......... 20

5. Conclusions 23

6. References 26

Report Documentation Page 33

List of Figures

Figure 1. Deterministic, probabilistic, and complex decision processes for visualization

of future events ........................................... 2

Figure 2. Views (2-D and 3-D) of the same terrain for the integrated display experiments

(St. John et al., 2002) ....................................... 6

Figure 3. SPAWAR knowledge wall concepts (Oonk et al., 2001) ................. 10

Figure 4. Risk management visualizations for missile defense simulations (Barnes,

Wickens, & Smith, 2000) .................................... 14

Figure 5. Three-dimensional visualizations showing possible options combined with

predictive software and configural displays showing the consequences of the

animated vignettes (Rozenblit et al., 2000) ......................... 22
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1. Background

Visualizing future battlespaces (including decidedly nonconventional ones) is an important goal

for the Objective Force and Future Combat Systems (FCS). Technological advances will result in a

knowledge-rich battlefield with virtual planning, multi-modal visualizations, disbursed operations,

and highly intelligent automated systems. Too often, however, technological advances are designed without enough consideration for the warfighters who use them (Barnes, 1997). Obviously,

the effects of visualization will cascade over the entire battlefield and will influence the common

and specialized views of the battle at all echelons. Yet the demand for improved technology

outpaces understanding of the benefits of the various techniques. The purpose of this report is to

develop an understanding of the theoretical and empirical underpinnings of visualization research

in order to create design principles for future military visualization systems. Visualization is the

process that commanders use to envision new tactical alternatives. It is a bridge between human

knowledge and “seeing” new solutions. However, the ability to visualize is based on heuristic

processes that have cognitive costs as well as benefits. Effective synergy between humans and

visualization systems must be based on design principles that engender human strengths and

limitations.

The U.S. Army definition of battlespace visualization implies not only “imaging” the battlespace

but also more significantly generating objectives and possible tactics to achieve these objectives:

The process whereby the commander develops a clear understanding of his

current state with relation to the enemy and environment, envisions a desired end

state which represents mission accomplishments, and then subsequently visualizes

the sequence of events that will move his forces from the current state to the end

state (Department of the Army, 1997).

Thus, it is important not to confuse visualization with the depiction of battlespace information.

Understanding the topographical, spatial, and force dispositions on the battlefield is a necessary

component of visualization but is not sufficient. The ultimate purpose of visualization aids is to

increase the commander’s ability to understand the battle dynamics, consider options, and predict

outcomes. For that reason, I have included a number of topics related to decision making in future

battlespaces, including visualization issues raised by uncertainty representation, naturalistic

decision making, automated systems, and complexity theory. The purpose of this report is to

identify important visualization issues that impact the commander’s ability to understand and

predict the combat situation. The discussion focuses on design and research issues likely to impact

FCS.
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2. Overview and General Model of Visualization

Figure 1 portrays the visualization process from the beginning of the planning procedures to

understanding the current state to mental projections of possible future states. Visualization is not

concerned solely with the perception of the current combat environment. It is both historical and

teleological; commanders are continuously planning and executing, based on past trends and

their relation to the desired “end state.” Perception is an important component of visualization,

but in reality, visualization is more concerned with problem solving than with immediate

awareness. However, the type of decision space in which the commander must operate is not

straightforward. To illustrate the type of decision processes that the commander uses to

“visualize the sequence of events that will move his forces from the current state to the end

state,” three basic decision types are shown as solid, short dashes, or long dashed lines. The

expert commander quite often is able to visualize the battlespace in terms of a single preferred

solution (Klein, 1999), and this type of visualization process is shown as the solid line

representing an almost deterministic process.

Figure 1. Deterministic, probabilistic, and complex decision processes for visualization of

future events.

However, many decision processes and much of the traditional decision research assumes that

the decision maker considers probabilities associated with multiple options (shown as dashed

lines). For this class of decisions, the commander must consider a set of options, uncertainties,

and consequences, all subject to cognitive biases that can distort the visualization process

(Wickens & Hollands, 2000). Finally, decisions must be made in which there are neither single

solutions nor probabilistic ones. These decisions are those that must be made in complex or

chaotic decision spaces. The long dashed line is meant to convey the notion that not all the

options are known or the decision space is so volatile that a stable probabilistic structure does not

exist (Waldrop, 1992). In truth, actual combat decisions may not fit neatly into any of these

categories. However, the categories subsume important research topics in decision making and

Initial Point Current Situation

Emerging Trends

Unexpected Event

Perceived Option 1

Favored Option

Perceived Option N

Unexpected Event

Initial Point Current Situation

Emerging Trends

Unexpected Event

Perceived Option 1

Favored Option

Perceived Option N

Unexpected Event
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the distinctions are useful in the delineation of many of the cognitive issues that impact human

visualization processes.

Two basic processes underlie visualization: current situational understanding and prediction.

Situational understanding entails the processes used to visualize (and understand) the current

situation. The visualization technologies to support situational understanding in a military

environment are fairly well developed; unfortunately, the human performance implications are

not as well defined. The focus in these sections is on the human and operational design issues for

the various technologies including terrain visualization systems, military symbology, knowledge

walls, and some of the more recent cognitive concepts for abstract visualizations. Although the

emphasis is on military related research, basic cognitive and human processing research is

discussed whenever appropriate. The second major division is prediction. Prediction includes

those processes that permit the warfighter to “see” possible solutions in order to achieve end

states. Discussion entails design issues related to the decision processes outlined in Figure 1. In

particular, military research based on traditional heuristic models of probabilistic decision

making is contrasted to results based on naturalistic decision-making (NDM) models. I conclude

that the two methods represent complementary approaches to understanding the role of visualization in prediction: NDM research indicates the efficiencies of expert decision making, whereas the heuristic research indicates the other side of the coin: cognitive biases. Visualization

processes related to automation are discussed as a binary decision involving biases toward

manual and automated choices, depending on the quality, presentation mode, and type of information presented. Finally, complex military environments are discussed in terms of various

decision aiding and visualization techniques that are being developed to reduce the complexity in

these environments. Again, the purpose of the report is to help designers and researchers understand the human issues related to visualizing future battlespaces. Design principles are suggested

when the data so warrant; in other cases, unresolved research issues are discussed in their stead.

Also, visualization is a large topic area; by necessity, this report covers only a limited portion.

3. Current Situational Understanding

3.1 Terrain

The history of warfare is replete with examples of the use of successful tactics that depend on a

thorough understanding of the local terrain. From Vicksburg to the Plains of Abraham to the

African deserts, victory depended on the use of terrain characteristics as part of the battle plan.

Modern display technology allows the operator to see realistic views of the battlespace that use

multiple angles, altitudes, and dimensional options. The visualization issue is to determine how

well combatants perform military tasks when they use different terrain-viewing technologies.

Dimensionality has been researched thoroughly in the aviation domain. Most of the research
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involves a comparison of two-dimensional (2-D) planar views with three-dimensional (3-D)

rendered views while slant range and angle are varied. The general finding is that there are as

many tasks in which 3-D displays impede performance as those for which they enhance performance (Banks & Wickens, 1999). This is not surprising; projecting a 3-D representation onto

a 2-D surface results in space distortions, ambiguous locations along the line of sight (LOS), and

foreshortening phenomena (St. John, Cowen, Smallman, & Oonk, 2001). For example, Banks

and Wickens reported that in judging the relative positions of aircraft, most studies showed

superior performance for 2-D formats, which indicated difficulties in keeping track of both the

vertical and horizontal information for the 3-D displays. In contrast, they found that when the

task involved terrain understanding, 3-D resulted in better performance. Naval researchers also

found many useful applications for 3-D renderings of littoral operations such as mine warfare,

command and control and air operations (Eddy & Kribs, 1999).

Specific Army-related experiments involved map tasks that used experienced cadres from the

United States Military Academy. In a series of experiments, the cadres performed various tactical map tasks using 3-D oblique, immersed 3-D, or 2-D planar terrain views rendered on a

silicon graphics system. For simple map tasks, distance judgments were superior with 2-D views,

mobility judgments were unaffected by view type, and LOS judgments showed increased accuracy with an immersed 3-D view of the mountainous terrain. For tasks that required tactical

decisions, immersion in the terrain appeared to cause cognitive tunneling among the surrogate

tank commanders. The participants ignored important information that was not in the frontal

field of view, even when this information was available on an insert; this was partly attributable

to the difficulty of switching attention between two displays (Wickens, Thomas, & Young, 2000;

Thomas & Wickens, 1999). In general, being immersed in the problem space has cognitive costs

as well as benefits; it is easy for the operator to narrow his or her attentional focus and ignore

important peripheral information. Other research has generalized these findings to more abstract

3-D domains which indicate that being immersed within the problem space helps one in navigating through and remembering local features; however, if the task is to comprehend global

features of this space, then “god’s eye” exocentric views (views from outside the problem space)

proved superior (McCormick, Wickens, Banks, & Yeh, 1998).

U.S. Navy researchers investigated the dimensionality issue (2-D versus 3-D) as well and found

very similar trends using terrain-related and abstract tasks. They concluded that 2-D displays

were superior for understanding relative positions in space but that 3-D rendered views aided in

tasks that required understanding terrain contour and depth cues (e.g., LOS judgments) (St. John

et al., 2001). In summary, the research indicates that no one view of the terrain is superior in all

situations. For force disposition and an overall tactical understanding, 2-D planar views have the

advantage of simplicity and veridical scale. On the other hand, an understanding of the terrain

itself, its general topography, and possible high and low spots is enhanced by 3-D views. Situations that require mobility through the terrain (such as rehearsal during the planning process)
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benefit from 3-D immersed displays with the caveat of the lowering of the attentional bandwidth

of the observer (Wickens & Hollands, 2000).

The problem is more complicated when 3-D displays are used for portraying dimensions other

than spatial ones. For example, Hollands, Pierce, and Magee (1998) investigated the ability of

observers to track vehicular movements on the X-Y plane with time forming the third potential

dimension. Judgments about the convergence or divergence of and distance between the two

vehicles were generally more accurate for 2-D versus 3-D presentations, which again suggests

that costs are associated with processing a third dimension. In an ensuing study, mesh and line

enhancements were added to the 3-D representation; the results indicated a clear benefit for the

enhanced 3-D conditions in contrast to the 3-D control and the 2-D conditions (Meserth &

Hollands, 1999). What was disquieting about the latter study was the evidence of optical illusions related to curvature, which caused the observer to be biased toward convergence judgments

when the vehicles were close together. Interestingly, the type of illusion seemed to interact with

the type of enhancement. Problems with linear perspective apparently caused a divergence bias

for vehicular tracks that were widely separated for conditions where a line was inserted between

the tracks as a visual cue. This is important because the original intent of the research was to

show that emergent cues aided in trend judgments; the experiments also suggest that emergent

cues can display illusionary information.

The point to all this is that multiple viewing capabilities will be required for any sufficiently complex battlespace to respond to changing task requirements and to monitor various battlespace

slices simultaneously (Barnes & Wickens, 1998). The commander will need to switch and compare battle views much like the television networks do in the so-called Monday night football

paradigm (the television camera operator chooses the best view from multiple camera shots). The

narrative of the battle situation must be maintained while views are switched something that has

proved to be a significant human performance issue. Attempting to maintain the correspondence

between two views is difficult; doing the same thing among multiple views will tax the observer

to an even greater extent. Somehow, a cognitive narrative needs to be woven so that the observer

sees the different views as components of a perceptual whole (cf, Woods’ notion of visual

momentum; Woods, 1984). For military tasks that required combining and switching between

2-D and 3-D views, the Navy-sponsored researchers investigated five view correspondence

strategies and found that two of the approaches were the most promising. Interestingly, the

obvious solution of a side-by-side view was not effective because of the view-switching problem

alluded to previously (see Figure 2). Based on operator performance for both the antenna-placing

task and the terrain correspondence test, the two superior options were the time “morph” (i.e.,

gradually shifting software views) and the overlay displays. The time morph allowed the observer

to replace a 2-D topographic map with a 3-D representation for a 2-second duration by holding

down the space bar. The overlay option combined the 2-D topographic and the 3-D renderings on

the same display. More research needs to be done to establish general principles and methods for

view switching, but the initial research indicates that a number of clever software techniques may
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help humans maintain correspondence among multiple views of the battlespace (St. John,

Smallman, & Cowen, 2002).

Figure 2. Views (2-D and 3-D) of the same terrain for the integrated display experiments (St. John et al.,

2002).

This research pertained mainly to 3-D rendering on a flat surface. There are, however, two additional techniques that produce 3-D visualization effects: motion-induced cues and stereopsis

(Kaiser & Profitt, 1992). At this time, few 3-D military displays use stereopsis or motion as

techniques for producing 3-D; this should change as virtual technology becomes more accessible

and the equipment required to obtain these effects becomes more cost efficient. Researchers

investigating the different types of 3-D views found that combining induced motion and stereoscopic cues with monocular cues strengthened the perceived 3-D effects in a linear fashion, that

is, each cue improved depth perception in an additive fashion (Sollenberger & Milgram, 1993).

Although these cues show performance gains for certain tasks, they also have their drawbacks,

including the obvious ones such as the specialized headgear necessary for some 3-D applications

(Kaiser & Profitt, 1992; Wickens & Hollands, 2000; Yeh & Silverstein, 1992). The benefits of

creating a virtual 3-D world with motion and stereoscopic cues need to be carefully assessed

against possible costs. Costs associated with 3-D in general and alarming reports of simulation

sickness in particular need to be investigated more thoroughly before these technologies are

widely adopted in military environments. The relationship of these technologies to human

performance is just beginning to be understood. Wickens and Hollands outline a variety of

performance costs for virtual environments (VE): serious time lags between the observer’s

actions and VE, perceptual illusions, observer disorientation, and simulation sickness. As the

technology improves, some of these costs will be ameliorated, but the prevalence of motion

sickness symptoms with more conventional flight simulators (with only visual cues) should

remind the designer that 3-D VE might cause serious disruptions if the human-related issues are

not evaluated thoroughly.
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3.2 Displaying Tactical Information

The military situation map and the use of standard symbols have been part of U.S. military tradition at least since the Civil War. These representations have allowed commanders and staff the

ability to plan, discuss, and even rehearse the coming battle with virtually no technological

overhead. The present discussion focuses on the perceptual and cognitive requisites for using

standard military symbols as a visualization tool. Searching for target symbols is among the most

primitive tasks that the commander performs in order to use the situation map, and yet, human

performance of this seemingly simple task is fairly complex. In general, search time and accuracy degrade as a function of the total amount of information on the situation display (Teichner

& Krebs, 1974; Teichner & Mocharnuk, 1979; Wickens & Hollands, 2000). However, at the

same time, humans have developed a number of specialized mechanisms that allow them to

process large amounts of information efficiently. The best understood of these skills is preattentive processing which early investigators noted in both the visual and auditory domains (cf,

Neisser, 1967). Basically, humans scan their environment to note important target items (usually

something out of place) without focusing attention on any particular item. This mechanism

allows humans to process complex scenes while focusing on important change cues. The same

processes can be used to search for target items in a complex display as long as the target item

contains features that contrast with the non-target items. Pre-attentive processing (defined as

search time being unrelated to the number of non-targets in the display) is particularly efficient

because we can detect the target feature in the general clutter with minimal interference from

non-targets. There are even cases when the target symbol is searched for more rapidly as the

number of non-targets increases (Bacon & Egeth, 1991) (presumably because the non-targets

create a contrasting background from which the target feature “pops out”).

However, the number of symbol sets in which this is possible is limited. For most symbol

searches, humans use focused attention, which is more time consuming and less efficient.

Military symbols that are similar to other symbols or those for which the warfighter must tally

more than a single attribute (e.g., unit type and status) must be searched for with the slower but

generally more accurate focused search strategy. Apparently, this is a not an all-or-nothing

phenomenon because certain target cues (such as color and size contrast) improve search rates

among non-targets (shallower slope of search time versus number of items) even when the

operator is searching for more than a single attribute (Treisman, 1982; Treisman & Paterson,

1984; Treisman & Sato, 1990). Even more interesting is the finding that pre-attentive search

processes can respond to cognitive as well as perceptual information. For example, the location

of a novel word is processed more rapidly when it is embedded in a set of familiar words and

digits seem to “pop out” of displays that contain mostly letters (Johnston, Hawley, & Farnham,

1993; Teichner & Krebs, 1974). A related phenomenon is the “automatic” processing capability

that allows humans to search for more than a single target type without sacrificing performance

efficiency. In general, each additional symbol type that the operator searches for puts additional

demands on short-term memory (e.g., simultaneously scanning for Taliban strongholds and
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Northern Alliance defense structures). However, if the relationship between potential targets and

responses is extremely well learned, then searching for multiple targets simultaneously is not

affected by the additional memory requirement (Schneider & Shiffrin, 1977). Both of these

phenomena have important visualization implications. The most important symbols should be

chosen carefully to be easily discriminable from other display symbols (and their background),

and the relationship between the symbol and the semantic response needs to be consistent and

well practiced. A good situation display is one in which the commander can notice change

instantly and be able to keep track of multiple situations concurrently with minimal cognitive

loading. Other cueing techniques such as blinking and sound augmentation can be used to

emphasize important changes, but in general, these techniques can be annoying and are apt to be

ignored if they are used.

Of all the codes used for modern symbology, color codes seem to have the most attractive

qualities. Color coding and color backgrounds are a favorite visualization technique to segregate

boundaries and to code various important tactical situations. However, choosing the optimal

color-coding schema is not trivial. In the 1930s, the International Commission on Illumination

(CIE) developed a color discrimination schema for reflective light. More recent standards have

been proposed for emissive light from cathode ray tube (CRT) displays (Carter, 1982). The

standards have been modified and validated with human performance data, which indicate that

the formula derived from the standards (CIE L*u*v) is a good predictor of human discrimination

performance (Carter). Algorithms based on variations of the formula have improved the engineer’s ability to pick color palettes that are maximally discriminable for CRT uses (De Corte,

1986). The problem is that it is difficult to use a formula to create optimal color schemes because

many military symbols have been assigned colors based on traditional conventions (red: enemy

or danger, blue: friendly, etc.), and map backgrounds are so variable. A related problem is that

the number of color codes that can be assigned to important categories is limited to five to nine.

Scenes can be portrayed in a nearly infinite array of color gradations, but the use of color codes

for symbols is limited by human working memory limitations; too many codes degrade search

performance (Wickens & Hollands, 2000). In general, color coding does not automatically

increase search or memory performance, especially when compared to well-learned non-color

codes. In fact, there are important instances when color coding leads to degraded performance

(Christ, 1975; Christ & Corso, 1983; Van Orden, Osga, & Luaben, 1991). Usually, problems

were found when the test subjects had to ignore color codes to search across another coding

dimension. Color attracts the user’s attention and it interferes with his or her ability to ignore

color codes that are not useful for a particular task. Another problem is that a portion of the male

population has problems with color discriminations and to make matters worse, color discrimination degrades with age. However, even when all the limitations and drawbacks of color are

considered, most researchers agree that color codes enhance situational awareness (Nugent,

Keating, & Campbell, 1995; Van Orden, Divita, & Shim, 1993).
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Symbology sets are used successfully in many civilian applications and the icons that represent

the various applications are chosen for their high associative value with the represented object

(Collins, 1982). It has even been suggested that pictograms could be used to represent a formal

universal language (Koler, 1969). However, symbology has been most successful when the

applications are limited and the symbol set is extremely intuitive. Because of the complexity of

military applications, military symbols have naturally inflated to subsume hundreds of different

applications, some of which are intuitive and many of which simply have to be learned. For

example, the original Navy Tactical Data System (NTDS) symbols were designed to be learned

and discriminated easily. However, NTDS became unwieldy as more applications such as the

Airborne Early Warning/Ground Environment Integration Segment (AEGIS) class cruisers (and

the concomitant symbols) were added. Although military symbols are updated periodically, the

basic Army symbols have remained fairly constant (cf, Department of the Army, 1985). The

symbols are not ideal but they have been used for generations and would require extensive

retraining if they were changed substantially (Weidenbacher & Barnes, 1997). The original

symbol sets were overlaid on military maps and later innovations allowed frequent symbol

changes with acetate overlays and grease pencils. As an unexpected bonus, the overlays allowed

the commander and staff to review the trends of the military situation by flipping through the old

overlays. New North Atlantic Treaty Organization (NATO) standards (NATO, 1990) have

resulted in the addition of more human friendly symbology and the adoption of color standards.

This in turn has influenced the United States to coordinate its symbol standards across services

and to reformat its symbols to be in accord with human engineering standards and the NATO

standards (Department of Defense, 1999). Still, the symbol sets remain fairly static for the

reasons discussed before. The introduction of digitized displays on television monitors has

precipitated its own problems. Many of the display surfaces are small, causing the symbols to

cover too much surface area; this occludes neighboring symbols and gives the commander only

an approximate location of a particular unit. Also, the color contrast between a symbol and its

map background is often poor because the color contrast among emissive displays is variable and

difficult to predict for all the possible map backgrounds that are used. In general, the traditional

military symbology conventions are less than ideal for modern display technology. Data suggest

that more compact symbols with greater contrast would lead to improved observer performance

(Weidenbacher & Barnes, 1997). However, traditional symbol conventions are ubiquitous in

military systems. The software replacement and training costs of introducing radically new

symbol sets would most likely preclude the adoption of more effective concepts in the near

future.

Future situation displays will be multi-media based and will combine knowledge sources from

varied commercial and military sources. The U.S. Navy Space and Naval Warfare Systems

Command (SPAWAR) system center is in the process of developing knowledge wall concepts to

replace the traditional situation maps that are ubiquitous in modern operations centers. The

purpose of the knowledge wall is to foster shared situational awareness, permit continuous

revision of the military situation, and enhance the senior staff’s ability to interact with supporting
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information systems. These concepts are still being developed. Figure 3 is only a notional

prototype of an early design but it at least captures the variegated information sources and the

live multi-media possibilities of the actual implementation. Imagery intelligence, smart

knowledge portals, media from anywhere in the world, video of ongoing conflicts, and

conferencing with world leaders and experts are some of the obvious possibilities for future

knowledge systems. Based on extensive interviews with 30 senior Naval staff officers, the

following crucial issues (directly quoted) were identified as important characteristics for the

knowledge wall (Oonk, Smallman, & Moore, 2001):

• Shared situation awareness among its users

• The integration of relevant mission status information

• An intuitive graphical interface

• Consistently formatted information

• A tactical focus for the displayed information

• The display of information to supplement tactical data

• The display of mission goals and Commander’s Critical Information Requirements (CCIR)

• The display of summary information provided by “anchor desk” or support staff

• The ability to connect and coordinate or collaborate with others at diverse locations

• A flexible configuration that can easily be changed by users

• The ability to drill down through displayed information for more detail

• Display of information age and reliability

• Tactical overlays to highlight different types of information

Figure 3. SPAWAR knowledge wall concepts (Oonk et al., 2001).

3.3 Abstract Processes

Ferren (1999), a design consultant for Walt Disney’s Imagineering Division, attacked the notion

that information overload was inevitable for battlespace visualization displays if the battle process were represented in detail. He argued that the problems were caused by poor design rather

than human limitations. His thesis is that symbols and icons are not the natural manner of human
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processing; rather, he posits that if information could be presented in a naturalistic narrative

format, most of the problems associated with cluttered situation displays could be circumvented.

Many of the design principles he discussed are based on research which indicates that humans

process information with holistic strategies rather than feature extraction processes. For example,

Pomerantz, Sager, and Stoever (1977) found what they referred to as a “configural superiority

effect”. Their subject’s task was to find simple physical features that were part of a larger

configuration instead of trying to identify the same features in isolation. Counter-intuitively,

adding information (as part of a configuration) decreased rather than increased processing time.

Apparently, individual features that were part of a configuration were perceived as patterns that

formed emergent cues, thus enhancing discrimination performance in contrast to the same cues

in isolation. A similar linguistic phenomenon was discovered which showed improved recognition of a letter embedded in a word contrasted with the same letter presented alone (Wheeler,

1970). The design implication is that humans are particularly efficient at recognizing visual

patterns compared to processing individual objects. This design principle holds for strategically

grouped objects as well as a single configural object. For example, the changing visual relationships among bar graphs can be used as the emergent cue that allows humans to quickly understand higher order interactions among the individual indices (Sanderson, Flach, Buttigieg, &

Casey, 1989).

However, an unresolved problem is that humans often require detailed symbolic information

(unit type, readiness, location, mobility, etc.), and understanding patterns by themselves is not

sufficient. There is evidence that when humans attend to global patterns, they miss changes in

the individual components. Bennett and his colleagues (Bennett & Flach, 1992; Bennett, Toms,

& Woods, 1993) attempted to solve this problem in the nuclear power plant domain by

combining principles for emergent higher order features with basic human factors design

principles to highlight individual components. The configural display they designed depicted

higher order process features related to overload conditions with specific details of the individual

components (steam and water flow information) annotated on the same display. The interactions

among the components were displayed in terms of a dynamically changing rectangle whereas

color-coded sides portrayed measurement data for individual components. Specifically, changes

in area, shape, and rate of change of the rectangle were the emergent cues that signaled important

state changes in the power plant process. After practice, the operators were able to respond to the

interaction (emergent) cues without losing information concerning the specific component cues.

This indicates that global and local processing cues can, at least in certain circumstances, be

attended to concurrently. Barnes and Suantak (1996) used the same configural concept to design

a command and control display that showed the relationship between combat readiness and

mobility information of maneuver battalions with an integrated configural representation.

Empirical results (with 16 officers at Fort Huachuca, Arizona) indicated that configural

representations resulted in more effective situational awareness and simple tactical decisions

than current Army display symbology.
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Possibly, the most widely used abstract representation is the spatial metaphor. The idea is simple:

display entities are arranged in space to show their relationships in the problem-solving domain

of interest. For example, a spreadsheet is arranged in some temporal and categorical order to

represent financial trends and relationships. For complex databases, the problem is to represent

the data in such a manner as to capture the local relations without losing the big picture. A

number of techniques have been suggested to visualize the abstract relationships among data

clusters. Two-dimensional approaches use “fish eye” techniques that emphasize the most

important current data clusters by displaying them in central locations, whereas other data

relations cover smaller areas on the periphery. There are also a number of techniques that use

cone representations, mathematical proximity functions, and various navigational and browsing

utilities for understanding 3-D databases (Wickens & Hollands, 2000; Fairchild, Poltrock, &

Furnas, 1988). An example of the mathematical techniques is “Pathfinder,” which generates a

network representation based on cognitive distances derived from expert judgments that use

psychometric scaling techniques (Minkowski r-metric). Pathfinder and similar proximity

functions can be used to display various cognitive relationships among data sources via map

metaphors (McDonald & Schvanveldt, 1988). Such techniques are not without their drawbacks;

there are no unique cognitive distance functions1, and these functions may vary a great deal

among experts. However, the military is beginning to use proximity functions to organize large

intelligence databases to allow the analyst to “see” the big picture and to focus on similarity

clusters that are pertinent to the current intelligence requirements.

On a more abstract level, Healy, Booth, and Enns (1998) demonstrated the use of pre-attentive

cues (curvature and color) for efficient visual parsing of multivariate data sets (cf, Treisman,

1982). More recently, Healy and Enns (1999) combined textural, height, and color cues to design

complex oceanographic displays that show shifting patterns of plankton density, current strength,

and sea surface temperature. In particular, they were interested in the combination of color and

textural cues as independent dimensions to code various oceanographic phenomena such as

typhoon development. They found that a search for color-coded information was unaffected by a

background of textural codes (density, size, height, regularity, etc.) but that there was a cost for

searching for textural coded targets in a multi-hued display. Most of the search times were

extremely rapid, which suggested again that color and textural codes could be processed preattentively. Perhaps the more interesting question of whether higher order emergent features

could be abstracted from their displays was not addressed. For example, it would have been

fascinating to know whether visualization patterns representing precipitation, wind, and ocean

current cues and their interactions could be used to predict incipient weather changes. As Ferren

(1999) noted, the human visualization system is able to extract complex cues to understand

important trends in our often volatile environment. Precisely how our perceptual system

 

1

For example, during the cold war, the United States and the Soviet Union were perceived as being close in terms of

military power but distant in terms of political philosophy.
13

accomplishes this feat is still mostly a matter of empirical investigation rather than theoretical

understanding.

4. Prediction

4.1 Visualization and Decision Making During Uncertainty

Because many military situations are inherently uncertain, displaying risk and associated

concepts such as uncertainty and utility are important issues for FCS. There is a tradition of

psychological research related to risky decisions that is still lively and controversial (cf,

Gigerenzer & Hoffrage, 1999; Klein, 1999; Lopes, 1986). One school of thought emphasizes

decision biases and the non-rational aspects of human decision making. Certainly, the type and

severity of the cognitive biases reported in the decision-making literature are extensive (Wickens

& Hollands, 2000). For example, in an Army-sponsored project, researchers found that nine

types of cognitive biases had important impacts on the way intelligence analysis is performed

(Thompson, Hopf-Weichel, & Geiselman, 1983). Much of the research suggests that humans do

not compute probabilities among an exhaustive set of options; instead, they visualize concrete

examples and use frequency judgments to assign uncertainty values. Thus, many of the decision

biases are a result of the heuristic processes that humans use to visualize the problem space

(Johnson-Laird, Legrenzi, Girotto, Legrenzi, & Caverni, 1999). The U.S. Army Research

Laboratory (ARL), working with the University of Illinois, investigated the effects of these

biases and possible visualization techniques to mitigate them in a series of missile defense

simulations. In the initial experiment, Air Force officers acted as operators defending against a

national missile attack. Figure 4 shows the visualizations for the simulation. The left-hand screen

showed the trace history of the incoming missiles and the inventory of ground-based interceptors

(GPIs). The right-hand top display portrayed the status of the incoming missiles and their

probable targets. The bottom right-hand display indicated the GPI allocation scheme for each

incoming missile. Periodically, the computer showed the uncertainties associated with successful

defense (or possible loss) of the defended cities, based on the current number of incoming

missiles and remaining inventory of interceptor missiles.

The first experiment, which used 16 trained national missile defense (NMD) potential operators

(the system is still being developed), investigated different formats for displaying uncertainty.

The NMD approach at the time of the experiment was to display probability of mission success

(overall and for each defended target), which proved to be abstract and somewhat confusing

because of the high degree of certitude required in such cases. The researchers displayed the

same information in terms of expected frequency of enemy “leakers” (the expected rate of enemy

missiles not intercepted), arguing that the expected frequency format was more concrete and

more easily visualized than abstract probabilistic representations (cf, Gigerenzer & Hoffrage,
14

1999). The operators’ overall situational awareness did improve with the concrete “leaker”

format in terms of superior memory and search performance. However, the operators’ decisions

whether to add more reserve missiles to the current mission (thus leaving defended cities

vulnerable to an ensuing attack) were not affected by the uncertainty format, which indicated that

no bias was introduced by the “leaker” metric. There was an interaction between current risk and

the probability of an ensuing attack. Basically, operators were responsive to the possibility of an

ensuing attack but they were much more concerned about the known risks involved in the current

mission. The results of this experiment suggest that a concrete representation of uncertainty in

terms of expected frequencies of “leaker” missiles is a promising format for aiding operators in

visualizing uncertainty, but more research needs to be done to generalize beyond the specific

task used for this experiment. What is particularly interesting is the fact that the visualization

improvement was not the result of pictorial or graphical variables but involved a simple change

in numerical format that allowed the operator to visualize risk in terms of concrete examples

rather than abstract concepts. There is also a disturbing suggestion that operators may focus too

heavily on the immediate fight to the detriment of planning for future engagements, which

suggests that visualization factors related to future shortages of interceptor missiles need to be

investigated.

Figure 4. Risk management visualizations for missile defense simulations (Barnes, Wickens, & Smith,

2000).

Another series of studies investigated biases related to how military personnel visualize

sequences of hits and misses of interceptor missiles against incoming missiles (Smith &

Wickens, 1999). In general, humans tend to visualize independent stochastic processes using

easily imaged heuristics such as things getting better (or worse) or they use anchoring stratagems

to perceive trends that do not exist. For example, test subjects tend to see trends in data that are,

in fact, stochastically independent even when they are informed of the mathematical relation-
15

ships (Wickens & Hollands, 2000). In two separate experiments, Patriot missile operators

perceived nonexistent trends, anchoring their decisions to the first or last piece of information

they received, which indicated individual differences as well as effects caused by slight changes

in experimental conditions (Adelman & Bresnick, 1992; Adelman, Bresnick, Black, Freeman, &

Sak, 1996). In a second NMD experiment, 16 Reserve Officer Training Corps students and 4

active duty officers stationed at the University of Illinois were used to investigate trend effects

related to observing the sequence of hits or misses of interceptors against the incoming enemy

missiles. The trend data were investigated in the simulation game mentioned; the participants

were informed of the stochastically independent properties of hits and misses in the simulation.

As in the Patriot case, the trend effects were complex. In particular, the perception of things

getting worse caused the operators to over-react and take more missiles out of reserve than they

would for an equivalent case when the hits and misses seem to be of a more random nature

(Barnes, Wickens, & Smith, 2000). In the real world, trends are often of the type where events

start to improve or deteriorate. The problem posed here is how to analyze and then present

information to the operator so that the difference between random fluctuations and actual battle

trends can be visualized. It is worth noting that the problems investigated for the NMD will

surface for many of the new automated systems being developed for military intelligence, Patriot

and other missile defense systems, and sensor-to-shooter field artillery systems (Thompson et al.,

1983). As evolving systems become more sophisticated, the display of states of uncertainty and

the concomitant cognitive biases will require innovative cognitive engineering solutions.

Visualization techniques and more effective ways of portraying uncertainty are at least part of

the solution.

4.2 Naturalistic Decision Making and Mental Simulation

Whereas the previous reported research focused on the negative aspects of human decision

making and their heuristic processes, many researchers have pointed out the efficiencies of the

same processes. The laboratory research has been criticized as being unrealistic and unrelated to

tasks that humans, especially experts, usually perform. In particular, a number of researchers

have questioned the generality and the construct validity of research that was not conducted in

more naturalistic situations (Klein, 1999; Lopes, 1986). Much of the early research about

decision making assumed a normative process underlying human decision making. Humans

deviated from this process but they could be trained to use prescriptive techniques that would

enhance their decision-making skills. This approach influenced U.S. Army doctrine, causing the

Command and General Staff College at Fort Leavenworth, Kansas, to train staff officers to

evaluate three courses of action (COAs) and enumerate their strengths and weaknesses (and the

inherent uncertainties involved) before making a recommendation to the commander. This

paradigm is very similar to the traditional structured approach based on the tenets of decision

analytical theory being taught at most university business schools. The theoretical implication is

that the successful decision maker needs to know what options are available, the uncertainties

and utilities involved, and the general structure of the decision space (usually some sort of a
16

decision tree) before he or she chooses an optimal solution. The principal weakness of this

paradigm is that it has little to do with how experts actually make decisions. For example, Klein,

in a now classic study, found that expert firemen did not generate options but simply “knew” the

correct thing to do. Klein found the same basic process for experienced business executives and

military officers. Experts, especially in time-constrained situations, focus on situational understanding and then make decisions rapidly, usually without considering options. Klein refers to

this process as recognition-primed decision making (RPD). Experts “see” familiar patterns and

visualize solutions using external cues that trigger memories of successful actions for similar

events. The evolutionary advantage is obvious; primitive man had scant opportunities to

construct decision trees. Klein does not assume that all decision making is this rapid; in more

ambiguous situations, the expert may visualize various scenarios and COAs, thus creating a

mental simulation of the possibilities and their consequences before making a decision.

The key to military decision making is not a normative process but the warfighter’s ability to

visualize a solution that is highly dependent on past experience and situational understanding.

Kobus, Proctor, Bank, and Holste (2000) investigated 52 marines making COA decisions in a

dynamically changing military situation during both high and low uncertainty conditions. The

most important differences were between experienced and inexperienced marines. The results

indicated that experienced marines took longer to make their situational assessment but made

COA decisions more accurately and rapidly than inexperienced marines. The experienced

marines were also less affected by the degrees of uncertainty concerning the validity of the

intelligence information they received. The advantage of experience seem to be that it allowed

the officers to visualize an effective COA rapidly once enough knowledge was digested for them

to understand the military situation. Also, other research confirms that introducing uncertainty

has a significantly more deleterious effect on inexperienced military decision makers than on

their more experienced peers (St. John, Callan, Proctor, & Holste, 2000).

In a different domain, researchers found that the ability of an expert to adapt to new problem sets

depends on a rich interconnectedness among the expert’s knowledge structures, allowing

generalization to the problem space. Experts are not necessarily more logical than the novice but

have more stored patterns (and their stored relationships to other patterns) to compare to the new

situation. For example, researchers found important differences in problem-solving abilities

between experts and less experienced Unix2 programmers. Experts were more adept at

combining Unix commands to solve novel programming problems than were programmers with

less than 2 years’ experience. Experts were able to visualize multi-step solutions, whereas the

relative novices were still at the individual software command level (Mannes & Doane, 1991;

Doane, Alderton, Sohn, & Pelligrino, 1996; Doane, Sohn, McNamara, & Adams, 2000).

Holyoak and Spellman (1993) point out that solving new problems requires more than RPD

processes. Humans must not only retrieve past memories but must also mix and match them to

the current problem until a new solution emerges. The thought processes themselves tend to be

 

2Unix® is a registered trademark of The Open Group.
17

“imagery” based and to depend on the investigation of more likely solutions rather than on an

assessment of all possible solutions (Johnson-Laird et al., 1999). The research results suggest

that training via visualization techniques and role playing in various military vignettes may aid in

the development of the “expertise” to adapt to new environments. Another product of naturalistic

approaches involves a better understanding of how humans tend to use visualizations in realworld situations. Interviews with experts suggest that humans visualize in terms of analogies to

past experiences, making metaphors and narratives effective forms of exposition. The implication is that the knowledge presentation should tell a story because a narrative presentation

involves observers in a mental dialogue with the presentation material, thus allowing them to fill

the blanks (Gershon & Page, 2001). Commanders do not need to be overwhelmed with details

but need to be presented only with that part of the story that directly impacts their decisions

(Ferren, 1999; Klein, 1999). This is an important contrast to the traditional wisdom of how to

present information. Humans were modeled as passive information receivers who at best could

decode all the information on the display (cf, information theory in Wickens & Hollands, 2000).

In the naturalistic view, humans are active creators of information, based on their past experience

and their ability to understand the current situation using analogical reasoning and metaphorical

examples. As intuitively appealing as the naturalistic view is, there are obvious drawbacks. It is

difficult to scientifically define “storytelling” formats and to show that narration improves

situational awareness or decision making more than well-designed information displays (or more

precisely, during what conditions it does or does not do so). The research implications are that

we need to collect data and evaluate concepts in realistic military environments. However, this is

a difficult and not easily accomplished task. Too often, data collected in realistic exercises are

not useful because of the complete lack of controls. A more measured approach would be to

combine the strengths of controlled experiments with the realism of field work via a hybrid

research program (Barnes & Beevis, in press). The same is true in investigating human biases as

opposed to expert strengths. It is a matter of a glass half full or half empty; both phenomena

exist, and any realistic research effort must investigate the warfighter’s limitations as well as

strengths. However, we do need to focus our efforts more on using realistic simulation and

experimental paradigms that capture the important issues of specific military domains if we are

going to understand these phenomena in the correct military context. Eventually, the resulting

concepts must be validated during actual military exercises.

4.3 Visualization Issues for Supervisory Control of Automated Battlefields

An important question in future combat environments is how much trust the commander will

have in automated systems. Commanders will never relinquish their decision prerogatives even

though processing and execution will become increasingly automated. The commander must be

able to understand and visualize the consequences of each of these systems as they execute on

the battlefield in order to achieve his or her intended end states. Trust does not mean that the

commander will assume that systems will operate perfectly. For the foreseeable future, an

operator will be in the loop at a supervisory level. Rather, appropriate trust is an ability to
18

visualize how automated systems will operate in a specific military environment and when to

intervene with redundant systems. At the operator level, appropriate trust in automated solutions

will become an increasingly important component of executing the commander’s intent. Early

research is pessimistic, indicating many cases of misuse (over-reliance on automated systems)

and disuse (over-reliance on manual solutions) (Parasuraman & Riley, 1997). The results can be

disastrous: airplane crashes when pilots relied too much on automated landing systems, train

crews ignoring the speed regulators leading to rail accidents, or the infamous K-007 Korean

aircraft incident when the crew ignored evidence of route deviation once the automatic pilot was

set (miss-set). The problem is fuzzy because there seem to be biases toward relying too little and

too much on automated systems. Researchers sponsored by ARL at Fort Sill, Oklahoma, have

done a number of studies investigating trust issues in Army-related tasks. They have varied

motivational, social, and cognitive factors and they found examples of misuse and disuse of

automated systems (Dzindolet, Pierce, Beck, & Dawe, 2002).

In a series of experiments using a simulated automatic target recognition (ATR) device,

Dzindolet et al. found that the test subjects were more willing to rely on their own estimates even

when they were informed that the ATR solutions were superior to their own, based on the last

200 trials. Subjects were also more willing to trust the same estimates if they were told that they

came from a peer. Thus, subjects in this series of experiments showed a bias against automation;

some of the participants even suggested that they had a moral imperative to trust their own

judgment over a machine's (Dzindolet et al., 2002). However, another study using a similar

paradigm (Dzindolet, Pierce, Beck, Dawe, & Anderson, 2001) indicated a complete shift in

strategy, with the participants more prone to misuse than to disuse. They tended to over-rely on

the aid when the aid was incorrect. The crucial factor was the order of the decision making. In

the latter experiment, the machine’s solution was displayed before the operator made a decision,

whereas in the former experiments, the operator’s decision preceded the machine’s. A comparison of the experiments indicates that the shift from a manual bias (“I make the decisions”) to an

automation bias (“let the machine do it”) is based on type of feedback and time stress but mainly

by whether the operator had already made a decision before the machine solution was known.

The results are important because it gave insight into the dynamics of automation bias, which has

some of the characteristics of social loafing (if the machine has already made the decision, the

operator’s uncertainty could be resolved simply by agreeing). However, if operators have already

set their minds by the time they see the automated decision, then they have a vested interest in

following their own dictates. Again, it is an example of a very simple manipulation, apparently

changing the operator’s visualization of the problem space from one where the operator is the

controller and the computer is the assistant (under-reliance) to one where the computer is

authority and the operator the backup (over-reliance).

Ensuing experiments indicated that the precision of the feedback as well as the decision order

was important in instilling appropriate trust in the operator. An experimental condition in which

the operator was given feedback about each trial and when obvious errors were not displayed to
19

the operator resulted in nearly optimal operator automation decisions. In this condition, the

operator was able to visualize a rational strategy in regard to the machine error rate and was able

to intervene appropriately (Pomranky, Dzindolet, & Peterson, 2001). One issue that became

obvious in this series of experiments and the NMD experiments is the importance of training as

well as improvements in visualization technology. The Pomranky et al. experiment showed that

optimal synergy depended on the operator learning the characteristics of the automated device.

The displayed information is only meaningful if combined with training with feedback that

stresses consequences (“you just missed an obvious target”). Visualization depends on memory

of past incidences combined with cues relating to the current situation. When these cues are

properly understood, the operator can visualize his or her appropriate role within the automated

system.

Another important issue related to supervisory control is the degree of automation and reliability

problem (Parasuraman, Sheridan, & Wickens, 2000). FCS will depend on unmanned aerial and

ground robotic systems with varying amounts of autonomy. The crucial issue for these systems is

how much autonomy to assign to the unmanned systems a question that depends on the operator’s situational awareness as well as the reliability and degree of automation of the unmanned

systems. For example, it may be beneficial from a systems point of view to keep operators

actively engaged in the control of a semi-autonomous vehicle because it forces them to maintain

good situational awareness for those instances when the operator is truly needed. Complacency

effects are a problem even when the automation is implemented only at the information level.

Horrey and Wickens (2001) found benefits and costs of partially automating military situation

displays. Overall, automated highlighting and the presentation of diagnostic information concerning enemy movements aided military decision making; however, their subjects were misled

by unreliable automation, causing them to miss important cues that may have been spotted in

manual conditions. Galster, Bolia, Roe, and Parasuraman (2001) found the same trends using a

military aviation display. More intriguing is the “automation paradox” effect found in a number

of military and applied experiments. Researchers found that the more reliable the automation, the

more likely the operator was to make mistakes when the automation proved unreliable (Rovira,

Zinni, & Parasuraman, 2002). This suggests that the engineering solution of designing more

reliable systems has its own human performance costs. Especially for more reliable aids, humans

can become too complacent and fail to anticipate automation failures (even when they know they

can occur) (Rovira & Parasuraman, 2002).

Trust is a pervasive combat issue that affects all command decisions. In the future, the issues will

not only involve subordinate commanders and their individual characteristics but also subordinate intelligent systems and their characteristics. The most important research issues involve

determining the cognitive engineering paradigms that best allow the commander to understand

the operations of automated systems so that the degree of autonomy and trust for systems is a

natural and effective part of the overall command process. Some combination of training and

improved visualization concepts is required so that the commander (and his or her operator
20

surrogates) can understand and predict automated performance. The research about the

automation paradox and complacency also implies that it is important to engage the operator in

the decision process even for completely automated systems. However, we need to know more

about the degree of autonomy, complacency, feedback effects, and the automation paradox in

realistic military settings before we can design effective visualization aids.

4.4 Visualization and Prediction in Complex Military Environments

Perhaps the most influential scientific change in the 20th century is a shift from a belief in a

deterministic Newtonian world to a scientific zeitgeist that accepts the notion of inherent uncertainties. More recently, even the notion of a being able to make probabilistic predictions has been

challenged. Chaos theory, for example, assumes that minor events can radically alter the

outcome of nonlinear processes in a world with many such processes. Complexity theory is a

group of related concepts that is predicated on two important concepts: information and emergence. The first involves the overwhelming number of possible outcomes in any large set of

interacting parts. The second involves the result of the possible interactions in a complex system.

For example, there are 2n possible states for a system with n components. For any one of the

myriad interactions, it is possible for unpredictable behaviors to “emerge” that can have minor or

major impacts on the system as a whole (Bar-Yam, 1997; Waldrop, 1992). Considering the

battlefield as a complex system, complexity theory implies that the dynamics of battlefield

skirmishes become unpredictable as the number of interacting elements increase. This is true

even in a successful battle. For these situations, the commander (through training, doctrine, and

tactical redundancy) has accounted for unexpected events, and the resulting divergence from the

battle plan has only minor impacts. However, as the inherent uncertainty of the situation

increases, “emergence” of unexpected events and chaotic behaviors will start to play an

increasingly important role in determining the outcome.

Unfortunately, future military missions will tend to be the type for which doctrine and preconceived notions of war fighting will be challenged in many different situations. In particular,

opposition forces have shown the ability to adapt to western preconceptions and to use asymmetric tactics to defeat superior force and firepower, especially when political and psychological

factors are being exploited. Complexity and the effects of unforeseen interactions have become

topics of intense debate among military researchers. However, empirical and applied research in

this area is still in the beginning stages. Currently, ARL is investigating new prediction and

visualization paradigms to enhance the commander’s situation understanding in these environments. New visualization techniques have been proposed to help us understand the historical

trends, political changes, ethnic conceptions, and changing perceptions of various combatant and

non-combatant groups in a particular area of concern (Zacharias & Hudlicka, 2001). One area of

particular concern for missions in unfamiliar environments is the use of information aids to assist

the intelligence analyst in interpreting the streams of data that can overwhelm military capacity

during critical situations. University of Illinois researchers in support of ARL have developed a

series of decision support systems using Bayesian belief nets that can be used as tools for an
21

individual analyst or in a collaborative environment to structure and categorize incoming

intelligence reports (Mengshoel & Wilkens, 2001; Asaro, Hayes, & Jones, 2001). The results are

promising but the systems are still very much laboratory tools. The main problem is that the

probability structure must be created to account for each new battle situation. The advantage of

these systems is that they handle large data streams and show inferential networks to the trained

analyst, which will allow him or her to visualize the emerging trends. More research needs to be

done to determine how robust these systems are when the military situation changes. They show

promise in helping to ameliorate an important consequence of complexity: the overwhelming

amount of battlefield information.

Another approach to predicting outcomes on the battlefield is the use of genetic algorithms to

“search” dynamically for the underlying structure by rapidly comparing possible solutions. The

solutions are generated by the combination of important characteristics (“genes”) to examine

hundreds of thousands of options with surprisingly little computer overhead. The FOX3 genetic

algorithm (GA), which was crafted at the University of Illinois, was developed to generate and

evaluate solutions to tactical problems in conventional warfare environments. The algorithm,

which is combined with a display that portrays the results, assisted surrogate commanders from

Fort Leavenworth, Kansas, by helping them visualize twice as many new COAs as they could

with the manual planning condition (Hayes, Fiebig-Brodie, Winkler, & Schlabach, 2001). Hillis

and Winkler (2001) have taken the genetic algorithm concept one step further by introducing coevolutionary solutions to FOX-GA. This approach allows the analyst to observe multiple iterations of the genetic solution by playing the red and blue force best solutions against each other.

The tactics of red and blue co-evolve as the best solution in game n-1 is challenged by the adversary in game n from which the best solution then determines the initial point of game n+1, etc.

The advantage is that solutions emerge that would go undetected in a simpler one-way game

such as the original FOX-GA. The empirical question is whether the added complexity aids or

hinders the analyst’s ability to understand the military environment. ARL is in the process of

investigating this issue by redesigning the basic algorithms that underlie the co-evolutionary and

genetic approaches and by generating visualization for the non-traditional multi-agent, volatile

situation such as anti-terrorist campaigns in Afghanistan. The results will be evaluated with

experienced analysts to help design better visualization systems and to pinpoint problems with

the basic concept. Tools based on co-evolutionary processes show particular promise for

understanding complexity in that they deliberately mimic the chaos of battlefield situations by

using dynamic algorithms that have no predetermined solutions and are particularly useful for

discovering unanticipated results (Hillis & Winkler, 2001).

An unfortunate mistake in trying to evaluate systems that are being designed for complex

environments is the belief that these systems must be subjected to realistic validation early in

their development process. Evaluations must be a multi-step process from early modeling with

expert feedback to controlled experiments to field validations. Too often in the attempt to capture

 

3Not an acronym
22

combat complexity, the temptation to skip steps leads to disastrous field experiences because

promising technologies are introduced prematurely (Barnes & Beevis, in press). An example of

an attempt to introduce realism and control early in the design process is an ARL-supported

project at the University of Arizona. The researchers are trying to circumvent these problems by

developing a simple simulation environment for initial evaluations of visualization and decision

support concepts. The purpose is to combine the efficiencies of iterative feedback from experts

and controls of laboratory experiments with at least some of the complexity of full-scale simulations. The 3-D visualization architecture has been used to investigate the utility of employing

various predictive and assessment aids to enhance the commander’s ability to operate in nonconventional situations such as Kosovo, Afghanistan, and Somalia (Rozenblit, Barnes, Momen,

Quijada, & Fichtl, 2000; Ziegler, Rozenblit, Barnes, & Hudlicka, 2000). This architecture allows

the experimental manipulation of visualization and algorithmic techniques via realistic military

vignettes with active duty officers as test subjects. Figure 5 portrays examples of concepts that

were demonstrated to groups of military experts to assess the military utility of various 3-D

animation and configural display concepts. Future efforts include data collection during realistic

simulated small-scale contingency operations.

Figure 5. Three-dimensional visualizations showing possible options combined with predictive software

and configural displays that show the consequences of the animated vignettes (Rozenblit et al.,

2000).
23

5. Conclusions

Visualization is a problem-solving stratagem that uses internal and external images to enhance

the commander’s ability to understand the current situation and to envision the actions necessary

to attain combat goals. Viewing battlefield terrain is a complicated process that requires 2-D

planar views for the understanding of tactical and spatial relationships, 3-D views for topographical understanding, and immersed 3-D views for mission rehearsal and detailed understanding of

the battlefield “dirt.” The principal research issue is to develop principles for the integration (or

separation) of various views so that battle managers can perceive tactical and topographical

battle conditions as a seamless narrative. Military symbols are products of tradition rather than

human engineering efficiency. Coding techniques such as color and creation of more uniform

symbol sets have increased the processing efficiency of modern symbol sets without solving the

basic problems of clutter and poor contrast. New display concepts based on research findings

related to pre-attentive processing and emergent perceptual cues may circumvent at least some of

the problems related to the portrayal of the complexity of modern battlefields. Also, knowledge

wall technologies are being developed to display all pertinent information (including live battlefield imagery) to the commander as a mosaic of various knowledge sources. The problem

remains as to how to present multiple sources of information with techniques that aid the commander in visualizing not only the current situation but also in predicting the most effective

responses and future COAs. The most important human-related design issues are

• Projecting a 3-D representation onto a 2-D surface results in space distortions,

ambiguous locations along the line of sight, and foreshortening phenomena. Visualization cues

such as mesh and line enhancements help us understand 3-D views.

• Tactical information is displayed more effectively with 2-D planar formats, whereas

understanding the topography is best visualized with 3-D formats.

• There is a cognitive cost in switching between dimensionality and slant range views.

Software utilities that allow the observer to morph between slant range and dimensionality views

of the same terrain have proved effective in laboratory experiments.

• In the future, warfighters may be able to visualize their military situation using VE.

However, many costs are currently associated with VE, which must be overcome before the

technology becomes used widely: motion sickness, perceptual illusions, time lags, and observer

disorientation.

• A small set of symbols with distinct shapes permits observers to use pre-attentive

processing mechanisms to maintain overall battle awareness.
24

• Military symbols in general and Army symbols in particular are only minimally

effective in portraying complex military situations. Because many of these symbol sets have

been used for generations, they are unlikely to be supplanted.

• Properly designed color coding, even with its own perceptual costs, can be used

effectively to enhance understanding of situation displays via military symbol sets.

• New visualization techniques, such as configural displays, proximity functions, and

narrative formats, which are based on human pattern recognition abilities, are promising

techniques to display complex military situations without clutter or information overload.

• Knowledge walls using modern digital technology are being designed for real-time,

multi-source, interactive, and shared command views of future battlefields.

• Expert users emphasize that future systems must be collaborative, intuitive, broad

based, have “drill-down” capabilities, and be predicated on the CCIRs.

Prediction is an important but controversial component of the visualization process. The literature about biases suggests that the use of heuristics will result in humans who are visualizing

only a limited portion of the underlying problem space, which will lead to sub-optimal decisions.

The literature about naturalistic decision making implies the opposite, mainly that heuristic

processes allow the expert to react to difficult situations by rapidly finding a satisfactory solution. There is some truth in both assertions; humans are prone to certain biases but also show an

amazing ability to adapt to difficult situations that fall into their area of expertise. The crucial

task for the designer is to find visualization cues that alert observers to critical decision parameters that are consonant with their level of expertise. The literature suggests that even simple

manipulations of displayed information (such as when information was presented in the decision

process, type of feedback, and how uncertainty was presented) can have positive effects on

performance. In contrast, cognitive biases such as complacency, anchoring effects, automation

paradox, and distrust of automated systems resulted in ineffective military decisions.

The general conclusion is that most of these biases could be overcome by a combination of

training and improved methods of visualization. In particular, visualization concepts based on

storytelling would seem to be a creative approach for both training and operational knowledge

presentation. However, these methods must be developed in realistic environments and evaluated

in actual field environments to be effective. Finally, cognitive technologies that are being

developed to help us better understand military complexity were assessed. Bayesian techniques

to sort masses of intelligence indicators into possible enemy actions and genetic and co-evolutionary algorithms were found to be potentially useful (but as yet unproven) techniques to help

an analyst visualize possible sources of complexity for conventional and contingency operations.

Methods to evaluate these technologies were also reviewed:

• Measures of uncertainty based on frequency counts offer a promising method for

displaying uncertainty.
25

• Because cognitive biases are often based on rational expectations of real-world

processes, training with realistic simulations is necessary to allow the operator to visualize the

outcome of these processes.

• Results from the NDM literature suggest that simulations, role-playing and storytelling exercises should improve the novice’s ability to visualize solutions to novel situations.

• When information is presented in the decision cycle, the type of feedback and type of

automation errors influence the operator’s perceived role in using automated systems.

• To achieve automation synergy, visualization systems must be designed to keep the

operator actively engaged and knowledgeable about the expected outcomes of the automated

decision process.

• Tools based on co-evolutionary processes show particular promise for visualizing

complexity in that they deliberately mimic the chaos of battlefield situations by using dynamic

algorithms that have no predetermined solutions and are particularly useful for discovering

unanticipated results.

• Evaluating visualization tools is a multi-step process that requires early modeling and

experimentation, continual user involvement, as well as realistic validation exercises.

We are just beginning to understand how to design a visualization architecture that is responsive

to the human’s natural decision-making style. However, certain features of this architecture are

evident. The top-level display process will focus on situational understanding. The visualization

will be knowledge rich but data sparse, focusing on the decision elements that allow the commander to follow the course of the battle narrative. Beneath the display surface, multiple battle

parameters will be continuously updated, and triggers will surface information that indicates

possible problems. More sophisticated predictive algorithms and collaborating humans will be

computing in the background and forecasting the battle process. These human and machine

analysts will interrupt only if objectives are threatened or unusual trends develop. A variety of

tools that address specific problems and planning requirements is also needed. This suggests that

the visualization architecture will be collaborative, with each component having private and

public visualization aids. The components will be widely disbursed and will consist of battle

staff, off-site expertise, and autonomous intelligent systems networked into a common cognitive

architecture. An important design issue will be to ensure that the individual components act

synergistically to create a unified and coherent view of the battlespace, which suggests the

importance of filters and knowledge management protocols in future systems. Principles based

on the use of narrative formats are possible cognitive solutions for improving coherency. The

visualization architecture will be analogous to human consciousness. The visualizations

themselves will be the top-level component that imparts meaning and understanding to the

diverse information and decision-making components serving the command structure.
26

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The Human Dimension of Battlespace Visualization: Research and Design Issues

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The purpose of the report was to explore the human dimension of battlefield visualization in order to generate design principles and suggest 

future research topics. The author reviewed cognitive engineering research sponsored by the U.S. Army Research Laboratory, other military 

related research, and pertinent open literature publications. A descriptive model of visualization is discussed in terms of the ability of the 

commander to understand the current combat situation, generate courses of action (COAs) and predict COA results. Specific topics covered 

as part of situation understanding included terrain visualization, situation displays that use symbology, techniques for abstract visualizations, 

and multi-media knowledge walls. Military decision-making research was reviewed in order to explore specific topics related to the 

commander’s ability to use visualization tools to predict combat results. Based on the reviewed research, the author discusses design 

principles for the following issues:

Dimensionality, slant range, degree of terrain immersion, and virtual environments

Methods of combining terrain views 

Limitations of and ways to enhance military symbology 

Configural, narrative, and pattern recognition advantages of abstract displays

Operator-derived principles for knowledge walls

Uncertainty representation, trend perceptions, and visualization biases 

Story telling as a visualization aid for naturalistic decision making

Information presentation, feedback, and the automation paradox effects on visualized automated systems

Visualization aids for complex and nonconventional military environments

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