The computation of Effectiveness (E) includes consideration of
the usual circadian rhythm in performance (C). This factor varies
from +10% to -10%, depending on the time of day and peaks at the
nominal value of 2000 hours. This factor combines additively with
the percent resource balance at the time of the performance (T).

Moderators

Theoretical background

Several well-established overall effects between stress and
cognitive performance have been documented. The Yerkes-Dodson Law
proposes that the general effect of stress on cognitive performance
is an inverted U-shaped function. Low levels of stress do not result
in best performance. However, moderate levels of stress do produce
optimal performance. Performance drops back to “low” levels at very
high levels of stress (Wickens, 1992). The drop from peak
performance at high levels of stress will begin sooner for more
complex tasks than for simple tasks.

Perceptual narrowing is another known effect of stress on
performance (Easterbrook, 1959). Under high stress conditions,
attentional focus becomes narrower and the range of cues that is
processed becomes smaller. This might be important in tasks that are
dependent upon recognizing and processing peripheral cues. Under
conditions of high stress, the likelihood that peripheral cues will
be recognized decreases.

Stress may also significantly affect decision making. The
picture that is emerging from a variety of studies of stress and
decision making is that the effects are strongly dependent on
training and experience. Based on the review of decision making
under stress, these factors greatly mitigate the effects of time
pressure but not the effects of, for example, fatigue, although this
later stipulation awaits experimental verification (Bowers, Weaver,
& Morgan, 1996).

Experience and training

When confronted with time pressure and work overload, the less
experienced decision-maker is subject to a variety of errors that
can degrade the quality of decisions in a variety of ways. As
Orasanu and Backer (1996) summarize the process:

10



e Situational influences, including battlefield stress events and
time pressure.

e Insufficient experience.

These effects modulate the effectiveness variable. Such effects take
into account the frequency and severity of stress events and take
into account time pressure.

b. The following factors may enhance performance in quantifiable
units: They serve to modulate, in a positive manner, the effects of
stress.

*Good to superior experience.

e Confidence-building events.
In Phase I, the experience variable and the confidence-building
effect modulated the effectiveness variable by decreasing the stress
effect.
c. Several factors affect performance in a currently
unquantifiable, though significant, manner; for example, a BCE may
choose a riskier COA that may produce a better outcome than a more
risk-aversive choice:

e Aggression versus risk~aversive.

e Emotion/motivation.

Additional issues that need to be examined here are such factors as
when a BCE becomes fatigued, does it take more chances?

a. There are interacting factors that need more examination. For
example, as stress increases, intelligence becomes less important
and experience becomes more important. (Locklear, Powell, and
Fiedler, 1988).

31



Tables 9 and 10 present data from mission replay mode, C3SIM
PH/PK algorithms without the HPCM. Again both BLUFOR and OPFOR were
largely destroyed enemy in zone; however the end state positions
from the Commanders’ Intents were again not achieved. Both BLUFOR
and OPFOR losses due to artillery were higher than for the actual
NTC mission data. In this mode, BLUFOR’s inability to take evasive
action due to indirect fires was compensated for by limited fires
placed on units.

Table 9. C3SIM Mission Replay (BLUFOR)

FULLY-MISSION CAPABLE

BLUFOR DESTROYED
M1/AL 29 19
IFV 36 12
scout
VULCAN
STINGER
M901 (TOW) 19 17
TOTALS 84 48
Survivability Index = 42.8 % ALL BF: 52% ALL TF: 50%

Table 10. C3SIM Mission Replay (OPFOR)

FULLY-MISSION CAPABLE

OPFOR DESTROYED
T-72 16 13
BMP 50 41
BRDM 8 3
RED AIR
HIND 4 4
ZSU
TOTALS 78 61

Survivability Index = 21.8% ALL OPFOR = 38%

Tables 11 and 12 present data from CGF BCE control mode, where
HPCM PCD was less than 50%. For this scenario, the HPCM setting for
the initial resource balance, or sleep reservoir, was set at 700
units. Individual capacity was a possible 2000 units. Thus the
hypothetical BCE had achieved less than half of necessary sleep for
proper cognitive functioning. The experience variable was set to
moderate; stressors and confidence building events and amplitudes
varied based upon battlefield action. The BLUFOR commander’s intent
was not met during this scenario.

27



Driskell, J. E. & Salas, E. (1992). Group decision-making under
stress. Journal of Applied Psychology.

Easterbrook, J. A. (1959). The effect of emotion on cue
utilization and the organization of behavior. Psychological Review,

66, 183-201.

Horne, J. A. (1988). Why We Sleep: The Functions of Sleep in
Humans and Other Mammals (pp. 13-75). Oxford: Oxford University
Press.

Horne, J. A. (1988). Sleep loss and “divergent” thinking
ability. Sleep 11(6), 528-536.

Hursh, S. R. & McNally, R. E. (1993). Modeling human performance
to predict unit effectiveness. In D. Kamely, K.A. Bannister, & R.M.
Sasmor (Eds.), The New Frontiers: Military and Civilian
Applications (pp. 309-328).

Janis, I. L. (1983). The patient as decision-maker. In D. Gentry
(Ed.), Handbook of behavioral medicine. New York: Wiley.

Kirschenbaum, S$. S. (1992). Influence of experience on
information-gathering strategies. Journal of Applied Psychology,
7(3), 343-352.

Klein, G. (1996). The effect of acute stressors on decision
making. In H.E. Driskell & E. Salas (Eds.), Stress and Human

Performance (pp. 49-88). Mahwah, NJ: Lawrence Erlbaum Associates.

Klein, G. (in press). The recognition-primed decision (RPD)
model: Looking back, looking forward. In C.E. Zsambok and G. Klein
(Ed.), Naturalistic decision making. Mahwah, NJ: Lawrence Erlbaum

Associates.

Klein, G., (1989). Recognition-primed decisions. In W. Rouse
(Eds.), Advances in man-machine systems research (Vol. 5, pp. 47-
92). Greenwich, CT: JAI Press.

Klein, G. A. & Calderwood, R. (1990). Investigations of
naturalistic decision making and the recognition-primed decision
model. ARI Technical Report 90-59. U.S. Army Research Institute for
the Behavioral and Social Sciences, Alexandria, VA.

Klein, G. A., Calderwood, R., & Clinton-Cirocco, A. (1996).
Rapid decision making on the fireground. Fire Engineering, 149(3),

89-90.

36



factors. Simulating such behavior will require a model that has
components specific to the task conditions being emulated as well as
modulating individual factors.

In addition to general theoretical investigations into these
stressors, modulators, and effects, recent research has attempted to
mathematically model these interactions and effects on decision-
making. Work from such modeling studies will be pooled and used to
develop computer simulations of CE decision-making. Thus, the
ultimate goal of the present work is to use previous research theory
and mathematical modeling about stress and its modulators on
performance to better simulate cognitive decision-making abilities
and performance under realistic stressful battle conditions. It
would not be feasible in the initial stages of the HPCM development
process to incorporate all facets of the model outlined above in.
Therefore, the most salient and most reliably researched and modeled
portions of this model will be used as a starting point to develop
the HPCM software.

Stressors

Based on reviews by Driskell and colleagues (Driskell, Mullen,
Johnson, Hughes, and Batchelor, 1992; Driskell, Hughes, Guy, Willis,
Cannon-Bowers, and Salas, 1991), the two most salient stressors that
are likely to apply to the CE are time pressure and fatigue brought
on by sustained operations, i.e., sleep deprivation.

Time pressure

The concept of time pressure generally refers to situations
whereby the time to act is limited, to the extent that the
perception of stress is induced or the ability to fully prepare for
the act is less than ideal. In the context of CE decision making,
time pressure may be characterized as a circumstance that requires
the CE to make a decision (or fail to make a decision) before being
able to adequately explore all decision alternatives. Consequently,
making a decision under time pressure requires at least one of
several possible adaptations including limiting the number of
decision alternatives considered, and limiting the time spent
contemplating each alternative. These decision making shortcuts may
result in a lower quality of decision making performance.

Driskell et al. (1992) have summarized the literature on time
pressure and have found that a relatively simple linear equation
relates the magnitude of time pressure to the size of the stress
effect. Equation 1 presents the magnitude of time pressure (MAG)
for a task performed without breaks.



The ability to process and react to events is modulated by the
time available. During a slowly developing operation with events
occurring infrequently in time, there is plenty of time to react to
events and take appropriate action. This tends to diminish the
effects of stressful events. Hence, the average value of battle
events is multiplied by a factor that represents time pressure (h).
For example, based on Table 3, a time pressure effect of .3 would
reflect a magnitude of time pressure of .587. Since it is not
possible at present to measure the magnitude of time pressure in an
actual scenario, the model may be exercised with a range of time
pressure values (h) from 0 to 1 that represent a range of time
pressure magnitudes shown in Table 3.

Finally, based on the previous discussion of naturalistic
decision making, it is clear that training and experience play a
Major role in determining the effects of stressful events and time
pressure on performance. A highly trained and experienced commander
can effectively deal with stressful events under time pressure by
relying on the recognition of typical situations that immediately
suggest corrective actions. Only unusual situations with unclear
solutions require time-consuming diagnosis and analysis to reach an
effective COA. This experience factor can greatly reduce the
performance-degrading effects of stressful events. This factor is
represented by the experience factor (x) that varies from 0 to 1
(low to high experience) and modulates stress by a factor of (1-x).
Experience cannot be computed, per se. We can only presume that
some commanders have low experience and others have high experience.
We represent these levels with various values of "x", ranging from 0
to 1 for low to high experience. Note that an experience level of
"1" causes the experience term (1-x) to go to zero, and that negates
the entire SE computation. Hence, experience (at its limit) causes
complete protection from stress. Obviously, it is unlikely that
anyone would actually achieve an experience level of "1". Everyone
can learn and improve their skills - but a level of "1" is the
doctrinally perfect commander.

Experience enters into the overall model in two areas: 1) in the
level of the individual cognitive reservoir (R’), which determines
the highest level of effectiveness that a particular individual can
achieve when fully rested relative to the ideal reservoir level of
2880 units, and 2) in modulating the effects of stress, as discussed
above as factor “x” that modulates the magnitude of the SE. To
maintain coordination between the two experience/training factors,
we may define an experience/training scale that relates individual
cognitive reservoir (R’) to experience modulation of stress effects
(x). Table 4 provides this coordinated input to the model for
experience/training.

14



The Problem Revisited: What Remains To Be Done?

Phase I research of quantifiable variables affecting human
behavior on the battlefield was successfully implemented in the HPCM
Version 1.0. The HPCM consequently influenced the selection of a
COA by a BCE. This is a promising beginning, but it is as yet an
incomplete solution to the problem of portraying realistic command
entity behaviors for CGFs. .The roadmap to a complete solution
includes at least the following steps:

Validate the effects of HPCM variables not validated in Phase I.

a. Incorporate/refine the following factors affecting performance in
a quantifiable or unquantifiable manner in the HPCM:

¢ Individual Differences
e aggression/risk-aversive tendencies
¢ emotion/motivation
*Danger, high-threat situational influences
e Suppression--the psychological effects of fire

eHeat and ballistic injury effects

b. A mental model of the CEs state of situational awareness should
be developed to improve performance. Therefore:

eAn SME-assisted Process Trace must be constructed

eThe DST must be carefully analyzed and enhanced

*Communications must be simulated

®Definitions for Good Conditions, Bad Conditions, Positive
Reactions, and Negative Reactions must be carefully refined and

codified

c. Further distinctions need to be made between actions that reflect
good training and actions that reflect good experience.

d. Good and bad judgement needs to be scaled to reflect superior
experience and training behaviors on behalf of the BCE.

32



COGNITIVE BEHAVIORS FOR COMPUTER GENERATED FORCES

FOREWORD

Human performance in combat is widely acknowledged to be the
most important factor in determining victory or defeat; however,
there is only indirect evidence of this in development of computer
generated forces to support constructive and virtual simulations.
The variability due to human performance factors is not well
represented in these models and the result is performance that is
frequently predictable, and beyond the scope of human capability.

Constructive and virtual simulations need better methods to
represent human performance variability in response to the stresses
of combat. Specifically, the underlying algorithms that produce
realistic behavior in Computer Generated Forces should be developed
based on behavioral science data and theory.

This report reflects research to develop a human performance
cognitive model that can be used to influence the performance of a
command entity in Computer Generated Forces. The human performance
cognitive model includes the effects of sleep deprivation,
experience, training, and time pressure on the quality of military
decision making. Results of this research have been widely briefed
including presentations to the Deputy Under Secretary of the Army
(Operations Research), Commander STRICOM, and the Project Manager,
WARSIM 2000.

ZEYA M. SIMUTIS
Technical Director



The KB allows the user to build new events through an easy-to-
use GUI and also works as a server for event objects to the C3SIM.
The KB sends events to the C3SIM using DCOM technology, allowing it
to communicate on the same computer or even across a network.

C3SIM Verification and Validation

The effectiveness of the HPCM based BCE is currently being
tested. Comparisons are being made between four sets of data:

e The actual NTC training data.

eC3SIM Mission Replay Mode, C3SIM PH/PK algorithms.
eHPCM based BCE Mode, HPCM PCD greater than 50%.
*HPCM based BCE Mode, HPCM PCD less than 50%.

The data presented in the tables below is offered as a proof of
concept that the variation in human and cognitive performance
variables such as those utilized in this study produce measurable
effects upon the performance of an artificial CE. The BCE
functionality for Phase I of this study was limited to its ability
to perform minimal situational assessment and COA selection. It
maneuvered four separate teams and issued fire and movement orders.
Phase II of this work is investigating a full range of situational
awareness and COA selection activities for the BCE. In addition,
interactions between battlefield events and HPCM calculations are
being improved, leading to greater realistic variability responses
on behalf of the BCE.

Three principle measures of mission success were examined for
this phase of the evaluation: BLUFOR and OPFOR OPORD Commander’s
Intent accomplished, BLUFOR and OPFOR Survivability Index, and
percent of OPFOR and BLUFOR destroyed.

The Commander's Intent for the BLUFOR was to: “Destroy the
Advance MRD in zone and seize key terrain for follow-on defense.
The end state is the destruction of the advance guard without
penetration of PL FLORIDA, occupy OBJECTIVE FORD, and OBJECTIVE
CHEVY with sufficient combat power to establish a defense in sector
to defeat follow-on enemy Motorized Rifle Regiments.”

The Commander’s Intent for the OPFOR was: “Find the enemy and
destroy his recon and lead elements with the MRB Advance Guard
battalions. Success is destroying enemy in zone while maintaining
sufficient combat power to seize the regimental subsequent
objectives.”

25



decision-makers use a small number of heuristics
(rules) in making their decisions (Tversky & Kahneman,
1973), fail to consider all possible decisions and
outcome options (Slovic, Fischoff, & Lichtenstein,
1977), are inconsistent in dealing with risk (Lopes,
1983),..[are] likely to display premature closure -
terminating the decisional dilemma without generating
all the alternatives and without seeking all available
information about the outcomes (Janis, 1983) (p.102).

Studies of experienced decision-makers under stress suggest that
a more streamlined decision strategy is used that is called
Naturalistic Decision-Making (Klein, Orasanu, Calderwood, & Zsambok,
1993; Orasanu.& Connoly, 1993; Klein, in press; Klein & Crandall,
1995). This strategy is best suited for settings in which the
decision task is unclear, the available information is incomplete,
unreliable, or continuously changing, and stressors such as time
pressure and high stakes are present (Orasanu & Connoly, 1993).
Thus, under conditions of high stress such as time limitations, an
individual does not have sufficient time to utilize serial-
exhaustive decision-making strategies. The individual does not
consider the utility of each alternative. Rather, strategies are
adopted that allow the possibility of quick solutions versus ideal
solutions. This process has been referred to as satisficing (Simon,
1955) whereby the search for a solution is based on finding the
first satisfactory solution versus examining all alternatives for
the ideal solution. Under such situations, it is impractical to
adopt an exhaustive prescriptive decision strategy that requires
complete data and is time-consuming.

Klein (1996; 1989; in press) has proposed that experienced
decision-makers faced with such stressful, dynamic, and ambiguous
situations use the Recognition-Primed Decision (RPD) model.
According to this model (Klein, 1989), the experienced decision-
maker can make rapid but effective decisions by using experience to
size up the situation and to generate and evaluate COAs one at a
time (as opposed to comparatively). In the simple case, the
situation is recognized as typical of ones encountered before and a
typical COA can be immediately applied. More complex situations
that are not typical lead to more diagnosis and require a more
deliberate analysis of COAs. The diagnostic strategy may involve
the construction of series of hypotheses or stories to explain the
available data. The most reasonable story is adopted. Once the
situation is diagnosed and a plausible story is constructed to
account for the data, the appropriate COA is usually obvious. When

11



Future Areas of Integration: Individual Differences

Intelligence

Very few studies have incorporated both experience and
intelligence into their consideration of stress and its effects.
One group of researchers who have done so is Locklear, Fiedler, and
Powell (1988). They examined the impact of individual experience
and intelligence of military leaders (lieutenants, captains, platoon
sergeants and above) on decision performance under varying degrees
of stress. Their experiment incorporated two levels of intelligence
(low, high) and two levels of experience (low, high) based on median
splits. Three levels of stress were included (low, medium, high).
Results indicated that:

e Intelligence was a benefit at all stress levels;

e Experienced leaders outperformed less experienced leaders in the
high stress condition;

eAt low and moderate stress levels, intelligence resulted in
better performance than experience, and;

eAt high levels of stress, experience resulted in better
performance than intelligence.

The effects and interactions of intelligence, experience, and
stress on performance can be summarized as follows:

Intelligence

e Increased intelligence positively affects performance at all
levels.

eHowever, even those higher in intelligence are negatively
affected by high stress levels

eAt high stress levels, greater experience more positively
affects performance than greater intelligence.

Experience

e Experienced individuals have little or no performance decrements
due to high stress; and

eNovices suffer significant performance decrement under high
stress.

Thus, the key characteristics related to individual experience,
intelligence, and stress, that may be incorporated into the HPCM can
be summarized as follows:

17



The data presented earlier on the magnitude of the effect of
time pressure from Driskell et al. (1992) does not consider the
modulating effects of experience on the magnitude of the time
pressure effect. Judging from the review of NDM (Klein, 1996), we
may propose that these accuracy changes may be reduced by 70% to 90%
in experienced decision makers, yielding the lower correlation with
Driskell et al’s. (1992) MAG performance accuracy effects presented
in Table 1. These revised effects that take into account experience
are presented in Table 2.

Table 3

Magnitude of Time Pressure Effect on Accuracy with Low and High

Experience

MAG Accuracy Effect: Accuracy Effect:
Low Experience High Experience

-481 -.10 -.02

-533 -.20 -.04

-587 -.30 -.06

644 -.40 ~.08

707 -.50 -.10

Modeling stress and experience

The effects of stress can degrade cognitive performance as
represented in the computation of Effectiveness (E) in Figure 2.
The stress effect (SE) as represented in this simplified model is
designed to reflect the influence of stressful events, time
pressure, and experience on effectiveness in making decisions. One
key factor in this model of stress is the occurrence of significant
events in the battle scenario that may either advance the mission
(positive or confidence building events) or hinder the mission
(negative or stressful events). The computation of the SE depends,
in part, on the frequency of those events (f) and their value or
severity. Mission advancing or confidence building events can range
in value from 0 to +1; hindering or stressful events can range in
value from 0 to -1. The overall stress effect at any moment
considers the average of these values over the preceding time
interval. This average value is the sum of the values of the events
divided by the total frequency of events (f).

13



NTC data sets used for the first mission were complete with the

exception of HINDs, eight T-72,
all artillery units for both BLUFOR and OPFOR.
HINDs, missing T-72s,
order to make up for the missing NTC data.

and ten BMP units for the OPFOR and
Operator controlled
BMPs and OPFOR artillery were utilized in
Artillery fires and

other OPFOR CGF attacks were orchestrated based upon the OPFOR
Operations Order for the mission and the OPFOR Doctrinal Support

Package.

Time limits for all missions were consistent with actual

NTC mission data.

Tables 7 and 8 present the results from the first actual NTC
training mission that provided the present data for this set of

simulation runs. Both BLUFOR and OPFOR destroyed enemy in zone.
However Commanders’ Intents were not achieved due to heavy losses on
both sides.

Table 7. Actual NTC Battle Statistics (BLUFOR)
FULLY-MISSION CAPABLE

BLUFOR DESTROYED

M1/Al1 26 20

IFV 20 9

SCOUT 4 2

VULCAN 3 2

STINGER 3

TOTALS 56 33

Survivability Index = 41.1%

Table 8. Actual NTC Battle Statistics (OPFOR)

FULLY-MISSION CAPABLE

OPFOR DESTROYED
T-72 10 9
BMP AT 32
BRDM 7 3
RED AIR 8 3
HIND 4 2
ZSU 2 2
TOTALS 78 51
Survivability Index = 34.6 %

26


sleep that restores 480 reservoir units.

STRESS EFFECT (SE)

Modulates Effectiveness based on Frequency(f) and Severity(v) of

Stress Events, Time Pressure (h), and Experience (x):
where:

PERFORMANCE
CIRCADIAN RHYTHM (C)

C= A* cos(aiT - 9/24)

, . DECISION PROCESSOR
A 26H Clock
sino EFFECTIVENESS P(cor) = e+(1-e)/N
A Ampituce () E= 100(RB/R) + C + SE ‘A P(cot) = Probability of correct decision
‘Ae = £/100, effectiveness as fraction
AN = Number of COA

IRESERVOIR DEFICIT}

SLEEP ACCUMULATION
s=at @=h+(R'-RB)B

4 Accumulation Rato ( a)

‘2 Winimum Accumulation Rato( >)

‘A Time Constant ( B) Controls Gain on

COGNITIVE RESERVOIR

RB 41) = RBy +S -P

4 Resource Balance (RB): Increased by Min of
Sleep, Reduced by Min of Performance

4. Cognitive Reservoir Capacity (A): Balance
needed to pertorm perfectly when full and
sustain performance without sleep for 4 days

4 Individual Capacity (R') depends on prior
twaining. unit cohesion. and experience

PERFORMANCE USE
paKt,

& Time Awake (t,)

‘A Resource Use Rate (x)= 5

FRAGMENTATION 5
Sleep Accumulation begins five minutes f
after a transition from awake to sleep

Figure 2. Model of the effects of sleep deprivation/fatigue,
circadian rhythm, stress, and experience on decision-making
performance.



Figure 1.
2.

LIST OF FIGURES

General Model of Stress Effects on Performance......
Model of the Effects of Sleep Deprivation/Fatigue,
Circadian Rhythm, Stress, Experience on Decision
Making Performance..... a
The Implementation of HPCIM and KB in C2SIM wee e ee eee

xi



program whenever an effect is generated that will alter the CE

profile.

Human Performance
Cognitive Model
: (HPCM)

Calculates probability of
correct decision based on
stress and other factors

HPCM sends Blue
Command entity probability
of correct decision.

Blue Command updatt
HPCM on current stres:
levels and confidencs
builders.

Blue Command requests a
new Event from the
Knowledge Base

Knowledge Base
(KB)

Data base of Courses of
Action, Decisions, and Goal
for the Blue Command Entit

to execute

Knowledge Base returns the
appropriate Event to the Blue
Command Entity which
carries out the actions in the
main simulation.

~

Blue Command

(BCE)

Red Command

Entity

C3SiM

Main Simulation

Simulation mechanics and current world state

SoBe

=

SusnS27g08 2:

Figure 3. The implementation of HPCIM

and KB in C3SIM.

The BCE checks the current C3SIM simulation state by testing to

see if goal conditions received from the KB have been achieved. The
outcome of these tests is subsequently sent to the HPCM, which
updates the current PCD by adding stressor or confidence builder
levels. When it is time for the BCE to make its next decision, it

acquires the updated PCD from the HPCM. The BCE then uses the new
PCD along with current simulation conditions to get a new event from
- the KB.

|
|
| Entity
'
|
23



Personality characteristics

The effects of stress may also be directly and indirectly
moderated by individual personality characteristics (see Driskell &
Salas, 1996). Direct effects of several personality traits on
decision making have been examined, including aggression, risk-
taking, self-confidence, locus of control, and trait anxiety. These
traits have been shown to become increasingly significant in
decision making when more reliable methods are not available such as
experience (Sanbonmatsu & Fazio, 1990). For instance, if an
individual has no prior experience with a situation and is highly
limited by time stress, they may use tendencies such as aggression
to facilitate the decision. However, as experience with a situation
increases, the direct impact of personality factors decreases.

More typically, personality indirectly affects the influence of
stress on decision making through an individuals reactivity to
stress. Several personality types have been shown to react
predictably when exposed to stressors. Type A personalities,
defined as “hard-driving, persistent, and highly work-involved
“behavior patterns” exhibit higher stress levels than non-Type A
personalities (Bowers, Weaver, & Morgan, 1996). Also, people who
are self-confident or who have a high locus of control exhibit lower
stress levels, or a stronger resistance to performance decrements
associated with high stress levels.

In the context of the HPCM, it may be hypothesized that the
indirect effects of personality will be more relevant than direct
effects under the assumption that commanders will all have had at
least some training or experience which is used to guide decisions.
Therefore, the direct effects of personality on decision making
under stress should be minimized. Currently efforts are underway to
quantify the effects of these personality variables for the HPCM to
properly represent the effect of personality variables on decision
making under varying levels of stress. These efforts will also
compare these influences to training, experience, and intelligence
and look for interactions among all categories of individual
differences.

In terms of quantifying the impact of personality factors on
decision making, although some literature exists in which the
relationship of personality traits with decision making, very little
literature is available within the context of NDM, particularly in
relation to stress effects. Further, the effect of experience as a
moderator of stress effects is also overlooked in the research
involving personality characteristics and decision-making.

20



In order to test and validate the effects of the human
performance variables examined in this study, a constructive
simulation was developed from an extant communications planning
simulation (Gillis, 1995). The results of the simulation with and
without the effects of the human performance variables was examined
and compared to actual NTC training mission outcomes.

Findings:

An examination of these preliminary data suggests similar
results between actual NTC battle statistics and Mission Replay
Mode, Command, Control, and Communications Simulation (C3SIM)
Probability of Hit/Probability of Kill (PH/PK) algorithms. Mission
goals were partially achieved by the OPFOR when the computer
generated Blue Command Entity was in a fatigued state. When the BCE
was in a well-rested state, it achieved all mission goals from the
BLUFOR Commander’s Intent. Superior maneuvering in all situations
was clearly one of the strongest reasons for the Blue Command
Entity’s success. Effective evasive maneuver drills at the unit
level also contributed to BLUFOR success.

Utilization of Findings:

This initial effort provides a set of cognitive and human
performance specifications for ADS. It also provides guidance for
the development of situational awareness and course of action class
specifications from the perspective of human performance and
cognitive modeling effects on ADS.

viii



COGNITIVE BEHAVIORS FOR COMPUTER GENERATED FORCES

EXECUTIVE SUMMARY

Research Requirement:

The basic problem addressed by this research concerns the need
for more intelligent and realistic behavior by Computer Generated
Forces (CGF) Command Entities (CEs) in Advanced Distributed
Simulation (ADS). The problem is best approached through the
utilization of better human performance and cognitive modeling R&D
leading to algorithms for inclusion into CGF CEs, which will enable
the CE to function more realistically.

Realistic cognitive modeling class specifications for CGF have
yet to be developed, especially for large scale simulations; hence
this is a needed research and development area. This lack of
research leads to inaccurate behavior of CGF CEs. It also impacts
the human resources required for CGF scenario development and
exercise control in ADS. They are inappropriately demanding and may
be off-loaded by more intelligent and realistic CGF behaviors.

Procedure:

The research methodology for this project includes efforts to:

° Review relevant literature on current human performance
factors and quantitative human performance models that
may aid in the development of more accurate CE behavior.

e Create a CGF Battalion Command Entity (BCE) ina
reconfigurable, constructive simulation using actual
National Training Center (NTC) training data for purposes
of human performance variable validation. NTC data
provides information from mission replay, unit behaviors,
and “stressor and confidence-building events" useful for
validating BCE behaviors.

*® Incorporate the military decision-making process (MDMP)
and an analysis of Mission, Enemy, Time, Terrain, and
Troops (METT-T) as it chooses battlefield courses of
action within the context of NTC mission data.

® vary BCE behavior based on “human performance stressors
and confidence builders” as it exercises the MDMP and
METT-T.

vii



Table 2

Initial Values of the Resource Balance (RB) of the Cognitive
Reservoir for Five Levels of Individual Capacity (R’) and Prior
Sleep Duration.

Individual Capacity | Starting Value of Resource Balance (RB) at Midnight of Scenario Day
(R) Average Hours of Sleep per Day for Prior 5 Days

Percent Units 8 7 6 5 4 3 2

100 2880 2400 2361 2308 2239 2129 1871 1006

95 2736 2260 2217 2164 2095 1986 1734 919

90 2592 2116 2073 2020 1951 1842 1596 826

85 2448 1972 1929 1876 1807 1698 1457 727

80 2304 1828 1785 1732 1663 1555 1318 622

Table 2 also accounts for differences in individual capacity.
The ideal individual attains a full capacity (R) of 2880 and is
capable of 100% performance when fully rested and at the circadian
peak. Not all individuals are able to attain this ideal level of
performance and have individual capacities (R’) less than 2880.
These levels are shown as rows in the table representing levels that
range from 100% to 80% of the ideal level. RB values are computed
separately for each R’ because they depend, in part, on the
operation of feedback in the model based on sleep deficit, which is
the individual capacity (R’) less the current RB.

During the scenario, the RB will continue to deplete and
potential cognitive effectiveness degrades with hours awake
according to the following formulas:

Effectiveness:
Circadian Rhythm:

i

E 100 (RB/R) +C

Cc A * cos (2nm(T - @)/24), where A =10,
T= time of day, and g=20
R

Cognitive Capacity: = 2880 units

Resource Balance: RBit+i) = RBiyy - P, where RB starts at the values
in Table 2 at midnight

Performance Use: P=k« ta , where ta is time awake in minutes

since the last computation of RB and K= .5



Table 1.

7.

10.
11.
12.
13.
14.

LIST OF TABLES

Relationships Between MAG Levels and Performance

J -\oXot) hat- (os gn 5
Initial values of the Resource Balance (RB) of the

Cognitive Reservoir for Five Levels of Individual

Capacity (R’) and Prior Sleep Duration.........-..6-- 9
Magnitude of Time Pressure Effect on Accuracy with
Low and High Experience...... 1. ee eee eee eee eee ee eeee 13

Experience/Training Ratings and Associated

Individual Cognitive Reservoir Capacity Values (R’)

and Experience Modulation Factors (X) .....---+-++-+-++++: 15
Sensitivity Analysis of P(cor) to Variation in

Number of COA (N) with Different Levels of

Effectiveness (€) ...... eee eee eee eee ee eeee . seeeeee 16
Cognitive Performance Matrix as a Function of.
Stress Level, Intelligence, and Experience 19

Actual NTC Battle Statistics (BLUFOR)
Actual NTC Battle Statistics (OPFOR) .
C3SIM Mission Replay (BLUFOR)
C3SIM Mission Replay (OPFOR)
C3SIM HPCM PCD <50% (BLUFOR)
C3SIM HPCM PCD <50% (OPFOR) ..
C3SIM HPCM PCD >50% (BLUFOR) ..
C3SIM HPCM PCD >50% (OPFOR)


Table 4

Experience/Training Ratings and Associated Individual Cognitive
Reservoir Capacity Values (R’) and Experience Modulation Factors (x)

Experience/Training| Individual Cognitive Experience
Rating Capacity, R’ : Factor, x
Exceptional 100 % 2880 -99
Outstanding 95% 2736 -85
Above Average 90% 2592 -70
Average 85% 2448 -50
Minimal 80% 2304 -00

The overall equation for the SE is shown in Figure 2. Within
the computation of the SE is a weighting factor, epsilon, that
determines the amplitude of the SE effect in comparison to the other
factors that influence effectiveness (E). Epsilon equals the +/-
range of SE in percent. SE combines as an additive factor with the
effects of sleep deprivation and circadian rhythm to yield a value
for overall effectiveness (E), expressed as a percent of maximum
effectiveness. When the stream of events has a negative average
value (stressful events) and the overall value of SE is negative,
then the value of E will be decreased by the absolute value of SE.
Note that experience does not directly eliminate the effects of
fatigue, although it does increase the individual cognitive capacity
and increases expected effectiveness when fully rested.

The Decision Processor

In any battle scenario, the commander is faced with a variety of
COAs to deal with each new set of conditions. We may represent that
list of COAs as ranging from 1 to N in number. Only one action is
the best for that situation - the correct COA. The probability that
the commander will select the correct COA (Probjcor)) is determined by
the current level of cognitive effectiveness based on the results of
sleep/fatigue, circadian rhythm, and stress. The probability that
any given choice will be correct can range from 1 (correct with
certainty) to random (1/N). As effectiveness increases, the
probability of correct response increases as a direct function of
current effectiveness, expressed as a probability fraction
(e=E/100), and the probability of a random response varies as the
complement of effectiveness (1l-e). The overall probability of

15



Technical Report 1113

Cognitive Behaviors for Computer Generated
Forces

Philip D. Gillis
U.S. Army Research Institute

Steven Hursh
Science Applications International Corporation

Michael A. Guest
University of Central Florida
Consortium Research Fellows Program

Bill Sweetman
Systems Engineering Associates

Jennifer A. Ehrlich
University of Central Florida
Consortium Research Fellows Program

March 2000

20010524 053

United States Army Research Institute
for the Behavioral and Social Sciences

Approved for public release; distribution is unlimited.



Simon. H. A. (1955). A behavioral model of rational choice.
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Slovic, P., Fischoff, B., & Lichtenstein, S. (1977). Behavioral
decision theory. Annual Review of Psychology, 28, 1-39.

Sanbonmatsu, D. M. & Fazio, R. H. (1990). The role of attitudes
in memory-based decision making. Journal of personality and social
psychology, 59(4), 614-623.

Steinwag, K. K. (1995). Dealing realistically with fratricide.
Parameters, Spring, 4-29.

Stokes, A. F. (1995). Sources of stress-resistant performance in
aeronautical decision making: The role of knowledge representation
and trait anxiety. Proceedings of the Human Factors and Ergonomics
Society 39°" Annual Meeting. San Diego, CA, pp. 887-890.

Stokes, A. F., Kemper, K. L., & Marsh, R. (1992). Time-stressed
flight decision making: A study of expert and novice aviators (TR
ARL-93-1/INTEL-93-1). Urbana-Champaign, IL: Institute of Aviation.

Thorne, D. R., Genser, S. G., Sing, H. C., & Hegge, F. W. (1983).
Plumbing human performance limits during 72 hours of high task
loading. In Proceedings of the 24th DRG Seminar_on the Human as a
Limiting Element in Military Systems, (pp.17-40). Toronto: Defence
and Civil institute of Environmental Medicine.

Tversky, A. & Kahneman, D. (1973). Availability: A heuristic
for judgment frequency probability. Cognitive Psychology, 5, 207-
232.

Whitmarsh, P. J. & Sulzen, R. H. (1989). Prediction of simulated
infantry-combat performance from a general measure of individual
aptitude. Military Psychology, 1(2), 111-116.

Wickens, C. D. (1992). Engineering Psychology and Human
Performance (2TM¢ ed.). New York, NY: HarperCollins Publishers Inc.

Wickens, C. D., Stokes, A. F., Barnett, B., & Hyman, F. (1991).
The effects of stress on pilot judgment in a MIDIS simulator. In Oo.
Svenson & J. Maule (eds.), Time Pressure and Stress in Human
Judgment and Decision Making. Cambridge, Eng.: Cambridge University
Press.

yates, J. F. (1990). Judgment and Decision Making. Englewood
Cliffs, NJ: Prentice-Hall.

38


The Human Performance Cognitive Model

CE software is designed primarily to simulate the determination
and selection of courses of action (COA). Specifically, it must
take into account and simulate the following functions in the
movement of forces:

eTarget selection and firing

e Survivability and risk to force
*Communication from higher headquarters
eCommunication to subordinate units
eEnvironmental and task specific stress factors
*Factors modulating stress

eMission analysis

eSituation analysis

*Determination and selection of COA

*Revision and execution of orders

In other words, the CE must simulate decision-making
capabilities and restrictions that would normally occur in a battle
situation. The modeling and simulation effort described in this
report will focus on several possible stress factors, some stress
modulators, and the resultant determination and selection of COAs.

One important facet in the cognitive modeling of decision-making
in naturalistic conditions is taking into account the effect of
stress on the ability of the commander to select appropriate COAs.
Although stress can be conceptualized as a single phenomenon, it is
actually multifaceted. It has a number of causes (stressors) as well
as mediating factors (modulators) that increase or decrease the
impact of these stressors on the individual.

Stressors originate from both the physical environment and task
variables. Modulators of stressors are functions of both individual
characteristics and social features of the situation. Interactions
among these stressors and modulators ultimately affect the
expression and magnitude of physical, psychological, and performance
decrements on a given individual in a given situation. Figure 1
presents these categories of stressors, modulators, and stress
effects associated with a general model of stress effects.


Cognitive Behaviors for Computer Generated Forces

Introduction
Problem

The basic problem this research addresses is the need for more
intelligent and realistic behavior by Computer Generated Forces
(CGF) Command Entities (CE) in Advanced Distributed Simulations
(ADS) through better human performance cognitive modeling (HPCM)
R&D.

Realistic cognitive modeling specifications for CGF have yet to
be developed, especially for large-scale simulations, such as WARSIM
2000 and JSIMS. This lack of research negatively impacts the
accurate behavior of CGF CEs, as well as the human resources
required for CGF scenario development and exercise-control in DIS.
More intelligent CGF behaviors should reduce these problems. The
HPCM is one such effort at intelligent modeling.

The accuracy of the resultant HPCM for CGF CEs needs to be
validated in a realistic simulation setting. The U.S. Army Research
Institute (ARI) has access to an extant distributed interactive
simulation (DIS) testbed, a Command, Control, and Communications
Simulation (C3SIM) and also has access to numerous National Training
Center/Combat Training Center (NTC/CTC) training missions that
provide a means for the empirical validation of the HPCM.

Objectives

The long-term objectives of this effort are to identify, develop,
and evaluate:

eA unified theory of human behavior on the battlefield;

eA HPCM based upon the effects of training, experience, combat
stress, fatigue, confidence-building events, morale, individual
differences, situational influences, miscommunication, and
based upon the physiological and psychological effects of
direct/indirect fire suppression and heat and ballistic injury
for CGF CE.

As a starting point, we will examine, model, and test the impact
of time pressure, sleep deprivation, experience, and training.
Several other factors for future inclusion are also reviewed.





The C3SIM architecture is based upon objects written in C++ for
robustness and ease of modification. Tanks, infantry, and other
combat entities are represented by the unit objects that have
properties that are instantiated at run time from a separate data .
file. This allows modifications to the attributes of combat units
without the need of modification of source code.

C3SIM is also a multithreaded application. This allows it to be
a real-time simulation where actions and events can happen at any
time. BLUFOR and OPFOR run in their own threads allowing
simultaneous actions. This is in contrast to other, less realistic,
simulations that are turn based.

C3SIM also uses distributed component (DCOM) technology to
optionally communicate with an external application or applications
running on the same computer or on a separate computer connected via
a network. This allows a user to control the BCE decision-making
from an external application. DCOM networking is used with the
Knowledge Base (KB) application to provide the BCE with all
currently possible COAs. DCOM can also be used with our newly
developed HPCM to determine stress levels and possibility of
choosing a correct action. Thus, C3SIM may be used to obtain
baseline data related to human performance variables or it may be
used to observe the effects of factors from the model being
developed in this research effort such as sleep deprivation,
battlefield stressors, experience and training.

Implementation of the HPCM in C3SIM

The HPCM is an add-on to the C3SIM system. It is designed to
work with and modify the functioning of the BCE (see Figure 3). The
HPCM calculates the probability of a correct decision based on
stress and other factors. The formulas and their underlying
rationales were described earlier. The HPCM takes a set of initial
values and creates a CE profile based upon current level of
stressors, time awake, number of COA’s, experience, and other
values. This profile is then updated by external calls on methods
that represent humanistic effects (i.e., confidence builders,
additional stressors, time, and other factors) which alter the
initial profile. When the profile gets changed, the HPCM computes a
new value for the current probability of current decision (PCD).
Updating the PCD value when the profile changes, instead of when a
new PCD value is requested, permits constant access to the current
PCD value. This value is then returned when the access method for
the PCD is called externally. These methods are bundled in a
interface object which allows the HPCM to be controlled externally
through DCOM. This allows the profile to be updated from the C3SIM

22



Therefore, quantifiable generalizations that would be practical in
the current model are difficult. Only broad directions are
indicated:

eAn individual may use personality traits such as aggression to
assist in decision making when other resources, such as training
and experience, are not sufficient for the current task
(Sanbonmatsu & Fazio, 1990);

e Personality traits become more prevalent under severe time
constraints or low experience and training;

eSeveral personality characteristics have been associated with
higher reactivity to stress (Wickens, 1992; Bowers, Weaver, &
Morgan, 1996):

-Type A individuals are more reactive than Type B;
-Higher trait anxiety individuals are more reactive;
-Lower self-confidence results in higher reactivity;
-Extroverts are more reactive than introverts;

-External locus of control results in higher reactivity.

C3SIM and the HPCM
C3SIM Background

C3SIM is a reconfigurable, constructive simulation that utilizes
the Task Force strength of BLUEFOR and OPFOR Battlefield Operating
Systems that may be directly input from archived NTC data. C3SIM
features mission replay and mission modification modes for both
BLUEFOR and OPFOR CE control. It is a WindowsTM application that
represents a realistic replay of battalion, company, platoon, and
troop events and movements using data collected from the NTC or from
a user-defined script. C3SIM supports the full command structure
from a BCE to the actual unit itself. The battalion commander can
issue movement and attack orders to the company level, and can also
receive field reports from platoons. The mission state analysis
routines provide intelligent interpretation of the data gathered and
recommend courses of action. C3SIM incorporates data from both
Close Combat Tactical Trainer (CCTT) and Joint Research Training
Center (JRTC) to provide the PH/PK (damage) analysis routines.

21



COGNITIVE BEHAVIORS FOR COMPUTER GENERATED FORCES

CONTENTS

IMtroduction 0... ee ccc cece cece eee eee eee cence eens
B= ao) of 1
Objectives ...... eee eee eee wees

The Human Performance Cognitive Model...
SULeESSOLS ..... eee eee ee eee
Time Pressure....
Sleep Deprivation

Bi Cole [5 ak- 1 oo) ok -
Theoretical Background .. .
Experience and Training.........

Modeling Stress and Experience...
The Decision Processor
Future Areas of Integration: Individual Differences............ 17
Intelligence ........... ES
Personality Characteristics
C3SIM and the HPCM.............. .
C3SIM Background ...............50008
Implementation of the HPCM in C3SIM
Implementation of the Knowledge Base in C3sim.............. 24
C3sim Verification and Validation
SUMMALY .. cee ee ee ee ee eee
Major Accomplishments to Date...
The Problem Revisited: What Remains to Be Done?
REECLENCES 2... ec ee cet ete tte e teen t teenies 35

ix



multiple CQAs are open to the decision-maker, then each is evaluated
for likely outcomes (Klein & Crandall, 1995). Those COAs that lead
to difficulties or unacceptable outcomes are rejected, often leaving
only one acceptable action, under the specific set of conditions.
Thus under time pressure much larger effects should be expected with
inexperienced commanders compared to experienced commanders. For
inexperienced decision-makers who cannot rely on a RPD strategy or
who attempt to use an exhaustive prescriptive strategy, the effects
of time pressure will be serious degradations in decision making.

Thus, the product of naturalistic decision making (NDM) is a
decision that is adequate and resistant to time pressure, if not
absolutely optimal, because alternative, more exhaustive strategies
are disrupted by time pressure and, consequently, yield decisions
that are flawed or not timely. However, proper NDM critically
depends on higher level or training and experience. The studies of
NDM indicate that experienced decision-makers are able to generate
reasonable solutions as the first ones they consider when performing
a stressful task (Klein, 1996, Klein, Wolf, Militello, & Zsambok,
1995; Stokes, Kemper, & Marsh, 1992; Yates, 1990, and Wickens,
1987).

An important facet of this theory is the explanatory power it
offers for the degraded decision making ability of novices compared
to experts in high stress situations. Davies and Parasuraman (1982)
have proposed stress mainly impacts working memory. Novices must
utilize working memory to generate and compare possible solutions;
an expert simply selects important environmental cues and matches
them to a solution stored in long-term memory (Davies &
Parasuraman). Because retrieval from long-term memory is not
strongly affected by stress, the impact of stress on cognitive
performance is minimized (Wickens, Stokes, Barnett, & Hyman, 1991).
Thus, training that emphasizes the importance of generating
experience through multiple condition scenarios, allowing trainees
to develop workable solutions into long-term memory can aid in
mitigating the effects of stress.

Similarly, Mullins, Fatkin, Modrow, and Rice (1995) found that
participants with less experience reported higher ratings of overall
stress. Several other studies have documented the benefits of
experience for cognitive performance under stress in NDM situations
(Kirschenbaum, 1992; Stokes, 1995; Klein, Calderwood, & Clinton-
Cirocco, 1996; Klein & Calderwood, 1990).

12



The BLUFOR mission goals from the Commander’s Intent were met
during this scenario. BLUFOR Teams destroyed the enemy in EA
TUSKER, then checked for enemy in EA CLOVER, OBJECTIVE WIDOW, and EA
POWER and destroyed the enemy in those areas before advancing to the
final objective, OBJECTIVE FORD. The Task Force was at 58% strength
upon reaching OBJECTIVE FORD; the OPFOR survivability index was down

to 19%.
Table 13. C3SIM HPCM PCD > 50% (BLUFOR)
FULLY-MISSION CAPABLE

BLUFOR DESTROYED
M1/A1L 29 13
IFV 36 9
SCOUT
VULCAN
STINGER
M901 (TOW) 19 5
TOTALS 84 27

Survivability Index = 67.9 % ALL BF: 64 % ALL TF: 58.33%

Table 14. C3SIM HPCM PCD > 50% Battle (OPFOR)
FULLY-MISSION CAPABLE
OPFOR DESTROYED
T-72 16 13
BMP 50 43
BRDM 8 3
RED AIR
HIND 4 4
ZSU
TOTALS 78 63

Survivability Index = 19.23 % ALL OPFOR: 23%

A comparison of mission success with these preliminary data
suggests similar results between actual NTC battle statistics and
Mission Replay Mode, C3SIM PH/PK algorithms. Mission goals were
partially achieved by the OPFOR when the BCE was in a fatigued
state. When the BCE was in a well-rested state, it achieved all
mission goals from the BLUFOR Commander’s Intent. Superior maneuver
in all situations was clearly one of the strongest reasons for the
BLUFOR BCE’s success. Effective evasive maneuver drills at the unit
level also contributed to BLUFOR success. Survivability indices are
slightly higher in all C3SIM utilized OPFOR data due to incomplete
NTC unit movement data.

29



Summary

Major Accomplishments to Date

As this report indicates, Phase I of this effort resulted in two
major accomplishments. First, was the development of HPCM Ver. 1.0.
This software prototype primarily utilized Walter Reed Army
Institute of Research data on the effects of continuous operations
and stress on individual performance. At the heart of the model is
a cognitive reservoir that maintains a balance of effective
performance units. An effectiveness ratio was produced as the
product of interactions between battlefield stressors, performance
circadian rhythm, performance use, confidence building events, and
training and experience variables. This effectiveness ratio was
then divided by the number of potential COAs among which a simulated
CE could decide, producing a probability of a correct decision. A
timing variable was also introduced; it used the HPCM
"effectiveness" ratio to produce a time lag for the selection of a
course of action by a possibly "fatigued and stressed" BCE.

The second major accomplishment of this project was the
refinement of a PC-based, NTC mission replay simulation that can be
used to test the effectiveness of the HPCM outputs. The resultant
simulation, C3SIM, is a reconfigurable, constructive simulation that
uses Task Force strength BLUEFOR and OPFOR Battlefield Operating
Systems that may be directly input from archived NTC data. C3SIM
features mission replay or mission modification modes for both
BLUEFOR and OPFOR BCE control. Thus, C3SIM may be used to obtain
baseline data related to human performance variables or it may be
used to observe the effects of sleep deprivation, battlefield
stressors, experience and training, and other factors.

The Phase I work allows us to formulate the following
preliminary findings:

a. The following factors degrade performance, as manifested in the
BCE’s selection of a COA and the BCE’s timeliness of decisions made.

eSleep deprivation--Such a factor is a quantifiable variable; its
effects on the performance reservoir are a continuous variable.

° Performance use--This is also a real number that varies over
time. As time goes by, the BCE’s performance uses results in
the decrement of the effectiveness variable, which consequently
decreases the BCE’s probability of making a correct decision.

30


*Intelligence positively affects performance at all levels but is
subject to performance decrement, at high stress levels,
depending on experience;

eAt high levels of stress, experience positively affects
performance more than intelligence;

e Experienced individuals have little or no performance decrement
due to high stress;

*Novices have a significant performance decrement under high
stress.

A proposed framework for representing the effects referenced is
presented below (see Table 6). This framework implements:

eA 3 unit performance decrement for novice individuals from low
to high stress, when intelligence remains constant;

eA 1.5 unit performance decrement for middle-experienced
individuals from low to high stress, when intelligence remains
constant;

eNo performance decrement for experts from low to high stress;

eA 1.0 unit increase in performance with a 1.0 unit increase in
intelligence, when experience and stress remain constant.

18



Technical Report 1113

Cognitive Behaviors for Computer Generated
Forces

Philip D. Gillis
U.S. Army Research Institute

Steven Hursh
Science Applications International Corporation

Michael A. Guest
University of Central Florida
Consortium Research Fellows Program

Bill Sweetman
Systems Engineering Associates

Jennifer A. Ehrlich

University of Central Florida
Consortium Research Fellows Program

Simulator Systems Research Unit
Stephen L. Goldberg, Chief

U.S. Army Research Institute for the Behavioral and Social Sciences
5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

March 2000
EEE!
Army Project Number Personnel Performance and
20262785A790 Training Technology

Approved for public release; distribution unlimited.

iti



Klein, G., Wolf, S., Militello, L., & Zsambok, C. (1995).
Characteristics of skilled option generation in chess.
Organizational Behavior _and Human Decision Processes (pp. 63-69).

Klein, G. & Crandall, B. W. (1995). The role of mental simulation
in problem solving and decision making. In P. Hancock, J. Flash, J.
Caird, & K. Vicente (Eds.), Local applications of the ecological
approach to human-machine systems (pp. 576-580). Dayton, OH: Human
Factors Society.

Klein, G. A., Orasanu, J., Calderwood, R. and Zsambok, C. E.
(1993). Decision Making in Action: Models and Methods. Norwood,
NJ: Ablex.

Locklear, J., Powell, C., & Fiedler, F. (1988). The effects of
individual experience and intelligence on decision performance.
Proceedings of the 30°. annual Conference of the Military Testing
Association. Arlington, VA, pp. 135-140.

Lopes, L. (1983). Some thoughts on the psychological concept of
risk. Journal of Experimental Psychology, 9, 137-144.

Mullins, L. L., Fatkin, L. T., Modrow, H. E., & Rice, D. J.
(1995). The relationship between cognitive performance and stress
perception in military operations. Proceedings of the Human Factors
and Ergonomics Society 39% annual Meeting. San Diego, CA, pp. 868-
872.

Orasanu, J. M. & Backer, P. (1996). Stress and Military
Performance. In J.E. Driskell & E. Salas (Eds.), Stress and Human
Performance (pp. 89-125). Mahwah, NJ: Lawrence Erlbaum Associates.

Orasanu, J., Connolly, T. (1993). The reinvention of decision
making. In G.A. Klein, J. Orasanu, R. Calderwood, & C.E. Zsambok
(Eds.), Decision Making in Action: Models and Methods (pp. 3-20).

Norwood, NJ: Able.

Pain, J. A. W., Between, J.R., & Johnson, E.J. (1988). Adaptive
strategy selection in decision making. Learning, Memory, and
Cognition, 14(3), 534-552.

Schneider, W. & Detweiler, M. (1988). The role of practice in
dual-task performance: Toward workload modeling ina
connectionist/control architecture. Human Factors, 30, 539-566.

37



References

Angus, R. G. and R. J. Heslegrave (1985). Effects of sleep loss
on sustained cognitive performance during a command and control
situation. Behavior Research Methods, Instruments, & Computers,
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Backer, P. R. & Orasanu, J. M. (1992). Stress, stressors, and
performance in military operations: A review (Contract No. DAAL03-
86-D-001). Alexandria, VA: Army Research Institute.

Banderet, L. E., Stokes, J. W., Francesconi, R., Kowal, D. M.,
Naitoh, P. (1981). Artillery teams in simulated sustained combat:
Performance and other measures. P 581-604 in DHHS (Department of
Health and Human Services) NIOSH (National Institute for
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eds. Washington, DC: Government Printing Office.

Belenky, G., Balkin, T. J., Thomas. M. L., Redmond, D. P., Kant,
J., Thorne, D. H., Sing, M. S., Wesensten, N. J. & Bliese, P.
(1993). Report of the sleep and performance work group division of
neuropsychiatry, Walter Reed Army Institute of Research.

Bowers, C. A., Weaver, J. L., & Morgan, B. B. (1996). Moderating
the performance effects of stressors. In Driskell, J. E. & Salas, E.
(Eds.), Stress and Human Performance. Mahway, NJ: Lawrence Erlbaum
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Davies, D. R. & Parasuraman, R. (1982). The Psychology of
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Driskell, J. E., Hughes, S. C., Guy, W., Willis, R. C., Cannon-
Bowers, J., & Salas, E. (1991). Stress. stressor, and decision-
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Driskell, J. E., Mullen, B., Johnson, C., Hughes, S., &
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Driskell, J. E. & Salas, E. (Eds.) (1996). Stress and Human
Performance. Mahway, NJ: Lawrence Erlbaum Associates.

35



MAG = longer time period/(longer period + shorter period)
Equation 1.

A task that normally is performed with high accuracy in 60
seconds and that must be performed in 42 seconds would have a MAG
value of .587. In establishing their guidelines for the effects of
time pressure on performance accuracy, Driskell et al. (1992) define
small (r = .1), medium (r = .3), and large (r = .5) effect sizes.
The relationships between effect size and time pressure are
presented in Table 1.

Table 1

Relationships Between MAG Levels and Performance Accuracy

MAG Performance Accuracy
481 -.1
-533 -.2
587 -.3
-644 -.4
707 -.5

Thus, an MAG of .481 is needed to produce even a small effect on
performance (e.g., a 60-second task performed in 59 seconds), while
a MAG of .707 is necessary to produce a large effect on performance
(e.g., a 60-second task performed in 25 seconds).

Sleep Deprivation

Modern combat augmented by night vision devices and electronic
means of navigation and communication is not constrained by time of
day and the cloak of darkness. The flow of battle may be relatively
continuous with few breaks for sleep and recuperation. Under these
conditions of continuous or sustained operations, sleep deprivation
and fatigue may be a natural human hazard. Moreover, studies of
sleep patterns in simulated combat at the NTC indicate that
commanders (Lieutenant Colonels and Colonels) in force-on-force
operations average just over four hours of sleep per day, about half
of the normal requirement for fully effective performance (Belenky,
Balkin, Thomas, Redmond, Kant, Thorne, Sing, Wesensten, & Bliese,
1993). Furthermore, laboratory studies of sleep deprivation
indicate that the most sensitive indicators of sleep deprivation are
decrements in cognitive operations, such as logical reasoning,
mathematical operations, short-term memory, and decision-making



which specifies a linear decrease in the cognitive reservoir with
each minute awake. Potential performance effectiveness is sum of
three terms, the level of the cognitive reservoir, expressed as a
percent of its maximum capacity, the performance circadian rhythm,
and the general stress effects, discussed below. Finally, the
model stipulates a delay between the start of sleep and the
beginning of sleep accumulation. This factor introduces a penalty
for interruptions in sleep, or sleep fragmentation.

The overall model is homeostatic because of the feedback loop
between sleep deficit and the rate of sleep accumulation. Within
specified limits, the model will increase the rate of accumulation
to partially offset the accumulation of a sleep debt. In other
words, when a person becomes fatigued, the model specifies that a
deeper, more restorative sleep state is achieved for each minute of
sleep. As the deficit is reduced, the depth of sleep and the
contribution to the sleep reservoir also decreases back to the
baseline level. As a result, a person that obtains only four hours
of sleep per day for an extended period will accumulate a sleep debt
for the first two or three days, with a resulting loss in
effectiveness. After that point, the person will achieve an
equilibrium state in which no additional deficit occurs and the
level of performance degradation when awake remains relatively
constant.

For the purposes of the current command simulation, the
duration of the scenario is short, relative to the effects of
sleep deprivation that accumulates over days. In addition,
during the scenario it is presumed that the CE will not have an
opportunity to sleep. As a consequence, the effects of sleep
deprivation will be based upon an accumulation of sleep debt
that has occurred prior to the start of the simulation and the
computation of sleep accumulation during the simulation will not
be necessary. The model has been applied off-line to compute
starting values of the cognitive reservoir (resource balance,
RB) that reflect preexisting levels of sleep deprivation. These
values are shown in Table 2 for six levels of fatigue
representing the effects of average amounts of sleep per day for
the preceding five days, ranging from seven hours to two hours
per day. These values are the level of RB at midnight prior to
the day of the scenario. Since the sleep model assumes that
all sleep periods start at midnight, the initial RB reflects the
effects of a full day awake without the usual sleep on the
following night. For example, the initial RB for the eight-hour
case is 2400 units, which is the full capacity less the effects
of a full day of performance for 16 hours (960 minutes x .5 =
480 units) and without the benefits of the next eight hours of



REPORT DOCUMENTATION PAGE

2. REPORT TYPE
Final

1. REPORT DATE (dd-mm-yy)
March 2000

3. DATES COVERED (from. . . to)
February 1997 — November 1997

4, TITLE AND SUBTITLE
Cognitive Behaviors for Computer Generated Forces

Sa. CONTRACT OR GRANT NUMBER

5b. PROGRAM ELEMENT NUMBER
62785

6. AUTHOR(S)

Philip D. Gillis (U.S. Army Research Institute), Steven Hursh
(Science Applications International Corporation), Michael A. Guest
(University of Central Florida), Bill Sweetman (Systems Engineering
(Associates), and Jennifer A. Ehrlich (University of Central Florida)

5c. PROJECT NUMBER
A790

5d. TASK NUMBER
201

5e. WORK UNIT NUMBER

HOt

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Army Research Institute for the Behavioral and Social Sciences
ATTN: TAPC-ARI-IF

12350 Research Parkway

Orlando, FL 32826-3276

8. PERFORMING ORGANIZATION REPORT NUMBER

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)
U.S. Army Research Institute for the Behavioral and Social Sciences
5001 Eisenhower Avenue

10. MONITOR ACRONYM
ARI

Alexandria, VA 22333-5600 11, MONITOR REPORT NUMBER

Technical Report 1113

12. DISTRIBUTION/AVAILABILITY STATEMENT
Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 words):

The basic problem addressed by this research concems the need for more intelligent and realistic behavior by Computer
Generated Forces (CGF) Command Entities (CEs) in Advanced Distributed Simulation (ADS) through the utilization of
better human performance and cognitive modeling (HPCM) R&D. Realistic cognitive modeling class specifications for
CGF have yet to be developed, especially for large scale simulations; hence this is a needed research and development
area. The lack of existing research impacts the accurate behavior of CGF CEs, and it also affects the human resources
required for CGF scenario development and exercise control in ADS. They are inappropriately demanding and may be
off-loaded by more intelligent and realistic CGF behaviors. In addition, the development of human performance cognitive
models (HPCM) for CGF entities and consequent CE behaviors needs to be validated in a realistic simulation setting. To
meet this need, the U.S. Army Research Institute for the Behavioral and Social Sciences has developed and used a
constructive simulation testbed, the Command, Control, and Communications Simulation (C3SIM), and has also used
National Training Center data as a means to validate HPCM-driven CE behavior.

15, SUBJECT TERMS
Computer Generated Forces Cognitive Modeling Constructive Simulation Performance Predictors

Advanced Distributed Simulation Human Performance Stress and Decision Making _ Combat Stress

. 2 ny | 19. LIMITATION OF | 20. NUMBER 21, RESPONSIBLE PERSON
ABSTRACT OF PAGES (Name and Telephone Number)
16. REPORT 17. ABSTRACT | 18. THIS PAGE Stephen L. Goldberg
Unclassified | Unclassified | Unclassified | Unlimited 47 DSN 970-3980



Table 11. C3SIM HPCM PCD < 50% (BLUFOR)

FULLY-MISSION CAPABLE

BLUFOR DESTROYED

M1/Al 29 24

IFV 36 13
SCOUT

VULCAN

STINGER

M901 (TOW) 19 5
TOTALS 84 42

Survivability Index = 50% ALL BF: 56% ALL TF: 36.7%

Table 12. C3SIM HPCM PCD < 50% (OPFOR)

FULLY-MISSION CAPABLE

OPFOR DESTROYED
T-72 16 10
BMP : 50 28
BRDM 8 3
RED AIR
HIND 4 2
ZSU
TOTALS 78 43

Survivability Index = 44.8% ALL OPFOR: 56 %

The OPFOR Commander’s Intent was met with mixed success. BLUFOR
was partially destroyed in zone, and the resulting 45% OPFOR SI was
perhaps “sufficient combat power to seize the regimental subsequent
objectives.”

Tables 13 and 14 present data from CGF BCE control mode, where
HPCM PCD was greater than 50%. For this scenario, the HPCM setting
for the initial resource balance, or sleep reservoir, was set at
individual capacity maximum of 2000 units, thus the hypothetical BCE
had received the maximum amount of sleep for optimal cognitive
functioning. The experience variable was again set to moderate;
stressors and confidence building events and amplitudes again varied
based upon battlefield action.

28



correct choice at any given time is the weighted sum of these two
extremes: e+(1-e)/N. Table 5 displays the results of computations
of Probicor) with variations in the number of COA (N) and with levels
of effectiveness (e) resulting from the joint effects of fatigue,
circadian rhythm, and stress. Notice that when e is equal to 1.0,
the probability of correct is also 1.0, indicating perfect choice of
the correct COA. When e is zero, the probability of correct choice
is equal to random choice among the possible COAs, or is equal to
1/N. Within the range of e from 1 to .5, the most common range of
effectiveness, the value of Probicor) is fairly insensitive to N. For
example, for an e value of .7, Probicor) varies by only .05 (from .76
to .71) for increases in N from 5 to 30. This is reasonable, since
the selection of a COA will be dominated by the most common or
likely choices, even when the total number of logical choices
increases.

Table 5

Sensitivity Analysis of P(cor) to Variation in Number of COA (N)
with Different Levels of Effectiveness (e€) ........-..--+-5----

Formula: P(cor) = e + (1-e)/!I

Effective Values of N

s

E 5 10 15 20 30

1 1 1 1 1 1 Perfect

0.99 0.992 0.991 0.991 0.991 0.990

0.95 0.96 0.955 0.953 0.953 0.952

0.92 0.936 0.928 0.925 0.924 0.923

0.9 0.92 0.91 0.907 0.905 0.903

0.85 0.88 0.865 0.86 0.858 0.855

0.8 0.84 0.82 0.813 0.81 0.807

0.7 0.76 0.73 0.72 0.715 0.71

0.6 0.68 0.64 0.627 0.62 0.613

0.5 0.6 0.55 0.533 0.525 0.517

0.1 0.28 0.19 0.16 0.145 0.13

0 0.2 0.1 0.067 0.05 0.033 Random

(1/N)

16


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

A Directorate of the U.S. Total Army Personnel Command : .

EDGAR M. JOHNSON
Director

Research accomplished under contract
for the Department of the Army

Science Applications International Corporation

Technical Review by

Michael J. Singer
Joseph Psotka

NOTICES

DISTRIBUTION: Primary distribution of this Technical Report has been made by ARI.
Please address correspondence concerning distribution of reports to: U.S. Army
Research Institute for the Behavioral and Social Sciences, Attn: TAPC-ARI-PO, 5001
Eisenhower Ave., Alexandria, VA 22333-5600.

FINAL DISPOSITION: This Technical Report may be destroyed when it is no longer
needed. Please do not return it to the U.S. Army Research Institute for the Behavioral
and Social Sciences.

NOTE: The findings in this Technical Report are not to be construed as an official
Department of the Army position, unless so designated by other authorized documents.



An event object contains both a time to be used and a list of
decision objects. When the simulation time is equal to the current
event time, the BCE examines each decision object. Each decision
object contains a positive action, a negative action, stress level,
confidence builder level, and conditions. If all conditions are
Satisfied, the BCE gets the next decision object. If the conditions
are not satisfied, the BCE gets the current PCD from the HPCM and,
depending on this value, sends out either the positive or the
negative action as an order. Positive actions increase confidence
builder values; negative actions increase stressor values. These
value changes are then sent back to the HPCM. The CE then waits for
the next event time to occur to repeat the process.

The HPCM communicates with the C3SIM remotely using DCOM
technologies. The C3SIM sends the HPCM stress and confidence
builder levels that the HPCM uses with other factors to determine a
PCD, which the HPCM then passes back to the C3SIM. The C3SIM uses
this information to produce either a correct or incorrect decision
for the BCE.

Implementation of the Knowledge Base in C3SIM

The KB is a database of events, actions, decisions, and
conditions for the CE to use. At the highest level it consists of
event objects that reference a set of decision objects to specific
times in the simulation. The decision objects contain conditions
that can be used to test unit strength level, unit position, enemy
in area, and other variables. Decision objects also contain actions
representing the orders the CE sends out. For example, an action
may be an order for a team to move to a specific position, or an
order to have a team cease fire. Stressor and confidence builder
values also reside at the decision level. They are sent to the HPCM
via the BCE, depending on the outcome of the condition tests. The KB
encodes battalion commander expert knowledge. The original
formulation of this knowledge was obtained from a subject matter
expert who generated an extensive set of tables correlating battle
scenario conditions with COA.

The KB represents the mind of the BCE. It contains the goals,
sub-goals, contingency plans, and actions for the BCE to carry out
in the main simulation. The BCE receives new events from the KB and
then breaks these events down into actions that it can send out as
orders to teams. In addition to actions, it also uses conditions,
built by the KB, which are tests for the CE to use to check on
current status of sub-goals. If these sub-goals are not met, the
event may contain an alternate action to try to achieve this sub-
goal on a second attempt.

24


e. Reactions, especially negative reactions, must be distinct and
mutually exclusive from other reactions.

£. Interviews with NTC TF commanders and background data checks need
to be performed to arrive at quantifiable metrics to account for the
difference in experience and training, aggression/risk-aversive
factors in CE behavior.

The refinement of the cognitive model in Phase II should include
reconfigurable, multiple skill levels, and should include varying
models to account for an individual’s capacity to process
information, his personality traits, level of aggression, etc.

These data may be obtained from such sources as Army Field Manuals,
ARTEP's, the WARSIM 2000 Functional Description of the BattleSpace
database, and NTC data (in the form of After Action Reviews, Take
Home Packages, etc.).

Two larger issues also need to be addressed in Phase II, as a
consequence of the R&D performed under Phase I of this effort. One
is the issue of "realism" in Semi-automated Forces (SAF). There are
both severe limitations and advantages in implementing human-like
behaviors in an artificially intelligent CE. One such issue
involves the crucial distinction between human and computer
cognition. A computer is capable of the "precise" cognizance of
numerous entities and events occurring within a simulation.
Conversely, a human being is not capable of the cognizance of such
entities and events. However, a human being is more capable of
acting upon this information using a plethora of heuristics. Such
issues will be addressed and defined in Phase II of this research.

The second issue that needs to be more comprehensively addressed
involves the development of a unified theory of cognition and human
behavior on the battlefield. The simple schema developed for Phase
I of this research postulated that certain stressors are modulated
producing stress effects. This model is inconclusive relative to
numerous events that occur on the battlefield that are of a non-
stressful nature. Phase II of this research will propound such a
unified theory, based upon all of the positive, as well as negative,
quantifiable or qualifiable events that occur on the battlefield.

33



Table 6

Cognitive Performance Matrix as a Function of Stress Level,
Intelligence, and Experience

STRESS LEVEL INTELLIGENCE EXPERIENCE PERFORMANCE
SCORE
1 1 1 4
1 2 1 5
1 3 1 6
1 1 2 5.5
1 2 2 6.5
1 3 2 7.5
1 1 3 7
1 2 3 8
1 3 3 9
2 1 1 1
2 2 1 2
2 3 1 3
2 1 2 4
2 2 2 5
2 3 2 6
2 1 3 7
2 2 3 8
2 3 3 9

Intelligence Levels (3):

1=low 2=middle 3=high
Stress Levels (2):

1=low 2=high

Experience Levels (3):

1=low 2=middle 3=high
9 Performance Levels:
l=worst..............0- 9=best

The above matrix results in the following regression equation
(Equation 2):

y' = 2.25(EXPERIENCE) + 1.0(INTELLIGENCE) - 1.5(STRESS) + 1.5
Equation 2.

19



MODULATORS |

Individual Factors

‘Traint
Experience
Personality
Motivation

Figure 1. General model of stress effects on performance.*
* Items in bold will be reviewed here.

Not all stressors alter performance in the same way. For
example, sleep deprivation and group pressure reduce both
performance speed and accuracy. Time pressure and extreme cold, on
the other hand, reduce accuracy but increases speed (Driskell, et
al., 1992). In addition, the size of the effects is dependent on
the type and magnitude of the stressor. Heat and noise have small
(although significant), varied effects (Orasanu & Backer, 1996) on
performance, while fatigue and high levels of group pressure have
relatively large, negative effects on performance (Orasanu & Backer,
1996).

The size of the effect on performance will also depend on the
magnitude of the stressor, as well as the presence of any modulating
factors and individual differences. For instance, although the
effect of fatigue increases with hours of sleep deprivation, it is
modulated by the physiological circadian rhythm.

Hence it is not reasonable to speak of the effects of stress on
performance as a single hypothetical construct with a
straightforward consequence. Rather, a variety of identifiable
environmental and task conditions can alter the capacity of someone
to perform in ways that are both specific to given conditions and
tasks, as well as a reflection of unique individual and situational



(Thornse, Genser, Sing & Hegge, 1983; Banderet, Stokes, Francesconi,
Kowal, & Naitoh, 1981; Horne, 1988; Angues & Heslegrave, 1985).
Hence fatigue can be a strong disrupter of command level
performance.

Science Applications International Corporation (SAIC) has been
developing a Sleep and Performance Model (SPM) in conjunction with
the Division of Neuropsychiatry of the Walter Reed Army Institute of
Research (WRAIR). The initial version was based on the U.S. Army
Unit Resiliency Analysis (AURA) sleep and performance module. A
stand-alone model was developed that could be modified to better
reflect current psychophysiological and performance research. The
final product was the first version of the WRAIR-SAIC Sleep and
Performance Model, written in FORTRAN (Hursh & McNally, 1993).

In a subsequent effort, funded by the U.S. Army and in
conjunction with the Federal Highway Administration and the Federal
Aviation Administration, the SPM was refined and optimized to serve
as an integral element of the actigraph-based Sleep and Activity
Monitor (SAM), a joint product of the WRAIR, SAIC, and the Precision
Control Design Corporation. This wrist-worn device has been
produced and provides real-time assessment of sleep duration and
cognitive-performance potential, based on activity records of sleep
and wakefulness. The new SPM algorithm developed for this device
was based on several long-term sleep deprivation studies and was
redesigned to better reflect the most recent observations of the
recuperative value of sleep. The model is a homeostatic control
system that relates hours asleep, hours awake, and circadian
variations to cognitive potential. The model currently exists in
three forms: a WindowsTM-environment spreadsheet with embedded
graphics designed for development and demonstrations, a stand-alone
C-language application for actigraph data analysis, and a machine
language code incorporated in the miniaturized actigraph for real-
time LCD readout of performance potential.

The major components of the model are diagrammed in Figure 2,
bottom portion. At the heart of the model is the cognitive
reservoir that maintains a balance of effective performance units.
During sleep, units are added to the cognitive reservoir according
to the sleep accumulation function, which specifies how many units
of effective performance are credited for each minute of sleep. The
rate of accumulation is responsive to the sleep deficit, the
difference between the current level of the cognitive reservoir and
its maximum capacity. During time awake, units are subtracted from
the cognitive reservoir according to the performance use function,