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OCR for page 109
F
Biomarkers
Specific biomarkers can be measured to indicate performance capability. For
instance, individuals exhibiting extensive neural connectivities between major
brain areas (as determined by increased fractional anisotropy on diffusion tensor
imaging, a magnetic resonance imaging technique) tend as a group to sustain
attention for longer periods of time than those with fewer such connectivities. Sub-
sequently, those individuals exhibiting extensive connectivities will perform at a
sustained level for longer time periods than those will with fewer connectivities. 1
As another example, the marked reduction in the slow wave from visual cortex
recordings following eye closure is indicative of fatigue and loss of vigilance. 2
1 Matthew D. Rocklage, Victoria Williams, Jennifer Pacheco, and David M. Schnyer, 2009,
“White Matter Differences Predict Cognitive Vulnerability to Sleep Deprivation,” Sleep 32(8):1100-
1103; Matthew D. Rocklage, W. Todd Maddox, Logan T. Trujillo, and David M. Schnyer, 2010,
“Individual Differences to Sleep Deprivation Vulnerability and the Neural Connection with Task
Strategy, Metacognition, Visual Spatial Attention, and White Matter Differences,” pp. 75-92 in Steven
Kornguth, Rebecca Steinberg, and Michael D. Matthews (eds.), Neurocognitive and Physiological
Factors During High-Temp Operations, Ashgate Publishing, Burlington, Vt.
2 Christian Cajochen, Daniel P. Brunner, Kurt Kräuchi, Peter Graw, and Anna Wirz-Justice, 1995,
“Power Density in Theta/Alpha Frequencies of the Waking EEG Progressively Increases During
Sustained Wakefulness,” Sleep 18:890-894; Christian Cajochen, Rosalba Di Biase, and Makoto Imai,
2008, “Interhemospheric EEG Asymmetries During Unilateral Bright-Light Exposure and Subsequent
Sleep in Humans,” American Journal of Physiology-Regulatory, Integrative and Comparative
Physiology 294:R1053-1060; Julian Lim and David F. Dinges, 2008, “Sleep Deprivation and Vigilant
Attention,” pp. 149-173 in Annals of the New York Academy of Sciences 1129:305-322; Ernst
Niedermeyer, 1999, “The /Normal EEG of the Waking Adult,” in Ernst Niedermeyer and Fernando
Lopes Da Silva (eds.), Electroencephalography: Basic Principles, Clinical Applications and Related
Fields (4th ed.), Williams and Wilkens, Philadelphia, Pa.
109
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110 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS
Certain biomarkers can be measured quantitatively in an operational set-
ting as shown in Chapter 3, Table 3.1. By monitoring multiple indicators, it may
be possible to provide a signature of probable performance degradation. The
measurement of these biomarkers in the deployed leader could then provide a
mechanism for anticipating potential positive and negative responses to threat
and thereby allow mitigation of undesired states. One possibility then would
be for extensive data sets of individual performance versus stress curves to be
developed for the leaders.
One challenge would be to develop better estimates of an individual’s state
(e.g., Is an individual in a rational, decision making mode versus an anger-
response mode, or in a state to detect the presence of a threat rapidly versus lack -
ing focus?). Objective assessments of individual states performed in a quantitative
and reproducible manner would require individual-based correlates of biomarker
measurements with performance capability. The varied experiences of individual
Marines suggest that biomarker outputs required for the assessment of logistics
leaders may differ from those needed by infantry leaders who may also have a
different set of critical markers from those for Marines involved in negotiations
with a local council leader.
Stress is coupled to performance in general as a U-shaped function: at very
low stress levels and at very high stress levels, performance degrades (see Figure
F.1). For example, performance can degrade either because of boredom (very low
stress) or because of overload (high stress). The maximum level of stress condu -
cive to high performance varies by individual.3 In studying individual differences,
it would be ideal to develop for each unit leader a plot of stress susceptibility
versus performance under high-tempo operations. The stresses assessed could
include sleep deprivation, fatigue, anxiety, isolation, and fear. Such a set of curves
could predict changes in the ability to make decisions and maintain vigilance,
situational awareness, and communication skills.
DECISION SPACE
Decision making in conducting enhanced company operations in hybrid
engagement, complex environments is carried out in a context of complexity,
the time duration of the mission, and geographical distribution. The complex -
ity of decision making is confounded by the conflicting goals of kinetic combat
conducted simultaneously with nonkinetic interactions involving noncombatants
with a strategic mission to “win the hearts and minds of the population.” The time
element can range from tactical issues that last for minutes to long-term strategic
issues that may last for weeks and months. The geographic distribution can range
from the local issues in a neighborhood of a small village to the large-scale inter-
3 PeterA. Hancock and James L. Szalma. 2008. Performance Under Stress (Human Factors in
Defense), Ashgate Publishing, Surrey, U.K.
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111
APPENDIX F
Excellent
Performance
Moderate
Poor
Low Moderate High
Stress
FIGURE F.1 Performance versus stress. SOURCE: Adapted from data by Peter A.
Hancock and Joel S. Warm, 1989, “A Dynamic Model of Stress and Sustained Attention,”
Figure F-1
Human Factors 31(5):519-537.
actions in a state or a region. These three variables (complexity, time, and space)
that characterize decision making can be plotted together. This plot in Figure F.2
can be thought of as the “decision space.”
The decision space also includes many coupled dependencies, such as the
impact of noncombatant and combatant casualties on psychological stress, time
urgency as a function of rapidly changing conditions, the logistics of supplying
needed support over a great distance, and the quantity and validity of the data that
contribute to situational awareness. And, of course, all of these interactions are
Decision Complexity
High Complex
Simple Complex
Space
Long time Focal Distributed
(local)
Short time
Time
Available
FIGURE F.2 The decision space, involving complexity, time, and space. The farther one
is from the origin, the more stressful and difficult the decision making task becomes, with
a higher likelihood of negative outcomes; the closer to the origin, the more manageable
the decision making is. Figure F-2
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112 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS
compounded by the various uncertainties associated with each of the decisional
factors (i.e., the real state of the world, the uncertainty associated with a weapon’s
effect and the associated collateral damage, etc.). As a result of these multiple
interacting variables, the outcome of any action will have high variability with
often-unexpected and sometimes undesirable outcomes.
A PROBABILISTIC APPROACH
Because precise predictions of the outcomes of various decisions are not
possible, a probabilistic risk management approach to decision making could be
applied. This probabilistic or engineering approach should include the expected
performance of individual decision makers at various points in the decision space.
In particular, the biomarkers of the individual making the decision could be a
particularly vital source of data that could be monitored and used to reduce the
risk involved in military operational decisions.
“Risk” is defined here as the product of the consequence of an outcome and
the probability of that outcome occurring. The desired outcome from improv -
ing the decision making capability of small unit leaders is to minimize the risk
involved in their decisions. Risk can be reduced by changing either the conse -
quence of an action or its probability of occurrence, or both. Improvements could
be achieved in decision making performance by using biomarkers to monitor the
physiological characteristics of individuals and providing mitigation to reduce
the probability or consequence of a particular suboptimal decision process. Fig -
ure F.3 illustrates how lowering the consequences and lowering the probability
lowers the risk.
Because of the inherent complexity of hybrid warfare, particularly in situ -
ations involving combatants and noncombatants, the consequences of an action
may be significant and the probability of an undesirable outcome may be high. An
example would be a patrol that has a high probability of noncombatant casualties
as a result of the co-location of noncombatants.
In all cases, the risk management approach requires the ability to make deci -
sions involving an analysis of a situation and an evaluation of the risk involved.
The performance of the decision maker will depend on such factors as perceived
rewards, cost, and social influences. Individual differences include intelligence,
adaptability, specific situational awareness, and training. It is well known that the
effectiveness of the decision maker is particularly impacted by chronic and acute
stressors as a function of time.4
Individual characteristics of adaptability can result in different levels of
continuing performance over a broad level of stress and over an extended period,
but for each individual there is a cumulative effect of chronic stress punctuated
4 PeterA. Hancock and James L. Szalma. 2008. Performance Under Stress (Human Factors in
Defense), Ashgate Publishing, Surrey, U.K.
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113
APPENDIX F
High
Consequence Medium Hi
gh
Ri
s k
M
ed
iu
m
Ri
sk
Low
Lo
w
Ri
sk
Medium
Low High
Probability
• Adaptability and resilience lower consequence.
• Training reduces probability.
• Improved situational awareness reduces probability.
• Real-time actionable intelligence (including sensors) lowers probability.
• Chronic and acute stress reduces performance and increases probability of
high consequence.
FIGURE F.3 Risk management. (The scales of the axes are logarithmic.)
Figure F-3
by acute incidents that can lead to a certain point at which the performance can
rapidly decline (based on the Yerkes-Dodson law illustrated earlier in Figure F.1).
In addition to the widely used methods of behavioral modeling, the data from
biomarkers on the physiological status of the decision maker could also be used
to anticipate or avoid this decrease in performance.
The critical technical issue that needs to be resolved is whether physiologi -
cal monitoring using biomarkers can be used to determine the performance stress
curves for individuals and whether those data can be used in a predictive manner
to improve decision making under realistic stress conditions.
