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5a. CONTRACT NUMBER
Neurotechnology to Accelerate Learning: During Marksmanship
Training
W911NF-12-1-0039
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6. AUTHORS
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Adrienne Behneman, Chris Berka, Ronald Stevens, Bryan Vila, Veasna
Tan, Trysha Galloway, Robin Johnson, Giby Raphael
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Washington State University
Office of Grants and Research
423 Neill Hall
Pullman, WA
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U.S. Army Research Office
P.O. Box 12211
Research Triangle Park, NC 27709-2211
61844-NS-DRP.2
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The views, opinions and/or findings contained in this report are those of the author(s) and should not contrued as an official Department
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14. ABSTRACT
This article explores the psychophysiological metrics during expert and novice performances in marksmanship,
combat deadly force judgment and decision making (DFJDM), and interactions of teams. Electroencephalography
(EEG) and electrocardiography (ECG) are used to characterize the psychophysiological profiles within all
categories. Closed-loop biofeedback was administered to accelerate learning during marksmanship training in
which the results show a difference in groups that received feedback compared with the control. During known
distance marksmanship and DFJDM scenarios, experts show superior ability to control physiology to meet the
15. SUBJECT TERMS
training, neuroscience, EEG, ECG, marksmanship
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Bryan Vila
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509-358-7711
Standard Form 298 (Rev 8/98)
Prescribed by ANSI Std. Z39.18
Report Title
Neurotechnology to Accelerate Learning: During Marksmanship Training
ABSTRACT
This article explores the psychophysiological metrics during expert and novice performances in marksmanship,
combat deadly force judgment and decision making (DFJDM), and interactions of teams. Electroencephalography
(EEG) and electrocardiography (ECG) are used to characterize the psychophysiological profiles within all categories.
Closed-loop biofeedback was administered to accelerate learning during marksmanship training in which the results
show a difference in groups that received feedback compared with the control. During known distance marksmanship
and DFJDM scenarios, experts show superior ability to control physiology to meet the demands of the task. Expertise
in teaming scenarios is characterized by higher levels of cohesiveness than those seen in novices. Learning a novel
task generally relies heavily on the conventional classroom instruction with qualitative assessment and observation.
Integration of neuroscience-based evaluation and training techniques could significantly accelerate skill acquisition
and provide quantitative and objective evidences of successful training. In a project supported by Defense Advanced
Research Projects Agency’s (DARPA) Accelerated Learning program, EEG (brain’s electrical activity) and ECG
(heart’s electrical activity) techniques were used to assess expertise in marksmanship, combat DFJDM, and team
neurodynamics.
REPORT DOCUMENTATION PAGE (SF298)
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Continuation for Block 13
ARO Report Number 61844.2-NS-DRP
Neurotechnology to Accelerate Learning: During...Marksmanship Training
Block 13: Supplementary Note
© 2012 . Published in IEEE Pulse, Vol. Ed. 0 (2012), (Ed. ). DoD Components reserve a royalty-free, nonexclusive and
irrevocable right to reproduce, publish, or otherwise use the work for Federal purposes, and to authroize others to do so
(DODGARS §32.36). The views, opinions and/or findings contained in this report are those of the author(s) and should not be
construed as an official Department of the Army position, policy or decision, unless so designated by other documentation.
Approved for public release; distribution is unlimited.
By Adrienne Behneman, Chris Berka, Ronald Stevens,
Bryan Vila, Veasna Tan, Trysha Galloway,
Robin R. Johnson, and Giby Raphael
During Marksmanship
Training
T
his article explores the psychophysiological metrics during expert and novice performances in
marksmanship, combat deadly
force judgment and decision
making (DFJDM), and interactions of teams. Electroencephalography
(EEG) and electrocardiography (ECG) are
used to characterize the psychophysiological
profiles within all categories. Closed-loop biofeedback was administered to accelerate learning during marksmanship training in which the
results show a difference in groups that received
feedback compared with the control. During known
distance marksmanship and DFJDM scenarios, experts
show superior ability to control physiology to meet the demands of the task. Expertise in teaming scenarios is characterized by higher levels of cohesiveness than those seen in novices.
Learning a novel task generally relies heavily on the conventional
classroom instruction with qualitative assessment and observation. Integration of neuroscience-based evaluation and training techniques could significantly
accelerate skill acquisition and provide quantitative and objective evidences of successful training. In a project supported by Defense Advanced Research Projects Agency’s (DARPA) Accelerated Learning
program, EEG (brain’s electrical activity) and ECG (heart’s electrical activity) techniques were used to assess
expertise in marksmanship, combat DFJDM, and team neurodynamics.
Digital Object Identifier 10.1109/MPUL.2011.2175641
Date of publication: 6 February 2012
60 IEEE PULSE
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JANUARY/FEBRUARY 2012
2154-2287/12/$31.00©2012 IEEE
© BRAND X PICTURES
Learning a novel
task generally
relies heavily
on conventional
classroom instruction
with qualitative
assessment and
observation.
Marksmanship skill acquisition was accelerated by first modeling the psychophysiological characteristics of expertise and then
developing sensor-based feedback to accelerate novice-to-expert transition. A preshot
peak performance profile was identified in expert marksmen, characterized by an increase in
theta and alpha power (derived from EEG) and
a decrease in heart rate in the seconds leading up
to a shot [1].
A recent study incorporated a novel device
called the adaptive peak performance trainer (APPT) into
a marksmanship training protocol with novice participants. The APPT provided real-time feedback of heart rate
and EEG alpha power to the trainee. A haptic motor clipped
to the collar vibrated in sync with the trainee’s heart beat and
stopped vibrating when alpha power was sufficiently elevated. The goal of the APPT was to indicate to the trainee when
he is in the ideal state to fire the weapon. All participants
completed eight trials of five shots each in a simulated indoor
shooting range. The training protocol incorporated videobased coaching on the fundamentals of marksmanship by
a qualified marksmanship coach. An instrumented weapon
was developed using off-the-shelf sensing components and
a demilitarized M16/A2 housing a pneumatic recoil system
designed to approximate the weight, noise, and action of a
real live fire weapon. Shots were directed against a scaled
projection of a circular target simulating a 20-in diameter
target at 200 yd. Marksmanship performance was measured
as the average dispersion of five shots from the shot group
center (inches), where lower dispersion represents a tighter
shot grouping and better performance. Performance trajectories of novices trained with the APPT (n = 37) were 2.3 times
greater than those of novices trained with an identical protocol without the APPT (n = 17) [1] (Figure 1). A one-way
ANOVA revealed a control versus APPT difference in both
final trials, F (1,51) = 6.65, P < 0.05, and percent improvement, F (1,52) = 5.34, P < 0.05.
Combat DFJDM
The decision of when to use deadly force is a complex skill
to assess and train. Split-second life-or-death decisions require a delicate balance between restraint and aggression.
The psychophysiology of DFJDM was evaluated using realistic video-based simulations to fully engage participants.
Twelve experts (infantry with urban combat experience and
police with $5 years patrol in active areas) and 12 novices
(civilians with no military or police experience) participated
in the study. All participants engaged in 24 DFJDM scenarios
(approximately 70% justifying the use of deadly force). Scenarios were filmed using professional actors and represented
characteristics of the most common deadly force situations
encountered by law enforcement. When participants perceived a threat requiring deadly force, they responded by firing a simulated Glock 17 with a laser barrel insert. Between
scenarios, participants were given a short rest (+2 min) to
recover from the previous scenario and prepare for the next
scenario. Participants were given a long rest (+40 min) after every three scenarios. EEG and ECG were measured
throughout the day-long session (Figure 2).
Experts had significantly lower low-frequency heart rate
variability (LF HRV) than novices during the scenarios [t (22)
= –2.79, P < 0.05]. Lower LF HRV indicates less influence by
Mean Shot Group Dispersion (in)
Accelerated Marksmanship
Skill Learning
19
18
17
16
15
14
13
12
11
10
9
Control (n = 17)
APPT (n = 37)
Baseline Trials
Final Trials
FIGURE 1 The performance trajectory for the APPT group is
2.3 times greater than the control group.
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IEEE PULSE 61
The psychophysiology
of DFJDM was
evaluated using
realistic video-based
simulations to fully
engage participants.
example, entropy, a meathe sympathetic nervous syssure of randomness or untem, suggesting a lower state
certainty of NSs expressed
of stress [2]. Using a twoover a specified time period,
way ANOVA (group # task
was significantly higher for
demands), experts showed
expert submarine piloting
greater suppression of right
and navigation teams comparietal EEG alpha power (8–
pared to less-experienced
12 Hz) during scenarios relateams. Entropy increased
tive to rest periods, F(3,44) =
as teams gained experience,
3.84, P < 0.05. Task-related
approaching the level seen
alpha suppression is associin experts. Transition matriated with increased attention
ces (a plot of the NS_E being
demands, specifically, inexpressed at time t versus
creased alertness and expecthat at time t + 1) showed
tancy [3]. A greater change in FIGURE 2 The DFJDM scenario conducted at Washington State
that the expert teams use
alpha power between resting University. (Photo courtesy of Scott L. Oplinger, Washington
more of the available NS_E
and scenario most likely in- State University.)
patterns available to them,
dicates that experts are more
possibly indicating a more flexible team and one that does
efficient at matching their psychophysiological state (e.g., state
not frequently get locked into a restricted pattern of enof alertness) to task demands (scenario versus rest).
gagement.
In Figure 3, the entropy metrics for two novices and one
Team Neurodynamics
expert
team are shown. Submarine Officer Advanced Course
Our goal for modeling team neurodynamics is to rapidly de(SOAC) novice team 1 did not complete session 5, while novtermine the functional status of a team to assess the quality
ice team 2 had technical issues that resulted in session 1 being
of performance and decision with the potential to adaptiveunavailable. However, the graphs in Figure 3(a) clearly demonly rearrange the team or task components to better optimize
strate that novices increase in the entropy measure as they gain
teamwork. Neurophysiologic synchronies (NS) are low-level
experience and plateau in sessions 3–5. Figure 3(b) compares
data streams that combine individual team members’ EEGthe expert entropy to the novice entropy in sessions 1–2 verbased measures of engagement into an aggregated profile
sus 3–5. A significant difference is shown between experts’ and
of team engagement. The metric of EEG engagement from
novices’ sessions 1–2.
which these team patterns are derived is related to informaNS expressions appear to be important constructs for studytion gathering, visual scanning, sensory load, and sustained
ing team dynamics as
attention [4]. Team engagement NS are normalized and pattern classified by self-organizing artificial neural networks.
▼ they change rapidly in response to short- and long-term
The temporal expression of these patterns can be mapped
changes in the task [5]
to team events such as the frequency of team conversation.
▼ they relate to the task and some established aspects of team
Team NS metrics can be collected and analyzed in near real
cognition such as speech [6]
time and realistic training settings such as submarine pilot▼ they can be rapidly reported for use by educators/training and navigation, high-school team problem solving, or
ers [7]
antisubmarine warfare tactical training. The team NS met▼ they can distinguish some aspects of novice/expert perforrics that are expressed during team performance have been
mances
proven to provide insight into the dynamics of the team. For
▼ they are sensitive to training effects.
62 IEEE PULSE
▼
JANUARY/FEBRUARY 2012
Team engagement
NS are normalized
and pattern classified
by self-organizing
artificial neural
networks.
SOAC Team T1
SOAC Team T2
4.5
NS_E Entropy
NS_E Entropy
4.5
4.3
4.1
3.9
3.7
3.5
4.3
4.1
3.9
3.7
3.5
1 2 3 4
Session Number
1 2 3 4
Session Number
Adrienne Behneman (abehneman@b-alert.com), Chris Berka
(chris@b-alert.com), Veasna Tan (vtan@b-alert.com), Robin
R. Johnson (rjohnson@b-alert.com), and Giby Raphael
(graphael@b-alert.com) are with Advanced Brain Monitoring, Inc.,
Carlsbad, California. Ronald Stevens (Immex_ron@hotmail.com)
and Trysha Galloway (Trysha@teamneurodynamics.com) are with the
Learning Chameleon and UCLA IMMEX Project, Culver City, California. Bryan Vila (vila@wsu.edu) is from Washington State University,
Spokane, Washington.
(a)
References
4.3
**
NS_E Entropy
4.2
(n = 5)
(n = 5)
4.1
4.0
**P < 0.001
Kruskal–Wallis
**
3.9
(n = 9)
3.8
Expert
Teams
SOAC
SOAC
Teams
Teams
(Sessions (Sessions
1 and 2)
3–5)
(b)
FIGURE 3 Entropy metric for two SOAC novice teams and one
expert team: (a) the Shannon entropy levels for two SOAC teams
that performed four SPAN simulations at different times during
their nine-weeks training and (b) the mean (± S.D.) entropy
levels for SPAN sessions performed by expert submarine navigation teams (Expert) and for SOAC teams on their first two SPAN
performances (sessions 1 and 2) and subsequent performances
(sessions 3–5).
Acknowledgment
This work was supported by the DARPA (government contract
numbers NBCHC070101 and NBCHC090054) and National Science Foundation Small Business Innovative Research award
0822020. The views expressed are those of the author and do
not reflect the official policy or position of the Department of
Defense or the U.S. Government.
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