Cognitive Training for Military Application: a Review of the Literature and Practical Guide

Abstract

In recent years, the potential to improve cognitive skills through training has captured the attention of academic researchers, the commercial market, and the general public. Numerous clinical and healthy populations have been identified as targets for cognitive training, and military personnel are one particular group that may be able to uniquely benefit from cognitive training interventions. Military operations involve a wide range of human performance skills, many of which are cognitive in nature. Use of cognitive training to improve these critical everyday skills for service members represents an untapped potential resource by which to improve operational readiness and warfighter performance. While much of the cognitive training research to date has been circumscribed within basic science pursuits, here we propose ways in which this research may start to be applied in a military setting. In the current review, we examine instances of military operations that may readily lend themselves to cognitive training. Further, we examine the existing literature from academic endeavors and pinpoint areas of exemplary efforts that can serve as a guide for military research to follow, as well as pitfalls to avoid. In particular, we identify and review evidence from the video game, working memory, and executive function training literatures. Finally, the goals of basic and applied science often differ, and that is certainly the case when comparing outcome-based research in a military context with mechanism-based research in an academic context. Therefore, we provide a guide for best practices when conducting cognitive training research specifically in a military setting. While cognitive training has attracted much controversy in both academia and commercial markets, we argue that utilizing near transfer effects in a targeted, outcome-based approach may represent a powerful tool to improve human performance in a number of military-relevant scenarios.

Full text

REVIEW
Cognitive Training for Military Application: a Review of the Literature
and Practical Guide
Kara J. Blacker
1,2
&Joseph Hamilton
3
&Grant Roush
1,2
&Kyle A. Pettijohn
1,2
&Adam T. Biggs
2
Received: 3 October 2017 /Accepted: 10 April 2018
#This is a U.S. government work and itstext is not subject to copyright protection in theUnited States; however, its text may be subject to foreign copyright
protection 2018
Abstract
In recent years, the potential to improve cognitive skills through training has captured the attention of academic researchers, the
commercial market, and the general public. Numerous clinical and healthy populations have been identified as targets for cognitive
training, and military personnel are one particular group that may be able to uniquely benefit from cognitive training interventions.
Military operations involve a wide range of human performance skills, many of which are cognitive in nature. Use of cognitive training
to improve these critical everyday skills for service members represents an untapped potential resource by which to improve operational
readiness and warfighter performance. While much of the cognitive training research to date has been circumscribed within basic
science pursuits, here we propose ways in which this research may start to be applied in a military setting. In the current review, we
examine instances of military operations that may readily lend themselves to cognitive training. Further, we examine the existing
literature from academic endeavors and pinpoint areas of exemplary efforts that can serve as a guide for military research to follow, as
well as pitfalls to avoid. In particular, we identify and review evidence from the video game, working memory, and executive function
training literatures. Finally, the goals of basic and applied science often differ, and that is certainly the case when comparing outcome-
based research in a military context with mechanism-based research in an academic context. Therefore, we provide a guide for best
practices when conducting cognitive training research specifically in a military setting. While cognitive training has attracted much
controversy in both academia and commercial markets, we argue that utilizing near transfer effects in a targeted, outcome-based
approach may represent a powerful tool to improve human performance in a number of military-relevant scenarios.
Keywords Cognitive training .Military research .Human performance .Transfer
Military operations stretch the gamut of human performance
and tap into many different cognitive abilities. The possibility
of using cognitive training to improve those diverse sets of
cognitive skills relevant to military operations represents an
exciting practical application. Cognitive training represents a
substantial opportunity to improve the performance of military
service members if the right cognitive training is provided for
the right circumstances. For example, inhibitory control has
already been linked to the likelihood of inflicting either a
civilian casualty (Biggs et al. 2015) or a friendly fire incident
(Wilson et al. 2013). One cognitive training study has already
demonstrated the potential to improve shooting performance
through inhibitory control training by reducing civilian casu-
alties inflicted during simulation (Biggs et al. 2015). Other
forms of cognitive training could have widespread military
applications, including how visual search training might im-
prove security screening or raw intelligence processing, work-
ing memory (WM) training might have applications for high
cognitive workload tasks such as flying modern military air-
craft, and attention training might have applications for
watchstanding duties. The cognitive training opportunities
are almost endless given the need for accurate and quick de-
cisions in some of the most high-intensity scenarios
imaginable.
Recent reviews have assessed the accumulated empirical
evidence and made some detailed recommendations about the
*Kara J. Blacker
kara.blacker.ctr@us.af.mil
1
The Henry M. Jackson Foundation for the Advancement of Military
Medicine Inc, Bethesda, MD, USA
2
Naval Medical Research Unit Dayton, 2624 Q Street Building 851,
Area B, Wright-Patterson AFB, OH 45433, USA
3
Randolph-Macon College, Ashland, VA, USA
Journal of Cognitive Enhancement
https://doi.org/10.1007/s41465-018-0076-1

best cognitive training methods (e.g., Simons et al. 2016).
Still, these best case recommendations often discuss the gen-
eral viability of cognitive training and assume complete ex-
perimental control. Cognitive training and field applications
face significant uphill challenges when incorporating these
best methods into an active duty military population. Some
methodology issues become more important, some issues be-
come less important, but all issues can be reviewed with the
specific goal of improving human performance for military
operations rather than hypothetical generalities. The current
manuscript will therefore review the cognitive training litera-
ture with the stated and specific intent of evaluating these
training initiatives for military purposes. Some examination
will touch upon well-covered topics such as near transfer ver-
sus far transfer, but this discussion will differ because the
focus will be on the relative value for military applications
or a military audience—not the general population or specific
clinical disorders. The goal is to provide a framework and
understanding for any future research initiatives or applica-
tions which may seek to provide a military benefit from cog-
nitive training. This review is oriented toward two groups of
researchers: basic scientists studying cognitive training who
seek to find real-world applications for training and applied
researchers working with military populations that seek to
develop and test novel methods for improving operational
skills. We attest that the successful implementation of cogni-
tive training for improvement of military performance will
require a collaborative effort between these two groups. The
discussion will begin with a general overview of the cognitive
training literature before moving into practical applications
and considerations for ongoing and future military operations.
Cognitive Training Overview
The beginning of the twenty-first century has witnessed an
explosion of interest in cognitive training both in basic science
and applied settings. The overarching goal of cognitive train-
ing is to improve specific or broad-ranging cognitive skills
and numerous approaches have been examined with this goal
in mind, such as video game training (Green et al. 2016;
Powers et al. 2013), working memory training (Au et al.
2015;MorrisonandChein2011; Shipstead et al. 2012), med-
itation training (Chiesa et al. 2011;Lutzetal.2008;Tangetal.
2015), non-invasive brain stimulation (Berryhill et al. 2014;
Parkin et al. 2015), aerobic exercise (Hillman et al. 2008), and
many others (for a comphrensive review see Simons et al.
2016; Strobach and Karbach 2016). However, each of these
approaches to cognitive training has been surrounded by some
degree of controversy, which typically stems from debate
about the effectiveness of training to Btransfer^to perfor-
mance on other untrained tasks. In the context of cognitive
training, transfer refers to the potential of training on one task
to elicit improvements on another untrained task. This idea of
transfer from training originates from Thorndike’stheoryof
identical elements (Thorndike and Woodworth 1901), which
proposed that only when the knowledge components between
two skills are identical can there be transfer between the two.
Starting with the most rudimentary concept of transfer,
there is little doubt that practice improves performance or that
practicing one task (e.g., shooting a handgun) will lead to
more efficient learning of a closely related skill (e.g., shooting
a rifle). From practice, we can move along the transfer spec-
trum to what is typically referred to as near transfer. A repre-
sentative example of near transfer comes from the WM train-
ing literature. For example, if an individual trains on a spatial
n-back task, where the order and locations of stimuli must be
remembered, it would be expected that performance following
training would improve for an object n-back task, where the
order and identity of stimuli must be remembered. The logic
here is that training on the spatial n-back version improves
one’s ability to maintain and update visuospatial information
in WM and therefore this improved skill will result in better
performance on an untrained n-back task that uses different
stimuli. Near transfer has great potential for elucidating cog-
nitive mechanisms, as well as practical use in applied settings.
Despite thepractical value of near transfer,the vast majorityof
research and its subsequent controversy has surrounded the
idea of far transfer.
Far transfer refers to training on a task and seeing improve-
ment on an untrained task that does not necessarily share ele-
ments with the trained task. Again, from the WM training
literature, the typical example is examining whether training
on a WM task like an n-back task will improve performance
on matrix reasoning tasks that are considered measures of
fluid intelligence (Gf). The logic here is that WM capacity is
a limiting factor for performing a wide range of cognitive
tasks. In particular, WM capacity is highly predictive of Gf
and therefore improving WM capacity via training may enable
individuals to perform better on measures of Gf and/or a range
of real-world outcomes. While far transfer is the Bholy grail^
of cognitive training and the marketing foundation of com-
mercial brain training products, evidence has proved
inconsistent.
Here, we consider transfer to be a spectrum ranging from
practice to far transfer. Thus, there are a variety of Bmid^level
transfer scenarios one can imagine. For example, WM training
has shown mixed evidence for modality specificity, whereby
visuospatial WM training may or may not elicit improvement
on untrained WM tasks in the verbal domain and vice versa
(Buschkuehl et al. 2014;Jaeggietal.2014;Schneidersetal.
2011). This type of cross-modality, but intra-skill transfer,
may be viewed as mid-level transfer. Alternatively, consider
an example where training on a task that requires inhibitory
control, such asa go/no-go task, yields improvement on a very
different task that taps into an overlapping cognitive
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mechanism, such as the Stroop task. In this review, our pri-
mary focus will be on the spectrum of transfer with the goal of
informing the application of cognitive training in military op-
erational outcomes. Human performance is a critical factor in
military operations, and cognitive training represents a poten-
tial untapped resource for improving that performance.
Task Analysis
One critical consideration in the application of cognitive train-
ing to real-world outcomes is that of task analysis. Here, we
aim to provide an overview of the cognitive training literature
in a basic science setting, as well as provide a guide to using
cognitive training in applied settings—specifically the mili-
tary. Outcomes are of primary importance to military opera-
tions, but especially important are outcomes for multifaceted
skills, as military operations inevitably require coordination of
multiple cognitive abilities. For example, there is not one spe-
cific laboratory-based task that encompasses all components
of room clearing, piloting a military aircraft, or navigating a
ship. With this in mind, determining what knowledge, skills,
and abilities are required for a particular job is a critical step in
determining what type of cognitive training may be benefi-
cial—otherwise known as a formal task or job analysis. There
are several studies which address ways to conduct a formal
task analysis (e.g., Brawley and Pury 2016; Morgeson and
Dierdorff 2011;PatrickandMoore1985). However, for re-
searchers conducting applied or Bfield^research, there are less
formal steps that can be taken to integrate some of the princi-
ples used in a task analysis.
For example, in a recent study exploring the impact of
cognitive training on Bshoot/do not shoot^performance, the
authors applied task analysis principles to help design a spe-
cific training program for military and law enforcement re-
search (Hamilton et al., under review). The researchers con-
ducted interviews with a group of 26 firearms training profes-
sionals including active federal Special Agents, special oper-
ations military personnel, state and local law enforcement of-
ficers, and an elite group of national and world champion
competitive shooters. These subject matter experts (SMEs)
provided insights into which cognitive abilities might apply
to a shoot/do not-shoot decision, and this information was a
critical factor in determining not only the cognitive training
regimen but the research design as well. Importantly, this
method also allowed them to incorporate feedback from ex-
perts in nearly every relevant occupation where individuals
maybeforcedtomakeaBshoot/do not shoot^decision with
a firearm. This approach started with the real-world applica-
tion to create a cognitive training regimen rather than design-
ing a cognitive training regimen and then trying to fit it into a
real-world application. It should be noted that, in this situa-
tion, the SMEs suggested cognitive tasks that aligned with the
limited amount of existing literature regarding shoot/do not
shoot performance (e.g., Biggs et al. 2015;Wilsonetal.
2013).
In short, after reviewing the relevant literature on a partic-
ular application of cognitive training, particularly on a topic
where there is very little, a worthwhile next step is to consult
with the recognized Bexperts^in the area that is being studied.
This process of starting from the Bground^up and incorporat-
ing qualitative data, such as in-depth interviews with SMEs, is
one that is often used in grounded theory and/or organizational
psychology (Brawley and Pury 2016;Creswell2007).
However, while this technique may be less common in basic
cognitive psychology, it may represent a necessary first step in
applied research especially when specialized populations are
being targeted. This notion may prove especially true in mil-
itary applications.
Cognition in the Military
Research on the improvement of cognitive function in military
populations has been primarily focused on medical interven-
tions for service members recovering from traumatic brain
injury (TBI; Bogdanova and Verfaellie 2012; Huckans et al.
2010; Twamley et al. 2014) or psychological disorders such as
post-traumatic stress disorder (PTSD; Bogdanova and
Verfaellie 2012; Haaland et al. 2016; McDermott et al.
2016). For example, there is a wealth of research on the impact
of TBI on cognitive performance in military populations, such
as the Vietnam Head Injury Study, which has followed 1221
Vietnam veterans with mostly penetrating brain injuries pro-
spectively (e.g., Grafman et al. 1990; Grafman et al. 1996;
Groswasser et al. 2002; Raymont et al. 2011). The prevalence
of brain injuries continues to increase in the modern era, as
The Defense and Veterans Brain Injury Center estimates that
between 2000 and 2017 more than 375,000 service members
have been diagnosed with TBI (BDoD Worldwide Numbers
for TBI,^2018). TBI is associated with a variety of cognitive
impairments that include impaired attention, impaired
visuospatial/verbal information processing, memory deficits,
communication problems, poor judgment, and impaired exec-
utive functioning (Barman et al. 2016). The most common
cognitive impairments following TBI are related to attention,
memory, and executive functioning (Arciniegas et al. 2002).
These deficits represent clear targets for cognitive training as a
rehabilitative tool. Further, the deficits seen following TBI
demonstrate the utility of potential training programs that elic-
it near transfer (e.g., improving WM in a population that has
suffered WM deficits following TBI).
However, in addition to clinical applications following a
physical or psychological injury, improvements or enhance-
ments in cognitive functioning could have several positive
preventative implications for service members. Increased
combat readiness among healthy service members may help
to mitigate some Bavoidable^combat injuries. An avoidable
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combat injury could be described as an injury or incident that
results from a cognitive processing error or Bmental mistake.^
Some examples could include friendly fire incidents, prevent-
able civilian casualties, or incorrect relay of grid numbers
during communication with air support or artillery units.
Furthermore, enhancing cognitive abilities may serve another
preventative purpose by improving recovery prognosis fol-
lowing a TBI.
One explanation for why there are differences in how indi-
viduals respond to brain injury or disease is cognitive reserve.
The assumption underlying cognitive reserve is that pre-
existing neurological differences underlie the differential re-
sponse (Stern 2002). Cognitive reserve is broadly classified as
either passive or active. Passive reserve refers to gross ana-
tomical differences, such as brain size or cranial volume that
serve as protective factors. Active reserve refers to the neural
mechanisms, such as more efficient networks or neural flexi-
bility that protect cognitive function (Stern 2009). While nei-
ther of these types of cognitive reserve reduce the damage
caused by disease or injury, they may allow the individual to
compensate for the effects of the damage during recovery.
Several studies have shown that pre-injury measures of cog-
nitive reserve are associated with better clinical outcomes. For
example, one study that examined cognitive functioning fol-
lowing TBI found that total intracranial volume, a measure of
passive reserve, was associated with higher post-injury func-
tion (Kesler et al. 2003). Other research that has looked at the
effects of active reserve has shown that WM capacity (Sandry
et al. 2015), standardized test scores (Kesler et al. 2003), var-
ious cognitive tests (Fay et al. 2010), and educational attain-
ment (Schneider et al. 2014) may protect against the effects of
brain injury. These results raise the question of whether it may
be possible to use training to increase cognitive reserve, thus
reducing the impact of a potential future injury. While some
factors that influence passive cognitive reserve (e.g., intracra-
nial volume) are fixed, others that affect active reserve might
be improved through training. One example of using cognitive
training in a preventative manner with military personnel has
shown some initial promise. Jha et al. (2010) found evidence
that mindfulness training served to protect WM capacity dur-
ing a high-stress pre-deployment interval. The authors con-
cluded that while stress is known to reduce WM, the service
members who received mindfulness training were less suscep-
tible to the functional impairments associated with those high
levels of stress during pre-deployment. This approach of
boosting cognitive resources and skills prior to insult/injury/
stress is in line with the work on cognitive reserve as a pro-
tective factor. In addition to serving as a preventative ap-
proach, enhancement in cognitive functioning may also rep-
resent a method for improving warfighter performance and
efficiency in a number of other tasks, scenarios, and domains.
There are several areas of cognitive functioning that likely
impact military personnel and specifically apply to the unique
situations they encounter. Often the very nature of combat is
unpredictable, hectic, and imposes an overload of information
on an individual. Battle plans, call signs, routes, frequencies,
and grid numbers are only a few of the factors that may change
several times over the course of a single mission—and almost
certainly change upon contact with a hostile adversary.
Keeping track of all these items is extremely taxing to an
individual’s WM, which is known to be capacity limited
(Luck and Vogel 1997). To ensure mission success, it is crit-
ical that combat leaders constantly monitor and update new
mission relevant information and discard old, irrelevant infor-
mation. Individuals in specialty roles, such as medics and
forward observers, may be required to shift between very dif-
ferent mental tasks or sets such as calling for fire (e.g.,
radioing information to coordinate an artillery strike) to ma-
neuvering to returning fire to rendering aid. Military personnel
are expected to be able to perform these demanding tasks and
at the same time exercise enough inhibition to appropriately
identify and engage the enemy while avoiding friendly fire or
inflicting civilian casualties. Cognitively speaking, much is
demanded of members of our armed forces, particularly as
the battlefield becomes more technologically advanced and
complex. Therefore, it is imperative that we investigate how
specific cognitive functions impact service members and at-
tempt to find ways to improve their capability in those areas.
Unfortunately, this is an area in which relevant research and
training is severely lacking.
To illustrate the variety of potential cognitive training op-
portunities that are relevant to military personnel, consider an
example scenario that is often encountered in urban combat:
room clearing. Service members are often tasked with going
through a building or house and searching it for enemy com-
batants. This type of situation is dynamic, unpredictable, and
extremely dangerous. Individuals must rely heavily on visual
and auditory perception to see and hear necessary input.
Deployment of visuospatial attention is key, as the entire rel-
evant area must be attended to and then quickly searched for
potential threats. Individuals must be able to quickly and ac-
curately identify potential threats as hostile or non-hostile. If a
hostile target is identified, that individual must decide on a
course of action (e.g., shoot or do not shoot) and then execute
the necessary motor response (i.e., aim and pull the trigger).
All of this is accompanied by procedural and declarative
knowledge that is learned during combat training, such as
what order individuals in a team enter a room or building
and which individuals are responsible for which specific tasks.
Finally, all of these cognitive efforts are compounded by ad-
ditional variables such as perceptual and attentional distrac-
tions (e.g., a dog barking in the background or smoke obscur-
ing one’s vision), psychological stress (e.g., anxiety), and
physiological stress such as heat or muscle fatigue from car-
rying a heavy pack. Each of these relevantskills may represent
a potential trainable target. While academic endeavors in
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cognitive training have focused heavily on far transfer, as
illustrated in this example, the military may be able to capital-
ize on near transfer of these specific perceptual, attentional,
and higher-order cognitive abilities. In this review, we will
focus on the effectiveness of particular forms of cognitive
training to elicit near transfer to skills that are operationally
relevant to military personnel.
Critical Factors in Cognitive Training Application
When considering application of cognitive training in the real-
world, such as use with military personnel, it is critical to not
only consider range of transfer but other components of train-
ing. In particular, learning theories suggest that both content
and context of learning or training are key to a learner’sout-
come. Content is highly related to the concept of transfer
discussed above, as we typically consider content to be the
knowledge and skills practiced in a training paradigm. Thus,
the degree of similarity or overlap in content between the
training task and any untrained task dictates what degree of
transfer we might expect. Cognitive training can be designed
to focus on a singular task (e.g., spatial n-back), a singular
content (e.g., multiple WM tasks), or multiple content areas
(e.g., WM, inhibitory control, and task switching tasks).
However, it is also important to recognize the influence of
context on training outcomes as well. The context of training
or learning can vary along multiple dimensions, including
knowledge domain, physical location and circumstances,
intended purpose, and whether the trained skill involves only
the individual or involves other people. In a cognitive training
environment, additional context variables to consider are mo-
tivation (e.g., intrinsic vs. extrinsic) and compliance.
Furthermore, there can be a multitude of interactions be-
tween content and context (Barnett and Ceci 2002)thatthe
cognitive training field has only begun to consider—and some
of these issues may only arise in military, law enforcement, or
security situations. For example, one important issue involves
the transfer of effects when using simulators versus live am-
munition for training purposes. There is always a push to
create realistic circumstances during training, but military op-
erations are often dangerous in nature, which complicates the
possibility of creating realistic content and context for transfer.
Forexample,shootingsimulatorscommonlyusedbymilitary
units can only provide a limited amount of realism because
eventually a service member must carry a live weapon with
live ammunition during the course of his or her duties.
Unfortunately, there is no comparable substitute for this con-
text other than placing a live weapon into his or her hands.
Meaningful transfer thus becomes a critical issue because
transferring the learned training procedures to a combat situ-
ation can literally mean the difference between life and death.
It also means overcoming the anxiety of using a weapon and
the fear involved in facing combat. The challenge becomes
how to best ensure the greatest degree of transfer from one
context to another, which may require targeting the underlying
cognitive mechanisms thereby underscoring the value of cog-
nitive training. In this sense, the value of cognitive training
can be seen in the relationship between the Soldier and the
combat environment. It is impossible to fully prepare an indi-
vidual for all possible situational variations that he or she may
encounter. This functional challenge makes combat training
complicated; however, it is possible to train the cognitive abil-
ities of the individual that are likely to be utilized in combat.
Put another way, it may be impossible to train for precisely
where an individual will be driving a car—but, we can im-
prove the type of car he or she will be driving.
Meaningful training-related improvements in cognition are
almost certainly underscored by neural plasticity. Knowing
which conditions are conducive to plasticity helps to establish
the circumstances under which we should begin to train and
the types of training initiatives to explore. Lovden et al. (2010)
argue that a fundamental prerequisite for successful cognitive
training is a mismatch or imbalance between environmental
demands and actual brain supply. Thus, plasticity denotes a
prolonged mismatch between cognitive resources and
situational demands. To create this prolonged mismatch,
cognitive training tasks must be challenging but manageable
with a high degree of effort. This balance between keeping a
task difficult enough to ensure the participant is not bored
while easy enough to ensure the participant does not get
frustrated is typically achieved through adaptive paradigms
that keep the effort and feasibility level consistent for each
individual participant. Lovden et al. (2010) delineate two
manifestations of plasticity: changes to representations and
changes to processes and their efficiency. For example,
training-induced plasticity could alter representation or
knowledge, which is similar to the concept of crystallized
intelligence (Horn and Cattell 1966) and relies on procedural
or declarative knowledge and abilities. On the other hand,
training-induced plasticity could alter the process or efficiency
with which one completes a task. WM training relies on the
logic that training-induced plasticity will either increase the
capacity of WM or enable an individual to more efficiently use
their existing capacity (e.g., improving distractor filtering; C.
H. Li et al. 2017), which represents an alteration to process or
efficiency, which in turn benefits performance on other WM
tasks.
Perhaps the greatest challenge in applying cognitive train-
ing to the military is the lack of understanding regarding the
specific links between cognitive processes and human perfor-
mance in military operations. Specifically, it is easy to assume
that WM is necessary in aviation operations, where a pilot
must coordinate information from the control tower, other
aircraft in the vicinity, various instruments, and the skies
around the aircraft. Some recent evidence has started to ex-
plore these links while also using cognitive training to
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demonstrate that these links are causal in nature rather than
purely correlative (e.g., Biggs et al. 2015). Still, these links are
more exploratory than anything else. Cognitive training re-
search will need to build upon the most established links or
provide foundational efforts first to provide the largest and
most efficient expenditure of military resources in pursuit of
improved training initiatives.
Lessons from Commercial and Academic
Efforts
There has been exponential increase in the for-profit brain
training industry in recent years. Unfortunately, there is lit-
tle—if any—convincing evidence to support the claims made
by these brain training companies (e.g., Federal Trade
Commission 2016). These companies tend to make claims
of vertical, far transfer based on weak or non-existent evi-
dence and with no clear path between training and application
(for a detailed review of the commercial brain training
literature, see Simons et al. 2016). Conversely, when dealing
with a pre-defined area of interest such as specific military
operations, the well-defined application provides a rigid stan-
dard of comparison in the effectiveness of any cognitive train-
ing. Put another way, far transfer is not necessarily the end
goal. We are not looking for one single training task or even
one group of training tasks that will improve warfighter per-
formance on all job-related tasks. While this one-training-fits-
all approach would be ideal, it seems far-fetched and unlike-
ly—especially given that operational military performance
can range from room clearing to combat casualty care to avi-
ation operations. Instead, the military has specific tasks and
problems that it needs to solve and training could be designed
for specific audiences. Previously in this review, we have used
shoot/do not shoot performance in the context of room clear-
ing as an example of a singular problem. While this task has
many interleaved cognitive processes involved, it is one high-
priority task for which the military aims to improve perfor-
mance. In this context, practice effects or near transfer are
appropriate outcomes in improving shoot/do not shoot effi-
ciency. Near transfer is a suitably reliable effect, having been
demonstrated in several training formats, and these types of
enhancement programs are certainly worth a military invest-
ment. Next, we will review these examples of positive near
transfer from the basic science literature as models that are
useful and may shed light on how training can be used in a
military context.
Action Video Game Training
One of the most consistent and robust findings within the
cognitive training literature comes from the effects of action
video game training on perceptual and attentional skills. The
idea that playing video games may serve as a useful tool to
improve cognitive and motor functioning dates back to the
early 1970s, when the game Pong was first released and re-
searchers tested whether playing Pong could improve func-
tional outcomes following a stroke (Cogan et al. 1977).
Modern research has focused specifically on first-person
shooter, Baction^video games. A seminal paper by Green
and Bavelier (2003) showed using both cross-sectional and
training study designs that action video game experience
was related to enhanced performance on measures of the spa-
tial distribution and temporal resolution of visual attention.
This initial paper sparked an explosion of research into the
effects of action video game experience.
The most robust and replicated findings surround action
video game training (AVGT) improving visual perception
and attention. For example, AVGT has been shown to improve
visual acuity, contrast sensitivity, and perceptual learning
(Bejjanki et al. 2014;R.Lietal.2009;R.Lietal.2010).
Several studies have found and replicated an enhanced spatial
distribution of attention following AVGT as measured by the
useful field of view (UFOV) or flanker tasks (Green and
Bavelier 2003,2006,2007). Further, AVGT has been shown
to improve the temporal resolution of attention and visual
speed of processing more generally (Dye et al. 2009; Green
and Bavelier 2003).
In addition to effects on perception and attention, several
studies have found evidence for AVGT improving a broad
range of other skills that are mechanistically related to visual
perception and attention. For example, AVGT has been shown
to increase visual working memory performance (Blacker
et al. 2014), to enhance mental rotation ability (Feng et al.
2007), to reduce attentional capture (Chisholm et al. 2010;
Chisholm and Kingstone 2012), and to improve selective at-
tention (Mishra et al. 2011). Finally, the effects of action video
game experience on higher-order executive functions have
been explored but less so, and with less consistent results.
These specific findings will be reviewed below in the section
on Executive Function training.
The AVGT literature provides a good example of the use of
methodologically rigorous experimental designs that include
training studies and adequate control groups. While not every
action video game study adheres to these standards, there are
higher proportions of studies with these designs compared to
other areas of cognitive training. Indeed Simons et al. (2016)
noted that studies on the benefits of action video game training
are Bamong the best designed^in the cognitive training liter-
ature. The inherent qualities of video games (e.g., adaptive,
engaging) make them a desirable training vessel for real-world
application. AVGT improves a core set of visual perception
and attentional skills that are critical in many everyday tasks.
One such clinical application of this work is the attempt to
improve vision in individuals with amblyopia, which is a de-
velopmental disorder that results from physiological
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alterations in visual cortex early in life (Ciuffreda et al. 1991).
Individuals with amblyopia experience a wide range of visual
deficits including reduced contrast sensitivity, high levels of
spatial uncertainty, spatial disorientation, and impaired read-
ing ability. However, early work has demonstrated that AVGT
in individuals with amblyopia can enhance a variety of visual
functions compared to a control training group (Vedamurthy
et al. 2015a,b). While this work is in its early stages, it repre-
sents a practical and focused use for AVGT.
In this review, we have focused on near transfer and quan-
tifiable outcomes following training. Both near transfer and
quantification are critical aspects of training with practical
value for military applications. The action video game litera-
ture is an interesting case in this regard. Is an improvement in
visual perception and attention reasonably considered near
transfer following AVGT? Indeed, identifying the exact com-
ponents of action video games that produce transfer is an
emerging area of research and will likely be a critical future
direction for the field (Dale and Green 2017). However, an-
other potential advantage of AVGT is the likelihood that it is
tapping into a range of skills, which are all being trained si-
multaneously. This notion has led Bavelier et al. (2012)to
propose that transfer from AVGT may reflect what is known
as Blearning to learn^(also reviewed in, Green et al. 2016).
The learning to learn model proposes that complex training
environments, like action video games, actually foster brain
plasticity and learning. The idea here is that training on BTask
A^may not necessarily produce immediate transfer to BTask
B,^but instead the training on BTask A^allows BTask B ^to
be learned more quickly. Indeed, several studies have found
evidence that action video game players and non-video game
players start out at the same performance level, but action
video game players improve their performance more rapidly
throughout the course of the new task (e.g., Bejjanki et al.
2014; Gozli et al. 2014). This theory of cognitive training, if
supported, avoids the curse of specificity whereby learning is
typically specific to a single task. This so-called curse of spec-
ificity is a primary limiting factor in most practical applica-
tions like job training, education, or rehabilitation. Given the
myriad duties and tasks that military personnel are asked to
learn and execute, a training approach that increases the speed
at which new skills can be acquired is particularly appealing.
Indeed, learning to learn is already a high military priority.
Admiral John Richardson is the current U.S. Chief of Naval
Operations—theseniorleaderoftheU.S.Navy—and he re-
leased a document entitled BA Design for Maintaining
Maritime Superiority^(Richardson 2016). In this document,
he outlined the need to achieve high velocity learning at every
level as a key line of effort for current and future naval oper-
ations. This need fits with the general challenge of naval ser-
vice in the high variety of tasks and duties required of so many
personnel. Sailors and Marines have to deal with challenges
on sea, air, and land, which creates context and content
transfer issues among such different environments.
Moreover, shipboard duties for Sailors might differ substan-
tially from urban combat duties of Marines, yet some Sailors
may have responsibilities that include both shipboard opera-
tions and close-quarters combat operations if they have to
board an enemy ship. In short, the scope of military duties is
complicated, far-ranging, and the same individual may be re-
quired to engage in many different types of duties throughout
a career. If learning to learn is a primary component of cogni-
tive training in general or AVGT specifically, then these train-
ing initiatives offer immense value to current and ongoing
military operations.
Working Memory Training
Unlike AVGT, WM training specifically emerged on the scene
with the hopes of achieving broad-ranging far transfer. A sem-
inal study in 2008 sparked much excitement and ensuing con-
troversy by demonstrating that training on a dual n-back task
improved performance on Raven’s progressive matrices, a
measure of Gf (Jaeggi et al. 2008). While WM training has
been surrounded by the most controversy due to its initial
focus on far transfer, it may also bear the greatest potential
for informing application of cognitive training. A full review
of the entire WM training literature is beyond the scope of this
review (however, for reviews see Konen et al. 2016;Morrison
and Chein 2011; Shipstead et al. 2012;Simonsetal.2016;
also see Au et al. 2015 for a recent meta-analysis). Therefore,
here, we will focus on two major aspects of WM training: the
logical link between training and outcome and near transfer
effects.
WM capacity is limited, varies widely across individuals,
and is highly related to many other cognitive skills, including
Gf (e.g., Engle et al. 1999). Many theories have emerged
about the nature of the relationship between WM and Gf,
but consistent evidence for several decades has demonstrated
that WM capacity and performance on measures of Gf like
matrix reasoning tests have a high, positive correlation (e.g.,
Ackerman et al. 2005; Conway et al. 2003;KaneandEngle
2002; Mogle et al. 2008; Unsworth et al. 2014). The ability to
improve Gf via training is particularly intriguing because the
construct is highly predictive of many real-world skills such as
academic performance, creativity, reading comprehension, job
performance, and even mortality (Dreary 2008; Kuncel et al.
2004). The logic of WM training states that WM capacity is a
limiting factor for performing a wide range of cognitive tasks,
including Gf, and thus if WM capacity can be improved with
training, then all of these other related skills may also reap the
benefits. While WM training’s efficacy in eliciting far transfer
to Gf has been inconsistent (for reviews and recent meta-anal-
yses, see Au et al. 2015; Morrison and Chein 2011;Shipstead
et al. 2012; Simons et al. 2016), the logical link between WM
and Gf represents a laudable approach. The goal is to find a
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process or system that (a) underlies cognitive skills and abil-
ities, (b) is malleable or sensitive to training, and (c) may
benefit other skills if it can be improved.
While far transfer associated with WM training has been
inconsistent, near transfer effects have been far more consis-
tent. A number of recent meta-analyses and reviews agree that
WM training produces significant improvements to untrained
WM tasks across the lifespan (e.g., Karbach and Verhaeghen
2014; Melby-Lervag and Hulme 2013; Schwaighofer et al.
2015). For example, training on one type of n-back task trans-
fers to improvements on untrained versions of the n-back task
(Blacker et al. 2017; Colom et al. 2013;Jaeggietal.2010;S.
C. Li et al. 2008). Similarly, training on one type of complex
span WM task transfers to improvements on other complex
span tasks (Chein and Morrison 2010; Richmond et al. 2011).
Given the critical nature of WM to everyday functioning, this
evidence of near transfer represents a promising step in utiliz-
ing WM training in an applied setting such as the military.
Further, there are also other methods for improving WM itself
through training that would be considered near transfer. For
example, a commercial training program called NeuroTracker
can be used to have participants complete adaptive 3D multi-
object tracking training, which heavily involves WM storage
and updating processes. In fact, one study in particular has
demonstrated in a military population that this form of training
can improve WM span performance (Vartanian et al. 2016).
We propose that the military value of this type of near transfer
is clear and overwhelming. In a military setting, any human
performance improvement could translate to lives saved on
the battlefield and thus the distance of transfer from training
task to outcome measure becomes less of a concern. This
outcome-focused approach stems from the inherent need for
military operations to achieve tangible goals for the safety of
our population. Thus, military training may be able to utilize
WM training itself, which has shown reliable near transfer
results or use WM training as a model training approach that
yields consistent improvements on a critical cognitive skill.
One interesting aspect of WM training is the relative ob-
scurity of what general behavioral or neural mechanisms un-
derlie training-based improvement or transfer to untrained
tasks. Two general and not mutually exclusive mechanisms
are typically discussed. First, it may be the case that WM
training simply enhances the capacity of WM, meaning it
allows for more information to be stored than prior to training.
Second, it may be that WM training enhances the efficiency
with which information is stored in WM. This second option
could be achieved through strategy use, such as rehearsal or
chunking, or through an improvement in underlying process-
es, such as selective attention. For example, individual differ-
ences in the ability to select task-relevant and filter out task-
irrelevant information is highly predictive of individual WM
capacity (Vogel et al. 2005). Improved WM efficiency could
take the form of better distractor filtering, faster encoding
speed, and/or higher precision of maintained representations.
It is also a possibility that WM training improves both capac-
ity and efficiency. To use an oversimplified analogy, if WM
were a suitcase, training could allow you to increase your
Bphysical capacity^by using the extra expandable zipper;
training could simply provide you with a method for more
efficiently packing your items into the same, non-expanded
space; or finally, training could achieve both, more space by
use of the expandable zipper plus a more efficient packing
approach. Understanding how WM training influences perfor-
mance on this mechanistic level is of great interest in
laboratory-based investigations of cognitive training and sure-
ly represents a critical next step in advancing the field.
However, in a real-world application such as military training,
the end goal of packing more items into that suitcase is suffi-
cient and how it occurs is less pertinent as long as human
performance is improving. These distinct goals embody the
difference between basic science done in academia and ap-
plied work done in military research. Both lines of research
and communication between entities will help to move the
entire field forward as a whole.
A particularly relevant example of the use of WM training
near transfer comes from clinical application. In a clinical
setting, both rehabilitation (e.g., from stroke) and psychother-
apy rely on the engagement of viable and healthy systems to
compensate for or re-innervate function that is lost due to
illness or injury (Light and Swerdlow 2015). Understanding
an individual’s assets, in turn, can be leveraged in the service
of therapy or treatment. For example, individuals with schizo-
phrenia often exhibit a deficit in WM (e.g., Goldman-Rakic
1994; Lee and Park 2005; Silver et al. 2003), but cognitive-
behavioral therapies used to treat schizophrenia place de-
mands on WM with the goal of helping the individual to
develop compensatory strategies for learning and applying
information. Indeed, previous work has shown that WM train-
ing can produce significant near transfer and changes to neural
activity in prefrontal cortex among individuals with schizo-
phrenia (Haut et al. 2010).
A future step in this line of research may be to test the
efficacy of WM training or other types of cognitive training
as a supplement to typical cognitive-behavioral therapy. By
boosting one’s initial ability, subsequent training or therapy
may end up being more effective or result in a faster rate of
improvement. This notion, while untested to the best of our
knowledge, bears resemblance to Bavelier and colleagues
Blearning to learn^model from the action video game litera-
ture. Perhaps boosting a critical ability like WM will allow an
individual to benefit more from a subsequent learning or ther-
apy opportunity. For example, cognitive training that focuses
on WM improvement prior to training on a specific operation-
al task like aviation could dramatically reduce the time needed
to train a new pilot or immediately increase his or her opera-
tional efficiency. Pilot training costs millions of dollars and
JCognEnhanc

takes months or years—per pilot—which makes any improve-
ments to the training process highly advantageous. Training
that increases the spatial distribution of attention prior to train-
ing could likewise aid pilots in reducing spatial disorientation,
which is the number one cause of Class A mishaps in military
aviation. These predictions have yet to be tested but may re-
flect critical and practical ways for cognitive training to be
applied to standard military training procedures.
Executive Function Training
While WM is considered a higher-order executive function in
many influential models, training WM has received more at-
tention than all other forms of executive function training
combined. Here, we will briefly review the state of the training
literature for executive functions other than WM that have not
yet been covered. Different theories propose different catego-
ries of executive functions, but here we will adhere to the
theory put forth by Miyake et al. (2000). Thus, we will con-
sider WM or Bupdating^as already discussed above and con-
tinue our discussion focused on the remaining two functions:
shifting and inhibition. Although there is also a burgeoning
literature on training of dual task performance, we will not
include this topic here as it was not identified as one of the
three core components of executive function by Miyake and
colleagues (however, for a review on dual task training see,
Strobach et al. 2014). Finally, we will review of a specific
form of inhibition training that may be particularly relevant
to the military, response inhibition training. These different
forms of executive function training may represent an un-
tapped potential for improving human performance in the mil-
itary. Notably, these executive functions control one’sbehav-
ior when performing in demanding and/or complex situations
in which the management of different tasks or task sequences
is required.
Shifting Mental set shifting or task switching involves
switching back and forth between multiple discrete tasks, op-
erations, or mental sets (Monsell 2003). Throughout this re-
view, we will simply refer to this function as Bshifting.^
Shifting is a critical skill for service members, particularly
those in an infantry or maneuver role. Some examples of this
are first line leaders coordinating movements, a forward ob-
server calling for artillery support, or a combat medic render-
ing aid. These individuals must be able to not only perform
these specialized roles but to also shift their focus to another
task—such as someone calling in for artillery support may
need to return small arms fire until the artillery strike is com-
pleted. This type of shifting may occur dozens or even hun-
dreds of times during a prolonged engagement with the
enemy.
Two major approaches have been used in the training of
shifting in basic science: video game training and direct
shifting training. In the gaming literature, action video game
players have demonstrated smaller task-switching costs
(Colzato et al. 2010; Green et al. 2012; Strobach et al.
2012). However, in a separate study, these smaller costs were
attributed to action gamers’specific benefit in controlling se-
lective attention, rather than better task switching-related cog-
nitive control processes (Karle et al. 2010).
The second approach has typically compared training that
involves switching between two disparate tasks in random or
predictable order and training that involves the same two tasks
but practiced in single blocks or sessions where no shift or
switch must occur (Minear and Shah 2008). This rigorous
design allows for strong conclusions on the effects of training
the skill of shifting specifically. There is substantial evidence
for the successful improvement of shifting following this type
of training in a range of populations, including older adults
(for a meta-analysis, see Karbach and Verhaeghen 2014), chil-
dren and adolescents (for a review, see Karbach and Unger
2014), and in individuals with ADHD (Kray et al. 2011).
These findings typically involve practice effects, whereby
the cost shifting between tasks is reduced as training pro-
gresses. However, there is also promising evidence for near
transfer following shifting training, whereby training on one
shifting task also reduced switch or shift costs in an untrained
task (Blacker et al. 2017; Karbach and Kray 2009;Minearand
Shah 2008). For example, after 4 weeks of training on an
adaptive, rapid rule learning and switching task, participants
saw a significant reduction in switch costs on an untrained
task that used distinct stimuli, compared to two active control
groups that completed four weeks of WM training (Blacker
et al. 2017). Thus, training shifting through action video
games or directed training seems to produce robust and con-
sistent practice and near transfer effects that may be useful to
military personnel who are consistently placed in environ-
ments that require rapid and numerous shifts between key
operational tasks.
Inhibition Inhibition or inhibitory control refers to the ability
to deliberately inhibit or stop a dominant, automatic, or pre-
potent response (e.g., Hallett 1978;Logan1994; Stroop
1935). In the basic science realm, tasks that are often used to
measure inhibition broadly include go/no-go (GNG), stop sig-
nal reaction time (SSRT), flanker tasks, and Stroop tasks.
Fratricide, or Bblue on blue^incidents, is one readily apparent
military implication for this category of executive functioning.
Interestingly, despite tremendous advances in military tech-
nology, the rate of these incidents has increased since WW2
(Rasmussen 2007). Even as individual weapons systems be-
come more advanced and more effective, they are still ma-
chines that lack the ability to discern or discriminate between
targets. Ultimately, the role and efficacy of the weapon relies
on decisions made by the individual employing it. With that in
mind, the most effective way to mitigate fratricide seems to be
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increasing the individual performance of military operators as
it pertains to their ability to make accurate friend or foe deci-
sions. Importantly, recent research suggests that a lack of in-
hibitory control may contribute to friendly-fire or other inci-
dents of unintended casualties (Biggs et al. 2015; Wilson et al.
2015). This important issue for military personnel makes the
possibility of improving inhibitory control via training an ex-
citing avenue for exploration.
Studies examining inhibitory control training are scarce
and mostly done in young children. For example, Thorell
et al. (2009) trained preschoolers on three different inhibition
tasks (i.e., GNG, SSRT, and a flanker task) and found signif-
icant practice effects on two of the three training tasks, but
found no evidence of near transfer to untrained inhibition
tasks. On the other hand, an earlier study of inhibitory control
training in 4–6 year olds found that training yielded far trans-
fer to a matrix reasoning test that is thought to measure Gf
(Rueda et al. 2005). Further, studies with younger and older
adults have typically involved training on a Stroop task, which
a body of work has demonstrated that training results in prac-
tice effects whereby response times (RT) are reduced for in-
congruent trials, effectively decreasing the magnitude of the
interference effect (i.e., incongruent RT–congruent RT;
reviewed in Strobach et al. 2014). However, evidence for
transfer tends to be non-existent or highly stimulus-specific
(e.g., Reisberg et al. 1980). While this area of research does
not provide many leads in the effective use of inhibitory train-
ing in applied settings, there is a related but notably distinct
area of research that specifically examines response inhibition
training as it relates to appetitive stimuli such as unhealthy
foods, alcohol, and drugs.
Response Inhibition Training Response inhibition training
techniques include cognitive tasks that require a stopping ac-
tion or elicit an avoidance response. This approach is frequent-
ly used for intervention with individuals exhibiting overindul-
gence and impulsive behaviors such as those struggling with
obesity and/or addiction. GNG and SSRT paradigms are ex-
amples that have been used for this form of response inhibi-
tion training. A common strategy is to use cue-specific stimuli,
such as pictures of snack foods for those dieting or photos of
glasses of beer for those attempting to control drinking.
The GNG task, when designed to include stimulus-specific
stopping responses, has shown to be effective in dietary inhi-
bition treatment for decreasing chocolate intake (Houben and
Jansen 2011,2015), decreasing portion sizes (van
Koningsbruggen et al. 2014), resisting unhealthy foods
(Veling et al. 2013), devaluation of food items (Chen et al.
2016), decreasing food intake (Lawrence et al. 2015), as well
as overall weight loss (Veling et al. 2014). Further, similar
efforts have demonstrated that alcohol intake can be reduced
with a GNG task modified to include a no-go response to
photos of alcohol (Houben et al. 2012; Houben et al. 2011),
but the converse was also supported whereby participants re-
ported drinking more following training that involved a go
responses to photos of alcohol (Houben et al. 2011).
The SSRT task differs from a traditional GNG task in that
every trial begins with a go signal and may or may not change
to a stop signal at a variable duration. This stop signal delay
varies based on an individual’sperformanceandallowsforan
individualized measure of how long it takes for one to stop an
initiated response. This type of task has also been used as a
training paradigm with the goal of improving inhibitory
control. However, Allom and Mullan (2015)wereunsuccess-
ful in producing a difference in eating behavior for a food-
specific stimuli condition. Likewise, Jones and Field (2013),
when using photos of beer, showed no difference in consump-
tion rate for heavy drinkers. Guerrieri et al. (2012) adjusted the
SSRT to compare different proportions of go and stop trials
and the results indicated no change in caloric intake for the
inhibitory (more no-go stimuli) group, but resulted in an in-
crease in intake for the impulsivity (more go stimuli) group.
As such, it is possible that inhibition training from the SSRT
task does not apply well to a stimulus-specific training design
such as reducing consumption behaviors.
Verbruggen and Logan (2008) argued against distinct func-
tionality of cognitive tasks attributed to top-down inhibition
(SSRT) and bottom-up control (GNG) respectively, and others
have suggested that combinations of training might work best
(Jones and Field 2013). In general, there seems to be a con-
sensus that inhibition training using stimuli associated directly
with the targeted inhibitory action elicits better results than
using general stimuli. Although the SSRT obtains a measure
of individual differences in stopping time, the GNG task has
yielded more consistent results in training inhibition.
Indeed, at least one recent training study has directly ex-
amined the effects of inhibitory training on an operationally
relevant issue—specifically, shoot/do not-shoot performance.
Using a simulated shoot/do not-shoot scenario via a video
game, Biggs et al. (2015) tested participants’performance
before and after response-inhibition training (i.e., GNG and
SSRT tasks) or a visual search training paradigm. The re-
sponse inhibition training group showed a significant reduc-
tion in civilian casualties post-training compared to the active
control group (i.e., visual search training). Moreover, these
findings have been replicated—albeit using a different train-
ing design—by having trained police officers fire live ammu-
nition at images holding either guns or cell phones (Hamilton
et al., under review). Both training studies demonstrated sig-
nificant improvement in shoot/do not-shoot decision making
through inhibitory control training, and each did so with vary-
ing degrees of realism in the shooting tasks.
These findings are potentially relevant to military person-
nel when engaged in room clearing. As mentioned previously,
room clearing is one of the most dangerous and dynamic tasks
that a soldier can participate in and is often unavoidable in
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urban warfare. Perhaps unsurprisingly, in an urban
environment, the most dangerous areas are generally those
that are more densely populated by enemy fighters.
However, the enemy fighters themselves are not the only
risk. Research done by Wilson et al. (2015) suggests that this
type of Btarget rich^environment can also lead to a prepotent
response to fire that is very difficult to inhibit. Obviously,
failure to withhold an inappropriate response (such as firing
at friendly personnel) places coalition forces at great risk to
one another. In Wilson and colleagues’study, increased speed
and high proportions of foe targets during friend-foe room
clearing tasks had a significant, negative impact on perfor-
mance. This issue is of particular concern when enemy com-
batants may be mixed among civilian non-combatants and
other friendly forces. Friend-or-foe or shoot/do not-shoot
training research among military populations is scarce.
Given the urban nature of modern warfare combined with
constant increases in weapon lethality, it is more important
than ever that soldiers are equipped to mitigate collateral dam-
age and friendly fire incidents. Response inhibition training,
like that used by Biggs et al. (2015), may represent a viable
method for improving friend-foe decision making in room
clearing scenarios often encountered in urban warfare. With
these ideas in mind, it is critical that research into cognitive
training application in the military build on the existing basic
science and utilize the best practices for experimental design
and evaluation of training efficacy.
Practical Considerations for Military
Application
Thus far in this review, we have detailed many potential areas
of cognitive training that might be useful for military applica-
tion. The ultimate goal involves improving warfighter perfor-
mance, but doing so requires rigorous experimental design
and evaluation of efficacy. In a recent thorough review of
the cognitive training literature, Simons et al. (2016)included
several best practices or suggestions for conducting and eval-
uating cognitive training research for researchers, reviewers,
policymakers, and funding agencies. However, the goals of
the military are often different from those of basic scientists
in academia or individuals working in for-profit commercial
or industrial settings. There are also numerous practical con-
siderations when evaluating cognitive training or
implementing cognitive training designs into active duty mil-
itary training. Under normal circumstances, cognitive training
for clinical issues can be carefully designed and controlled
when administered to a clinical population. An ineffective
training method primarily risks wasting time. For military
training, especially as far as combat training is concerned,
there are rigorous schedules to consider and wasted time could
mean wasted alternative training opportunities—in combat,
the consequence of wasted training opportunities is that a less-
er prepared unit could mean increased battlefield casualties.
The grave importance of these field applications makes it nec-
essary to consider all potential aspects of cognitive training so
that the eventual training opportunities provided to military
personnel will fully prepare them for the challenges to be
faced.
Here, following the suggestions of Simons et al. (2016)and
other cognitive scientists, we outline what we view as the most
critical best practices for military researchers investigating po-
tential cognitive training uses toimprove human performance.
We also describe some salient individual differences that may
influence training efficacy especially in a military population
such as baseline performance and motivation. These discus-
sions will also address the advantages of using each method to
evaluate cognitive training for military purposes, and potential
workarounds for those instances when the best empirical con-
trols cannot be implemented. Finally, we end this section with
a brief description of methods that may be able to boost the
effects of traditional cognitive training approaches.
Control Groups and Expectations
Standard cognitive training designs need to differentiate be-
tween simple practice or placebo effects and tangible improve-
ments due to cognitive enhancement. Military training bene-
fits are no different—particularly if one objective is to deter-
mine whether the finding involves only a practice effect, or a
sufficient warfighter performance improvement to justify sig-
nificant military investment. Adequate control groups are one
critical step to evaluation of training efficacy. Control groups
are generally approached one of two ways: passive or active
control groups. In passive control designs, the control group
does not receive any training and generally does not have any
contact with the experimenters. For example, participants
might not be contacted during the data collection period, or
they may be told that they are on a waitlist and will receive
training when it becomes available. While these designs pro-
vide a comparison group to detect any effects of test-retest
practice effects or the passage of time, they do not equate
contact with the experimenters or the expectations of the two
groups. Simply participating in some form of training may
cause the experimental group to expect that they should see
an improvement in performance, much like a placebo.
Active control groups are one way to better match the
groups’expectations. In these designs, the control group per-
forms a similar task as the experimental group but without the
critical component of the training. Active control groups come
in several forms. Two groups may receive identical training
tasks, but the experimental group will experience adaptive
training that gradually gets more difficult, whereas the active
control group will perform the same task at the same (easy)
difficulty level throughout training. As an example, a task that
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trains visual search might present increasingly difficult arrays
as a participant improves (e.g., more distractor objects, in-
creased spatial jitter). The active control group would perform
the same task for the same amount of time but without any
increase in difficulty. Another approach is to have an active
control group that trains on a similar task that simply lacks the
critical Bingredient(s)^thought todrive the experimental train-
ing. An example of this approach comes from the video game
training literature. It has been proposed that action video game
training improves visual cognitive skills at least in part be-
cause of the fast-paced nature of the games and the use of
first-person perspective. For example, a typical study might
compare training on an action game that is fast-paced and
first-person perspective with a strategy game that is slower
paced and third-person perspective. Use of active control
groups helps to equate participants’expectations about the
outcome of training, as well as the amount of contact partici-
pants have with experimenters. This type of design is partic-
ularly useful in elucidating mechanistic features of training
paradigms in elicited transfer. A final approach is to choose
an active control training program that is expected to improve
a different set of skills. For example, a WM training group
would be expected to improve on an untrained WM task,
whereas a task shifting training group, the active control,
would not be expected to improve on the WM task.
Similarly, in the same design, the task shifting training group
would be expected to improve on an untrained shifting task,
whereas the WM training group would not be expected to
improve (for an example of these results, see Blacker et al.
2017). This type of outcome allows for a robust interpretation
of the results that reduce the possibility of external effects like
expectations and practice effects. We would argue that this
design is most effective at evaluating training efficacy and
therefore is most suitable for military research, which focuses
on outcome more often than mechanism. This design also has
the potential to test two distinct forms of training with two
distinct operationally-relevant outcomes in one single study,
thus conserving military personnel’s time and research funds.
As cognitive training continues to pervade public aware-
ness, the need to control for and be aware of the effects of
expectations becomes critical. One recent study related to vid-
eo game training required participants to view clips of two
video games, one game that is typically used as an experimen-
tal training game (e.g., an action game) and the other a typical
control training game (e.g., a non-action game). Even after a
relatively short exposure, people expected differences in per-
formance on measures of visual attention following training
on each of the two types of games (Boot et al. 2013). While
the action video game training literature is lauded for its rig-
orous use of active control groups, expectations remain an
important area to be explored. A related point is that the way
participants are recruited may also effect expectations. For
example, two groups were recruited with flyers that contained
either suggestive content (e.g., referred to Bcognitive
enhancement^and Bfluid intelligence^) or neutral content
(e.g., BParticipate in a study^) and then received the same
training on a dual n-back task. Those who responded to the
suggestive flyer showed a significant improvement on two
measures of Gf, but those who responded to the neutral flyer
did not (Foroughi et al. 2016). These points may be particu-
larly salient when using a military population who may have
very specific and deep-rooted expectations about their train-
ing—be it cognitive, or otherwise. Our recommendation for
best dealing with expectations in a military population is two-
fold: (1) when possible, record subjective measures of expec-
tations before and after training has occurred as basic scientists
are starting to do in laboratory-based cognitive training, and
(2) attempt to maintain similar expectations for cognitive
training as a service member would have when completing
any other required training (e.g., physical training, marksman-
ship training, etc.).
Blinding
The gold-standard approach in intervention research is to uti-
lize a double-blind study design, which involves having both
the participants and the experimenters unaware of what con-
dition or training group each participant is enrolled. However,
in cognitive training research, it is not possible to completely
blind a participant to their training because they are actively
engaged in it and therefore know what it entails. While a drug
study can give a participant a pill and keep them unaware
whether it is an active dose or a sugar pill, cognitive training
research does not allow this level of blinding. Thus, it is pru-
dent for experimenters to be blind to group assignment in
order to ensure equal treatment and reduce any potential de-
mand characteristics. On the participant’send,itispossibleto
ensure that any one participant is at least unaware of the alter-
native training program being used and if possible, unaware
that there is any other training group.
In a military setting, this is particularly relevant because if a
training study is implemented within a unit or group of service
members that work closely together, it may be tempting for
individuals to discuss their training with other members of the
group, which can undermine the blinding procedure. Military
participants should be given strict instructions to not discuss
their study participation or the nature of their training with
other members of the group until everyone has completed
the study. Still, the assumption should be that all training par-
ticipants will be discussing the training despite experimenter
instructions. One important workaround may be to use units
from different bases—but with the same basic training—when
comparing active and control groups. The point is that some
further consideration will be necessary to ensure that partici-
pants are not discussing study procedures. Finally, one recom-
mendation that can alleviate the lack of blinding of
JCognEnhanc

participants in cognitive training studies is to assess expecta-
tions whenever possible (Boot et al. 2013). If a full blinding
procedure is not possible, then expectations management be-
comes the next reasonable mitigation technique.
Randomization and Sample Size
Random assignment is imperative for a controlled design.
Each individual enrolled in a training study should have an
equally likely chance of being assigned to any one training
group in that study. One goal of randomization is to ensure
that all training groups have equivalent baseline performance.
Acquiring a large sample size increases the effectiveness of
this procedure. The military has the benefit of utilizing collab-
oration of research units if only small samples are available at
each location. Further, when multiple locations are being used
to achieve a larger sample size, every effort should be made to
equate study procedures and expectations across locations and
testing site should always be included as a covariate in any
analyses. When selecting a sample size, it is important to
consider the number of measures implemented so that after
correcting for multiple outcomes there is sufficient statistical
power to observe the expected effect size.
Still, several practical challenges exist with respect to sam-
ple size and expectations. It is a defining aspect of elite forces
that there are fewer elite operators than regular operators. For
example, an infantry unit may have sufficient numbers to pro-
vide training groups and control groups, but a special opera-
tions unit may not have the sample sizes necessary unless
every member of the unit agrees to participate in training. In
these instances, the population itself might be an issue to con-
sider—where both groups receive the same training, but one
group includes special operations personnel, and one group
includes regular infantry personnel. The critical issue is that an
experimenter simply may not be able to design a fully double-
blind, randomized-controlled design with limited numbers of
potential participants with results that may or may not apply to
operators outside of special forces. Likewise, expectations
management is a critical consideration about how individuals
regard the training and how experimenters interpret the value
of said training. For example, a cognitive training design may
induce nothing more than a placebo effect that improves con-
fidence among personnel and improves post-test performance.
This outcome would lead many cognitive scientists to dismiss
the training as nothing important or valuable, yet for military
purposes, a sustainable and repeatable placebo effect could
prove invaluable. The difference again becomes an emphasis
on the end state performance. The goal is to improve human
performance, and even training that merely improves confi-
dence could be of substantial use in military training. Whether
sample size issues or expectations management, it is important
to remember that operational circumstances may prevent a
perfectly designed training experiment, sample sizes may
complicate training designs, and even placebo effects may
have significant value if those effects can be sustained.
Our suggestion for achieving adequate sample sizes when
addressing operationally relevant questions about training ef-
ficacy is two-fold. First, if care is taken to equate experimental
procedures across locations and times, then multiple units can
be tested. This may involve using separate groups of individ-
uals who rotate through a training facility and collecting data
over multiple time periods. It also may involve recruiting vol-
unteers from disparate military groups, including all branches
of the military, both standard operators and special forces. A
second recommendation is to consider non-military popula-
tions that often engage in the same task of interest. For exam-
ple, in studying lethal force decision making, a law-
enforcement group could be used in addition to a military
group. Furthermore, groups such as air traffic controllers or
commercial airline pilots may have overlapping duties with
those of interest to military operations.
Outcome Measures and Multiple Comparisons
The outcome measures utilized by military researchers may be
very different from those used by basic scientists. For exam-
ple, a study may include a form of neurofeedback training to
improve a pilot’s ability to identify symptoms of hypoxia dur-
ing flight. This type of outcome measure is very different than
assessing improvement on a computer-based n-back task fol-
lowing WM training. However, some of the same principles
apply to these real-world outcome measures. Outcome mea-
sures should be reliable, meaning that if you test someone on
that measure multiple times, the end results should be compa-
rable. In the example here, hypoxia symptoms are known to
be reliable, meaning that each individual tends to experience
the same symptoms each time they are hypoxic (i.e., a hypoxia
signature). While there is wide inter-individual variability in
symptoms, intra-individual differences are small, making it a
reliable outcome measure to study in a training study (Singh
et al. 2010;Smith2008). The improvement tasks should in-
corporate some operational aspect to gauge the training effec-
tiveness. For example, some assessments may include pure
cognitive tasks to assess changes in the underlying cognitive
mechanism, but these tasks should be paired with operation-
ally relevant tasks based upon the intended field improvement.
Cognitive training to improve RTs may then want to include
simple vigilance task measurements as well as marksmanship
tasks to determine the time needed to fire the first shot. This
operational link will improve any evaluation of the training
efficacy and help training officers make determinations about
when and how to utilize these training opportunities. One
concrete suggestion for choosing appropriate training tasks
that are likely to improve the specific outcome measure of
interest is using a task analysis approach, as detailed above.
Consulting SMEs and breaking down a complex outcome
JCognEnhanc

measure into its constituent tasks or jobs is a recommended
first step for applied researchers.
The use of multiple outcome measures is also an issue that
needs to be taken into consideration. Some researchers argue
that cognitive training outcomes should be evaluated at the
construct level, where multiple tests of one cognitive construct
should be used. For example, instead of simply administering
Raven’s Progressive Matrices before and after a training inter-
vention, a study should administer multiple tests all thought to
assess Gf, and then the outcome should be evaluated for the
construct of Gf, not the individual tests (Shipstead et al. 2012).
While this may be a robust approach to elucidating mecha-
nism and degree of transfer, this approach does not fit partic-
ularly well with the goals of military research. It may be useful
to evaluate a specific training paradigm on multiple measures
of operational readiness, such as friend-or-foe shooting per-
formance, marksmanship, and performance in prolonged
room clearing simulations, but these measures are distinct
from looking at construct level transfer in basic science. One
consideration that military researchers should note is the need
to correct for multiple comparisons when using multiple out-
come measures. While military outcomes tend to be more
specific, it is still possible that multiple outcome measures will
be tested. Given this stringent need to correct for multiple
outcome measures, these measures should be chosen carefully
based on operational relevance and reliability of the measure.
Individual Differences
The current approach used in the cognitive training field is to
implement a Bone-size-fits-all^training program and have a
sizable group of participants train on that program, and then
compare group level improvement pre- and post-training com-
pared to an active control group. However, as this review and
others have detailed, these results can be inconsistent or weak.
These shortcomings have initiated a recent turn toward under-
standing individual-level effects and responsivity to training.
As with physical exercise, psychotherapy, or any other formal
intervention, it may be unreasonable to expect that every in-
dividual will respond in the same way to a cognitive training
intervention. Indeed, there is a rich literature spanning multi-
ple decades focusing on individual differences in a number of
cognitive functions like WM, attention, and cognitive control
(e.g., Braver et al. 2007; Cantor and Engle 1993;Daneman
and Carpenter 1980; Machizawa and Driver 2011; Shamosh
et al. 2008; Tuholski et al. 2001; Vogel et al. 2005). Thus,
individual differences like baseline performance, motivation,
and expectations may be key to applying cognitive training in
the real world. The individual differences consideration is es-
pecially important when considering the wide array of training
needs of military missions. Drone operators have different
training needs from combat medics and front line infantry.
Differences between roles should be considered with an eye
toward how wide a particular training can be administered.
Likewise, there could be issues of homogeneity with certain
populations. The particular concern here is among special op-
erations forces, where the requirements become so extreme
that individual skills and competencies are more homogenous
than among the general military population. However, the
corresponding advantage is that training can be tailored to
specific populations if special operations personnel are the
intended training recipients.
Baseline Performance Baseline performance is likely the area
that has received the most attention in cognitive training be-
cause it can be investigated without any additions to the typ-
ical study design. In a cognitive training study, individuals
perform an assessment task or often a battery of tasks before
training, then train for a period of time, and then are re-tested
on those assessment tasks to look for improvement. However,
there is great individual variation at baseline before training
begins (e.g., Blacker et al. 2017;Jaeggietal.2014). Let us
consider a typical WM training study that measures Gf as a
transfer measure to illustrate two influences of baseline per-
formance. There are at least two possible scenarios for seeing
improvement following training: magnification or compensa-
tion. Magnification is often conceptualized as the Brich get
richer^outcome, whereby individuals who have higher initial
Gf scores will show the most improvement following training
(assuming they are not at or near ceiling during baseline as-
sessment). The notion here is that individuals with more re-
sources will either learn more or learn more quickly during
training, thereby magnifying the effects of training. For exam-
ple, in a study with older adults using WM training paired with
transcranial direct current stimulation (tDCS), there was evi-
dence that more educated participants experienced greater
benefits from training and brain stimulation than those with
less education (Berryhill and Jones 2012).
On the other hand, compensation represents a scenario
where individuals with lower initial Gf show greater improve-
ments because they have more Broom to improve.^Indeed,
several studies have shown evidence for compensation, such
as two studies by Zinke and colleagues demonstrating that
individuals who performed worse at baseline across multiple
training paradigms experienced larger training gains on the
training tasks themselves (Zinke et al. 2012; Zinke et al.
2014). Similarly, multiple studies have shown that the amount
of improvement on the training task itself is related to the
amount of transfer gains seen following training (Chein and
Morrison 2010;Jaeggietal.2011; Schmiedek et al. 2010).
However, none of these studies directly tested or manipulated
how baseline performance on an untrained task influences
training transfer. Blacker et al. (2017) found that low WM
individuals, as measured by a pre-training untrained n-back
task, improved more following training on that same task fol-
lowing both dual n-back training and complex span training,
JCognEnhanc

compared to their high WM counterparts. On the other hand,
Foster and colleagues recently showed that individuals with
high WM at baseline showed greater gains from training com-
pared to their low WM counterparts (Foster et al. 2017). These
opposite findings may result from differences in study popu-
lations (e.g., where the study was conducted, whether all un-
dergraduates were used, age of the sample). Finally, there is
evidence that the type of training used matters, whereby
process-based training results in greater gains for individuals
with lower initial performance, but strategy-based training re-
sults in greater gains for individuals with higher initial perfor-
mance (Karbach and Unger 2014). These findings suggest that
baseline performance can have a wide range of effects on the
outcome of training.
This issue is particularly salient in a general military pop-
ulation, which is more diverse than the typical university un-
dergraduate sample. For example, the first formal requirement
for applying to enlist is to complete the Armed Forces
Qualification Test (AFQT), which is comprised of four tests
from the Armed Services Vocational Aptitude Battery
(ASVAB), including Arithmetic Reasoning, Mathematics
Knowledge, Word Knowledge, and Paragraph
Comprehension. The ASVAB was renormalized in 2004
(Moore et al. 2000) and the minimum percentile score needed
to enlist is a 31 (Chu 2007). However, there are exceptions,
whereby individuals who score between the 10th and 30th
percentile may be admitted if they have completed a high
school diploma. All of this is to say that any particular military
sample recruited for a cognitive training study could poten-
tially span 90% of the population as it relates to performance
on this standardized test, which we would speculate encom-
passes a much larger range of cognitive skills than a typical
academic study conducted on undergraduate students.
Further, it has been suggested that the ASVAB is largely a
measure of crystallized intelligence and that the battery would
benefit from the addition of tests that tap into Gf and/or WM
(National Research Council 2015). The ASVAB also serves as
a method for personnel selection for specific jobs. For exam-
ple, there is evidence that WM capacity and multitasking abil-
ity are linked (Redick 2016) and thus jobs that require multi-
tasking may benefit from a measure of WM capacity as a
means of personnel selection. One such attempt to broaden
the scope of skills assessed in military personnel was the de-
velopment of the Enhanced Computer-Administered Test
(ECAT) battery, which aimed to improve the validity of the
ASVAB. The ECAT is comprised of nine tests designed to
measure non-verbal reasoning, spatial ability, psychomotor
skill, and perceptual speed (Alderton et al. 1997). One such
measure, the Mental Counters test (MCt), was accepted for
possible inclusion in the ASVAB with the goal of increasing
the validity of predictors for military occupations and mini-
mizing minority difference in testing performance (Alderton
et al. 1997; Larson and Saccuzzo 1989; Russell et al. 2014;
Sager et al. 1997). However, a hurdle to implementing the
MCt was that one of the requirements of the ASVAB is that
any assessment that is included must not be susceptible to
cheating, compromise, practice, or coaching. As we have de-
tailed in this review, there are numerous methods for improv-
ing WM skills through practice and training. To date, the MCt
has been included in the ASVAB, but only for Navy appli-
cants. The state of the ASVAB and its inclusion/exclusion of
measures of fluid cognitive skills have the potential to change
personnel selection criteria and ultimately the applicability of
cognitive training for specific jobs within the military.
Baseline performance levels can influence training outcome
and further work into their effects would benefit from more
nuanced methods of personnel selection criteria.
Both magnification and compensation represent important
considerations in cognitive training, and either could have
military use. Magnification could mean that special operations
personnel are the ideal recipients of the training.
Compensation could mean that cognitive training is a poten-
tial method to prevent individuals from washing out during
training (i.e., failing to complete the necessary training re-
quirements). Either could create useful training opportunities,
but each potential would alter the manner in which the training
opportunities are provided. A hybrid possibility is that cogni-
tive training could help individuals who may want to become
special operators, but they may narrowly fail a particular train-
ing requirement. This possibility is especially important given
the high costs involved in selecting and training special oper-
ators and the operational value they bring to ongoing military
operations. For example, consider a special operations group,
like the U.S. Navy SEALs. Like other special operations
groups, they substantially contribute to operational efficiency
of the military, but among those individuals who qualify to
train as a SEAL, fewer than one in five complete the training
(Waller 1994). If cognitive training can help reduce the attri-
tion rate, then it could prove to be an invaluable asset to spe-
cial operations training.
In sum, the variability in individual differences in cognitive
ability in a military population is bound to be greater than that
of a typical university sample. Given this consideration, re-
search into cognitive training effectiveness in military popu-
lations should consider both group-level and individual-level
analysis approaches. While group-level improvements may be
of greater interest to the military, identifying individual factors
that interact with training may allow for personnel selection
criteria and further refinement of training for specific sub-
groups of service members.
Motivation Potentially one of the most salient factors to con-
sider when applying cognitive training to a military popula-
tion are contextual factors such as compliance, motivation,
and expectations about outcome or efficacy of training. The
types of real-world tasks that military personnel encounter
JCognEnhanc

often have life or death consequences. This fact along with the
mandated nature of military training make it likely that moti-
vation and compliance may be much higher than a typical
study in an academic setting. However, there are still several
factors to consider such as beliefs about the flexibility of cog-
nition and expectations about whether training will benefit
real-world performance. For example, Jaeggi et al. (2014)
found that individuals who endorsed the malleability of intel-
ligence showed more transfer to Gf following WM training
compared to individuals who endorsed the fixed nature of
intelligence.
Motivation certainly impacts military training efficacy, but
similar to the magnification and compensation issues, motiva-
tion challenges may differ based upon the personnel involved.
The motivation challenges may also differ from standard cog-
nitive training investigations. It may be necessary to incorpo-
rate cognitive training into other training (e.g., physical train-
ing) to either increase motivation to train, increase transfer
effects, or ensure that the training itself is completed on a
regular basis. A larger issue involves how any cognitive train-
ing would be implemented on a long-term basis, particularly if
any training is to be done during deployments. For example,
motivation is a challenge when trying to have any population
complete computer-based training (Mohammed et al. 2017).
Different platforms use gamification to increase motivation
and willingness to engage in the training tasks (Deveau et al.
2015). During a military deployment, increased stress and
workload are likely to lower an individual’s motivation to do
even more training, particularly if the task is boring.
Gamification may be one method to increase motivation to
engage in the training tasks when individual willingness to
participate is especially likely to wane (Deveau et al. 2015;
Mohammed et al. 2017).
Methods for Boosting Cognitive Training
Two major limitations have blocked the immediate applica-
tion of cognitive training in real-world settings: inconsistent
findings and small effects. Here, we have addressed some of
the inconsistencies, which primarily surround the potential of
training to elicit far transfer, such as improving Gf.Instead,we
have focused on the mostly consistent effects of training on
near transfer because near transfer represents a sufficient out-
come for a real-world application like human performance in
military operations. However, the second limiting factor of
small effect sizes plagues both near and far transfer attempts
alike. While improvements on untrained WM tasks following
WM training are consistently demonstrated, the degree or size
of improvement may not be meaningful in the real world.
Interestingly, there have been several other areas of research
that have tested alternative methods for improving cognitive
function that may be able to be combined with cognitive train-
ing to produce meaningful advances. There is evidence that
non-invasive brain stimulation techniques, neurofeedback,
and pharmacological interventions may boost the effects of
cognitive training. While an extensive review of each of these
topics is beyond the scope of this review, we will briefly dis-
cuss these three methods for enhancing the effects of cognitive
training and point the reader toward more topic-specific
literature.
Each of the following methods can be used in conjunction
with cognitive training in an attempt to boost or enhance the
efficacy of cognitive training or to speed up the learning pro-
cess. First, the most common form of non-invasive brain stim-
ulation that has been used in this capacity is transcranial direct
current stimulation (tDCS), which involves passing a small
electrical current through the scalp that alters the resting state
of underlying neural populations (Nitsche and Paulus 2000,
2001; Stagg and Nitsche 2011). Pairing tDCS with cognitive
training has been shown to be effective in a number of cogni-
tive domains (for reviews see, Berryhill et al. 2014; Kuo and
Nitsche 2012; Parkin et al. 2015). Second, neurofeedback has
also been used in conjunction with training, whereby individ-
uals learn to control their own brain activity via systematic
feedback regarding their own internal states (Sherlin et al.
2011). This approach is most commonly done using EEG
paired with a variety of cognitive training tasks including de-
clarative learning, episodic memory retrieval, mental rotation,
and others (e.g., Enriquez-Geppert et al. 2014;Gruzelier2014;
Hanslmayr et al. 2005;Hoedlmoseretal.2008; Keizer et al.
2010;Klimeschetal.2005; Zoefel et al. 2011). Finally, efforts
have been made to boost the effects of cognitive training with
pharmacological interventions. For example, drugs such as
Ritalin (e.g. Agay et al. 2010) or Modafanil (e.g., Battleday
and Brem 2015; Gilleen et al. 2014), and the amino acid ty-
rosine (Jongkees et al. 2015) have all been shown to enhance
the effects of cognition and cognitive training, specifically.
These enhancing methods may be of particular interest to
military application insofar as they may allow for faster or
more effective results following cognitive training.
Conclusions
As modern warfare continues to evolve and change with the
advent of new technological, biological, and nuclear threats,
one aspect of military performance persists: the ability of our
service members to perform their operational tasks under a
myriad of circumstances is critical. While technological ad-
vances now may allow for us to send in a drone instead of a
manned-aircraft, that drone still requires a human operator.
The foreseeable future still has human performance as a key
element for military effectiveness and as new threats arise, our
service members will be asked to juggle increasing cognitive
demands in a variety of operational settings. Cognitive
JCognEnhanc

training may represent an exciting future step for improving
operational readiness and effectiveness in our military
personnel.
To date, the majority of cognitive training research has
been centered in basic science settings in academia, or in
for-profit settings like commercial brain training. As we de-
tailed in this review, lessons can be learned from both of these
settings and can be harnessed to begin to develop an area of
applied research. A key factor to keep in mind is that the goals
of the military differ greatly from those of academic or com-
mercial entities. Military research is often focused on
outcome-based measures and improving warfighter perfor-
mance. For this reason, we have argued that many of the
instances of near transfer in the cognitive training literature
may be of great value to military application. Near transfer
has received very little attention in basic science, likely be-
cause of the heightened focus and controversy surrounding far
transfer results. However, when performance outcomes be-
come the key measure of training success, near transfer has
the potential to be a very powerful tool.
Here, we have made several concrete suggestions that will
be critical in translating basic science into military application,
as it relates to cognitive training interventions. First, the use of
active control groups can have two important implications: (1)
an active control group provides a rigorous and acceptable
control by which to compare effects in the experimental group
and (2) if a second training effort is used as the active control,
two distinct training programs can be tested simultaneously in
the same study preserving military personnel’s time and re-
sources. Second, the role of expectations in cognitive training
has been receiving much attention and is particularly salient in
a military population who may hold very firm beliefs about
the nature of their physical and mental training. We recom-
mend that all training evaluations include measures of expec-
tations (i.e., what expectation(s) does a participant enter a
study with and how does that change, if at all, through the
course of the training intervention). Third, given the close
interaction of military personnel who are likely to be enrolled
in the same training study, strict instructions should be given
to participants to not discuss the training group activities that
have been assigned to them. The goal of these instructions is
to maintain blinding to other groups of the study. Fourth, to
attain adequate sample sizes, it may be necessary to collect
data over multiple time points, multiple groups of participants
in separate locations, and/or use non-military personnel with
similar skills to fill in the gaps. Fifth, the use of task analysis
via SMEs is critical in determining appropriate outcome mea-
sures that will relate back to the real-world tasks that service
members are performing. Finally, individual differences are
bound to be greater in a military population compared to a
typical university sample. These differences can be harnessed
by using individual-level analyses in addition to typical
group-level analysis approaches. Together, these
recommendations provide a practical approach that can be
used to drive efforts forward in testing and implementing cog-
nitive training for military application.
The notion that we might be able to improve human cog-
nition through training is not a novel idea that arose in the
twenty-first century. At least as long as modern psychology
has existed, the idea has been discussed and empirically test-
ed. However, the modern era of cognitive training research
has built to a point where application may now be a reasonable
expectation. Military application in particular has great poten-
tial to not only improve the performance of our service mem-
bers but also to test the bounds of how far these skills can be
improved. The parallel investigation of basic questions about
the mechanisms driving cognitive training effects and the ap-
plied questions of how far cognitive performance can be en-
hanced is likely to move the field forward in a fast-paced and
exciting way.
Acknowledgements The views expressed in this article are those of the
author and do not necessarily reflect the official policy or position of the
Department of the Navy, Department of Defense, nor the U.S.
Government.
Funding This work was supported by an Office of Naval Research award
H1602 to ATB.
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no conflict of
interest.
Copyright Statement LT Adam T. Biggs is a military service member.
This work was prepared as part of his official duties. Title 17 U.S.C. §105
provides that BCopyright protection under this title is not available for any
work of the United States Government.^Title 17 U.S.C. §101 defines a
U.S. Government work as a work prepared by a military service member
or employee of the U.S. Government as part of that person’s official
duties.
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