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Opportunities in Neuroscience for Future Army Applications
3
Training and Learning
This chapter discusses opportunities to expand on and improve the Army’s current behavioral approaches to training and learning by incorporating recent or emerging advances in neuroscience. The discussion is organized under five application areas:
Evaluating the efficiency of training regimes and learning paradigms,
Individual capability and response to training,
Monitoring and predicting changes in individual performance efficiency,
Soldier selection and assessment, and
Monitoring and predicting social and group interactions.
The final section summarizes applications of neuroscience to Army training and learning in terms of when practical application can be expected: 5 years (near term), 10 years (medium term), or 20 years (far term). Enhancing the utility and predictive power of traditional behavioral and psychological methods by incorporating the insights and tools of neurophysiological monitoring in these and other application areas—but focused on understanding how individual choices are made in nonmilitary contexts—is a principal goal of neuroeconomics.1 Results from neuroeconomics research are cited frequently in this chapter and Chapter 4.
EVALUATING THE EFFICIENCY OF TRAINING REGIMES AND LEARNING PARADIGMS
Neuroscience offers new ways to assess how well current training paradigms and accepted assumptions about learning achieve their objectives. For example, does overtraining in specific paradigm responses increase trainees’ agility in responding to new threats, or does training in probabilistic assessment make them more adaptable? Two examples illustrate how each methodology produces benefits in specific cases. From a behavioral perspective, overtraining reduces time to response by reducing consideration of alternatives in favor of an anticipated favorable outcome to the trained-in response (Geva et al., 1996). By contrast, a medical doctor is trained to diagnose any given set of clinical presentations (e.g., presenting symptoms, past individual and family medical history) in terms of a probabilistic etiology based on environment, current illnesses in the community, and other factors. This differential diagnosis strategy helps focus possible treatment modes on likelihood of outcomes. When sufficient evidence accrues, the most likely cause may not be the most frequent cause.
Neuroscience-Based Models of Learning
Over the past decade, enormous progress has been made toward describing the neurological basis for learning skills and procedures. We now have fairly complete models that describe how the brain learns the values of actions (see, for example, Niv and Montague, 2009) and uses these values to guide future decisions, a process often called reinforcement learning. Suppose a subject is offered the opportunity to search in one of two locations for a reward on each of hundreds of sequential trials. With repeated sampling, the subject learns the relative values of the two locations and shapes his behavior to maximize his reward. Although human subjects show idiosyncratic behavior under this training regime, the learning models are now well enough developed that an individual’s behavior can be well characterized by a single parameter. Given the value of this parameter for an individual, his future choices can be predicted with accuracies approaching 90 percent (Corrado et al., in press). These learning models thus both describe the behavior observed in humans and animals and predict how a subject, once char-
1
Neuroeconomics is characterized in a current survey of the field as the convergence of normative models of choice—the province of economics—with the psychological and neurobiological processes (or algorithms) by which individuals (animal and/or human) make choices (Glimcher, in press).
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acterized, will behave in dynamic environments (Balleine et al., 2009).
Similarly, tremendous advances have been made in understanding movement generation, including development of skills, habits, and automatic performance (Poldrack et al., 2005; Yin and Knowlton, 2006). Moreover, consider an individual who is learning to execute a complex movement accurately—for example, tracking a moving target with his finger (Poulton, 1974). We understand quite precisely how the incentives and feedback provided shape both performance and learning (Newell, 1996; Trommershäuser et al., 2009). We also have evidence that the fastest way to train a movement is not to provide strictly accurate feedback. The speed and effectiveness of such training can often be maximized by providing feedback regimens that take advantage of this inherent accommodation to variability (Schmidt and Lee, 2005; Kording et al., 2007).
In short, advanced models of reinforcement learning and movement control learning have implications for both training and prediction of learning efficiency. We know in principle how to develop optimal training regimes under many conditions, and we know how to predict agent behavior with precision under a range of conditions. Both assets could be leveraged to improve not only training but also data presentation (for situational awareness) and prediction of threat/enemy behavior.
The combination of neuroimaging tools, cognitive neuroscience, and experimental or cognitive psychology has resulted in the development of models of how the brain may process information. For example, recent accounts of brain processing that occurs in the dorsolateral prefrontal cortex (PFC) are based on interpreting cognitive control as altering ongoing behaviors in order to adjust to the changing context of the environment (Botvinick et al., 2001). The resulting computational models reveal which aspects of cognitive performance are altered as information changes—a field of study called “computational neuroscience”—and can be used to predict performance on behavioral tasks (Brown and Braver, 2007).
Computational neuroscience uses mathematical models to study how neural systems represent and transmit information. The discipline may be roughly divided into two schools. The first school uses detailed biophysical models of individual neurons, detailed models of neuronal networks, and artificial neural network models to study emergent behaviors of neural systems. The second school develops signal-processing algorithms, computational models, to analyze the growing volumes of data collected in neuroscience experiments. In these computational models, adaptation to new information is represented as changes in one or more parameters. By combining the models of both schools with information from neuroimaging tools (sometimes called “systems neuroscience”) and behavioral neuroscience, the possible causes of underperformance and the conditions conducive to improved performance can be quantitatively constrained. Eventually, it may be possible to develop specialized interventions aimed at improving performance in soldiers.
Subject Populations for Army-Specific Studies of Learning and Training
A perennial issue for behavioral and neurological testing is the degree to which experimental findings from a specific sample can be extrapolated to a target population. Many of the activities in which soldiers and their leaders engage depend critically on rigorous preparatory training and task-specific expertise. The majority of behavioral research is performed with subjects who are either patients in a clinical setting or volunteers from a university community (mostly undergraduate students). A critical question is how far results based on these study populations of convenience transfer to a soldier population. In cases where the research hypothesis addresses Army-relevant issues directly, the typical research subject populations may not be sufficiently representative of the population to which the Army wants the results to apply. In short, neither clinical patients nor university undergraduates are good surrogates for a soldier.
As the Army seeks to apply research results from the neuroscientific literature, the extent to which they can be transferred to a military (specifically, Army) population should itself be a subject of Army research. In particular, how far do results from typical civilian samples represent those to be expected from an Army population? Although some of the research reviewed by the committee has used actual soldiers or cadets, for the most part the human subjects in potentially relevant studies do not compare well to the soldier population in cardiovascular fitness, psychological drive to perform, and learning/training experiences that clearly affect neurobehavioral response—e.g., boot camp, intense training for operational performance, and actual operations.
One alternative to constraining Army-usable results to just the few studies that use soldiers (or even military cadets) is to seek subject populations that more closely resemble Army soldiers in such key characteristics as cardiovascular fitness, psychological motivation to perform, and training/learning in immersive, demanding environments. High-performance athletes are one such subpopulation, and there is an extensive literature of behavioral and neuropsychological research on them. Appendix C lists a sampling of the research literature from 2001 through 2007 on training methods for high-performance athletes: performance evaluation/assessment of athletes in training, including under stress; social interaction with other athletes; and issues with performance anxiety and other psychological issues including depression in ex-athletes (references 1-109 in Appendix C). Several studies have investigated the use of mental imagery in training athletes and its effects on performance (references 110-123). Performance after mild concussions and determining when the subject can return to a normal (strenuous) routine is a hot topic for both athletes
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and soldiers, and some longer-term studies investigating the effects on athletes of multiple concussions have also been published (references 124-143). Studies of training and performance issues for female athletes go well beyond the well-researched Female Athlete Triad2 (references 144-151). The relationship between athletes’ risk-taking behavior and athletic performance is the topic studied in references 152-157. Other relevant topics in this literature include the relationship between the lifestyle of athletes and changes in immune function (references 158-160), and the effect of music therapy on athletic performance (reference 161) and of caffeine (reference 162).
While it is important to consider how well the subject populations in the research studies match the population of interest (here, Army soldiers), the context of the research must also be considered. For example, in testing of futuristic military decision support systems, military subject-matter experts perform worse than novice populations, owing to cultural biases (Graham et al., 2007). When designing a futuristic system, it may therefore be better to test it using a subject population likely to possess the required skills (including constructive attitudes toward novel representations), which current military personnel may lack.
INDIVIDUAL CAPABILITY AND RESPONSE TO TRAINING
Given the increasing technology- and threat-driven dynamic complexity on the battlefield, it has become more and more critical to optimize the capability of individual soldiers. One solution to the great differences from one individual to the next in human capability and expertise is to tailor human–system interfaces to human capabilities and to adapt training regimes to the individual. Although classic experimental psychologists tended to downplay individual differences in their theories of human cognition, educators have consistently reminded psychologists of the need to understand individual differences in cognition and performance (Mayer, 2003). A similar need exists in understanding how the neural systems underlying cognition differ among individuals (Posner and Rothbart, 2005). Understanding the neural substrates of individual differences in cognition can help in characterizing the differences, developing training methods tailored to them, matching individuals to assignments for which they are well suited, and optimizing human–machine and individual–system interfaces.
Recent advances in neuroimaging make this endeavor possible (Miller et al., 2002; Miller and Van Horn, 2007). Patterns of brain activity as measured by functional magnetic resonance imaging (fMRI) appear to provide unique identifying characteristics. These are unlike fingerprints, because individual brain activity may not be epiphenomenal in the sense of simply being a developmental outcome that does not causally influence individual behavior. Rather, individual patterns of brain activity may reflect (or underlie) the unique characteristics of individual minds, and they may capture aspects of an individual mind that cannot be obtained using conventional behavioral measures or self-reporting. The better we understand the sources of individual variability in brain activity through systematic experimentation and analysis, the more fully we can determine whether, and to what extent, these individual differences can be used to assess and ultimately train that person.
Individual Variability in Brain Activity
Efforts by neurologists and neuropsychologists to understand individual differences in brain processes go back to at least the mid-1800s, when the neurologist Paul Broca concluded, from examining the common area of damage across a group of patients exhibiting similar speech production deficits, that speech production could be localized to the third convolution of the left inferior frontal gyrus. Around the same time, the neurologist John Hughlings Jackson argued against a centralized region for speech, basing his opinion on his observations of wide variations in the extent and location of damage in patients exhibiting similar problems in speech perception and wide variations in symptoms in patients with similar damage. More than a century later, Broca’s view of brain organization became the dominant paradigm. An example is the Wernicke-Geschwind model, which was developed to explain language function. Today, however, the Broca paradigm is often disregarded, largely because of enormous individual variability in the underlying brain processes. This variability is evidenced by the fact that a growing number of neurosurgeons painstakingly map out individual brains just prior to surgery, after a portion of the patient’s skull has been removed but while the patient is still conscious and responsive.
Researchers using neuroimaging to understand the relationship between the brain and the mind have recently encountered a similar paradigm shift. Most neuroimaging studies localize cognitive functions in the brain by conducting a statistical analysis across a group of subjects; this analysis identifies common areas of activation. While this can be a useful approach to understanding the modular organization of the brain, it disregards the not-common areas of activation that can be observed at the individual level and that may also be critical for that function in a given individual. Recent studies have shown that the individual patterns of brain activity during a memory task are enormously variable, sometimes with areas of activation that do not even overlap between two subjects (Miller et al., 2002; Miller and Van Horn, 2007). Furthermore, such studies have found that individual variations in brain activity could not be attributed to random noise because the pattern for an individual is stable
2
This is a condition of three syndromes common in high-performing female athletes of all ages, though especially in their teen years, and includes disordered eating, amenorrhea, and osteoporosis. An athlete can experience one, two, or all three syndromes in the triad.
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over time. Understanding these individual differences in brain activity has not only significant theoretical implications but also pragmatic implications for the Army in trying to understand how individuals respond to and remember events in battlefield and nonbattlefield situations and in characterizing the mental traits of individual soldiers and officers.
Individual variability in brain activity could come from one or two things that are not mutually exclusive. One possibility is that individual differences in brain anatomy and physiology lead to extensive differences in brain activity (for example, as measured by the BOLD signal in fMRI) despite the tremendous efforts undertaken to normalize brain spatial variability to a standard spatial representation. Structural differences in brain anatomy and physiology take many forms. It is known, for instance, that the size and shape of individual brains can vary greatly, and genetic markers associated with this variability have been found (Tisserand et al., 2004). There are also well-documented individual differences in the tissue structure of specific regions of the cerebral hemispheres (cytoarchitectonics) and in the orientation and location of specific fissures and gyri (Rajkowska and Goldman-Rakic, 1995). Theoretically, these regions are spatially normalized in fMRI analyses using sophisticated algorithms so that brain regions are consistent across subjects, but some differences have been shown to exist even after extensive spatial normalization. Other structural differences may also exist, despite the fact that brain development is generally universal (Kosik, 2003; Rakic, 2005).
How anatomical differences affect cognition is not well understood, but there are intriguing possibilities. For example, the size and location of the planum temporale is thought to affect language processing (Hutsler et al., 1998). Other recent studies have linked individual differences in white-matter connectivity to individual differences in cognition (Baird et al., 2005; Ben-Shachar et al., 2007). Understanding the extent to which structural differences account for variability among individuals in brain activation will greatly enhance our knowledge and understanding of individual minds, allowing human–system interfaces to be better tailored.
The second possible source of variability in brain activity is individual differences in cognitive styles, abilities, and strategies. These differences, which appear important to how individuals perform many cognitive tasks, often can be correlated with significant differences in BOLD activity, despite procedural efforts to constrain and control the psychological state when test subjects are performing the same experimental task. For example, a recent study found that brain activations underlying a standard memory task were extremely variable from subject to subject. These differences extended well beyond spatial normalization or relatively small differences in the location of brain structures (Miller et al., 2002). A significant portion of this variability correlates with differences in retrieval strategies (Donovan et al., 2007).
Episodic memory, which relies on an extensive hippocampal-cortical network for the consolidation, storage, and utilization of information, provides a useful model for understanding how individual differences in cognitive style and strategy may affect patterns of activations (Tulving, 2002; Squire et al., 2004). A hypothesis supported by patient studies and animal models is that the hippocampus is not involved in the permanent storage of information per se but rather serves to facilitate consolidation of a distributed cortical memory trace. A principal characteristic of this distributed network is that it allows the rapid and flexible formation of multimodal memories. In this emerging picture of brain activity, episodic retrieval makes use of several distinct brain regions, some of which may be involved in the cognitive processing that is peripheral to the actual retrieval of stored information. The same individual may, at particular times, differentially engage those brain regions based on the context and strategy of the moment. One potential implication of this architecture is that the same behavioral outcome—such as an “old” response on a recognition test—could be based on distinct sets of information and distinct combinations of neural circuits in two different subjects. The recent development of new neuroimaging techniques that enable the systematic study of individual differences in brain activity can be used to improve understanding of the optimal brain activity that underlies, for example, optimal performance on the battlefield or in other demanding environments and task contexts.
To improve the assessment of individual soldiers, research programs should systematically investigate the structural and cognitive factors that may account for the extensive individual variability that has been observed in brain activity across normal subjects using fMRI. Three basic questions need to be answered:
Why do individuals’ patterns of brain activity differ so much from each other?
Can individual differences in brain activity be accounted for by differences in anatomy or physiology? Can information about individual brain activity be indicative of limitations and constraints in the kinds of cognitive strategies and skills that a particular individual is capable of or tends to engage in?
Can individual differences in brain activity be accounted for by differences in cognitive style and strategy? Can information about individual brain activity be used to assess the thought processes of individuals engaged in a variety of tasks?
Identifying Conceptual Change in Individual Learning
The potential utility for the Army of monitoring individual differences in brain activity to track and evaluate individual learning can be illustrated by recent work in correlating differences in brain activity with success in assimilating a basic concept of physics that is nonintuitive. Teachers and researchers have found that beginning physics students retain a naive “impetus” theory of motion that differs from
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the fundamental concepts of force and motion central to Newtonian physics. Many physics courses may be needed before the Newtonian concepts become a student’s “natural” way of seeing the world (Clement, 1982; McCloskey, 1983; Mestre, 1991).
A number of studies have examined the degree to which major changes (through either education or experience) in the way people view the world can be captured using fMRI. Recently, fMRI has been used to compare the brain activity patterns of students who had taken no high school or college physics with the patterns of students who had taken at least five college-level physics courses (Dunbar et al., 2007). Students were shown similar movies of two balls falling at either different rates or at the same rate. The students were asked whether the movie they viewed was consistent or not with their expectations. The researchers were particularly interested in movies where balls of different sizes (and therefore different apparent mass) either fell at different rates (as expected in an “impetus physics” view) or fell at the same rate (as expected in a “Newtonian physics” view). The fMRI data showed that a particular brain region associated with error detection, the anterior cingulate cortex, was activated in the nonphysics students when they saw the balls of different sizes drop at the same rate, while the same region was activated in physics students when they saw the balls drop at different rates. Conversely, neither group showed the characteristic error detection activity when the movie they saw fit the expectations of their engrained physics concepts. The researchers concluded that the physics students had indeed fully assimilated the Newtonian concepts. In the context of Army applications of neuroscience, this example illustrates how fMRI could be used as an indicator of whether soldiers have learned and assimilated key concepts fully enough to act on them instinctively.
MONITORING AND PREDICTING CHANGES IN INDIVIDUAL PERFORMANCE EFFICIENCY
This section begins by outlining some conditions in which neuroscience approaches may be useful in assessing changes in the performance efficiency of individual soldiers that are due to the stresses of extreme environments and combat operations. Advantages and disadvantages of potential neuroscience approaches are illustrated with a few examples. Finally, short-, medium-, and long-term opportunities for Army R&D on these approaches are discussed.
The Effects of Environmental Stressors on Individual Performance
Extreme environments, including but not limited to combat environments, are characterized by the high demand they place on physiological, affective, cognitive, and social processing in the individuals exposed to them. In short, the stressors present in extreme environments strongly perturb the body and mind, which in response initiate complex cognitive and affective coping strategies. Research on expedition members, soldiers, elite athletes, and competitors in extreme athletic events provides substantial evidence that exposure to extreme situations profoundly affects performance. Different environmental stressors can place different demands on individuals or groups exposed to them. For example, exposure to extreme cold during an Antarctic expedition may result in social deprivation, whereas exposure to combat may result in affective overload. However, beyond these differences in response that correspond to differences in the environmental stressors, individual cognitive and affective responses to the same stressor vary just as widely.
Neuroscience offers some distinct advantages over behavioral assessment or self-reporting for assessing and even predicting how an individual’s baseline performance is affected prior to, during, and following exposure to a particular environmental stressor. One key limitation of standard self-reports and observer reports is their limited ability to predict future behaviors. Moreover, a number of studies have shown that individuals do not always report their current psychological, mental, or emotional status accurately (Zak, 2004). Although there is still no single neural measurement tool that can unequivocally replace self-reporting, the current tools can depict an individual’s status more fully, as a complement to, rather than a replacement for, self-reporting and behavioral assessments. Moreover, recent insights enable researchers to parse cognitive and emotional processes into more basic modules such as attention, working memory, cognitive control, and others. These modules can be assessed efficiently and quantitatively by linking behavioral paradigms to measurements made with electroencephalography, fMRI, or other imaging modalities.
Finally, behavioral tasks that have been developed recently for use with these imaging modalities are parametric in the sense that the imaging results can be used to quantify the degree to which performance is altered. Quantifying the degree of underperformance (the performance deficit relative to the individual’s baseline) is crucial to designing and administering countermeasures. (Chapter 5 discusses in more detail some countermeasures to stressors that degrade soldiers’ cognitive performance.)
A major challenge for neuroimaging used in this way is to determine its sensitivity and specificity for monitoring performance in extreme environments.3 Thus far, most
3
As discussed in Chapter 2, sensitivity and specificity have rigorous definitions that apply here. Thus, “sensitivity” measures the proportion of the actual positive cases that a test identifies as positive. Mathematically, it is the ratio of true positive test results to the sum of the true positive tests and the false negative tests (false negatives should have been positive). “Specificity” measures the proportion of actual negative cases that the test identifies as negative. It is the ratio of true negative test results to the sum of the true negative tests and the false positive tests (false positives should have been negative). The aim of having a test with both high sensitivity and high specificity is to identify all the positive cases efficiently while also distinguishing positive from negative cases.
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imaging studies have revealed intriguing results on a group-averaged level. But a key goal of a neuroscience-based approach to quantifying behavior and performance is greatly improved ability to predict the future behavior of individuals (individual-specific prediction). It is not enough to predict the distribution in performance of a group (or parameters of that distribution such as the average performance or a “normal range”). The goal is to predict which individuals will perform well and which will not, and even why indi viduals perform as they do. Recent results from neuro imaging studies indicate that such predictions are possible. Most imaging studies have demonstrated large effect sizes, which supports the idea that differences among individuals and at different times for the same individual may be large enough to be meaningfully measured.
The next step is to determine if neuroimaging or other neuroscience approaches that combine sensitivity and high specificity can be used to generate quantitative predictions of individual behavior in response to environmental stressors. To be more useful as a predictor of performance than current approaches, neural monitoring methods such as neuroimaging need to track performance states closely, including considering whether or not the individual is self-reporting a change in performance and whether or not the self-reporting is as accurate as objective measures of performance. To be useful for identifying (and eventually predicting) performance deficits that are due to extreme environments, the neural monitoring methods must do more than distinguish poor-performing individuals from normal performers. They must also consistently distinguish altered activity from normal activity in specific brain structures of those individuals who subjectively report performing at normal levels but whose performance has deteriorated by objective measures. The last-mentioned capability will enable the identification of individuals whose performance in future missions is at high risk of deteriorating, as well as the identification of design factors for military equipment. The example in Box 3-1 illustrates the potential of neuroscience methods to make such predictions.
SOLDIER SELECTION AND ASSESSMENT
Currently the Army selects most of its individual soldiers using two basic tests, both administered by the Department of Defense: the Armed Services Vocational Aptitude Battery (ASVAB), for selecting enlisted personnel, and the Scholastic Aptitude Test (SAT), for selecting officers. These tests are used for assessing both an individual’s fitness for the Army and his or her suitability for specific assignments. Neither test assesses personality traits or neuropsychological traits of applicants.
There has been little attention to how the Army could select, from a general pool of applicants, those individuals who, by inherent or acquired ability, would add value to a particular unit. Even for assignments that require expensive and specialized training, the Army knows little about a candidate’s neuropsychological traits before that individual starts training. Little is known about how to identify soldiers whose individual traits would enhance the performance of a unit to which they could be assigned. Indeed, for many high-performance assignments, one cannot state with certainty which psychological or behavioral traits correlate with superior performance. For example, the training for an attack helicopter pilot costs about $225,000, but candidates for this training are selected today largely on their expressed interest in becoming an attack helicopter pilot rather than on their ability or fitness for this high-value assignment.
At present, the Army has low washout rates even for
BOX 3-1
Predicting Future Behavior in Extreme Environments
Imaging techniques could be used to detect individuals who are at high risk for experiencing deterioration of performance on future Army missions. Paulus et al. (2005) used fMRI to scan the brains of 40 methamphetamine-addicted men who had been sober for 3 to 4 weeks. This imaging technique can map brain regions involved in specific mental activities. Scans were performed while the men were involved in a decision-making task to identify brain regions stimulated by the task. Approximately 1 year later, the researchers correlated the fMRI results with subsequent drug abuse in the 18 men who relapsed and the 22 who remained abstinent (drug-free).
In the scans made at the beginning of the study, the scientists observed low activation patterns in the brains of some of the men in structures that are known to participate in making decisions. These regions were the right middle frontal gyrus, the right middle temporal gyrus, and the posterior cingulate cortex. Lower activity in these structures correlated with early relapse to methamphetamine use. The scans also showed that reduced activation in the insula and the dorsolateral prefrontal, parietal, and temporal cortices correlated with drug relapse in 94 percent of cases. By comparison, significant activation in these same regions correlated with nonrelapse in the 86 percent of the men who remained abstinent after 1 year. Thus, the initial brain scans provided an indicator of which individuals were at greatest risk of relapsing and which were at least risk. Furthermore, these differences in activation pattern show both specificity and sensitivity as a predictive indicator for risk of relapse.
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difficult jobs because trainees are free to repeat the training sequence until they are able to meet minimum performance requirements. Less than 5 percent of helicopter pilot trainees, for example, wash out of training. This approach obviously increases training costs. It may also reduce unit performance; a soldier who requires three times longer than average to achieve minimal standards of proficiency during flight training may or may not be a good helicopter pilot. At the moment, however, the Army has no validated means of measuring how this approach affects unit performance.
Current Enlisted Soldier Selection
The ASVAB, which is administered to roughly 500,000 applicants each year, is a computer-administered multiple-choice test with two components: the Armed Forces Qualification Test (AFQT) and five subject area tests. The goals of the AFQT are to predict the likelihood of service completion by the applicant and to predict overall performance. The AFQT has four parts: word knowledge, paragraph comprehension, arithmetic reasoning, and mathematics knowledge. AFQT scores are percentile rankings that have been normed to the ASVAB applicant pool and are correlated with the likelihood that the applicant will complete a 2-year tour of duty. The higher the score, the more likely the applicant is to complete that tour. Interestingly, the AFQT prediction of tour completion is most accurate for applicants who hold a traditional high school diploma. The AFQT score is a poor predictor of tour completion for applicants who did not graduate from high school or who have a graduate equivalency diploma.
To address the limitations of the AFQT, the Army Research Institute (ARI) has developed an alternative test for candidates who did not complete high school. This “noncognitive” test, called the Assessment of Individual Motivation (AIM), was designed to assess conscientiousness, stress tolerance, and openness to new experiences. The AIM in combination with a measure of body mass index is called the Tier Two Attrition Screen and is now in limited use as a complement to the ASVAB.
The second part of the ASVAB consists of five tests of the applicant’s factual knowledge in general science, automobile and shop information, mechanical comprehension, electronics information, and assembling objects. The scores on these tests by soldiers who have successfully completed a tour of duty in a particular area of specialization are used as a benchmark or score profile that is associated with successful tour completion in that area of specialization. The score profiles are used by recruiters and recruits as guides to a recruit’s likely job performance when the recruiter and recruit together select an area of specialization. Essentially, the score profiles from the five subject tests are derived by simple linear regression. Their actual predictive power is low compared with that of best practices in the vocational assessment and training community.
Advanced Soldier Selection Tools
For many high-value soldier assignments, the ASVAB may only weakly predict how long it will take to train a recruit, how likely the recruit is to complete a tour of duty, or even whether he or she will be effective in the assignment. The Army recognizes the poor predictive power of the ASVAB and has commissioned ARI to construct secondary selection tests for a number of high-value specialties that call for a substantial investment in training. There are now specialized selection tests for helicopter pilots, improvised explosive device (IED) detection experts, Special Forces, drill sergeants, and recruiters. In general, ARI has developed these specialized selection tests as psychological tests.
By developing and using these tests as selection tests—even if only to a limited extent—the Army has in fact implemented a screening process for those interested in certain high-value assignments. For example, the helicopter pilot test allows some recruits to become helicopter pilots and excludes others from the required training. The question, then, is not whether applicants should be screened, at least for demanding and high-value positions, but rather how well the screening instruments do their job. To date there appears to have been little effort to ensure that these tests have both specificity and sensitivity with respect to predicting actual performance.
To illustrate the state of practice in Army selection testing and highlight the challenges where neuroscience-based approaches can help, the discussion will focus on the Army’s current test for helicopter pilots, the Alternate Flight Aptitude Selection Test (AFAST) and the test proposed as its replacement. After taking the ASVAB, Army recruits who want to train as helicopter pilots are invited to take the AFAST, which is a computer-based test. The Army uses the AFAST score to assess the suitability of the recruit for flight training. Two factors limit the effectiveness of the AFAST as a screening tool. First, the security of the AFAST has been compromised; answers to the test can be found on the Internet. Second, according to the briefing ARI gave to the committee, the AFAST has almost no predictive ability.4
In recognition of the limitations of the AFAST, ARI was tasked with developing an alternative, the Selection Instrument for Flight Training (SIFT). As an instrument for soldier selection, the SIFT model is based more closely than AFAST on the selection techniques used in private industry, although it is still limited to conventional behavioral testing. The SIFT is a secure test that measures, among other things, a number of personality features, perceptual speed and accuracy, and flexible intelligence. It has almost triple the predictive accuracy of the AFAST for helicopter pilot selection, based on subsequent pilot performance and aptitude evaluations by peers and instructors. The SIFT score has not yet been correlated with in-theater performance of helicopter pilots who
4
Lawrence Katz, research psychologist, Army Research Institute, briefing to the committee on April 28, 2008.
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took it prior to training. Unfortunately, there are no definite plans to gather the data to establish this correlation, a final assessment that is critical for such a tool.
Despite its advantages over the AFAST, the SIFT nevertheless lacks any neuropsychological measurements, nor does it look at neurochemical or genetic indicators of neuropsychological traits. ARI does not have expertise in these neuroscience-based measurements. Enhancing the SIFT by giving it the ability to identify established neuropsychological correlates of the personality and cognitive traits characteristic of highly successful helicopter pilots would greatly improve its value as a screening instrument. For example, research has found that individuals whose hypothalamo-pituitary axes are highly reactive to stress are unlikely to complete Navy SEAL training (Taylor et al., 2006, 2007). However, such information is not yet used by the Army in its recruit assignment process.
Even after advanced selection instruments have been developed, implementing them throughout the Army will be a challenge. Recruiting stations throughout the United States must be able to administer specialized selection tests like the SIFT, whose implementation cost has been estimated at roughly $200,000. Although this implementation cost is less than the cost of training a single helicopter pilot, ARI has not yet succeeded in getting the SIFT implemented generally in the recruitment process.
A similar project is under way to improve the test instrument used for identifying trainees in explosive ordnance disposal. This project, which is conducted jointly by ARI and the Joint Improvised Explosive Device Defeat Organization, was not evaluated by the committee because it involves classified information.
Neuropsychological Testing in the Army: The Automated Neuropsychological Assessment Metrics
The only Army testing instrument that contains significant elements of neuropsychology and cognitive neuroscience is the Automated Neuropsychological Assessment Metrics (ANAM). ANAM evolved from a series of neurological assessment tools initially developed during the Vietnam war. The goal was to develop behavior-based tests for determining whether the cognitive capability of a soldier had been impaired by exposure to a chemical agent and for testing protective agents for side effects. A group of such tests was consolidated by a multiservice group into what later become the ANAM. In its current form, ANAM is a computerized neuropsychological battery of 30 test sets, some of which have not been normed. ANAM support and development are currently under the direction of the Center for the Study of Human Operator Performance at the University of Oklahoma.
An ANAM assessment ranges from simple tests of reaction time to dual-task interference tests, which are useful for assessing executive function. The test sets are essentially standard neuropsychological tests packaged for easy analysis and administration. A tester first selects a number of test sets from the battery. The test sets might, for example, be selected to gain a baseline assessment of the neuropsychological functioning of an individual or to allow the state-specific assessment of neuropsychological function. For example, the ANAM can be used to assess mental function after a concussion, after a period of sleep deprivation, or after exposure to pharmacological agents. ANAM test sets can also be selected to obtain a clinical assessment of medical disorders such as Parkinson’s disease or Alzheimer’s disease.
The high incidence of IEDs on the contemporary battlefield has heightened the interest of the Army’s mission commanders in ANAM. A number of commanders have instituted predeployment baseline testing for all their troops with a subset of ANAM well-suited for identifying neural function changes caused by traumatic brain injury. Such testing allows for comparing an individual’s neurological functioning before and after exposures that increase the risk of traumatic brain injury, such as the blast from an IED. The ANAM support center estimates that over 50,000 troops were screened before deployment in 2008.
Summary: Status of Soldier Selection and Assessment and the Potential for Neuroscience-Based Improvements
The ASVAB, which the Army currently uses to assess the fitness of most candidate recruits for the Army and their suitability for specific assignments, does not assess personality traits or neuropsychological traits. Even in the case of high-value assignments for which the Army must make a substantial investment in training, trainees are free to repeat the training sequence until they can meet minimum performance requirements, and the Army knows little about a candidate’s neuropsychological traits before the training begins.
To address limitations in the ASVAB, the Army has developed a complementary test that attempts to assess a few basic behavioral traits considered valuable for completing a tour of duty, such as conscientiousness, stress tolerance, and openness to new experiences. For a number of high-value specialties, the Army uses screening tests, but the specificity and sensitivity of these tests have not been evaluated as predictive of performance in the specialties for which they are used. Even when a screening test that incorporates conventional behavior-based factors—for example, the SIFT for helicopter pilot training—has been developed to replace an older test with much less predictive power, the Army has not incorporated the improved test in its recruitment process.
The problem is that even improved tests like the SIFT do not measure the neuropsychological traits that neuroscience research has been identifying. Based on the progress seen when neuropsychological indicators are applied in vocational settings, adding fairly simple neuropsychological testing to the current mix of soldier assessment techniques
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appears likely to improve soldier selection immensely, particularly for high-value assignments where the training investment is high and individual performance is critical to the accomplishment of a unit’s mission.
The ANAM, which is currently deployed and being administered to assess neural function, seems a natural starting point for expanding the Army’s selection of testing instruments. As more and more soldiers are screened with ANAM before being deployed in combat theaters, the Army is building a large experience base that, if captured in an analytically useful medium, could provide essential feedback for improving recruit selection processes. If the Army can identify soldiers who excel in their areas of specialization, it could use their ANAM test data to identify characteristics common to high performers but less likely to be found in lower-performing individuals in that area of specialization. The ANAM data could thus be used to develop a set of tools for relating neuropsychological assessments to mission performance.
MONITORING AND PREDICTING SOCIAL AND GROUP INTERACTIONS
The Scope of Social Neuroscience
Most of a soldier’s actions involve other people, including fellow soldiers, commanders who are giving orders, the enemy, and noncombatants. Recent findings in social neuroscience can inform and improve Army training, tactics, and leadership for dealing with these social interactions.
Social neuroscience, also called social cognitive neuroscience, investigates the neurophysiological basis for social behaviors. Topics examined are those traditionally studied in social psychology, including attraction to others and attachment, altruism, speech recognition, affiliation, attitudes toward other individuals and groups, empathy, identification of others, kin recognition, cooperation and competition, self-regulation, sexuality, communication, dominance, persuasion, obedience, morality, contagion, nurturance, violence, and person memory. Social neuroscience research integrates information about a person’s physiological state, social context, experience, and cognition to understand his or her social behaviors. Studies in social neuroscience draw heavily on findings from affective neuroscience, as many studies have shown that social behaviors have a strong affective (emotional) component (Ochsner and Lieberman, 2001). What social neuroscience adds is explicit attention to brain activity and neurophysiology when studying social behaviors.
The techniques used to measure brain activity directly during experiments involving social tasks include magnetic resonance imaging generally and fMRI in particular, computerized tomography, electroencephalography and magnetoencephalography, positron emission tomography, transcranial magnetic stimulation, and event-related potentials. Other techniques are used as surrogates for brain activity: electrocardiography, electromyography, galvanic skin response, eye tracking, and genotyping. Still other methods used in social neuroscience include drug infusion studies, comparisons with patients who have neurological disorders or focal brain lesions, and comparison with animal models.
Although it is difficult to measure brain activity in a laboratory setting during natural social interactions, techniques that approach a natural interaction have been developed. For example, Montague developed a technique called “hyperscanning” to simultaneously measure brain activity using fMRI while two or more people interact (Montague et al., 2002). More simply, brain activity can be monitored in one person while he or she interacts in real time with one or more individuals whose brain activity is not measured (see, for example, Eisenberger et al., 2003). Many studies use a computer-simulated “person” with whom the study subject interacts without knowing the interaction is with a computer-based simulation. This approach appears to activate the same brain regions associated with actual social behavior (see, for example, McCabe et al., 2001).
Relevance of Social Neuroscience to the Army
There are numerous psychosocial factors that contribute to stress in the armed forces, including unpredictability of danger, concern about resiliency and sustaining performance in combat, inability to control situations, separation from family, recovery from injury, death or injury of comrades, even the anticipation of returning to civilian life. As the neurophysiological conditions that correlate with and perhaps underlie the behavioral and psychological (“mental experience”) responses to these and other psychosocial factors are uncovered, it will become easier to understand both their detrimental (stressing) and beneficial (stimulating) consequences. This section highlights several of many applications where research using the methods of social neuroscience is likely to produce results of value to Army training and learning.
Using the methods of social neuroscience, tasks with structured, well-defined parameters of interaction can be used to gauge an individual’s commitment to group goals while studying the brain processes associated with individual goals. When the group in question is an Army unit or a surrogate for one, this approach can provide an objective measure of effective training and an individual’s likely performance as a team member. By adding factors to the structured interaction—for example, time constraints on decisions, sleep deprivation, environmental stressors, or gunshots and other disturbances typical of combat—group cohesion under stress and the neuropsychological correlates of the observed behaviors can be examined for insights into whether and why cooperation is sustained or fails. Adding neuroscience methods and understanding to established techniques for training and evaluating group interactions and
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unit performance can improve the diagnostic and predictive value of current practices.
Behavioral measurements obtained through the use of structured interactions, such as measurements based on game theory constructs, can predict how soldiers in a particular unit will interact and respond during combat as well how they will interact and respond within their unit or with other “friendly” groups. Behavioral measurements can be structured to emphasize cooperation, competition, punishment, or a blend of all three. Many structured interactions, or games, have been formulated and studied in the context of behavioral economics for small groups (two to four players). A challenge for the Army will be to extend the results to larger groups in contexts that reflect different points along the full spectrum of warfare. The tasks can be modified to better relate to a field operation, while incorporating the basic structure of a formal game whose behavioral and neurological aspects have previously been established and confirmed.
Social neuroscience research on leadership is a nascent effort that could inform the training of both officers and the soldiers under their command. For example, characteristics of leaders under stress can be studied with the methods of social neuroscience as can the impact of a specific leader’s characteristics on the performance of those he or she leads. Behavioral game theory uses insights from human behavior to improve decisions (Camerer, 2003), and, as discussed above, the neural correlates of effective decision strategies are now being mapped. One direct application of this work would be to teach leaders some of the findings from experiments in behavioral game theory in order to improve their awareness of the factors that influence their own decision making behavior as well as that of others. Understanding the behavioral and psychological factors in how choices are made, including the brain mechanisms that support effective choices, can contribute to improving the performance of Army officers and the training regimens for soldiers.
As described in Chapter 8, an important trend in long-term research is the continuing discovery of performance indicators that can be linked to neural state, including biomarkers such as the small biomolecules involved in brain functioning that are relatively easy to monitor. Recent research has shown that when an individual receives an intentional and tangible signal of trust from another individual, the brain releases one of these biomolecules, the neuropeptide oxytocin (OT) (Kosfeld et al., 2005; Zak et al., 2005). OT has been shown to reduce anxiety (Heinrichs et al., 2003). It has been known for some time that OT is associated with bonding to offspring and spouses. Moderate stress also induces the release of OT, but fear and great stress inhibit it. By knowing this brain target, training could be redesigned to induce OT release.
Another aspect of social neuroscience relevant to leadership training concerns the neurophysiological correlates that are being identified for the cognitive constructs by which we interact with other persons as conscious agents. This attribution of a “mind” to others is referred to as the theory of mind (ToM), whereby in order to understand another person and respond to his or her behavior, an individual assumes the other person has a conscious mind directing his or her observed behavior (see Box 3-2).
A large number of studies have allowed brain processes related to ToM attributions to be localized to specific cortical regions (Gallagher and Frith, 2003). Neurophysical monitoring techniques can be used to determine whether these regions of the brain are activated during Army-relevant activities—for example, battle simulation training—to optimize soldier learning and retention. The brain’s ToM regions also appear to be active during moral judgments (Young et al., 2007). Neural processes in these regions may affect decisions soldiers make when they apply the Uniform Code of Military Justice to combatants and noncombatants. Thus, the techniques of social neuroscience can be adapted to monitor trainee responses in unaccustomed or problematic situations, to improve training, and to test retention of leadership, group dynamics, and moral judgment skills.
Social neuroscience methods can also enhance Army- relevant research on how group dynamics affect
BOX 3-2
Theory of Mind
ToM, a construct that was introduced by Premack and Woodruff (1978) to characterize the mental ability of higher apes, refers to the ability of individuals to attribute independent mental states to self and others in order to explain and predict behavior (Fletcher et al., 1995). This approach has been particularly important to characterize cognitive development in children (Frith and Frith, 2003) and dysfunction of cognitive development in autism and autism spectrum disorders (Happé et al., 1996) as well as in psychosis (Doody et al., 1998). More recently, several conceptual connections have been made between ToM and other important psychological constructs. For example, it appears that the neural representation of self (Happé, 2003) and, more generally, self-generated beliefs (Leslie et al., 2005) are closely related to the ability of ToM. This proposition is supported by some imaging studies that suggest the importance of the medial prefrontal cortex as part of both self-relevant as well as ToM-related processing (Wicker et al., 2003). Therefore, ToM is an important construct that can be used to examine one’s ability to infer mental states, related to self, and beliefs but more important is accessible to experimental modulation using neuroscience approaches.
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an individual’s ability to process information and make choices based on that information. Most army operations involve soldiers trained to operate as a team. However, in most studies—fMRI, positron emission tomography, and magnetoencephalography—of neural information processing the subjects are isolated from other persons, and the study is not designed to assess differences in performance related to the influence of other team members.
With the increasing importance of network-centric concepts in planning for the future Army, an important issue is the performance of individuals operating as a de facto team that results from being connected by communications links. Social neuroscience methods and insights can help to answer two questions raised by these network-created team situations:
Do soldiers respond more efficiently to perceived human–human communication than to machine–human communication?
What is the most efficient size (number of members) needed by a particular team to respond to a specific threat?
Recent findings suggest that a person interacting with another person over a communications link has a different pattern of brain area activation than a person interacting with a computer. In the human–human situation just the dorsolateral PFC is likely to be stimulated, whereas in machine–human interaction the medial PFC also becomes activated (McCabe et al., 2001).
Based on this work, neuroimaging experiments could indicate whether military operators are receiving information from a computer-based system or from another person. In these experiments, which would combine behavioral monitoring and neuroimaging, key variables would be the nature (human or machine) and number of communication channels. One hypothesis is that the responses of team members may vary significantly if, under information overload, they begin to attend in different ways to communications perceived to come from human counterparts and to communications perceived to come from computers, depending on the degree of confidence they have in the two sources. A competing hypothesis is that the team members will in fact operate more efficiently if their sense of social interaction with other humans increases their trust in the information received and encourages them to be concerned about the likelihood of low-probability, high-consequence events.
To explore optimal team size for these de facto teams, experiments could be framed to examine how individuals prune their information load to concentrate on what they perceive to be essential. For example, does a small-world network structure emerge, in which there are highly interconnected groups with sparse connections between groups? (In this type of network structure, each node has a low number of connections compared with the total number of possible connections if every node is fully linked with every other node in the net, but information can pass rapidly between any two nodes in the net.) This question also arises when attempting to maximize the efficiency of road networks, and such research might reveal that in some cases adding a communications channel would actually impede overall information flow.
SUMMARY
Neuroscience techniques (neuroimaging, physiological measures, biochemical assays of brain function) can be used to measure the training status of individual soldiers. Specifically, these techniques can be used to determine (1) functional status, (2) recovery time, or (3) level of training. The sensitivity, specificity, and accuracy of these approaches are unknown and require further research. Thus, a primary short-term goal for the Army should be to conduct this research, which will allow for assessing the training status of soldiers. These efforts should last a relatively short time (<5 years).
There is emerging proof of principle that neuroscience techniques could be useful not just for short-term predictions about outcomes and behaviors but also for predicting the performance, behavior, and potential of individuals over the long term. It is not clear which targets would be most valuable for the Army—for example, the best target population and what outcomes the Army should be most interested in. Close collaboration between neuroscience laboratories and Army leadership would help to develop a common agenda that would benefit both communities. First, the Army would improve its ability to predict performance and, second, the laboratories would obtain further proof of principle of the practicality of their methods. A collaborative effort could be viewed as a long-term initiative with high potential payoff over the next 10 years.
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