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Current Cognitive Neuroscience Research and Technology: Selected Areas of Interest

INTRODUCTION

Cognitive neuroscience and related technologies constitute a multifaceted discipline that is burgeoning on many fronts. Based on the expertise of its members, and realizing that it could not possibly cover the entire range of science within the discipline, the committee chose to discuss three specific areas of interest: (1) challenges to the detection of psychological states and intentions via neurophysiological activity, (2) neuropsychopharmacology, and (3) functional neuroimaging. Even then, the study’s timeline made it impossible to provide an exhaustive review. Despite these limitations, however, the following discussions accurately depict the current state of cognitive neuroscience research in the selected fields. The chapter also serves as the scientific foundation for Chapter 3.

CHALLENGES TO THE DETECTION OF PSYCHOLOGICAL STATES AND INTENTIONS VIA NEUROPHYSIOLOGICAL ACTIVITY

Overview

There is little doubt that great progress has been made over the last quarter century, particularly the last 10 to 15 years, in understanding the physiological and neural bases for psychological processes and behavior. Furthermore, there is a high likelihood that more progress will be made as more sophisticated theoretical models are developed and tested using ever more sophisticated assessment technology. In the applied sector, scientists will probably be better able to identify valid neurophysiological indicators of performance. For example, modeling the human genome will help researchers to index affective, cognitive, and motiva-



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2 Current Cognitive Neuroscience Research and Technology: Selected Areas of Interest INTRODUCTION Cognitive neuroscience and related technologies constitute a multifaceted discipline that is burgeoning on many fronts. Based on the expertise of its mem- bers, and realizing that it could not possibly cover the entire range of science within the discipline, the committee chose to discuss three specific areas of inter- est: (1) challenges to the detection of psychological states and intentions via neurophysiological activity, (2) neuropsychopharmacology, and (3) functional neuroimaging. Even then, the study’s timeline made it impossible to provide an exhaustive review. Despite these limitations, however, the following discus- sions accurately depict the current state of cognitive neuroscience research in the selected fields. The chapter also serves as the scientific foundation for Chapter 3. CHALLENGES TO THE DETECTION OF PSYCHOLOGICAL STATES AND INTENTIONS VIA NEUROPHYSIOLOGICAL ACTIVITY Overview There is little doubt that great progress has been made over the last quarter century, particularly the last 10 to 15 years, in understanding the physiological and neural bases for psychological processes and behavior. Furthermore, there is a high likelihood that more progress will be made as more sophisticated theoreti- cal models are developed and tested using ever more sophisticated assessment technology. In the applied sector, scientists will probably be better able to identify valid neurophysiological indicators of performance. For example, modeling the human genome will help researchers to index affective, cognitive, and motiva- 

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 CUrrENT COGNITIVE NEUrOSCIENCE rESEArCH AND TECHNOLOGY tional states and evaluate the effectiveness of training techniques or to determine the readiness of combat units. The vast majority of neuroscientific research has been conducted at the group, or aggregate, level rather than at the individual level, and this trend is likely to continue. To achieve sophisticated and highly sensitive neuro- physiological assessment of psychological states at the individual level, many significant challenges must be overcome. At a minimum, the neurophysiological indicators will probably have to be individually “tuned” to each user, given the issues of individual variability and plasticity described below. To accurately assess psychological states using neurophysiological measures, basic neurophysiological work needs to be accomplished over the next two decades. The committee identified and discussed a nonexhaustive list of issues that need to be addressed and questions that need to be answered. These included the nature of psychological states compared to “mind reading,” the nature of neurophysiological and neural activity, and barriers to identification of mental states and intentions. An important qualification about the parameters necessary for determining psychological state became apparent during the committee’s deliberations—the end use of information about the inferred psychological state. Because technol- ogy to infer a psychological state or intention could be put to a broad range of alternative uses, it is important to recognize that acceptable levels of error depend on the differential consequences of a false positive or a missed identification. The technology being applied to determine psychological state could even be derived from an incomplete model of brain function as long as it had sufficient predic- tive power to accomplish the desired goal. For instance, one would not need a complete model of brain function to construct a brain–computer interface that could improve the self-piloting capabilities of unmanned air vehicles. But the tolerance for error will be much less if a technology is used to determine whether an individual is lying about an act of treason, because the consequences of an error will be greater. The committee believes that it is critical to fully understand the relationship between neurophysiological markers and actual mental states when the applica- tion is the detection of deception. Mind reading and psychological States It has proven difficult since the beginning of modern psychology 150 years ago to achieve agreement, even among psychologists and other behavioral scien- tists, on explicit definitions of psychological constructs. Such agreement is impor- tant because most psychological constructs bear labels borrowed from common language. Dictionary meanings and usage tempt many scientists to assume that they know the scientific definition of a psychological construct without consulting the scientific literature, where such constructs are explicitly defined.

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0 EMErGING COGNITIVE NEUrOSCIENCE AND rELATED TECHNOLOGIES Typical didactic schemes for organizing psychological constructs imply a more rigid separation between them than actually exists and operates. Today, the main organizing constructs for understanding psychology at the individual level are affect, cognition, and motiation.1 However, such organization does not necessarily reflect how affective, cognitive, and motivational processes interact. Indeed, attempting to understand each construct in isolation rather than the three as an interdependent triumvirate is to wander in an epiphenomenal domain rather than a realistic psychological domain. If scientists could, for example, accu- rately determine how a particular soldier processes information about a member of the enemy force (cognition), that knowledge would do very little to help us understand how the soldier will behave toward that enemy unless scientists also take into account how he or she feels about that enemy (affect) and how both constructs play into motivational processes. When behavioral scientists ask why individuals behave in certain ways, they typically are asking a motivational question. During the first half of the twentieth century, psychologists focused on external environmental factors such as rein- forcement to explain motivation. In the latter half of that century, they focused on internal processes to explain affect (moods and emotions) and cognition (informa- tion processing, memory) but without knowing details of the causal interconnec- tions among the processes. Today, psychologists understand that behavior occurs between interrelated affective, cognitive, and motivational processes on the one hand and environmental factors and processes on the other. This complex set of interrelated factors must be understood and accounted for to detect a psychologi- cal state—that is, to “read” a mind—using any technology. There has been growing use of the term “mind reading” in the popular press and in a few circumscribed areas of the Department of Defense (DOD). Because the precise meanings of the terms that are used to communicate understanding are critical to the scientific endeavor, the committee believes it is important that the DOD and IC communities understand what is meant in this study by “mind reading” and “psychological state.” Mind reading typically refers to the capac- ity (imparted by an external mechanism—that is, some form of technology) to determine precisely what an individual is thinking or intending, whether or not the individual is willing to communicate that state of mind. As discussed below, to “read” minds scientists must understand how minds really work to come up with a technology that is of real use, and there are several formidable barriers to 1 Individual psychology is also determined by important factors such as fundamental biological drives and programming of behavior, cognition, and affect by all levels of biology, including genes, proteins, receptors, synapses, and nuclei, among others. In addition, endogenous and genetic drivers dominate cognition, affect, and behavioral capability—for example, in human development, sleep and circadian rhythms of cognition and affect, eating, the need for social affiliation and for salt and water, sexual drive, aggression, and nurturing—and dominate human behavior. This section of the report discusses in some detail environmental factors relating to individual psychology, but this is not meant to de-emphasize the importance of biological factors such as the ones just described.

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 CUrrENT COGNITIVE NEUrOSCIENCE rESEArCH AND TECHNOLOGY achieving such an understanding any time in the next two decades. In contrast, “psychological state” sometimes refers to a broad range of mental activities asso- ciated with cognition, affect, and motivation, but more often refers to a discrete and definable mental state, for example, sustained attention (cognition), anger (affect), or hunger (motivation). The committee believes that experimentation, with the careful control of any number of possibly confounding variables, will result in important progress toward understanding the nature of psychological states over the next two decades, using current and yet-to-be developed tech- nologies. It must be understood, however, that much neuroscientific research still infers psychological state based on the experimental controls. Barriers to being able to read minds as well as the hurdles that must be overcome to accurately determine psychological state are discussed below. The Nature of Neurophysiological Actiity The progress being made by scientific discovery in the field of biology is truly amazing, particularly at the molecular level. At the level of the neural system, however, current knowledge is more speculative. This is understandable given the complexity of the brain. Estimates are that each of the (approximately) 100 billion neurons in the brain synapses—that is, connects—with as many as 50,000 other neurons, making for a large and complicated control network that will likely take decades more of scientific work to map out. This level of complexity also makes it unnecessary to identify neural centers of activity that are responsible for or associated with specific psychological “modules” of activity. It has been shown that although the neural activity in some brain loci appears to increase or decrease during specified mental activities, these brain loci represent only a small fraction of ongoing neural activity (Raichle, 2007). The rest of the brain is still active, and much more of the operation of the brain system must be understood to develop a firm scientific basis for reliably inferring psychological states. Barriers to Identifying Psychological States and Intentions via Neural Activity A science of the relations of mind and brain must show how the elementary ingredients of the former correspond to the elementary functions of the latter. (James, 1890) The hurdles that must be surmounted in order to detect individual psycho- logical states in a scientifically valid way are quite challenging. Here, several of these challenges are identified and discussed.

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 EMErGING COGNITIVE NEUrOSCIENCE AND rELATED TECHNOLOGIES Technological Limitations and Adances The impediment to detection of psychological states via neurophysiological states that is currently the most tractable is availability of technology to monitor and measure putative neurophysiological and neural processes with high spatial and temporal resolution. Although the assessment of peripheral somatic and autonomic systems has been possible for many years (Shapiro and Crider, 1969), advances in the assessment technology have come only recently. Inexpensive, noninvasive endocrine assays (Dickerson and Kemeny, 2004) and noninvasive, high-density electroencephalographic and functional brain imaging technology with high spatial and temporal resolution of brain processes have advanced rapidly. However, scientists must be cautious about what to expect of these tech- nologies in the next quarter century. Technology is yielding new and powerful measurement tools. However, these tools will require sound scientific methods to be of benefit. Errors in Logic and the Scientific Method Given that the challenge set forth in the statement of task is to help the intel- ligence community (IC) and Department of Defense (DOD) “better understand, and therefore forecast, the international neurophysiological and cognitive/neural science research landscape,” members of the committee believe that individuals who are not members of the neuroscientific community tend to make several com- mon errors of logic when they interpret the findings of various technologies that are used to infer psychological states. These errors tend to occur because people misunderstand the relationship between the neurophysiological measurements and the actual mental state that the scientist is attempting to measure. A height- ened awareness of the potential for such errors may help the IC and DOD make the best possible decisions when evaluating the scientific claims of researchers in other countries as well as the United States. Furthermore, because technological innovation is as elemental to certain branches of neuroscientific investigation as the neuroscience itself is, the IC and DOD are likely to encounter two approaches to developing end-user applications of neuroscience, one favored mainly by neural and behavioral scientists, the other by engineers. Both approaches have their strengths, but when evaluating neuroscience, there are important differences. The first approach, as articulated by Cacioppo and Tassinary (1990a), places a premium on plausible scientific theory and the causal relationships underlying the psychological construct and the physiological index. This approach emphasizes the discovery of causal rela- tionships so the theory can be refined and more and more precise hypotheses can be posited, helping to avoid misinterpretation of the data—that is, third-variable confounding, as discussed below. The second approach (the “engineering” one) is to propose, demonstrate, or purport that a given device or technology or method works from a signal detection point of view—for example, “with this technol-

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 CUrrENT COGNITIVE NEUrOSCIENCE rESEArCH AND TECHNOLOGY ogy we can tell when a pilot is too tired to fly with 92.3 percent accuracy.” Any underlying causal model is secondary to the correlated effects. This approach is appealing, works well for many applications, and fits well with the DOD’s proactive approach to problem solving. One significant problem with the largely atheoretical “engineering” approach in neuroscience is that it leaves one open to third-variable confounding, because without a model it is not possible to predict potential confounding. Furthermore, if problems do develop in implementation, there is no model from which to predict the next step. In contrast, the theory- based approach is one of successive approximations by which the underlying theory is continually refined and built upon through the use of models describing the underlying causal relationships. relationship Between Neurophysiological Measures and psychological State First and foremost, it is important that the reader understand the nature of neuroscientific investigation. When a neuroscientist is studying the biochemistry or the physics involved in brain function—changes in amino acids or the flow of ions, for example—these physiological changes are the phenomenon of interest and the focus of the study. However, when a neuroscientist is studying a psycho- logical state such as attention or anger, changes in brain activity or chemistry are the correlates, or the means by which scientists study the mental state, which is the phenomenon of interest. Whereas physiological changes may regularly accompany a shift in mental state, scientists cannot assume that the mental state bears a one-to-one correspondence with the neural changes they are measuring. A discerning reader might argue, “But what if (and this is a very large if) scien- tists knew everything about how the brain functions, and knew how to measure it; would they then, in fact, be measuring mental states?” This line of reasoning, which is often followed by the lay community, is actually a philosophy of science known as reductionism. Reductionism, introduced by Descartes in the seven- teenth century, argued that complex things can be fully explained and predicted by reducing them to the interactions of their parts, which are simpler or more fundamental things. He said that the world was a machinelike system that could be understood by taking apart its pieces, studying them, and then putting them back together to see the larger picture. Taken to its logical extreme, measuring the biological mechanisms associated with a mental activity would be equivalent to measuring the mental activity itself rather than just a correlate. Although a reductionist philosophy of science is accepted in many areas of modern science, including much of physics, chemistry, and microbiology, reductionism to these levels of analysis has never taken hold in the behavioral sciences, probably with good reason. Although reductionists (see, for example, Wilson, 1998) believe that behavior can best be explained by genetic biology and/or the operation of neural control mechanisms, most other scientists argue that reductionist assumptions limit scientific understanding of complex systems.

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 EMErGING COGNITIVE NEUrOSCIENCE AND rELATED TECHNOLOGIES If this is true, mental states may be more than the sum of their parts and may not be amenable to measurement even if the underlying neural activity is fully understood. Stated another way, mental states may emerge only at a psychological level of analysis and cannot be described in terms of purely neurophysiological activity even though the mental states are assumed to be caused by the brain. If reductionism is indeed correct, then at the current level of knowledge about the complexity of neural systems, science is indeed a very long way from being able to read minds from genetic or neural information. This argument is important because neuroscientists realize that they are measuring the correlates of some mental state, not the mental state itself. As such, the issue of how closely the measures of neural activity map on to the mental state of interest (discussed below) becomes important. This point is of less concern to certain applications of technology to infer brain states (such as augmenting cognition to facilitate the piloting of unmanned aircraft), but it becomes critical when aspects of the psychological state can have legal rami- fications, as, for example, in the determination of deception or intent to harm. The knowledge that scientists do not know that the neural activity corresponds one to one with the actual mental state (deceiving) must be weighed very care- fully in these instances. Mapping Measurements of Neurophysiological Actiity to psychological States The most critical barrier to the identification of a psychological state from its neural signature is the fact that the neural activity underlying the psychologi- cal state subserves multiple tasks, so there can therefore be no one-to-one cor- respondence between neural activity and any psychological state. An excellent example of this point is that of deception detection, or credibility assessment. William Marston, the father of the polygraphy technique for deception detection, believed that there was a unique physiological response during deception. This has proved not to be the case, and few investigators since Marston, including current researchers investigating the use of neural activity measurements to infer deception, believed a unique signature associated with deception would ever be found. Whereas investigators expect to find some consistency in neural response during deception, they do not expect the activated neurons to fire only when the individual is being deceptive and at no other time. Rather, these same neurons are likely to also fire during other types of cognitive and emotional states besides deception (e.g., anxiety, dealing with a heavy cognitive load, inhibiting a pre- potent response). Whereas some low-level physiological processes may have a one-to-one correspondence with neural activation, no higher-order phenomenon on the order of a mental state has been found to have this type of neural pattern. Accordingly, researchers investigating the neural correlates of psychological states must control for many other variables, including other mental states, that could account for the neural activity they are measuring to be more certain that

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 CUrrENT COGNITIVE NEUrOSCIENCE rESEArCH AND TECHNOLOGY their results are indeed due to the construct under investigation (say, anxiety rather than deception or vice versa). Fortunately, there is a very useful approach to proper inference between indexes and psychological constructs or states originally suggested by Cacioppo and Tassinary (1990b), who elucidated four types of neurophysiological index for psychological constructs: outcomes, concomitants, markers, and invariants. Aware- ness of this typology helps us to recognize important inferential problems associ- ated with putative neurophysiological and neural indices of psychological states. Whereas the goal of a neurophysiological index for a psychological construct may be a symmetric, one-to-one relationship between the index and the variable based on a plausible and verifiable scientific theory, in practice this is rare.2 To be sym- metric, the presence of the variable must always be accompanied by the presence of the index and vice versa, and the two must covary systematically. To be based on a plausible scientific theory, the underlying causal relationships between the psychological construct and the physiological index should be valid ones. More commonly, neurophysiological indices are outcomes and concomitants. Outcomes and concomitants are merely associations or correlations between a physiological response (or set of responses) and a psychological construct that are context bound or context free, respectively (see Figure 2-1). Neither enjoys a symmetric one-to-one relationship between the response and the construct. For instance, the sympathetically driven autonomic responses indicative of stress is an outcome within the Cacioppo and Tassinary framework (1990b)—that is, it is context dependent and asymmetric. In a different context (the diagnostic one Erasistratus found himself in), such responses could be related to different psychological states (e.g., love or anxiety). Markers and invariants are associations between a physiological response and a psychological construct that are context-bound or context-free, respectively, but do enjoy a symmetric one-to-one relationship (Figure 2-1). There are few (see below) well-validated symmetric peripheral or central nervous system (CNS) neurophysiological markers of affective, cognitive, and motivational psychologi- cal constructs. This paucity is partially due to poor/insufficient understanding of how neurophysiological systems operate and the resulting lack of sophisticated and validated biopsychosocial theory, which have facilitated the development of valid markers and invariants of psychological states. Symmetric (one-to-one correspondence) relationships have rarely, if ever, been shown to exist between psychological constructs and their neurophysio- logical indicators. This lack of a symmetric relationship is a major problem for 2 “Symmetric” means “If A then B *and* if B then A.” For example, if there is a symmetric rela- tionship between a lie (A) and a neurophysiological response (B) then every time the lie occurs the specific neurophysiological response occurs *and* every time the neurophysiological response occurs the lie occurs. “Asymmetric” means “If A, then B, but not vice versa.” For example, if a lie is accompanied by a neurophysiological response that does not mean every time the neurophysiological response occurs that a lie has occurred.

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 EMErGING COGNITIVE NEUrOSCIENCE AND rELATED TECHNOLOGIES One to one Invariant Marker Specificity Concomitant Many to one Outcome Context Dependent Context Independent FIGURE 2-1 Associations between a physiological response (or set of responses) and a 2-1.eps psychological construct. SOURCE: Cacioppo and Tassinary (1990b) ©1990 by the Ameri- can Psychological Association. Adapted with permission. The use of APA information does not imply endorsement by the APA. detecting psychological states from the indicators. Neurophysiological and neural activities are almost always multifunctional when it comes to causing underlying psychological constructs. So, even if every time an individual enters a psychologi- cal state (e.g., “love”) the same portion of cortex (i.e., the left prefrontal cortex) is activated, does not mean that every time that portion of the cortex is activated the person is in that psychological state—that is to say, neurological measures may be sensitive, but are rarely specific. Good science avoids this logical error known as the “affirmation of the consequent.” These errors of inference can be avoided by precisely specifying the cir- cumstances or “controls” under which the data can be interpreted by limiting the number psychological states. For instance, a brain–computer interface designed to assess attention to an external task and that has been accompanied by indi- vidual training for the user may place sufficient limits on both the environment and the user’s possible states to allow accurate and useful interpretation of the neural responses. This highly controlled scenario, however, which has controls similar to the experimental controls that are used to interpret neuroimaging data does not amount to mind reading or to determining intent from the raw neural signals. Rather, a cognitive state is inferred based on the controls placed on the situation and is still subject to potential signal detection errors (e.g., false posi- tives, false negatives, misses).

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 CUrrENT COGNITIVE NEUrOSCIENCE rESEArCH AND TECHNOLOGY Aoiding Errors of Inference Fortunately, a reductionist philosophy of science is not a requisite for drawing valid inferences about psychological states from neurophysiological and neural activity if one accepts the “identity thesis” as a basic metaphysical assumption. This assumption states that all psychological phenomena occur via bodily processes and is widely shared by behavioral scientists and neuroscientists (Cacioppo and Tassinary, 1990a; Blascovich, 2000; Blascovich and Seery, 2007). Accordingly, there is nothing ethereal about human behavior, and all psychologi- cal states are embodied somehow. If one can associate certain neurophysiological data with certain psychological states, then identifying psychological states from such information is a potentially tractable, though very difficult, challenge. Sev- eral logical and inferential issues, including those associated with the section below on the third variable problem, cause this challenge to be daunting. The Third Variable problem. When two variables, such as a psychological state and some specified neurophysiological measure, are related probabilistically, even if perfectly so, scientists cannot assume that they are causally related. For example, the correlation between shoe size and reading ability in children might be a spurious correlation. There is no doubt that both increase with age; how- ever, correlation does not imply causality. Correlation is necessary for causality, but two other criteria must be met to imply causality: (1) time ordering (the cause must occur before the effect) and (2) third variables must be ruled out. Whereas an engineering approach can be used to determine time sequencing, a scientific model could allow ruling out third variables as the cause of a cor- relation; however, a poor or incomplete model will allow for many interpreta- tions of an effect that might have an altogether different cause. This becomes a significant problem, for instance, when one wishes to decide whether a person is lying on a polygraph test; even if there is a high correlation between guilt and strong autonomic reactions to certain questions, it would be a mistake to conclude that guilt is causing the stronger reactions if anxiety, not guilt, can produce those same reactions. The goal of a neurophysiological index of a psychological construct is a symmetric, one-to-one relationship between the index and the variable based on a plausible and verifiable scientific theory. To be symmetric, the variable must always be accompanied by the index and vice versa, and the two must covary systematically. To be based on a plausible scientific theory, the underlying causal relationships between the psychological construct and the physiological index should be valid. Brain plasticity Brain plasticity refers to changes that occur in brain organization and function as a result of experience. There is now considerable evidence that brain activity

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 EMErGING COGNITIVE NEUrOSCIENCE AND rELATED TECHNOLOGIES associated with a psychological state or process can change throughout life as a function of factors such as sleep, maturation, experience, damage, exogenous (e.g., pharmacological) agents or a combination of these. Indeed, most poststroke rehabilitation therapy (e.g., relearning walking, talking) would be ineffective if such change were not possible. Brain plasticity is manifested in at least three ways. One involves functional shifts and changes that occur when control of motoric behavior reorganizes itself in a different area of the cortex as a result of experience. A second way, termed synaptic plasticity, involves changes in neuroreceptor production and/or sensitivity that potentiate or antagonize the likelihood of synaptic transmission. A third way, at least speculatively, brain plasticity may manifest itself is changes in brain structure; that is, actual changes in the number of neurons and synapses, the most obvious examples of which are increases occurring early in life and decreases occurring as a result of lesions or aging. Brain plasticity represents a challenge to those seeking to develop neuro- nal indexes for psychological states—i.e., outcomes, concomitants, markers, and invariants—on an individual level, because structural, organizational, and functional differences between individuals—and within them over time—will have to be accounted for. It is also possible that a high degree of plasticity-based error in any given index could reduce its sensitivity and specificity and, hence, its practical value for “reading” individual minds. However, this remains an open question, for scientists do not yet know how plasticity might affect any given set of measures across various populations. Variability Within and Between Indiiduals Two important challenges to using brain states to index psychological states are variability between individuals and also within a single individual. It seems likely that brain plasticity, along with genomic factors, may be one of the under- lying causes of such variability, which apparently exists. However, “it is not easy to change the habits of people who are comfortable with traditional ways of doing things, and developers of cognitive models have continued to rely for support mainly on the fitting of functions such as curves of learning, retention, and generalization to averaged data” (Estes, 2002). Estes has examined the relationships between typical brain scan images aggregated across individuals and those of the individual cases from which aggregated images are derived. Figure 2-2 illustrates the problem of individual variability for location of episodic memory in the brain. The leftmost image is the group or aggregate image. The next three images illustrate some of the individual cases from which the aggregate image was derived. None of the individual images match the group image. Hence, it would be inappropriate to base a neural index of the operation of episodic memory on the aggregate picture without adjustment for individual differences.

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 CUrrENT COGNITIVE NEUrOSCIENCE rESEArCH AND TECHNOLOGY mental use. fNIRS systems are not as susceptible to movement artifact as fMRI, and algorithms are being refined for the removal of such artifacts (Izzetoglu et al., 2004). fNIRS depends on measurements of energy outside the visible spectrum. fNIRS was first used during World War I to monitor the blood oxygenation of bomber crews, a critical measurement before pressurized cabins were introduced in B-29s. Although fNIRS research has been ongoing since the late 1930s, the recent breakthroughs in both fNIRS and fMRI research have renewed interest in this technology. The capacity to translate findings from fMRI into fieldable, user- friendly, wearable devices is of significant interest. For instance, fNIRS has been shown to have promise in the detection of deception (Bunce et al., 2005), being both affordable and fieldable. The potential experimental uses of this technol- ogy are very exciting and include ecologically valid brain–computer interfaces; neurofeedback for guided facilitation of neural plasticity; and wearable neural monitors. Research groups in Japan (Haida et al., 2000), Ireland (Coyle et al., 2007), and the United States are working on brain–computer interfaces that allow locked-in patients (patients with no motor control, such as amyotrophic lateral sclerosis (ALS) patients) to communicate. In addition, Singapore has asked researchers in the United States to develop a brain fingerprint to identify specific brain signatures using fNIRS. Some advantages of the technology are its moder- ate cost (between $25,000 and $300,000) (Duscheck and Schandry, 2003) and its temporal resolution, which is similar to although somewhat lower than that of fMRI (1 cm3).13 It is also portable, wireless, and completely noninvasive. These attributes allow fNIRS to be used with children and with patients who may find confinement in an fMRI magnet very unpleasant. A number of sensor applications exist, including caps, tension straps, and medical-grade adhesives. fNIRS is quiet and comfortable and therefore amenable to sensitive protocols such as the induction of positive moods and to integration with other technolo- gies, including EEG. Appendix D provides additional information on the cost of the technology. NIRS can theoretically be combined with EEG, transcranial sonography, and other functional neuroimaging sensors. Unlike fMRI, where the subject is confined to the bore of the magnet, NIRS movement artifacts can be limited by proper affixation of sensors to the scalp. The major limitation is that NIRS best measures the first 2 or 3 centimeters of cortex so that deep brain imaging, at least through an intact skull, is challenging. Ongoing work, however, suggests that soon they will be able to image up to 5 cm deep. Monitoring Advanced Cognitive Processes via Neuroimaging There is a large body of published research on the use of various neuro- imaging modalities to investigate the neural circuitry associated with deception. 13 Currently, NIRs can localize hemodynamic changes within about 1 cm while the best fMRI scanner can localize changes within a few millimeters.

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 EMErGING COGNITIVE NEUrOSCIENCE AND rELATED TECHNOLOGIES Recent PET and fMRI studies have provided insights, with specific areas in the prefrontal cortices and amygdala being the most commonly implicated regions (Abe et al., 2006, 2007; Mohamed et al., 2006; Davatzikos et al., 2005; Kozel et al., 2004a,b, 2005; Langleben et al., 2002, 2005; Lee et al., 2002, 2005; Nuñez et al., 2005; Phan et al., 2005a; Ganis et al., 2003; Spence et al., 2001). Recent NIRs studies of deception have also implicated prefrontal brain regions in the neural circuitry associated with deception (Bunce et al., 2005). Another recently pub- lished study that correlated fMRI measurements with standard skin conductance measurements during a concealed information paradigm had interesting results (Gamer et al., 2007). There are other possible uses for fMRI and other neuro- imaging technologies that would indirectly provide information about deception and that are far more likely to be successful in the near future. These indirect measures would not require any response from the subject but would provide passive information about the subject’s experience. fMRI can already be used to judge recognition of items on a trial-by-trial basis. For example, one can imag- ine showing a subject a series of pictures of other people or crime scenes, and using fMRI to detect those that are familiar to the subject. The fMRI data could then be compared to the subject’s own statements about familiarity; this would be an indirect measure of lying. Such trial-by-trial measures are already under active investigation in the fMRI field, and it is entirely possible that they could be enhanced to aid in detection of deception using targeted research funding. Finding 2-5. Functional neuroimaging is progressing rapidly and is likely to produce important findings over the next two decades. For the intelligence com- munity and the Department of Defense, two areas in which such progress could be of great interest are enhancing cognition and facilitating training. Additional research is still needed on states of emotion; motivation; psychopathology; lan- guage; imaging processing for measuring workload performance; and the differ- ences between Western and non-Western cultures. REFERENCES Published Abe, Nobuhito, Maki Suzuki, Etsuro Mori, Masatoshi Itoh, and Toshikatsu Fujii. 2007. Deceiving others: Distinct neural responses of the prefrontal cortex and amygdala in simple fabrication and deception with social interactions. Journal of Cognitie Neuroscience 19(2):287-295. Abe, Nobuhito, Maki Suzuki, Takashi Tsukiura, Etsuro Mori, Keiichiro Yamaguchi, Masatoshi Itoh, and Toshikatsu Fujii. 2006. Dissociable roles of prefrontal and anterior cingulate cortices in deception. Cerebral Cortex 16(2):192-199. Abraham, Michael H., Harpreet S. Chadha, and Robert C. Mitchell. 1994. Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. Journal of pharma­ ceutical Sciences 83(9):1257-1268.

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