cognitive science research topics

The School of Humanities, Arts, and Social Sciences

Cognitive science, cognitive science research focus areas.

Artificial Intelligence

We take AI to be the field devoted to either engineering computational systems whose behavior is on par, or at least approaches, that of humans; or, computational systems whose intelligence is regarded to be at once high by humans  but qualitatively different from the capacities seen in humans . Of course, AI can be pursued in different ways. Here, given how we view AI, guidance as to how to engineer the relevant systems often comes from careful study of the cognitive powers of humans, including what forms of intelligence those powers classify as truly impressive.

Cognitive Engineering

Cognitive engineering is the application of cognitive science theories to human factors problems. Putting cognitive theories to the test of real-world applications is a means of maintaining a focus on the truly important cognitive issues. At Rensselaer, cognitive engineering has two components: (1) research directed at solving applied problems, and (2) research directed at developing engineering tools that others with less cognitive training can use to solve applied problems.

Cognitive Robotics

Cognitive robotics is a field devoted to engineering robots whose actions are a function of knowledge, belief, preferences, plans, and so on. In short, a cognitive robot acts on the basis of the things that underlie the actions of human beings.

Computational Cognitive Modeling

Understanding an integrated cognitive system can be very complex. The possibilities for interaction among cognitive, perceptual, and action operations is astounding. The interplay of each of these with the other and with the external world cannot be simply predicted. Computational cognitive models provide a vehicle to manage this complexity with the goal of making progress toward understanding how integrated cognitive systems affect and are affected by their environment.

Computational Linguistics

We focus on language use that involves a deep understanding of semantics and intent. Work in the department includes integrating language parsing with reasoning about the world and people’s beliefs and desires; logically controlled languages for learning by reading; and using human language to retrieve and analyze information from heterogeneous sources.

Human and Machine Reasoning

Foci include logic-based and knowledge-based AI, theorem-proving, and psychology of reasoning. The multidisciplinary group of researchers involved is known as the Rensselaer Reasoning Group, which works out of the Rensselaer AI & Reasoning (RAIR) Lab.

Perception and Action

This area of research focuses on perception with an emphasis on its role in the performance of both routine and skilled goal-directed action. Current research topics include visually guided locomotion in real and virtual environments, the coordination of eye and hand movements, and the integration of perception and action with higher-level cognition (e.g., learning and attention). At Rensselaer, these topics are investigated from various theoretical perspectives, including ecological psychology, dynamical systems theory, and computational cognitive modeling.

Research Sponsors:

Air Force Research Laboratory, Air Force Office of Scientific Research, Disruptive Technology Office, IBM Research, National Science Foundation, Defense Advanced Research Projects Agency, Intelligence Advanced Research Projects Activity, Science Applications International Corporation 

View the Cognitive Science 2024 Lecture Series

Check out previous Cognitive Science lecture series here .

For details of Cognitive Science research labs please click here.

The School of Humanities, Arts, and Social Sciences Russell Sage Laboratory (SAGE) 5304, 110 8th Street, Troy, NY 12180 (518) 276-6575

The Open Encyclopedia of Cognitive Science is the new, updated edition of the definitive, multidisciplinary guide to understanding the mind and cognition, the MIT Encyclopedia of the Cognitive Sciences. Forthcoming 2024.

Why an Open Encyclopedia of Cognitive Science?

From large language models to political polarization, cultural diversity to animal cognition, our intellectual lives are permeated by questions about the nature of minds: what they are, how they grow, how they interact, and how they differ from one another. But no single discipline holds the answers to these fundamental questions. Cognitive science is the interdisciplinary nexus devoted to the study of the mind.

Interdisciplinary conversations rely on shared referents including foundational philosophical concepts, approaches to the measurement of behavior from psychology, observations of human variation from anthropology, representations and formalisms from linguistics, mechanistic insights from neuroscience, and computational methods taken from computer science, statistics, and beyond. This background is complex and distributed across a bewildering array of books, journals, and other resources. Further, the research literature is not organized to provide definitions of foundational concepts.

The Open Encyclopedia of Cognitive Science (OECS) aims to provide an accessible introduction to this set of issues. The goal is to provide a roadmap that is suitable for a broad and informed non-specialist audience, providing tools to understand what is at stake in the study of cognition and intelligence, especially as it shapes fundamental issues facing society today. Articles will both showcase cutting-edge debates in the field of cognitive science as well as introducing core subfields, central concepts, significant phenomena, and key methods.

What is OECS?

OECS (pronounced “Oh-Ex”) is organized around a set of basic principles that distinguishes it from other reference works.

Open. OECS offers short, approachable, free articles that provide interdisciplinary context and definition for the major component topics and debates of cognitive science.

Accessible. OECS introduces key aspects of any given topic in a form that is readable by a college student, not just by a scholar who is already familiar with the content.

Authoritative and Scholarly . OECS articles are written by leading scholars in a particular field and peer-reviewed for accuracy and balance. They are fully-referenced, citable articles with permanent, versioned links and digital object identifiers (DOIs). Each article also includes a short, annotated bibliography with key readings.

Broad but Focused. Because cognitive science touches so many different disciplinary areas, providing full coverage of core concepts in each would be an unfathomable task. The goal of OECS is to feature articles on those questions, concepts, phenomena, and methods that have been a key focus of interdisciplinary interest, especially those topics for which no similar reference exists.

Fundamentally Interdisciplinary . OECS aims to emphasize the permeability of disciplinary boundaries. Articles are designed to give a multidisciplinary perspective and to situate specialist debates in a broader context, making connections to real-world issues where appropriate.

OECS is an open access resource published by the MIT Press with generous support from James S. McDonnell Foundation and the Allen Institute for AI.

The Cognitive Science Society began publication of  Topics in Cognitive Science  (topiCS) in 2009.

As its name suggests,  topiCS focuses on current topics in cognitive science. The journal provides a platform for presenting a subject or subfield in cognitive science with both greater depth and scope, and ideally from a broader range of perspectives, than stand-alone articles typically provide. Proposals for a topic and the individual contributions to them are rigorously assessed in terms of quality, relevance, and diversity (see Editor’s Welcome Address ).

Topics in Cognitive Science (topiCS)  began publication in January, 2009.

ACCESSING THE JOURNAL

Access to all Issues  (Members Only) Non-member Subscription to Journal Information about forthcoming issues may be found  here . The  TopiCS Journal  App is available from the Apple app store. The New York Times features a TopiCS  article . Editorial Introduction is  here .

INFORMATION FOR TOPIC PROPOSERS

Members of the Editorial Board Guidelines for Proposals Submission via Editorial Manager Reprint Permissions

Topics Executive Editor

Authors’ rights.

Cognitive Science is a premier outlet for innovative research and theory, and Topics in Cognitive Science provides a unique venue for collections of papers that focus on new and emerging topics or which are a bit off the mainstream, but of broad interest.

You might not know that the rights that our Authors have are nearly as unique as our journals. In contrast to most journals, the copyright to each paper is owned by the Society not the Publisher. Our negotiated contract with Wiley-Blackwell explicitly states that our authors have the right to:

A. Use the published PDF of their article immediately with no limitations on personal use (of course, commercial use is something else entirely). B. Post the final, electronic versions of the PDF produced by our publisher, Wiley-Blackwell, immediately, on your personal websites.

The final, electronic version of each issue of the journal is free to access on Wiley Online Library after a 1 year embargo period. Furthermore, the Society has the right to post the final, electronic versions of each issue of both journals on our archive site after a 3 year embargo. This means that all of our content can be downloaded for free by anyone (members or not), anywhere in the world.

This access policy is conventionally known as “green open access.”

society secretariat

Email Address

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 08 June 2021

Metacognition: ideas and insights from neuro- and educational sciences

• Damien S. Fleur   ORCID: orcid.org/0000-0003-4836-5255 1 , 2 ,
• Bert Bredeweg   ORCID: orcid.org/0000-0002-5281-2786 1 , 3 &
• Wouter van den Bos 2 , 4  

npj Science of Learning volume  6 , Article number:  13 ( 2021 ) Cite this article

35k Accesses

44 Citations

11 Altmetric

Metrics details

• Human behaviour
• Interdisciplinary studies

Metacognition comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and to regulate them (metacognitive control). Research in educational sciences has amassed a large body of evidence on the importance of metacognition in learning and academic achievement. More recently, metacognition has been studied from experimental and cognitive neuroscience perspectives. This research has started to identify brain regions that encode metacognitive processes. However, the educational and neuroscience disciplines have largely developed separately with little exchange and communication. In this article, we review the literature on metacognition in educational and cognitive neuroscience and identify entry points for synthesis. We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive constructs and processes, (ii) implement experiments to identify neural substrates necessary for metacognition based on protocols used in educational sciences, (iii) study the effects of training metacognitive knowledge in the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature from educational sciences regarding the domain-generality of metacognition.

Similar content being viewed by others

The development of human causal learning and reasoning

Sleep quality, duration, and consistency are associated with better academic performance in college students

Whole-cortex in situ sequencing reveals input-dependent area identity

Introduction.

Metacognition is defined as “thinking about thinking” or the ability to monitor and control one’s cognitive processes 1 and plays an important role in learning and education 2 , 3 , 4 . For instance, high performers tend to present better metacognitive abilities (especially control) than low performers in diverse educational activities 5 , 6 , 7 , 8 , 9 . Recently, there has been a lot of progress in studying the neural mechanisms of metacognition 10 , 11 , yet it is unclear at this point how these results may inform educational sciences or interventions. Given the potential benefits of metacognition, it is important to get a better understanding of how metacognition works and of how training can be useful.

The interest in bridging cognitive neuroscience and educational practices has increased in the past two decades, spanning a large number of studies grouped under the umbrella term of educational neuroscience 12 , 13 , 14 . With it, researchers have brought forward issues that are viewed as critical for the discipline to improve education. Recurring issues that may impede the relevance of neural insights for educational practices concern external validity 15 , 16 , theoretical discrepancies 17 and differences in terms of the domains of (meta)cognition operationalised (specific or general) 15 . This is important because, in recent years, brain research is starting to orient itself towards training metacognitive abilities that would translate into real-life benefits. However, direct links between metacognition in the brain and metacognition in domains such as education have still to be made. As for educational sciences, a large body of literature on metacognitive training is available, yet we still need clear insights about what works and why. While studies suggest that training metacognitive abilities results in higher academic achievement 18 , other interventions show mixed results 19 , 20 . Moreover, little is known about the long-term effects of, or transfer effects, of these interventions. A better understanding of the cognitive processes involved in metacognition and how they are expressed in the brain may provide insights in these regards.

Within cognitive neuroscience, there has been a long tradition of studying executive functions (EF), which are closely related to metacognitive processes 21 . Similar to metacognition, EF shows a positive relationship with learning at school. For instance, performance in laboratory tasks involving error monitoring, inhibition and working memory (i.e. processes that monitor and regulate cognition) are associated with academic achievement in pre-school children 22 . More recently, researchers have studied metacognition in terms of introspective judgements about performance in a task 10 . Although the neural correlates of such behaviour are being revealed 10 , 11 , little is known about how behaviour during such tasks relates to academic achievement.

Educational and cognitive neuroscientists study metacognition in different contexts using different methods. Indeed, while the latter investigate metacognition via behavioural task, the former mainly rely on introspective questionnaires. The extent to which these different operationalisations of metacognition match and reflect the same processes is unclear. As a result, the external validity of methodologies used in cognitive neuroscience is also unclear 16 . We argue that neurocognitive research on metacognition has a lot of potential to provide insights in mechanisms relevant in educational contexts, and that theoretical and methodological exchange between the two disciplines can benefit neuroscientific research in terms of ecological validity.

For these reasons, we investigate the literature through the lenses of external validity, theoretical discrepancies, domain generality and metacognitive training. Research on metacognition in cognitive neuroscience and educational sciences are reviewed separately. First, we investigate how metacognition is operationalised with respect to the common framework introduced by Nelson and Narens 23 (see Fig. 1 ). We then discuss the existing body of evidence regarding metacognitive training. Finally, we compare findings in both fields, highlight gaps and shortcomings, and propose avenues for research relying on crossovers of the two disciplines.

Meta-knowledge is characterised as the upward flow from object-level to meta-level. Meta-control is characterised as the downward flow from meta-level to object-level. Metacognition is therefore conceptualised as the bottom-up monitoring and top-down control of object-level processes. Adapted from Nelson and Narens’ cognitive psychology model of metacognition 23 .

In cognitive neuroscience, metacognition is divided into two main components 5 , 24 , which originate from the seminal works of Flavell on metamemory 25 , 26 . First, metacognitive knowledge (henceforth, meta-knowledge) is defined as the knowledge individuals have of their own cognitive processes and their ability to monitor and reflect on them. Second, metacognitive control (henceforth, meta-control) consists of someone’s self-regulatory mechanisms, such as planning and adapting behaviour based on outcomes 5 , 27 . Following Nelson and Narens’ definition 23 , meta-knowledge is characterised as the flow and processing of information from the object level to the meta-level, and meta-control as the flow from the meta-level to the object level 28 , 29 , 30 (Fig. 1 ). The object-level encompasses cognitive functions such as recognition and discrimination of objects, decision-making, semantic encoding, and spatial representation. On the meta-level, information originating from the object level is processed and top-down regulation on object-level functions is imposed 28 , 29 , 30 .

Educational researchers have mainly investigated metacognition through the lens of Self-Regulated Learning theory (SRL) 3 , 4 , which shares common conceptual roots with the theoretical framework used in cognitive neuroscience but varies from it in several ways 31 . First, SRL is constrained to learning activities, usually within educational settings. Second, metacognition is merely one of three components, with “motivation to learn” and “behavioural processes”, that enable individuals to learn in a self-directed manner 3 . In SRL, metacognition is defined as setting goals, planning, organising, self-monitoring and self-evaluating “at various points during the acquisition” 3 . The distinction between meta-knowledge and meta-control is not formally laid down although reference is often made to a “self-oriented feedback loop” describing the relationship between reflecting and regulating processes that resembles Nelson and Narens’ model (Fig. 1 ) 3 , 23 . In order to facilitate the comparison of operational definitions, we will refer to meta-knowledge in educational sciences when protocols operationalise self-awareness and knowledge of strategies, and to meta-control when they operationalise the selection and use of learning strategies and planning. For an in-depth discussion on metacognition and SRL, we refer to Dinsmore et al. 31 .

Metacognition in cognitive neuroscience

Operational definitions.

In cognitive neuroscience, research in metacognition is split into two tracks 32 . One track mainly studies meta-knowledge by investigating the neural basis of introspective judgements about one’s own cognition (i.e., metacognitive judgements), and meta-control with experiments involving cognitive offloading. In these experiments, subjects can perform actions such as set reminders, making notes and delegating tasks 33 , 34 , or report their desire for them 35 . Some research has investigated how metacognitive judgements can influence subsequent cognitive behaviour (i.e., a downward stream from the meta-level to the object level), but only one study so far has explored how this relationship is mapped in the brain 35 . In the other track, researchers investigate EF, also referred to as cognitive control 30 , 36 , which is closely related to metacognition. Note however that EF are often not framed in metacognitive terms in the literature 37 (but see ref. 30 ). For the sake of concision, we limit our review to operational definitions that have been used in neuroscientific studies.

Metacognitive judgements

Cognitive neuroscientists have been using paradigms in which subjects make judgements on how confident they are with regards to their learning of some given material 10 . These judgements are commonly referred to as metacognitive judgements , which can be viewed as a form of meta-knowledge (for reviews see Schwartz 38 and Nelson 39 ). Historically, researchers mostly resorted to paradigms known as Feelings of Knowing (FOK) 40 and Judgements of Learning (JOL) 41 . FOK reflect the belief of a subject to knowing the answer to a question or a problem and being able to recognise it from a list of alternatives, despite being unable to explicitly recall it 40 . Here, metacognitive judgement is thus made after retrieval attempt. In contrast, JOL are prospective judgements during learning of one’s ability to successfully recall an item on subsequent testing 41 .

More recently, cognitive neuroscientists have used paradigms in which subjects make retrospective metacognitive judgements on their performance in a two-alternative Forced Choice task (2-AFC) 42 . In 2-AFCs, subjects are asked to choose which of two presented options has the highest criterion value. Different domains can be involved, such as perception (e.g., visual or auditory) and memory. For example, subjects may be instructed to visually discriminate which one of two boxes contains more dots 43 , identify higher contrast Gabor patches 44 , or recognise novel words from words that were previously learned 45 (Fig. 2 ). The subjects engage in metacognitive judgements by rating how confident they are relative to their decision in the task. Based on their responses, one can evaluate a subject’s metacognitive sensitivity (the ability to discriminate one’s own correct and incorrect judgements), metacognitive bias (the overall level of confidence during a task), and metacognitive efficiency (the level of metacognitive sensitivity when controlling for task performance 46 ; Fig. 3 ). Note that sensitivity and bias are independent aspects of metacognition, meaning that two subjects may display the same levels of metacognitive sensitivity, but one may be biased towards high confidence while the other is biased towards low confidence. Because metacognitive sensitivity is affected by the difficulty of the task (one subject tends to display greater metacognitive sensitivity in easy tasks than difficult ones and different subjects may find a task more or less easy), metacognitive efficiency is an important measure as it allows researchers to compare metacognitive abilities between subjects and between domains. The most commonly used methods to assess metacognitive sensitivity during retrospective judgements are the receiver operating curve (ROC) and meta- d ′. 46 Both derive from signal detection theory (SDT) 47 which allows Type 1 sensitivity, or d’ ′ (how a subject can discriminate between stimulus alternatives, i.e. object-level processes) to be differentiated from metacognitive sensitivity (a judgement on the correctness of this decision) 48 . Importantly, only comparing meta- d ′ to d ′ seems to give reliable assessments metacognitive efficiency 49 . A ratio of 1 between meta- d’ ′ and d’ ′, indicates that a subject was perfectly able to discriminate between their correct and incorrect judgements. A ratio of 0.8 suggests that 80% of the task-related sensory evidence was available for the metacognitive judgements. Table 1 provides an overview of the different types of tasks and protocols with regards to the type of metacognitive process they operationalise. These operationalisations of meta-knowledge are used in combination with brain imaging methods (functional and structural magnetic resonance imaging; fMRI; MRI) to identify brain regions associated with metacognitive activity and metacognitive abilities 10 , 50 . Alternatively, transcranial magnetic stimulation (TMS) can be used to temporarily deactivate chosen brain regions and test whether this affects metacognitive abilities in given tasks 51 , 52 .

a Visual perception task: subjects choose the box containing the most (randomly generated) dots. Subjects then rate their confidence in their decision. b Memory task: subjects learn a list of words. In the next screen, they have to identify which of two words shown was present on the list. The subjects then rate their confidence in their decision.

The red and blue curves represent the distribution of confidence ratings for incorrect and correct trials, respectively. A larger distance between the two curves denotes higher sensitivity. Displacement to the left and right denote biases towards low confidence (low metacognitive bias) and high confidence (high metacognitive bias), respectively (retrieved from Fig. 1 in Fleming and Lau 46 ). We repeat the disclaimer of the original authors that this figure is not a statistically accurate description of correct and incorrect responses, which are typically not normally distributed 46 , 47 .

A recent meta-analysis analysed 47 neuroimaging studies on metacognition and identified a domain-general network associated with high vs. low confidence ratings in both decision-making tasks (perception 2-AFC) and memory tasks (JOL, FOK) 11 . This network includes the medial and lateral prefrontal cortex (mPFC and lPFC, respectively), precuneus and insula. In contrast, the right anterior dorsolateral PFC (dlPFC) was specifically involved in decision-making tasks, and the bilateral parahippocampal cortex was specific to memory tasks. In addition, prospective judgements were associated with the posterior mPFC, left dlPFC and right insula, whereas retrospective judgements were associated with bilateral parahippocampal cortex and left inferior frontal gyrus. Finally, emerging evidence suggests a role of the right rostrolateral PFC (rlPFC) 53 , 54 , anterior PFC (aPFC) 44 , 45 , 55 , 56 , dorsal anterior cingulate cortex (dACC) 54 , 55 and precuneus 45 , 55 in metacognitive sensitivity (meta- d ′, ROC). In addition, several studies suggest that the aPFC relates to metacognition specifically in perception-related 2-AFC tasks, whereas the precuneus is engaged specifically in memory-related 2-AFC tasks 45 , 55 , 56 . This may suggest that metacognitive processes engage some regions in a domain-specific manner, while other regions are domain-general. For educational scientists, this could mean that some domains of metacognition may be more relevant for learning and, granted sufficient plasticity of the associated brain regions, that targeting them during interventions may show more substantial benefits. Note that rating one’s confidence and metacognitive sensitivity likely involve additional, peripheral cognitive processes instead of purely metacognitive ones. These regions are therefore associated with metacognition but not uniquely per se. Notably, a recent meta-analysis 50 suggests that domain-specific and domain-general signals may rather share common circuitry, but that their neural signature varies depending on the type of task or activity, showing that domain-generality in metacognition is complex and still needs to be better understood.

In terms of the role of metacognitive judgements on future behaviour, one study found that brain patterns associated with the desire for cognitive offloading (i.e., meta-control) partially overlap with those associated with meta-knowledge (metacognitive judgements of confidence), suggesting that meta-control is driven by either non-metacognitive, in addition to metacognitive, processes or by a combination of different domain-specific meta-knowledge processes 35 .

Executive function

In EF, processes such as error detection/monitoring and effort monitoring can be related to meta-knowledge while error correction, inhibitory control, and resource allocation can be related to meta-control 36 . To activate these processes, participants are asked to perform tasks in laboratory settings such as Flanker tasks, Stroop tasks, Demand Selection tasks and Motion Discrimination tasks (Fig. 4 ). Neural correlates of EF are investigated by having subjects perform such tasks while their brain activity is recorded with fMRI or electroencephalography (EEG). Additionally, patients with brain lesions can be tested against healthy participants to evaluate the functional role of the impaired regions 57 .

a Flanker task: subjects indicate the direction to which the arrow in the middle points. b Stroop task: subjects are presented with the name of colour printed in a colour that either matches or mismatches the name. Subjects are asked to give the name of the written colour or the printed colour. c Motion Discrimination task: subjects have to determine in which direction the dots are going with variating levels of noise. d Example of a Demand Selection task: in both options subjects have to switch between two tasks. Task one, subjects determine whether the number shown is higher or lower than 5. Task two, subjects determine whether the number is odd or even. The two options (low and high demand) differ in their degree of task switching, meaning the effort required. Subjects are allowed to switch between the two options. Note, the type of task is solely indicated by the colour of the number and that the subjects are not explicitly told about the difference in effort between the two options (retrieved from Fig. 1c in Froböse et al. 58 ).

In a review article on the neural basis of EF (in which they are defined as meta-control), Shimamura argues that a network of regions composed of the aPFC, ACC, ventrolateral PFC (vlPFC) and dlPFC is involved in the regulations of cognition 30 . These regions are not only interconnected but are also intricately connected to cortical and subcortical regions outside of the PFC. The vlPFC was shown to play an important role in “selecting and maintaining information in working memory”, whereas the dlPFC is involved in “manipulating and updating information in working memory” 30 . The ACC has been proposed to monitor cognitive conflict (e.g. in a Stroop task or a Flanker task), and the dlPFC to regulate it 58 , 59 . In particular, activity in the ACC in conflict monitoring (meta-knowledge) seems to contribute to control of cognition (meta-control) in the dlPFC 60 , 61 and to “bias behavioural decision-making toward cognitively efficient tasks and strategies” (p. 356) 62 . In a recent fMRI study, subjects performed a motion discrimination task (Fig. 4c ) 63 . After deciding on the direction of the motion, they were presented additional motion (i.e. post-decisional evidence) and then were asked to rate their confidence in their initial choice. The post-decisional evidence was encoded in the activity of the posterior medial frontal cortex (pMFC; meta-knowledge), while lateral aPFC (meta-control) modulated the impact of this evidence on subsequent confidence rating 63 . Finally, results from a meta-analysis study on cognitive control identified functional connectivity between the pMFC, associated with monitoring and informing other regions about the need for regulation, and the lPFC that would effectively regulate cognition 64 .

Online vs. offline metacognition

While the processes engaged during tasks such as those used in EF research can be considered as metacognitive in the sense that they are higher-order functions that monitor and control lower cognitive processes, scientists have argued that they are not functionally equivalent to metacognitive judgements 10 , 11 , 65 , 66 . Indeed, engaging in metacognitive judgements requires subjects to reflect on past or future activities. As such, metacognitive judgements can be considered as offline metacognitive processes. In contrast, high-order processes involved in decision-making tasks such as used in EF research are arguably largely made on the fly, or online , at a rapid pace and subjects do not need to reflect on their actions to perform them. Hence, we propose to explicitly distinguish online and offline processes. Other researchers have shared a similar view and some have proposed models for metacognition that make similar distinctions 65 , 66 , 67 , 68 . The functional difference between online and offline metacognition is supported by some evidence. For instance, event-related brain potential (ERP) studies suggest that error negativities are associated with error detection in general, whereas an increased error positivity specifically encodes error that subjects could report upon 69 , 70 . Furthermore, brain-imaging studies suggest that the MFC and ACC are involved in online meta-knowledge, while the aPFC and lPFC seem to be activated when subjects engage in more offline meta-knowledge and meta-control, respectively 63 , 71 , 72 . An overview of the different tasks can be found in Table 1 and a list of different studies on metacognition can be found in Supplementary Table 1 (organised in terms of the type of processes investigated, the protocols and brain measures used, along with the brain regions identified). Figure 5 illustrates the different brain regions associated with meta-knowledge and meta-control, distinguishing between what we consider to be online and offline processes. This distinction is often not made explicitly but it will be specifically helpful when building bridges between cognitive neuroscience and educational sciences.

The regions are divided into online meta-knowledge and meta-control, and offline meta-knowledge and meta-control following the distinctions introduced earlier. Some regions have been reported to be related to both offline and online processes and are therefore given a striped pattern.

Training metacognition

There are extensive accounts in the literature of efforts to improve EF components such as inhibitory control, attention shifting and working memory 22 . While working memory does not directly reflect metacognitive abilities, its training is often hypothesised to improve general cognitive abilities and academic achievement. However, most meta-analyses found that training methods lead only to weak, non-lasting effects on cognitive control 73 , 74 , 75 . One meta-analysis did find evidence of near-transfer following EF training in children (in particular working memory, inhibitory control and cognitive flexibility), but found no evidence of far-transfer 20 . According to this study, training on one component leads to improved abilities in that same component but not in other EF components. Regarding adults, however, one meta-analysis suggests that EF training in general and working memory training specifically may both lead to significant near- and far-transfer effects 76 . On a neural level, a meta-analysis showed that cognitive training resulted in decreased brain activity in brain regions associated with EF 77 . According to the authors, this indicates that “training interventions reduce demands on externally focused attention” (p. 193) 77 .

With regards to meta-knowledge, several studies have reported increased task-related metacognitive abilities after training. For example, researchers found that subjects who received feedback on their metacognitive judgements regarding a perceptual decision-making task displayed better metacognitive accuracy, not only in the trained task but also in an untrained memory task 78 . Related, Baird and colleagues 79 found that a two-week mindfulness meditation training lead to enhanced meta-knowledge in the memory domain, but not the perceptual domain. The authors link these results to evidence of increased grey matter density in the aPFC in meditation practitioners.

Research on metacognition in cognitive science has mainly been studied through the lens of metacognitive judgements and EF (specifically performance monitoring and cognitive control). Meta-knowledge is commonly activated in subjects by asking them to rate their confidence in having successfully performed a task. A distinction is made between metacognitive sensitivity, metacognitive bias and metacognitive efficacy. Monitoring and regulating processes in EF are mainly operationalised with behavioural tasks such as Flanker tasks, Stroop tasks, Motion Discrimination tasks and Demand Selection tasks. In addition, metacognitive judgements can be viewed as offline processes in that they require the subject to reflect on her cognition and develop meta-representations. In contrast, EF can be considered as mostly online metacognitive processes because monitoring and regulation mostly happen rapidly without the need for reflective thinking.

Although there is some evidence for domain specificity, other studies have suggested that there is a single network of regions involved in all meta-cognitive tasks, but differentially activated in different task contexts. Comparing research on meta-knowledge and meta-control also suggest that some regions play a crucial role in both knowledge and regulation (Fig. 5 ). We have also identified a specific set of regions that are involved in either offline or online meta-knowledge. The evidence in favour of metacognitive training, while mixed, is interesting. In particular, research on offline meta-knowledge training involving self-reflection and metacognitive accuracy has shown some promising results. The regions that show structural changes after training, were those that we earlier identified as being part of the metacognition network. EF training does seem to show far-transfer effects at least in adults, but the relevance for everyday life activity is still unclear.

One major limitation of current research in metacognition is ecological validity. It is unclear to what extent the operationalisations reviewed above reflect real-life metacognition. For instance, are people who can accurately judge their performance on a behavioural task also able to accurately assess how they performed during an exam? Are people with high levels of error regulation and inhibitory control able to learn more efficiently? Note that criticism on the ecological validity of neurocognitive operationalisations extends beyond metacognition research 16 . A solution for improving validity may be to compare operationalisations of metacognition in cognitive neuroscience with the ones in educational sciences, which have shown clear links with learning in formal education. This also applies to metacognitive training.

Metacognition in educational sciences

The most popular protocols used to measure metacognition in educational sciences are self-report questionnaires or interviews, learning journals and thinking-aloud protocols 31 , 80 . During interviews, subjects are asked to answer questions regarding hypothetical situations 81 . In learning journals, students write about their learning experience and their thoughts on learning 82 , 83 . In thinking-aloud protocols, subjects are asked to verbalise their thoughts while performing a problem-solving task 80 . Each of these instruments can be used to study meta-knowledge and meta-control. For instance, one of the most widely used questionnaires, the Metacognitive Awareness Inventory (MAI) 42 , operationalises “Flavellian” metacognition and has dedicated scales for meta-knowledge and meta-control (also popular are the MSLQ 84 and LASSI 85 which operate under SRL). The meta-knowledge scale of the MAI operationalises knowledge of strategies (e.g., “ I am aware of what strategies I use when I study ”) and self-awareness (e.g., “ I am a good judge of how well I understand something ”); the meta-control scale operationalises planning (e.g., “ I set a goal before I begin a task ”) and use of learning strategies (e.g., “ I summarize what I’ve learned after I finish ”). Learning journals, self-report questionnaires and interviews involve offline metacognition. Thinking aloud, though not engaging the same degree self-reflection, also involves offline metacognition in the sense that online processes are verbalised, which necessitate offline processing (see Table 1 for an overview and Supplementary Table 2 for more details).

More recently, methodologies borrowed from cognitive neuroscience have been introduced to study EF in educational settings 22 , 86 . In particular, researchers used classic cognitive control tasks such as the Stroop task (for a meta-analysis 86 ). Most of the studied components are related to meta-control and not meta-knowledge. For instance, the BRIEF 87 is a questionnaire completed by parents and teachers which assesses different subdomains of EF: (1) inhibition, shifting, and emotional control which can be viewed as online metacognitive control, and (2) planning, organisation of materials, and monitoring, which can be viewed as offline meta-control 87 .

Assessment of metacognition is usually compared against metrics of academic performance such as grades or scores on designated tasks. A recent meta-analysis reported a weak correlation of self-report questionnaires and interviews with academic performance whereas think-aloud protocols correlated highly 88 . Offline meta-knowledge processes operationalised by learning journals were found to be positively associated with academic achievement when related to reflection on learning activities but negatively associated when related to reflection on learning materials, indicating that the type of reflection is important 89 . EF have been associated with abilities in mathematics (mainly) and reading comprehension 86 . However, the literature points towards contrary directions as to what specific EF component is involved in academic achievement. This may be due to the different groups that were studied, to different operationalisations or to different theoretical underpinnings for EF 86 . For instance, online and offline metacognitive processes, which are not systematically distinguished in the literature, may play different roles in academic achievement. Moreover, the bulk of research focussed on young children with few studies on adolescents 86 and EF may play a role at varying extents at different stages of life.

A critical question in educational sciences is that of the nature of the relationship between metacognition and academic achievement to understand whether learning at school can be enhanced by training metacognitive abilities. Does higher metacognition lead to higher academic achievement? Do these features evolve in parallel? Developmental research provides valuable insights into the formation of metacognitive abilities that can inform training designs in terms of what aspect of metacognition should be supported and the age at which interventions may yield the best results. First, meta-knowledge seems to emerge around the age of 5, meta-control around 8, and both develop over the years 90 , with evidence for the development of meta-knowledge into adolescence 91 . Furthermore, current theories propose that meta-knowledge abilities are initially highly domain-dependent and gradually become more domain-independent as knowledge and experience are acquired and linked between domains 32 . Meta-control is believed to evolve in a similar fashion 90 , 92 .

Common methods used to train offline metacognition are direct instruction of metacognition, metacognitive prompts and learning journals. In addition, research has been done on the use of (self-directed) feedback as a means to induce self-reflection in students, mainly in computer-supported settings 93 . Interestingly, learning journals appear to be used for both assessing and fostering metacognition. Metacognitive instruction consists of teaching learners’ strategies to “activate” their metacognition. Metacognitive prompts most often consist of text pieces that are sent at specific times and that trigger reflection (offline meta-knowledge) on learning behaviour in the form of a question, hint or reminder.

Meta-analyses have investigated the effects of direct metacognitive instruction on students’ use of learning strategies and academic outcomes 18 , 94 , 95 . Their findings show that metacognitive instruction can have a positive effect on learning abilities and achievement within a population ranging from primary schoolers to university students. In particular, interventions lead to the highest effect sizes when they both (i) instructed a combination of metacognitive strategies with an emphasis on planning strategies (offline meta-control) and (ii) “provided students with knowledge about strategies” (offline meta-knowledge) and “illustrated the benefits of applying the trained strategies, or even stimulated metacognitive reasoning” (p.114) 18 . The longer the duration of the intervention, the more effective they were. The strongest effects on academic performance were observed in the context of mathematics, followed by reading and writing.

While metacognitive prompts and learning journals make up the larger part of the literature on metacognitive training 96 , meta-analyses that specifically investigate their effectiveness have yet to be performed. Nonetheless, evidence suggests that such interventions can be successful. Researchers found that metacognitive prompts fostered the use of metacognitive strategies (offline meta-control) and that the combination of cognitive and metacognitive prompts improved learning outcomes 97 . Another experiment showed that students who received metacognitive prompts performed more metacognitive activities inside the learning environment and displayed better transfer performance immediately after the intervention 98 . A similar study using self-directed prompts showed enhanced transfer performance that was still observable 3 weeks after the intervention 99 .

Several studies suggest that learning journals can positively enhance metacognition. Subjects who kept a learning journal displayed stronger high meta-control and meta-knowledge on learning tasks and tended to reach higher academic outcomes 100 , 101 , 102 . However, how the learning journal is used seems to be critical; good instructions are crucial 97 , 103 , and subjects who simply summarise their learning activity benefit less from the intervention than subjects who reflect about their knowledge, learning and learning goals 104 . An overview of studies using learning journals and metacognitive prompts to train metacognition can be found in Supplementary Table 3 .

In recent years, educational neuroscience researchers have tried to determine whether training and improvements in EF can lead to learning facilitation and higher academic achievement. Training may consist of having students continually perform behavioural tasks either in the lab, at home, or at school. Current evidence in favour of training EF is mixed, with only anecdotal evidence for positive effects 105 . A meta-analysis did not show evidence for a causal relationship between EF and academic achievement 19 , but suggested that the relationship is bidirectional, meaning that the two are “mutually supportive” 106 .

A recent review article has identified several gaps and shortcoming in the literature on metacognitive training 96 . Overall, research in metacognitive training has been mainly invested in developing learners’ meta-control rather than meta-knowledge. Furthermore, most of the interventions were done in the context of science learning. Critically, there appears to be a lack of studies that employed randomised control designs, such that the effects of metacognitive training intervention are often difficult to evaluate. In addition, research overwhelmingly investigated metacognitive prompts and learning journals in adults 96 , while interventions on EF mainly focused on young children 22 . Lastly, meta-analyses evaluating the effectiveness of metacognitive training have so far focused on metacognitive instruction on children. There is thus a clear disbalance between the meta-analyses performed and the scope of the literature available.

An important caveat of educational sciences research is that metacognition is not typically framed in terms of online and offline metacognition. Therefore, it can be unclear whether protocols operationalise online or offline processes and whether interventions tend to benefit more online or offline metacognition. There is also confusion in terms of what processes qualify as EF and definitions of it vary substantially 86 . For instance, Clements and colleagues mention work on SRL to illustrate research in EF in relation to academic achievement but the two spawn from different lines of research, one rooted in metacognition and socio-cognitive theory 31 and the other in the cognitive (neuro)science of decision-making. In addition, the MSLQ, as discussed above, assesses offline metacognition along with other components relevant to SRL, whereas EF can be mainly understood as online metacognition (see Table 1 ), which on the neural level may rely on different circuitry.

Investigating offline metacognition tends to be carried out in school settings whereas evaluating EF (e.g., Stroop task, and BRIEF) is performed in the lab. Common to all protocols for offline metacognition is that they consist of a form of self-report from the learner, either during the learning activity (thinking-aloud protocols) or after the learning activity (questionnaires, interviews and learning journals). Questionnaires are popular protocols due to how easy they are to administer but have been criticised to provide biased evaluations of metacognitive abilities. In contrast, learning journals evaluate the degree to which learners engage in reflective thinking and may therefore be less prone to bias. Lastly, it is unclear to what extent thinking-aloud protocols are sensitive to online metacognitive processes, such as on-the-fly error correction and effort regulation. The strength of the relationship between metacognitive abilities and academic achievement varies depending on how metacognition is operationalised. Self-report questionnaires and interviews are weakly related to achievement whereas thinking-aloud protocols and EF are strongly related to it.

Based on the well-documented relationship between metacognition and academic achievement, educational scientists hypothesised that fostering metacognition may improve learning and academic achievement, and thus performed metacognitive training interventions. The most prevalent training protocols are direct metacognitive instruction, learning journals, and metacognitive prompts, which aim to induce and foster offline metacognitive processes such as self-reflection, planning and selecting learning strategies. In addition, researchers have investigated whether training EF, either through tasks or embedded in the curriculum, results in higher academic proficiency and achievement. While a large body of evidence suggests that metacognitive instruction, learning journals and metacognitive prompts can successfully improve academic achievement, interventions designed around EF training show mixed results. Future research investigating EF training in different age categories may clarify this situation. These various degrees of success of interventions may indicate that offline metacognition is more easily trainable than online metacognition and plays a more important role in educational settings. Investigating the effects of different methods, offline and online, on the neural level, may provide researchers with insights into the trainability of different metacognitive processes.

In this article, we reviewed the literature on metacognition in educational sciences and cognitive neuroscience with the aim to investigate gaps in current research and propose ways to address them through the exchange of insights between the two disciplines and interdisciplinary approaches. The main aspects analysed were operational definitions of metacognition and metacognitive training, through the lens of metacognitive knowledge and metacognitive control. Our review also highlighted an additional construct in the form of the distinction between online metacognition (on the fly and largely automatic) and offline metacognition (slower, reflective and requiring meta-representations). In cognitive neuroscience, research has focused on metacognitive judgements (mainly offline) and EF (mainly online). Metacognition is operationalised with tasks carried out in the lab and are mapped onto brain functions. In contrast, research in educational sciences typically measures metacognition in the context of learning activities, mostly in schools and universities. More recently, EF has been studied in educational settings to investigate its role in academic achievement and whether training it may benefit learning. Evidence on the latter is however mixed. Regarding metacognitive training in general, evidence from both disciplines suggests that interventions fostering learners’ self-reflection and knowledge of their learning behaviour (i.e., offline meta-knowledge) may best benefit them and increase academic achievement.

We focused on four aspects of research that could benefit from an interdisciplinary approach between the two areas: (i) validity and reliability of research protocols, (ii) under-researched dimensions of metacognition, (iii) metacognitive training, and (iv) domain-specificity vs. domain generality of metacognitive abilities. To tackle these issue, we propose four avenues for integrated research: (i) investigate the degree to which different protocols relate to similar or different metacognitive constructs, (ii) implement designs and perform experiments to identify neural substrates necessary for offline meta-control by for example borrowing protocols used in educational sciences, (iii) study the effects of (offline) meta-knowledge training on the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature in educational sciences regarding the domain-generality of metacognitive processes and metacognitive abilities.

First, neurocognitive research on metacognitive judgements has developed robust operationalisations of offline meta-knowledge. However, these operationalisations often consist of specific tasks (e.g., 2-AFC) carried out in the lab. These tasks are often very narrow and do not resemble the challenges and complexities of behaviours associated with learning in schools and universities. Thus, one may question to what extent they reflect real-life metacognition, and to what extent protocols developed in educational sciences and cognitive neuroscience actually operationalise the same components of metacognition. We propose that comparing different protocols from both disciplines that are, a priori, operationalising the same types of metacognitive processes can help evaluate the ecological validity of protocols used in cognitive neuroscience, and allow for more holistic assessments of metacognition, provided that it is clear which protocol assesses which construct. Degrees of correlation between different protocols, within and between disciplines, may allow researchers to assess to what extent they reflect the same metacognitive constructs and also identify what protocols are most appropriate to study a specific construct. For example, a relation between meta- d ′ metacognitive sensitivity in a 2-AFC task and the meta-knowledge subscale of the MAI, would provide external validity to the former. Moreover, educational scientists would be provided with bias-free tools to assess metacognition. These tools may enable researchers to further investigate to what extent metacognitive bias, sensitivity and efficiency each play a role in education settings. In contrast, a low correlation may highlight a difference in domain between the two measures of metacognition. For instance, metacognitive judgements in brain research are made in isolated behaviour, and meta-d’ can thus be viewed to reflect “local” metacognitive sensitivity. It is also unclear to what extent processes involved in these decision-making tasks cover those taking place in a learning environment. When answering self-reported questionnaires, however, subjects make metacognitive judgements on a large set of (learning) activities, and the measures may thus resemble more “global” or domain-general metacognitive sensitivity. In addition, learners in educational settings tend to receive feedback — immediate or delayed — on their learning activities and performance, which is generally not the case for cognitive neuroscience protocols. Therefore, investigating metacognitive judgements in the presence of performance or social feedback may allow researchers to better understand the metacognitive processes at play in educational settings. Devising a global measure of metacognition in the lab by aggregating subjects’ metacognitive abilities in different domains or investigating to what extent local metacognition may affect global metacognition could improve ecological validity significantly. By investigating the neural correlates of educational measures of metacognition, researchers may be able to better understand to what extent the constructs studied in the two disciplines are related. It is indeed possible that, though weakly correlated, the meta-knowledge scale of the MAI and meta-d’ share a common neural basis.

Second, our review highlights gaps in the literature of both disciplines regarding the research of certain types of metacognitive processes. There is a lack of research in offline meta-control (or strategic regulation of cognition) in neuroscience, whereas this construct is widely studied in educational sciences. More specifically, while there exists research on EF related to planning (e.g. 107 ), common experimental designs make it hard to disentangle online from offline metacognitive processes. A few studies have implemented subject reports (e.g., awareness of error or desire for reminders) to pin-point the neural substrates specifically involved in offline meta-control and the current evidence points at a role of the lPFC. More research implementing similar designs may clarify this construct. Alternatively, researchers may exploit educational sciences protocols, such as self-report questionnaires, learning journals, metacognitive prompts and feedback to investigate offline meta-control processes in the brain and their relation to academic proficiency and achievement.

Third, there is only one study known to us on the training of meta-knowledge in the lab 78 . In contrast, meta-knowledge training in educational sciences have been widely studied, in particular with metacognitive prompts and learning journals, although a systematic review would be needed to identify the benefits for learning. Relative to cognitive neuroscience, studies suggest that offline meta-knowledge trained in and outside the lab (i.e., metacognitive judgements and meditation, respectively) transfer to meta-knowledge in other lab tasks. The case of meditation is particularly interesting since meditation has been demonstrated to beneficiate varied aspects of everyday life 108 . Given its importance for efficient regulation of cognition, training (offline) meta-knowledge may present the largest benefits to academic achievement. Hence, it is important to investigate development in the brain relative to meta-knowledge training. Evidence on metacognitive training in educational sciences tends to suggest that offline metacognition is more “plastic” and may therefore benefit learning more than online metacognition. Furthermore, it is important to have a good understanding of the developmental trajectory of metacognitive abilities — not only on a behavioural level but also on a neural level — to identify critical periods for successful training. Doing so would also allow researchers to investigate the potential differences in terms of plasticity that we mention above. Currently, the developmental trajectory of metacognition is under-studied in cognitive neuroscience with only one study that found an overlap between the neural correlates of metacognition in adults and children 109 . On a side note, future research could explore the potential role of genetic factors in metacognitive abilities to better understand to what extent and under what constraints they can be trained.

Fourth, domain-specific and domain-general aspects of metacognitive processes should be further investigated. Educational scientists have studied the development of metacognition in learners and have concluded that metacognitive abilities are domain-specific at the beginning (meaning that their quality depends on the type of learning activity, like mathematics vs. writing) and progressively evolve towards domain-general abilities as knowledge and expertise increase. Similarly, neurocognitive evidence points towards a common network for (offline) metacognitive knowledge which engages the different regions at varying degrees depending on the domain of the activity (i.e., perception, memory, etc.). Investigating this network from a developmental perspective and comparing findings with the existing behavioural literature may improve our understanding of the metacognitive brain and link the two bodies of evidence. It may also enable researchers to identify stages of life more suitable for certain types of metacognitive intervention.

Dunlosky, J. & Metcalfe, J. Metacognition (SAGE Publications, 2008).

Pintrich, P. R. The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Pract. 41 , 219–225 (2002).

Article   Google Scholar  

Zimmerman, B. J. Self-regulated learning and academic achievement: an overview. Educ. Psychol. 25 , 3–17 (1990).

Zimmerman, B. J. & Schunk, D. H. Self-Regulated Learning and Academic Achievement: Theoretical Perspectives (Routledge, 2001).

Baker, L. & Brown, A. L. Metacognitive Skills and Reading. In Handbook of Reading Research Vol. 1 (ed. Pearson, P. D.) 353–395 (Longman, 1984).

Mckeown, M. G. & Beck, I. L. The role of metacognition in understanding and supporting reading comprehension. In Handbook of Metacognition in Education (eds Hacker, D. J., Dunlosky, J. & Graesser, A. C.) 19–37 (Routledge, 2009).

Desoete, A., Roeyers, H. & Buysse, A. Metacognition and mathematical problem solving in grade 3. J. Learn. Disabil. 34 , 435–447 (2001).

Article   CAS   PubMed   Google Scholar  

Veenman, M., Kok, R. & Blöte, A. W. The relation between intellectual and metacognitive skills in early adolescence. Instructional Sci. 33 , 193–211 (2005).

Harris, K. R., Graham, S., Brindle, M. & Sandmel, K. Metacognition and children’s writing. In Handbook of metacognition in education 131–153 (Routledge, 2009).

Fleming, S. M. & Dolan, R. J. The neural basis of metacognitive ability. Philos. Trans. R. Soc. B 367 , 1338–1349 (2012).

Vaccaro, A. G. & Fleming, S. M. Thinking about thinking: a coordinate-based meta-analysis of neuroimaging studies of metacognitive judgements. Brain Neurosci. Adv. 2 , 10.1177%2F2398212818810591 (2018).

Ferrari, M. What can neuroscience bring to education? Educ. Philos. Theory 43 , 31–36 (2011).

Zadina, J. N. The emerging role of educational neuroscience in education reform. Psicol. Educ. 21 , 71–77 (2015).

Meulen, A., van der, Krabbendam, L. & Ruyter, Dde Educational neuroscience: its position, aims and expectations. Br. J. Educ. Stud. 63 , 229–243 (2015).

Varma, S., McCandliss, B. D. & Schwartz, D. L. Scientific and pragmatic challenges for bridging education and neuroscience. Educ. Res. 37 , 140–152 (2008).

van Atteveldt, N., van Kesteren, M. T. R., Braams, B. & Krabbendam, L. Neuroimaging of learning and development: improving ecological validity. Frontline Learn. Res. 6 , 186–203 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Hruby, G. G. Three requirements for justifying an educational neuroscience. Br. J. Educ. Psychol. 82 , 1–23 (2012).

Article   PubMed   Google Scholar  

Dignath, C., Buettner, G. & Langfeldt, H.-P. How can primary school students learn self-regulated learning strategies most effectively?: A meta-analysis on self-regulation training programmes. Educ. Res. Rev. 3 , 101–129 (2008).

Jacob, R. & Parkinson, J. The potential for school-based interventions that target executive function to improve academic achievement: a review. Rev. Educ. Res. 85 , 512–552 (2015).

Kassai, R., Futo, J., Demetrovics, Z. & Takacs, Z. K. A meta-analysis of the experimental evidence on the near- and far-transfer effects among children’s executive function skills. Psychol. Bull. 145 , 165–188 (2019).

Roebers, C. M. Executive function and metacognition: towards a unifying framework of cognitive self-regulation. Dev. Rev. 45 , 31–51 (2017).

Clements, D. H., Sarama, J. & Germeroth, C. Learning executive function and early mathematics: directions of causal relations. Early Child. Res. Q. 36 , 79–90 (2016).

Nelson, T. O. & Narens, L. Metamemory. In Perspectives on the development of memory and cognition (ed. R. V. Kail & J. W. Hag) 3–33 (Hillsdale, N.J.: Erlbaum, 1977).

Baird, J. R. Improving learning through enhanced metacognition: a classroom study. Eur. J. Sci. Educ. 8 , 263–282 (1986).

Flavell, J. H. & Wellman, H. M. Metamemory (1975).

Flavell, J. H. Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am. Psychol. 34 , 906 (1979).

Livingston, J. A. Metacognition: An Overview. (2003).

Nelson, T. O. Metamemory: a theoretical framework and new findings. In Psychology of Learning and Motivation Vol. 26 (ed. Bower, G. H.) 125–173 (Academic Press, 1990).

Nelson, T. O. & Narens, L. Why investigate metacognition. In Metacognition: Knowing About Knowing (eds Metcalfe, J. & Shimamura, A. P.) 1–25 (MIT Press, 1994).

Shimamura, A. P. A Neurocognitive approach to metacognitive monitoring and control. In Handbook of Metamemory and Memory (eds Dunlosky, J. & Bjork, R. A.) (Routledge, 2014).

Dinsmore, D. L., Alexander, P. A. & Loughlin, S. M. Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educ. Psychol. Rev. 20 , 391–409 (2008).

Borkowski, J. G., Chan, L. K. & Muthukrishna, N. A process-oriented model of metacognition: links between motivation and executive functioning. In (Gregory Schraw & James C. Impara) Issues in the Measurement of Metacognition 1–42 (Buros Institute of Mental Measurements, 2000).

Risko, E. F. & Gilbert, S. J. Cognitive offloading. Trends Cogn. Sci. 20 , 676–688 (2016).

Gilbert, S. J. et al. Optimal use of reminders: metacognition, effort, and cognitive offloading. J. Exp. Psychol. 149 , 501 (2020).

Boldt, A. & Gilbert, S. Distinct and overlapping neural correlates of metacognitive monitoring and metacognitive control. Preprint at bioRxiv https://psyarxiv.com/3dz9b/ (2020).

Fernandez-Duque, D., Baird, J. A. & Posner, M. I. Executive attention and metacognitive regulation. Conscious Cogn. 9 , 288–307 (2000).

Baker, L., Zeliger-Kandasamy, A. & DeWyngaert, L. U. Neuroimaging evidence of comprehension monitoring. Psihol. teme 23 , 167–187 (2014).

Google Scholar  

Schwartz, B. L. Sources of information in metamemory: Judgments of learning and feelings of knowing. Psychon. Bull. Rev. 1 , 357–375 (1994).

Nelson, T. O. Metamemory, psychology of. In International Encyclopedia of the Social & Behavioral Sciences (eds Smelser, N. J. & Baltes, P. B.) 9733–9738 (Pergamon, 2001).

Hart, J. T. Memory and the feeling-of-knowing experience. J. Educ. Psychol. 56 , 208 (1965).

Arbuckle, T. Y. & Cuddy, L. L. Discrimination of item strength at time of presentation. J. Exp. Psychol. 81 , 126 (1969).

Fechner, G. T. Elemente der Psychophysik (Breitkopf & Härtel, 1860).

Rouault, M., Seow, T., Gillan, C. M. & Fleming, S. M. Psychiatric symptom dimensions are associated with dissociable shifts in metacognition but not task performance. Biol. Psychiatry 84 , 443–451 (2018).

Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J. & Rees, G. Relating introspective accuracy to individual differences in brain structure. Science 329 , 1541–1543 (2010).

Article   CAS   PubMed   PubMed Central   Google Scholar  

McCurdy, L. Y. et al. Anatomical coupling between distinct metacognitive systems for memory and visual perception. J. Neurosci. 33 , 1897–1906 (2013).

Fleming, S. M. & Lau, H. C. How to measure metacognition. Front. Hum. Neurosci. 8 https://doi.org/10.3389/fnhum.2014.00443 (2014).

Galvin, S. J., Podd, J. V., Drga, V. & Whitmore, J. Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions. Psychon. Bull. Rev. 10 , 843–876 (2003).

Metcalfe, J. & Schwartz, B. L. The ghost in the machine: self-reflective consciousness and the neuroscience of metacognition. In (eds Dunlosky, J. & Tauber, S. K.) Oxford Handbook of Metamemory 407–424 (Oxford University Press, 2016).

Maniscalco, B. & Lau, H. A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Conscious Cognition 21 , 422–430 (2012).

Rouault, M., McWilliams, A., Allen, M. G. & Fleming, S. M. Human metacognition across domains: insights from individual differences and neuroimaging. Personal. Neurosci. 1 https://doi.org/10.1017/pen.2018.16 (2018).

Rounis, E., Maniscalco, B., Rothwell, J. C., Passingham, R. E. & Lau, H. Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness. Cogn. Neurosci. 1 , 165–175 (2010).

Ye, Q., Zou, F., Lau, H., Hu, Y. & Kwok, S. C. Causal evidence for mnemonic metacognition in human precuneus. J. Neurosci. 38 , 6379–6387 (2018).

Fleming, S. M., Huijgen, J. & Dolan, R. J. Prefrontal contributions to metacognition in perceptual decision making. J. Neurosci. 32 , 6117–6125 (2012).

Morales, J., Lau, H. & Fleming, S. M. Domain-general and domain-specific patterns of activity supporting metacognition in human prefrontal cortex. J. Neurosci. 38 , 3534–3546 (2018).

Baird, B., Smallwood, J., Gorgolewski, K. J. & Margulies, D. S. Medial and lateral networks in anterior prefrontal cortex support metacognitive ability for memory and perception. J. Neurosci. 33 , 16657–16665 (2013).

Fleming, S. M., Ryu, J., Golfinos, J. G. & Blackmon, K. E. Domain-specific impairment in metacognitive accuracy following anterior prefrontal lesions. Brain 137 , 2811–2822 (2014).

Baldo, J. V., Shimamura, A. P., Delis, D. C., Kramer, J. & Kaplan, E. Verbal and design fluency in patients with frontal lobe lesions. J. Int. Neuropsychol. Soc. 7 , 586–596 (2001).

Froböse, M. I. et al. Catecholaminergic modulation of the avoidance of cognitive control. J. Exp. Psychol. Gen. 147 , 1763 (2018).

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S. & Cohen, J. D. Conflict monitoring and cognitive control. Psychol. Rev. 108 , 624 (2001).

Kerns, J. G. et al. Anterior cingulate conflict monitoring and adjustments in control. Science 303 , 1023–1026 (2004).

Yeung, N. Conflict monitoring and cognitive control. In The Oxford Handbook of Cognitive Neuroscience: The Cutting Edges Vol. 2 (eds Ochsner, K. N. & Kosslyn, S.) 275–299 (Oxford University Press, 2014).

Botvinick, M. M. Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cogn. Affect. Behav. Neurosci. 7 , 356–366 (2007).

Fleming, S. M., van der Putten, E. J. & Daw, N. D. Neural mediators of changes of mind about perceptual decisions. Nat. Neurosci. 21 , 617–624 (2018).

Ridderinkhof, K. R., Ullsperger, M., Crone, E. A. & Nieuwenhuis, S. The role of the medial frontal cortex in cognitive control. Science 306 , 443–447 (2004).

Koriat, A. The feeling of knowing: some metatheoretical implications for consciousness and control. Conscious Cogn. 9 , 149–171 (2000).

Thompson, V. A., Evans, J. & Frankish, K. Dual process theories: a metacognitive perspective. Ariel 137 , 51–43 (2009).

Arango-Muñoz, S. Two levels of metacognition. Philosophia 39 , 71–82 (2011).

Shea, N. et al. Supra-personal cognitive control and metacognition. Trends Cogn. Sci. 18 , 186–193 (2014).

Nieuwenhuis, S., Ridderinkhof, K. R., Blom, J., Band, G. P. & Kok, A. Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task. Psychophysiology 38 , 752–760 (2001).

Overbeek, T. J., Nieuwenhuis, S. & Ridderinkhof, K. R. Dissociable components of error processing: on the functional significance of the Pe vis-à-vis the ERN/Ne. J. Psychophysiol. 19 , 319–329 (2005).

McGuire, J. T. & Botvinick, M. M. Prefrontal cortex, cognitive control, and the registration of decision costs. Proc. Natl Acad. Sci. USA 107 , 7922–7926 (2010).

Hester, R., Foxe, J. J., Molholm, S., Shpaner, M. & Garavan, H. Neural mechanisms involved in error processing: a comparison of errors made with and without awareness. Neuroimage 27 , 602–608 (2005).

Melby-Lervåg, M. & Hulme, C. Is working memory training effective? A meta-analytic review. Dev. Psychol. 49 , 270 (2013).

Soveri, A., Antfolk, J., Karlsson, L., Salo, B. & Laine, M. Working memory training revisited: a multi-level meta-analysis of n-back training studies. Psychon. Bull. Rev. 24 , 1077–1096 (2017).

Schwaighofer, M., Fischer, F. & Bühner, M. Does working memory training transfer? A meta-analysis including training conditions as moderators. Educ. Psychol. 50 , 138–166 (2015).

Karbach, J. & Verhaeghen, P. Making working memory work: a meta-analysis of executive-control and working memory training in older adults. Psychol. Sci. 25 , 2027–2037 (2014).

Patel, R., Spreng, R. N. & Turner, G. R. Functional brain changes following cognitive and motor skills training: a quantitative meta-analysis. Neurorehabil Neural Repair 27 , 187–199 (2013).

Carpenter, J. et al. Domain-general enhancements of metacognitive ability through adaptive training. J. Exp. Psychol. 148 , 51–64 (2019).

Baird, B., Mrazek, M. D., Phillips, D. T. & Schooler, J. W. Domain-specific enhancement of metacognitive ability following meditation training. J. Exp. Psychol. 143 , 1972 (2014).

Winne, P. H. & Perry, N. E. Measuring self-regulated learning. In Handbook of Self-Regulation (eds Boekaerts, M., Pintrich, P. R. & Zeidner, M.) Ch. 16, 531–566 (Academic Press, 2000).

Zimmerman, B. J. & Martinez-Pons, M. Development of a structured interview for assessing student use of self-regulated learning strategies. Am. Educ. Res. J. 23 , 614–628 (1986).

Park, C. Engaging students in the learning process: the learning journal. J. Geogr. High. Educ. 27 , 183–199 (2003).

Article   CAS   Google Scholar  

Harrison, G. M. & Vallin, L. M. Evaluating the metacognitive awareness inventory using empirical factor-structure evidence. Metacogn. Learn. 13 , 15–38 (2018).

Pintrich, P. R., Smith, D. A. F., Garcia, T. & Mckeachie, W. J. Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educ. Psychol. Meas. 53 , 801–813 (1993).

Prevatt, F., Petscher, Y., Proctor, B. E., Hurst, A. & Adams, K. The revised Learning and Study Strategies Inventory: an evaluation of competing models. Educ. Psychol. Meas. 66 , 448–458 (2006).

Baggetta, P. & Alexander, P. A. Conceptualization and operationalization of executive function. Mind Brain Educ. 10 , 10–33 (2016).

Gioia, G. A., Isquith, P. K., Guy, S. C. & Kenworthy, L. Test review behavior rating inventory of executive function. Child Neuropsychol. 6 , 235–238 (2000).

Ohtani, K. & Hisasaka, T. Beyond intelligence: a meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacogn. Learn. 13 , 179–212 (2018).

Dianovsky, M. T. & Wink, D. J. Student learning through journal writing in a general education chemistry course for pre-elementary education majors. Sci. Educ. 96 , 543–565 (2012).

Veenman, M. V. J., Van Hout-Wolters, B. H. A. M. & Afflerbach, P. Metacognition and learning: conceptual and methodological considerations. Metacogn Learn. 1 , 3–14 (2006).

Weil, L. G. et al. The development of metacognitive ability in adolescence. Conscious Cogn. 22 , 264–271 (2013).

Veenman, M. & Spaans, M. A. Relation between intellectual and metacognitive skills: Age and task differences. Learn. Individ. Differ. 15 , 159–176 (2005).

Verbert, K. et al. Learning dashboards: an overview and future research opportunities. Personal. Ubiquitous Comput. 18 , 1499–1514 (2014).

Dignath, C. & Büttner, G. Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacogn. Learn. 3 , 231–264 (2008).

Hattie, J., Biggs, J. & Purdie, N. Effects of learning skills interventions on student learning: a meta-analysis. Rev. Educ. Res. 66 , 99–136 (1996).

Zohar, A. & Barzilai, S. A review of research on metacognition in science education: current and future directions. Stud. Sci. Educ. 49 , 121–169 (2013).

Berthold, K., Nückles, M. & Renkl, A. Do learning protocols support learning strategies and outcomes? The role of cognitive and metacognitive prompts. Learn. Instr. 17 , 564–577 (2007).

Bannert, M. & Mengelkamp, C. Scaffolding hypermedia learning through metacognitive prompts. In International Handbook of Metacognition and Learning Technologies Vol. 28 (eds Azevedo, R. & Aleven, V.) 171–186 (Springer New York, 2013).

Bannert, M., Sonnenberg, C., Mengelkamp, C. & Pieger, E. Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Comput. Hum. Behav. 52 , 293–306 (2015).

McCrindle, A. R. & Christensen, C. A. The impact of learning journals on metacognitive and cognitive processes and learning performance. Learn. Instr. 5 , 167–185 (1995).

Connor-Greene, P. A. Making connections: evaluating the effectiveness of journal writing in enhancing student learning. Teach. Psychol. 27 , 44–46 (2000).

Wong, B. Y. L., Kuperis, S., Jamieson, D., Keller, L. & Cull-Hewitt, R. Effects of guided journal writing on students’ story understanding. J. Educ. Res. 95 , 179–191 (2002).

Nückles, M., Schwonke, R., Berthold, K. & Renkl, A. The use of public learning diaries in blended learning. J. Educ. Media 29 , 49–66 (2004).

Cantrell, R. J., Fusaro, J. A. & Dougherty, E. A. Exploring the effectiveness of journal writing on learning social studies: a comparative study. Read. Psychol. 21 , 1–11 (2000).

Blair, C. Executive function and early childhood education. Curr. Opin. Behav. Sci. 10 , 102–107 (2016).

Clements, D. H., Sarama, J., Unlu, F. & Layzer, C. The Efficacy of an Intervention Synthesizing Scaffolding Designed to Promote Self-Regulation with an Early Mathematics Curriculum: Effects on Executive Function (Society for Research on Educational Effectiveness, 2012).

Newman, S. D., Carpenter, P. A., Varma, S. & Just, M. A. Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Neuropsychologia 41 , 1668–1682 (2003).

Sedlmeier, P. et al. The psychological effects of meditation: a meta-analysis. Psychol. Bull. 138 , 1139 (2012).

Bellon, E., Fias, W., Ansari, D. & Smedt, B. D. The neural basis of metacognitive monitoring during arithmetic in the developing brain. Hum. Brain Mapp. 41 , 4562–4573 (2020).

Download references

Acknowledgements

We would like to thank the University of Amsterdam for supporting this research through the Interdisciplinary Doctorate Agreement grant. W.v.d.B. is further supported by the Jacobs Foundation, European Research Council (grant no. ERC-2018-StG-803338), the European Union Horizon 2020 research and innovation programme (grant no. DiGYMATEX-870578), and the Netherlands Organization for Scientific Research (grant no. NWO-VIDI 016.Vidi.185.068).

Author information

Authors and affiliations.

Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands

Damien S. Fleur & Bert Bredeweg

Departement of Psychology, University of Amsterdam, Amsterdam, the Netherlands

Damien S. Fleur & Wouter van den Bos

Faculty of Education, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands

Bert Bredeweg

Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany

Wouter van den Bos

You can also search for this author in PubMed   Google Scholar

Contributions

D.S.F., B.B. and W.v.d.B. conceived the main conceptual idea of this review article. D.S.F. wrote the manuscript with inputs from and under the supervision of B.B. and W.v.d.B.

Corresponding author

Correspondence to Damien S. Fleur .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary materials, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Fleur, D.S., Bredeweg, B. & van den Bos, W. Metacognition: ideas and insights from neuro- and educational sciences. npj Sci. Learn. 6 , 13 (2021). https://doi.org/10.1038/s41539-021-00089-5

Download citation

Received : 06 October 2020

Accepted : 09 April 2021

Published : 08 June 2021

DOI : https://doi.org/10.1038/s41539-021-00089-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Relation of life sciences students’ metacognitive monitoring to neural activity during biology error detection.

• Mei Grace Behrendt
• Carrie Clark
• Joseph Dauer

npj Science of Learning (2024)

The many facets of metacognition: comparing multiple measures of metacognition in healthy individuals

• Anneke Terneusen
• Conny Quaedflieg
• Ieke Winkens

Metacognition and Learning (2024)

Towards a common conceptual space for metacognition in perception and memory

• Audrey Mazancieux
• Michael Pereira
• Céline Souchay

Nature Reviews Psychology (2023)

Predictive Validity of Performance-Based Metacognitive Testing is Superior to Self-report: Evidence from Undergraduate Freshman Students

• Marcio Alexander Castillo-Diaz
• Cristiano Mauro Assis Gomes

Trends in Psychology (2023)

Normative data and standardization of an international protocol for the evaluation of metacognition in Spanish-speaking university students: A cross-cultural analysis

• Antonio P. Gutierrez de Blume
• Diana Marcela Montoya Londoño
• Jesus Rivera-Sanchez

Metacognition and Learning (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Areas of Research

The MIT Department of Brain and Cognitive Sciences has an ambitious mission: to understand how the mechanisms of the brain give rise to the mind. To advance this vision, we bring together researchers, students, and faculty who study brain science at all levels.

Our researchers often cross the boundaries of established fields, or invent new disciplines entirely. Conceptually, however, we think of our research in four broad categories:

BCS faculty who are conducting research in this area can be found here.

Research in cellular and molecular neuroscience strives to understand the brain at its most fundamental level by studying the mechanisms that control construction and maintenance of cellular and molecular circuits.

Work in this area creates a window into how neurons are born and migrate, and how they form synaptic connections. Understanding how synapses function and undergo plasticity also allows insights into the molecular underpinnings of memory formation in the brain. Studying the ways that neurons operate will move us closer to understanding how the brain develops and responds to outside stimuli. The interplay of the complex molecular machinery of the neuronal membrane with the dynamics of electrical potentials is critical to understanding the synaptic contacts where neurons communicate with each other. This leads to important questions at the systems level. The plasticity of these contacts, expressed by neuronal axons, allows robust behavioral modification to changing environmental stimuli and internal representations. 

Disruptions of the molecular machines that underlie neuronal development and function are also at the heart of most neurological and psychiatric diseases. This provides strong motivation to define how these molecular and cellular pathways allow neurons to connect and communicate, and how they go awry in brain diseases.

Cellular and molecular neuroscience is a deep mystery, but it brings exciting and critical bridges to other facets of brain and cognitive science. Researchers at BCS are using the latest tools and technologies to unlock critical applications of molecular science, including the prospects of future genetic intervention that might one day lead to cures for brain diseases. 

Our focus in these important areas will help bring about new treatments for both neurodevelopment diseases like autism, as well as late-onset neurodegenerative diseases like Alzheimer’s. These studies also promise new insights into how other brain-related disorders associated with aging alter the functional interplay of neuronal function and connectivity.

In systems neuroscience, researchers use animal models to emulate core cognitive processes. This allows for more detailed study of algorithms and neural circuits that produce the representations of the mind. Scientists examine how patterns of neuronal connections (circuits) give rise to patterns of neuronal activity, and how those patterns of neural activity give rise to overt behavioral and different internal neural states.

Systems neuroscience studies the processes that occur within our central nervous system. Animal models allow much more precise study and intervention in the neural circuits that underlie higher cognitive function. Although these models do not capture the full mental abilities of humans, they are selected such that they likely share evolutionarily conserved neuronal processing mechanisms that will generalize to human brain function.  

This research is important to all aspects of our work. It provides detailed data that is used to build computational models of cognitive processes. It also allows us to test hypotheses about brain function by precisely intervening in the system in ways that are not possible in humans, such as neural or genetic manipulations. 

These experiments are critical to building our understanding, as captured by computational models. They are also central to our exploration of possible ways to repair or augment broken neural circuits in diseased or disrupted states. 

Because systems neuroscientists seek to understand the basis for cognitive, motivational, sensory, and motor processes, their work overlaps with that of our other research disciplines.  These connections are critical in uncovering answers to basic questions about how we move, learn and feel. 

Cognitive science is the scientific study of the human mind. It is a highly interdisciplinary field, combining ideas and methods from psychology, computer science, linguistics, philosophy, and neuroscience. The broad goal of cognitive science is to characterize the nature of human knowledge – its forms and content – and how that knowledge is used, processed, and acquired. 

Active areas of cognitive research in the Department include language, memory, visual perception and cognition, thinking and reasoning, social cognition, decision making, and cognitive development. 

The study of cognitive science within BCS illustrates the department’s philosophy that understanding the mind and understanding the brain are ultimately inseparable, even with the gaps that currently exist between the core questions of human cognition and the questions that can be productively addressed in molecular, cellular or systems neuroscience. To bridge these gaps, several cognitive labs maintain a primary or secondary focus on cognitive neuroscience research. There are many opportunities for interaction and collaboration between cognitive and neuroscience labs across BCS and its related centers.

Computational neuroscience uses the tools of mathematics and computers to develop theoretical models that test and expand our understanding of the workings of brain and behavioral processes. Unlike the related field of artificial intelligence, computation seeks not just to create intelligence out of machines, but to illuminate the processes that underlie sensation and perception, control of action, learning and memory, language, and other cognitive processes. 

These theoretical studies offer the prospect of connecting diverse research constructs and paradigms, and of providing a new understanding of the algorithms that drive our “mental machinery.” 

BCS scientists are focused on three key areas of computation:

• The study of the data representations and algorithms that autonomous systems might build to perform tasks that are important for human survival (closely related to artificial intelligence).
• The implementation and testing of circuits that are constrained by neuronal data but aim to accomplish the tasks above.  
• The development of analysis and statistical tools for analyzing and visualizing neuroscience data. 

Understanding something as complex as the human mind requires computational models that accurately translate the system’s internal workings. Models help us build formal bridges between any two levels of analysis. For example: from gene expression programs to regulation of neuronal connections (synapses), or from neuronal circuit connections to patterns of neuronal activity. Other examples include from patterns of neuronal activity to behavioral report and mental states, and last, from mental states to cognitive function. 

As we work to build a complete picture of the neural mechanisms of the mind, it is necessary for us to link models of all levels. Models allow us to make predictions about behavior, to emulate key aspects of neural computations in other devices (brain inspired computing), and to consider the best ways to repair or augment key functions.  

Topics, Methods, and Research-Based Strategies for Teaching Cognition

• Reference work entry
• First Online: 17 December 2022
• Cite this reference work entry

• Maya M. Khanna 5 &
• Michael J. Cortese 6  

Part of the book series: Springer International Handbooks of Education ((SIHE))

1167 Accesses

In this chapter, we review the basic contents and structure of our courses in cognition and cognitive psychology as well as pedagogical approaches to teaching. Topics range from an historical overview of the areas of science that lead up to the formation of cognitive science to detailed discussions of published articles within each of the major subfields of cognition (e.g., perception, attention, short-term working memory, long-term memory, language, and decision-making). Throughout our courses, we also focus extensively on the practical applications to cognitive theory. Furthermore, we emphasize the importance of research design and data analyses and discuss how we guide our students in the practice of using theory to arrive at specific numerical predictions. In addition, we discuss our major learning objectives that we hope our students achieve in completing our courses and highlight ways that we assess student work toward these objectives. We also share some of the best practices for teaching cognition that we have developed ourselves and ones that we acquired from others. In particular, we discuss our style of teaching the course as well as examples of in-class activities and demonstrations. Finally, we share a list of resources that interested readers can review to help in the design of their courses on cognition, or in any courses, in general. This overview can serve as both a good starting point for beginning instructors and a useful resource for more experienced instructors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Adelman, J. S., & Estes, Z. (2013). Emotion and memory: A recognition advantage for positive and negative words independent of arousal. Cognition, 129 , 530–535. https://doi.org/10.1016/j.cognition.2013.08.014 .

Article   Google Scholar  

Agarwal, P. K., Bain, P. M., & Chamberlain, R. W. (2012). The value of applied research: Retrieval practice improves classroom learning and recommendations from a teacher, a principal, and a scientist. Educational Psychology Review, 24 , 437–448.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York, NY: Academic.

Google Scholar  

Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftus, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project: A users guide. Behavior Research Methods, 39 , 445–459. https://doi.org/10.3758/BF03193014 .

Balota, D. A., Yap, M. J., Hutchison, K. A., & Cortese, M. J. (2012). Megastudies: Large scale analyses of lexical processes. In J. S. Adelman (Ed.), Visual word recognition Vol. 1: Models and methods, orthography and phonology (pp. 90–115). New York, NY: Psychology Press.

Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59 , 617–645. https://doi.org/10.1146/annurev.psych.59.103006.093639 .

Bernstein, D. A., Frantz, S., & Chew, S. L. (2020). Teaching psychology: A step by step guide (3rd ed.). New York, NY: Routledge.

Chomsky, N. (1965). Aspects of the theory of syntax . Cambridge, MA: MIT Press. ISBN:9780262260503.

Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82 (6), 407–428.

Coltheart, M. (2013). How can functional neuroimaging inform cognitive theories? Perspectives on Cognitive Science, 8 ( 1 ), 98 – 103 . https://doi.org/10.1177/1745691612469208 .

Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108 , 204–256. https://doi.org/10.1037//0033-295X.108.1.204 .

Connell, L., & Lynott, D. (2009). Is a bear white in the woods? Parallel representation of implied object color during language comprehension. Psychonomic Bulletin & Review, 16 ( 3 ), 573–577. https://doi.org/10.3758/PBR.16.3.573 .

Conrad, R. (1960). Serial order intrusions in immediate memory. British Journal of Psychology, 51 (1), 45–48.

Cortese, M. J., & Fugett, A. (2004). Imageability ratings for 3,000 monosyllabic words. Behavior Research Methods, Instruments, & Computers, 36 (3), 384–387.

Cortese, M. J., Khanna, M. M., & Hacker, S. (2010). Recognition memory for 2,578 monosyllabic words. Memory, 18 , 595–609. https://doi.org/10.1080/09658211.2010.493892 .

Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11 , 671–684.

Donders, F. (1868-69/1969). On the speed of mental processes. In W.G. Koster (ed.) Acta psychologica 30: Attention and performance II (pp. 412-431). Amsterdam, The Netherlands: North-Holland Press Company.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14 , 4–58.

Goodglass, H., & Kaplan, E. (1972). The assessment of aphasia and related disorders . Philadelphia, PA: Lea & Febiger.

Hauk, O., Johnsrude, I., & Pulvermu ̈ller, F. (2004). Somatotopic represen-tation of action words in human motor and premotor cortex. Neuron, 41 , 301–307.

Hutchison, K. A., Balota, D. A., Neely, J. H., Cortese, M. J., Cohen-Shikora, E., Tse, C., Yap, M. J., Bengson, J. J., Niemeyer, D., & Buchanon, E. (2013). The semantic priming project. Behavior Research Methods, 45 , 1099–1114. https://doi.org/10.3758/s13428-012-0304-z .

Joanisse, M. F., & Seidenberg, M. S. (1999). Impairments in verb morphology after brain injury: A connectionist model. Proceedings of the National Academy of Sciences, 96 , 7592–7597. https://doi.org/10.1073/pnas.96.13.7592 .

Keuleers, E., & Balota, D. A. (2015). Megastudies, crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments. The Quarterly Journal of Experimental Psychology, 688 , 1457–1468. https://doi.org/10.1080/17470218.2014.945096 .

Khanna, M. M. (2015). Ungraded pop-quizzes: Test-enhanced learning without all the anxiety. Teaching of Psychology, 42 (2), 174–178.

Khanna, M. M., Badura Brack, A. S., & Finken, L. L. (2013). Short- and long-term effects of cumulative finals on student learning. Teaching of Psychology, 40 (3), 175–182.

MacWhinney, B., & Snow, C. (1985). The Child Language Data Exchange System. Journal of Child Language, 12 , 271–295.

Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1996). Rule learning by seven-month old infants. Science, 283 ( 1 ), 77–80. https://doi.org/10.1126/science.283.5398.77 .

McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102 (3), 419–457.

McClelland, J. L., et al. (2010). Letting structure emerge: Connectionism and dynamical systems approaches to cognition. Trends in Cognitive Science, 14 (8), 348–356.

McDaniel, M. A., Agarwal, P. K., Huelser, B. J., McDermott, K. B., & Roediger, H. L. (2011). Test-enhanced learning in a middle school science classroom: The effects of quiz frequency and placement. Journal of Educational Psychology, 103 , 399–414.

Morris, C. D., Bransford, J. D., & Franks, J. J. (1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior, 16 ( 5 ), 519 – 533 . https://doi.org/10.1016/s0022-5371(77)80016-9 .

O’Reilly, R. C., Bhattacharyya, R., Howard, M. D., & Ketz, N. (2014). Complimentary learning systems. Cognitive Science, 38 ( 6 ), 1229–1248. https://doi.org/10.1111/j.1551-6709.2011.01214.x .

Patterson, K., & Lambon Ralph, M. (2015). The hub-and-spoke hypothesis of semantic memory. In G. Hickok & S. L. Small (Eds.), Neurobiology of language (pp. 765–773). London, UK: Academic. https://doi.org/10.1016/B978-0-12-407794-2.00061-4 .

Chapter   Google Scholar  

Pinker, S. (1999). Words and rules: The ingredients of language (1st ed.). New York, NY: Basic Books.

Roediger, H. L., & McDermott, K. B. (1995). Creating false memories: Remembering words not presented in lists. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21 (4), 803–814.

Roediger, H. L., Agarwal, P. K., McDaniel, M. A., & McDermott, K. B. (2011). Test-enhanced learning in the classroom: Long-term improvements from quizzing. Journal of Experimental Psychology: Applied, 17 , 382–395.

Rumelhart, D. E., & McClelland, J. L. (Eds.). (1986). Parallel distributed processing: Explorations in the microstructure of cognition (V1 and V2) . Cambridge, MA: MIT press.

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, New Series, 274 (5294), 1926–1928.

Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96 ( 4 ), 523–568. https://doi.org/10.1037/0033-295X.96.4.523 .

Smith, E., & Jonides, J. (1999). Storage and executive processes in the frontal lobes. Science, 283 , 1657–1661.

Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders’ method. Acta Psychologica, Amsterdam, 30 , 276–315.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18 (6), 643–662.

Watson, J. M., Balota, D. A., & Roediger, H. L. I. I. I. (2003). Creating false memories with hybrid lists of semantic and phonological associates: Over-additive false memories produced by converging associative networks. Journal of Memory and Language, 49 (9), 95–118.

Wickens, D. (1975). A test of four proposed new dimensions of semantic space. Bulletin of the Psychonomic Society, 6 (4), 381–382.

Woollams, A. M., Madrid, G., & Lambon Ralph, M. A. (2017). Using neurostimulation to understand the impact of pre-morbid individual differences on post-lesion outcomes. Proceedings of the National Academy of Sciences, 114 ( 46 ), 12279–12284. www.pnas.org/cgi/doi/10.1073/pnas.1707162114 .

Yap, M. J., & Balota, D. A. (2009). Visual word recognition of multisyllabic words. Journal of Memory and Language, 60 , 502–529. https://doi.org/10.1016/j.jml.2009.02.001 .

Zevin, J. D., & Balota, D. A. (2000). Priming and attentional control of lexical and sublexical pathways during naming. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26 , 121–135. https://doi.org/10.1037//0278-7393.26.U21 .

Zwaan, R. A., & Yaxley, R. H. (2003). Hemispheric differences in semantic relatedness judgments. Cognition, 87 , B79–B86. https://doi.org/10.1016/S0010-0277(02)00235-4 .

Download references

Author information

Authors and affiliations.

Department of Psychological Science, Creighton University, Omaha, NE, USA

Maya M. Khanna

Department of Psychology, University of Nebraska at Omaha, Omaha, NE, USA

Michael J. Cortese

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Maya M. Khanna .

Editor information

Editors and affiliations.

Department of Educational Research, University of Salzburg, Salzburg, Austria

Joerg Zumbach

Department of Psychology, University of South Florida, Bonita Springs, FL, USA

Douglas A. Bernstein

School of Science - Faculty of Psychology, Psychology of Learning and Instruction, Technische Universitaet Dresden, Dresden, Sachsen, Germany

Susanne Narciss

Department of Human, Philosophical and Educational Sciences (DISUFF), University of Salerno, Fisciano, Italy

Giuseppina Marsico

Section Editor information

University of Salzburg, Salzburg, Austria

Department of Psychology, University of South Florida, Tampa, FL, USA

Psychologie des Lehrens und Lernens, Technische Universität Dresden, Dresden, Deutschland

Department of Human, Philosophic, and Education Sciences, University of Salerno, Salerno, Italy

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this entry

Cite this entry.

Khanna, M.M., Cortese, M.J. (2023). Topics, Methods, and Research-Based Strategies for Teaching Cognition. In: Zumbach, J., Bernstein, D.A., Narciss, S., Marsico, G. (eds) International Handbook of Psychology Learning and Teaching. Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-030-28745-0_11

Download citation

DOI : https://doi.org/10.1007/978-3-030-28745-0_11

Published : 17 December 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-28744-3

Online ISBN : 978-3-030-28745-0

eBook Packages : Education Reference Module Humanities and Social Sciences Reference Module Education

Share this entry

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

National Research Council (US) Committee on Military and Intelligence Methodology for Emergent Neruophysiological and Cognitive/Neural Research in the Next Two Decades. Emerging Cognitive Neuroscience and Related Technologies. Washington (DC): National Academies Press (US); 2008.

Emerging Cognitive Neuroscience and Related Technologies.

• Hardcopy Version at National Academies Press

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 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

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 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 neurophysiological 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 technology 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 predictive 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 application 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 scientists, on explicit definitions of psychological constructs. Such agreement is important 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.

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 motivation . 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, accurately 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 reinforcement to explain motivation. In the latter half of that century, they focused on internal processes to explain affect (moods and emotions) and cognition (information processing, memory) but without knowing details of the causal interconnections 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 psychological 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 capacity (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 achieving such an understanding any time in the next two decades. In contrast, “psychological state” sometimes refers to a broad range of mental activities associated 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 technologies. 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 Activity

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 psychological states in a scientifically valid way are quite challenging. Here, several of these challenges are identified and discussed.

Technological Limitations and Advances

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 technologies 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 intelligence 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 common 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 heightened 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 relationships 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 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 psychological 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 ) scientists 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 seventeenth 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. 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 ramifications, 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 carefully in these instances.

Mapping Measurements of Neurophysiological Activity 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 psychological state subserves multiple tasks, so there can therefore be no one-to-one correspondence 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 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. Awareness of this typology helps us to recognize important inferential problems associated 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 symmetric, 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 ).

Associations between a physiological response (or set of responses) and a psychological construct. SOURCE: Cacioppo and Tassinary (1990b) ©1990 by the American Psychological Association. Adapted with permission. The use of APA information does (more...)

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 psychological 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 neurophysiological indicators. This lack of a symmetric relationship is a major problem for 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 psychological 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 circumstances 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 individual 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 positives, false negatives, misses).

Avoiding 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 psychological 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. Several 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; however, 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 correlation; however, a poor or incomplete model will allow for many interpretations 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 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 neuronal 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 Individuals

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 underlying 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.

Individual variability in the case of the putative location of episodic memory in the brain. SOURCE: Reprinted with permission from unpublished work (Miller, 2007).

Miller concludes that “investigations into the sources of unique individual brain activity may be necessary in order to understand the dynamic patterns of brain activity that underlie widely distributed, strategy-filled tasks like episodic memory” (Miller, 2007). There appears, moreover, to be variability across tasks, with some tasks showing more or less variability of locus than others. Furthermore, Miller reported that within-subject location appeared to be relatively stable across time, such that the same areas were activated in the same individual by the same task performed 6 months apart (Miller et al., 2002a,b). Individualized approaches to functional mapping, however, are feasible under certain circumstances. For instance, if a government wanted to outfit trained combat pilots flying high-tech aircraft with some type of neural interface, it would make sense to invest in determining individualized neurophysiological markers. Accounting for individual variability is both reasonable and feasible, because within-subject variability across time (after accounting for reorganization due to learning) tends to be low for cognitive tasks (Miller, 2007). This cost-benefit argument is more of a problem when attempting to apply a generic (averaged) algorithm to a given individual without individual tuning.

Specificity of Psychological States Within Contexts

The likelihood of developing valid neurophysiological indexes for inferring psychological states or intentions depends on the required specificity of the state itself and the specificity of the context within which it occurs. It is typically easier to develop an accurate index of a given psychological state if the number of candidate states can be limited by experimental controls. Consider, as an example, a vigilance scenario in the cognitive domain. It would be simpler to develop a neurophysiological index that marks a person as consciously processing information at a general level (e.g., having or not having the resources to cope with the situation he or she is in) than at a more specific level (e.g., having or not having specific resources). In the affective domain, it would be simpler to develop an index that distinguishes the polarity of a superordinate affective state (e.g., negative versus positive) than an index that distinguishes between more specific superordinate states (e.g., anger or fear). In the motivational domain, it would be easier to develop an index that distinguishes between fairly general superordinate motivational states (e.g., avoidance or approach) than to develop one that distinguishes more specific states (e.g., take flight or surrender).

Simultaneously indexing superordinate categories of general cognitive, affective, and motivational psychological states based on neurophysiological information can have great practical value because these general categories of psychological states are highly interdependent and simultaneous. Hence, if one could index all three categories simultaneously in a given context (say, warfighting), the resulting combined index would be more useful for drawing inferences and making predictions than any single index. Furthermore, one would be able to make these predictions without asking the warfighter questions and one could refine the predictions to reflect these continuously available indexes.

Indeed, one can probably infer psychological or behavioral intentions more accurately based on the combination index. If the cognitive index revealed that a soldier evaluates his or her resources as sufficient to meet the perceived needs in the particular context (e.g., a battle), if the affective index revealed that the soldier is experiencing positive affect (e.g., challenge) rather than negative affect (e.g., threat), and if the motivational index revealed that the soldier is motivated to approach rather than avoid the situation, one would could reasonably conclude that the soldier was prepared or even intended to do battle. On the other hand, if one index revealed that the soldier cognitively evaluates his or her resources as insufficient, another revealed that he or she is experiencing negative affect, and a third revealed that the soldier is avoidance-oriented, one could reasonably conclude that the individual was ready to retreat.

Existing peripheral neurophysiological measures include the following: (1) hemodynamic markers of cognitive resource/demand evaluations—for example, reciprocal changes in cardiac output and total peripheral resistance (Blascovich and Tomaka, 1996), (2) electromyographic indexes of affect—for example, relative reciprocal increases and decreases in zygomaticus major and corrugator supercillii muscle movements in the face (Cacioppo et al., 1986), and (3) cardiac indexes of approach/avoidance motivation—for example, increases or decreases in cardiac ventricular contractility and heart rate (Blascovich and Tomaka, 1996). If one could develop CNS indexes of superordinate categories of cognitive, affective, and motivational state, there would be a useful redundancy.

Assumptions About Base Rates of Psychological States and Intentions

Just as they like to rely on averaged data (Estes identified this propensity in 2002), many scientists also assume that base rates of dichotomous psychological states are 50/50. Much as scientists know that not everyone or every brain is average, they also realize that the probability that a person will act in one way rather than another is not necessarily 50 percent. This realization notwithstanding, development and even validational work on indexes of psychological states often assumes a base rate of 50 percent. 3

If base rates of psychological states, such as intentions to commit an act of terrorism or to defect, were 50 percent, individuals with such intentions would be much easier to identify. Unfortunately, many important psychological states are infrequent events, with the consequence that indexes validated assuming a 50 percent base rate produce a very large number of mistaken detections. Green and Swets’s classic paper on signal detection theory (1966) detailed the logic and mathematics for inferring the existence of target states from signals (or indexes) as a function of base rates.

Mistaken detections can be avoided only when a target signal is in almost perfectly symmetric (i.e., 1 to 1) correspondence with the target event in the biological or other system. Additionally, systems for transmitting and identifying the target signal must be almost perfectly accurate (Imrey, 2007). Imrey further argues that (1) combinations of these two circumstances are rare; (2) intuition on this point is notoriously poor, partly because of the ways accuracy is commonly expressed; (3) known biases in data collection invariably lead to overoptimism; (4) overoptimism means overconfidence in the reliability of results, and (5) this means underestimation of false positives and false negatives. A specific discussion on base rates and the detection of deception is found later in the chapter under the case study of detecting deception.

Finding 2-1. Cognitive neuroscientists can identify neurophysiological markers of general psychological states—for example, positive versus negative affect, automatic versus controlled cognitive processing, approach versus avoidance motivation, attention versus inattention—within individuals in specified contexts but are not yet able to identify highly specific psychological states and intentions—that is, exactly what a particular person is thinking, intending, or doing. Given the current state of knowledge about brain neurophysiology, it is highly likely that any advances in collective knowledge about individual psychological states over the next two decades will continue to occur in highly controlled situations where the number of candidate mental states is limited. The ability to determine a person’s mental state strictly from neurophysiological markers without environmental controls is unlikely to be gained any time in the next two decades.

Detection of Deception as an Example of Efforts to Identify Accurate Neurophysiological Indexes of Specific Psychological States in Individuals

The concept of the detection of deception was recently broadened and is now known as “credibility assessment,” and includes additional factors such as source verification and witness corroboration. The concept is of considerable interest to the IC and DOD. Detection of deception can serve to illustrate the considerations discussed above as barriers to the identification of accurate neurophysiological indices of specific psychological states in individuals (Feinberg and Stern, 2005).

At the societal level, the capacity to detect deception is valued for many social, legal, and medical reasons. At the government level, the capacity to detect deception is valued because those having or seeking authority and/or power need to be able to safeguard the national interest. At the individual level, detection of deception helps to identify individuals who threaten some important value of a social group. In addition, it often makes sense to help determine the reliability of information or witnesses before resources are allocated. As such, technologies for the detection of deception entail identifying a specific kind of psychological state at the individual, or idiographic, level.

Not unlike in the scientific community at large, there was considerable debate among members of the committee about the ability to develop useful neurophysiological markers of deception. The committee believed, however, that it is critical for the IC and DOD to understand where opinions differed. Box 2-1 summarizes the key points of agreement and disagreement on detection of deception.

Committee Agreements and Disagreements on Detection of Deception. Agreement The committee agreed that, as outlined in previous NRC reports, traditional measures of deception detection technology have proven to be insufficiently accurate. Second, the committee (more...)

Previous Research on Detection of Deception

No known lie-detection technology has been sufficiently accurate (i.e., virtually inerrant) to be legally acceptable, especially given the relatively low base rates for many types of threats to a social order. As described above, the main technology used during the last 100 years in the United States and a few other countries (e.g., Israel, Canada, and Japan) is the polygraph, the generic name for an instrument that can record multiple physiological measurements which has come to be closely associated with the lie detection technique utilizing it. Conventional polygraphy relies on psychophysiological measures of the sympathetic nervous system response (respiration rate, heart rate, electrodermal activity) to detect anxiety associated with guilt or lying (OTA, 1990). As determinants of psychological states, however, these autonomic response suffer, in particular, from a lack of specificity and cannot differentiate guilt from other cognitive/ affective states—say anxiety—resulting in an unacceptably high level of false positives (Office of Technology Assessment, 1983; Iacono, 2000). A report on the polygraph and lie detection (NRC, 2003, p. 4) concluded that:

… in populations of examinees such as those represented in the polygraph research literature, untrained in countermeasures, specific-incident [i.e., criminal] polygraph tests can discriminate lying from truth telling at rates well above chance but below perfection.

However, this should not be understood to be the applied accuracy of polygraph testing in operational field situations. Readers should note that many of the examinees referred to in the quote above are research subjects who, for the most part, are often informed of the nature of the research beforehand and are not typically drawn from the population to whom polygraph tests are typically given (individuals being investigated for criminal activity, those seeking employment in sensitive national security positions, prisoners of war). Often participants are college students or other volunteers and are hardly representative of populations that are likely to threaten the social order. Hence, the NRC report goes on to conclude that “polygraph testing yields an unacceptable choice for … employee security screening between too many … employees falsely judged as deceptive and too many major security threats left undetected” (NRC, 2003, p. 6).

It is the lack of specificity, in particular, of these autonomic measures to discriminate deception from other affective states that leads to insufficient positive and negative predictive power. That is, autonomic responses, at least as measured by the polygraph, are “outcomes” only, could represent a number of psychological states, and must be interpreted within a highly specified context. It is not surprising, then, that both the IC and the DOD have been interested in more precise (more sensitive and specific) measures of deception detection. As for other deception detection techniques, the report concluded that:

Some potential alternatives to the polygraph show promise, but none has yet been shown to outperform even the polygraph. None shows any promise of supplanting the polygraph for screening purposes in the near term. (NRC, 2003, pp. 7-8)

However, the report (NRC, 2003, p. 174) goes on to state that:

Functional brain imaging techniques have important advantages over the polygraph, in theory, because they examine directly what the brain is doing…. Not enough is yet known about the specific cognitive or emotional processes that accompany deception, about their localization in the brain, or about whether imaging signals can differentiate the brain activity associated with these processes from brain activity associated with other processes to make an assessment of the potential validity of these techniques on the grounds of the basic science. Further research with fMRI, coupled with a scientifically based cognitive psychological approach to deception, will be needed to determine if these issues can be addressed…. If a research effort is undertaken to find improved scientific techniques for the detection of deception, basic research on brain imaging would be a top candidate for the research agenda.

For illustrative purposes, the committee next discusses deception detection work in light of the challenges described above. As noted, there was considerable debate among the committee members about what would constitute a practical or useful application of technology to the assessment of psychological states.

Deception and the Nature of Neurophysiological Measures

Understanding the nature of the relationship between physiological markers and the psychological states that are purportedly assessed by quantifying the markers is critical for several reasons. First, users of the technology must recognize the limitations of the tools they are using. The majority of polygraphers do not make the mistake of believing that they are measuring deception directly via neurophysiological information, avoiding an unwarranted reductionistic approach. Rather, they claim that certain neurophysiological changes (e.g., respiration, heart rate, blood pressure, electrodermal activity), as a set, pattern themselves differently during acts of deception than during acts of truth telling. This places a burden on the methods used to gather the psychophysiological or neurological data, as proper interpretation of the data fundamentally relies on appropriate methodological technique and controls.

While it can be tempting to assume that brain imaging or other neurophysiological measurements allow direct access to the psychological state under investigation, this assumption is not warranted. Based on research to date, it is highly improbable that there is any specific “lie circuit” in the brain that is dedicated to deception. Rather, the neural circuits that respond during deception will respond in other, nondeceptive circumstances, and only appropriate techniques will allow accurate interpretation of the data. The committee agrees that important legal decisions should not be made as if incontrovertible proof of deception existed if they are based only on the correlates of deception.

Unfortunately, the biopsychosocial theory underlying the putative relationships between the autonomic measures that are obtained with a traditional polygraph and their actual value as accurate indices of lying remains unsophisticated and essentially unchanged for nearly a century. Twenty-five years ago (1983), the United States Office of Technology Assessment issued a report prepared by a group of scientists who had evaluated the scientific validity of polygraph testing for lie detection (OTA, 1983). The report concluded that “the basic theory of polygraph testing is only partially developed and researched…. A stronger theoretical base is needed for the entire range of polygraph applications” (NRC, 2003, p. 6).

This statement largely reflects the paucity of well-designed and controlled studies for investigating the potential confounds when a strong inference is drawn from polygraphy data collected under a great variety of conditions. From a neuroscientific perspective, theory must be refined through experimentation. The report leveled the same criticism at the theoretical basis of polygraphic lie detection (NRC, 2003). Indeed, a long line of similar reports, including one in the archives of the National Research Council as early as 1917 and another in 1954 (Guertin and Wilhelm, 1954), made the same point.

This may be due to the fact that there are essentially two communities when it comes to judging the utility of the polygraph in determining deception, the polygraph community and the scientific community (Porges, 2006). These two communities base their judgments on different criteria. A critical difference between them has been their approach to research: The polygraph community has long taken an applied, problem-oriented approach, with efficacy as its goal, driven by perceived societal needs. The scientific community, in contrast, takes a basic, theory-driven approach that emphasizes understanding the mechanisms underlying the process of deception, driven by the principles of science as they relate to society. These two approaches are often conceptualized as inherently divergent, which they certainly have been in practice. However, lacking a scientific basis, the applied approach has failed to increase its efficacy or advance its credibility over several decades, as outlined above.

The literature on lie-detection research, which has long focused mostly on measurements of autonomic reactions, appears to value seemingly high correlations between particular neurophysiological responses and the prevarication they identify, independent of sound biopsychosocial theory. Continued reliance on a century-old, outdated physiology-based rationale has resulted in a failure to advance the underlying science. The best that can be said for the current physiological indexes of lying used in polygraph testing is that they are outcomes, the type of index that provides the logically weakest basis for inference (Cacioppo and Tassinary, 1990b). Put even more succintly, the lack of biopsychosocial theoretical sophistication underlying polygraphic lie detection techniques over the past century has led to entropy in the field of lie detection. Because no known physiological or neurophysiological measure enjoys a one-to-one relationship with deception, all correlates of deception have multiple causes, only one of which is deception. This being the case, protocols and uses that rely on very few or even a single response to determine deception will be highly susceptible to false positives, despite which such protocols continue to be used (see, for example, Tsiamyrtzis et al., 2007). This has been demonstrated many times from work in such fields as event-related potentials, where multiple trials have proved much more reliable than single trials. If a marker is a true correlate of the deceptive state, it should survive several response trials, capitalizing on Bayesian probability. (Although it is well known that autonomic responses habituate, they can serve as markers for a few trials.)

The committee agrees that an integration of basic, theory-driven science with an applied, problem-oriented approach could facilitate acceptable solutions to the assessment of credibility. According to Porges (2006), basic science can help delineate the neural processes underlying deception and the theoretical relationships among the psychological state(s), their neural and physiological markers, their measurement, and their application in the field.

Improvements in Technology

Given that standard polygraph technology is no different in basic measurement principles than it was a century ago and, according to the literature, has failed to produce sufficient reliability, it is not surprising that many lie-detection researchers have turned to newer CNS assessment technologies, including high-density electroencephalography, near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) to improve the accruracy with which it detects lies. A small formative body of published research on the neural circuitry associated with deception utilizes various neuroimaging techniques. Recent studies using positron emission tomography (PET) and fMRI have provided insights into the neural circuitry associated with deception, 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; Langleben et al., 2002, 2005; Lee et al., 2002, 2005; Nuñez et al., 2005; Phan et al., 2005a; Kozel et al., 2004a,b, 2005; Ganis et al., 2003; Spence et al., 2001). Recent fNIRS studies of deception have also implicated prefrontal brain regions in the neural circuitry associated with deception (Bunce et al., 2005). Consistent with the Bunce et al. (2005) observation, through the end of 2007, all published neuroimaging studies of deception except one (Langleben et al., 2002), including PET, fNIRS and fMRI technologies, studies of well-practiced vs. spontaneous lies (Ganis et al., 2003), studies of malingering, and cultural samples (Lee et al., 2005, with Chinese subjects, Bunce et al., 2005, with East Indian subjects) have found activation in a similar area of the dorsolateral/ventrolateral prefrontal cortex. Another recently published study correlated fMRI images with standard skin conductance measurements during a concealed information paradigm, with interesting results (Gamer et al., 2007). Although some members of the committee believed this preliminary body of work to be quite promising there has not yet been sufficient systematic research to determine if functional neuroimaging can meet the challenges to the neurophysiological detection of psychological states relevant to deception, as described above. Future research is warranted in the brain plasticity and variability; specificity of psychological states; and base rates.

Brain Plasticity and Individual Variability

An important divergence between traditional polygraphic lie detection and newer lie detection based on newer CNS assessment technologies is the latter’s susceptibility to problems created by the brain’s plasticity and resultant individual variability in CNS structure and function. For example, if the loci of CNS synaptic transmissions underlying prevarication vary across individuals or within an individual across time, those loci would not be accurate enough to be useful as neurophysiological indexes of lying for individual testing. This is still an open question because to date, no sufficient research on these issues has been published. If individual variability proves to be a significant factor, there will be no scientific basis for using the newer assessment technologies unless theoretical mechanisms relating such variability to the biopsychsocial basis for lying can be specified and validated. This does not mean that a theory could not be developed, and some preliminary models have already been proposed (Porges, 2006; Spence, 2004). However, a great deal of appropriate research needs to be done before a specific conclusion could be drawn about the validity of CNS measures of intentional deception, with an interactive process between the scientific model and the research. While this does not rule out any role for an applied, problem-oriented approach—for example, does the outcome or marker predict at a given level of sensitivity/specificity—a good model would help to avoid serious misinterpretation of the data.

In addition to CNS-based measurement, several other sensing techniques are being investigated for their potential ability to discern deception or concealed knowledge. These techniques may avoid some of the issues surrounding neural plasticity, but they must still meet appropriate criteria for accuracy. Some are remote, noncontact sensing techniques that measure autonomic function and are already in use. For instance, laser Doppler vibrometry is a remote sensing technique for heart rate, blood pressure, and several other physical properties. Although the technique does provide more information about heart function than a polygraph, it typically measures aspects of autonomic function associated with stress in response to threat. Other similar techniques include voice stress analysis, pupilometry, and infrared measures of the periorbital regions.

One measurement that appears to have a largely cognition-based etiology is that of eye-movement-based memory assessment (EMMA) (see, for example Marchak, 2006). This method is based on evidence that people scan faces they have previously seen in a different way than they scan novel faces (Altoff and Cohen, 1999). People reliably use fewer eye fixations, sample fewer regions, and fewer statistical constraints in viewing familiar rather than new faces. By tracking and quantifying these eye movement patterns using the appropriate technology and experimental controls, researchers can identify concealed knowledge. These patterns can also be combined with performance measures such as speed and accuracy, which show increased efficiency in the subject’s processing of previously learned materials. The technique has been applied to objects and scene recognition as well as faces. EMMA, which stems from a theory-driven model of memory and adaptive function, appears to be promising. Published levels of correct classification across studies (grand mean = 88.1 percent) are on a par with results from standard polygraphs. More work is needed on the level of positive and negative predictive power across various samples of individuals. In addition, the methodology itself is somewhat constraining because it requires a specific type of knowledge on the part of the subject and a specific set of stimuli.

Specificity of Psychological States

The specificity of a psychological state should be reflected in the specificity of the neurophysiological index created for it. The neurophysiological index for a more general or superordinate psychological state is likely to be more easily validated than the corresponding index for a more specific psychological state. Deception in everyday matters is not as general a psychological state as information processing or fear or anxiety, but it is not as specific as lying about something as portentous as, say terrorism. Developers of new technologies must avoid making the same type of error. Turning to measurement of CNS response to deception (may represent) an attempt to develop an appropriate level of analysis for deception.

Reconsideration of Base Rates

The challenge posed by base rates can be overcome by applying signal detection theory, as mentioned above. Here is a substantive example. Assume that deception has a base rate of 1 in 1,000, and an index that is 90 percent accurate and that 10,000 individuals are being screened using the index. This assumed base rate is probably high if scientists are attempting to identify spies or terrorists in organizations as large as the military or in the populations of individuals on a given day traveling by air to or within the United States. As Table 2-1 from a report on the polygraph and lie detection illustrates (NRC, 2003), 1,598 people, or 99.5 percent of those who failed the screen and were (incorrectly) identified as spies or terrorists, would be false positives and 2 people (20 percent of the actual spies) who passed the screen and were identified as nonspies or nonterrorists would be false negatives. Only 8 (0.5 percent) of the people who failed the screen would actually be spies or terrorists and therefore true positives.

Rates of False Positives and False Negatives from Polygraph Examinations.

If the detection threshold is lowered to reduce the number of false positives, as Table 2-2 from the NRC report illustrates, the number of spies or terrorists who passed the screen (false negatives) would increase. In this case, 8 people (80 percent) of the spies or terrorists would have passed the screen. This example emphasizes the importance of a highly accurate test when testing large numbers of people for low-base-rate events, and calls attention to the care that must be taken in using the information when a less accurate test is involved.

Increase in the Number of Spies or Terrorists Who Passed the Screen (False Negatives).

These statistics make evident an important conclusion. When looking for targets in settings with a low base rate, such as screenings for terrorists at airports, achieving high confidence in a positive detection requires using a technology with an extremely high discriminating capacity on the order of that associated with HIV testing or the even more accurate DNA testing. If such a level of accuracy cannot be achieved using the test technology alone, further information that additionally differentiates true targets, and is external to the test technology, needs to be obtained and brought to bear.

Finding 2-2. The committee recognizes the IC’s strong interest in improving its ability to detect deception. Consistent with the 2003 NRC study The Polygraph and Lie Detection , the committee uniformly agreed that, to date, insufficient, high-quality research has been conducted to provide empirical support for the use of any single neurophysiological technology, including functional neuroimaging, to detect deception.

Opinions differed within the committee concerning the near-term contribution of functional neuroimaging to the development of a system to detect deception in a practical or forensic sense. Committee members who conduct neuroimaging research largely agreed that studies published to date are promising and that further research is needed on the potential for neuroimaging to provide a more accurate method to determine deception. Importantly, human institutional review board standards require, at a minimum, that individuals not be put at any greater risk than they would be in their normal everyday lives. The committee believes that certain situations would allow such testing under “normal risk” situations; although the committee strongly endorses the necessity of realistic, but ethical, research in this area, it does not specify the nature of that research in this report.

Recommendation 2-1. The committee recommends further research on multimodal methodological approaches for detecting and measuring neurophysiological indicators of psychological states and intentions. This research should combine multiple measures and assessment technologies, such as imaging techniques and the recording of electrophysiological, biochemical, and pharmacological responses. Resources invested in further cognitive neuroscience research should support programs of research based on scientific principles and that avoid the inferential biases inherent in previous research in polygraphy.

• NEUROPSYCHOPHARMACOLOGY

Drugs and other mind-altering chemicals can influence all aspects of human psychology, including cognition, emotion, motivation, and performance. For known drugs, predictions of the type, onset, magnitude, and/or duration of effects in individuals or groups can be limited by incomplete knowledge of the interacting processes that govern drug effects. Clinical, field, and research experience reveals that drug effects in individual humans arise from interactions of multiple factors, including (but not limited to) the drug itself; its dose and route of use; the demand characteristics of the current situation; and the individual’s health, physiology, and experience with drugs and performance demands. Attempts to predict the effects of new drugs are hampered by the possible surprise in structure, targets, delivery, mechanisms of action, interactions with other drugs, and varying performance conditions.

In the following discussion the committee uses the term “drug” to refer to any chemical agent with the capacity to alter human affect, cognition, motivation, or performance. Agents that can do this include not only pharmaceutical drugs for preventing, treating, or mitigating disease symptoms, but also foods, hormones, intoxicants, nutrients, plants, poisons, supplements, toxins, and so on. The porous boundaries among these classifications is exemplified by botulinum toxin type A, available by prescription as Botox. The actions and effects of drugs may change with long-term use or under altered conditions; may interact with medical, occupational, physical and psychological conditions; may affect individuals differently; and may affect human functions other than psychology. Effects may be perceived as beneficial, harmful, or neutral, and such perceptions may change with conditions.

Most discussions of drugs and their effects are organized along the lines of current models of brain and nervous system functioning. This method of organization can help to identify likely effects of known drugs but probably should not be used to identify potential dual-use threats. Changes in models of brain function may create new and surprising ideas about how, when, where, or why drugs produce their effects; about what those effects are; about the kinds of chemicals that function as drugs to alter human functioning; and about ways to enhance, minimize, or counteract drug effects. It is particularly important to realize that the drugs that changed psychiatry in the mid-twentieth century were not predicted by many pharmacological or psychological models of their time, perhaps especially in the United States (Swazey, 1974). Rather, the brief history of neuropsychopharmacology illustrates how the expectations of a particular cultural, medical, and research climate may cause a failure to predict new drugs, new ways of using drugs, or new drug effects. Recent advances in neuropsychopharmacology that have the potential to be “game changers” include a much improved knowledge of brain function and delivery systems such as are enabled by nanotechnology that would allow substances to cross the blood-brain barrier.

Many current psychotherapeutic drugs—that is, drugs used in treatment and management of psychiatric disorders—and their likely mechanisms of action were not anticipated by prior research or theory (Barrett, 2007). Classic examples of psychotherapeutic drugs with unexpected mechanisms of action and/or unanticipated effects, and called to attention by alert clinicians, include lithium, chlorpromazine, monoamine oxidase (MAO) inhibitors, and tricyclic antidepressants (Cade, 1949; Jarvik, 1970; Swazey, 1974). Another unexpected discovery was lead poisoning, which had long-lasting psychological effects in the employees of a workshop where batteries were made (Hamilton, 1915; International Labour Office, 1934). These examples suggest that new drugs with marked effects on critical psychological functions are “black swans,” unanticipated events with effects that could not have been predicted, that may fail to be observed when they happen, or that have large, long-lasting consequences that go unrecognized (Taleb, 2007a,b). 4

One modern drug with psychological effects that were not predicted by initial descriptions of its clinical pharmacology is ketamine. Developed in the private sector as an anesthetic, then discovered in clinical use to have hallucinogenic effects and co-opted into the club drug scene as Special K, it is now under investigation in the public and private sectors as a rapidly acting antidepressant and for the treatment of chronic pain; the use of ketamine as a rapid acting antidepressant remains unreplicated and provocative. Other such drugs are opioid antagonists as a treatment for alcoholism and cannabinoids. These examples suggest that current models of neuropsychopharmacological effect account post hoc for psychological effects of drugs and may have poor predictive ability.

In spite of the difficulty of predicting their psychological effects, psychotherapeutic drugs have proven to have greater clinical effectiveness than many other treatments. This has had important consequences for medical and cultural expectations about drugs. First, the discovery and clinical use of drugs for treatment and management of psychiatric disease have had far-reaching effects on research and practice in psychiatry, psychology, cognitive science, and other mental health and behavioral sciences. Demonstration that particular drugs could effectively treat psychiatric illnesses suggested that such illnesses were treatable much like other illnesses and promoted the medicalization of psychiatry. Equally important, the clinical usefulness of the earliest such drugs provided insight into the brain mechanisms that mediate the drugs’ therapeutic efficacy and created opportunities for research to understand the chemical and biological bases of their effects. The critical linkage between drug effects and discoveries of brain receptors for neurotransmitters and other endogenous brain chemicals enabled the development of drugs with fewer side effects and more focused activity as well as a few truly new drugs that could target newly discovered chemical systems in the brain.

The appearance of clinically useful brain drugs occurred as the field of neuroscience coalesced so that medical and behavioral researchers could explore how biology manifests itself in behavior (Barrett, 2007; Dinges, 2007). One marker of the importance of the emerging field of neuroscience is the growth in attendance at the annual meeting of the premier professional association in the field, the Society for Neuroscience, from 1,396 in 1971 to 34,815 in 2005 (Society for Neuroscience, 2007). 5 Discoveries in neuroscience can be exploited to create new cellular or subcellular targets for drugs, new drug delivery systems, and new strategies to direct or control drug effects and achieve desired psychological effects. Novel classes of drugs approved by the Food and Drug Administration (FDA) illustrate the potential surprises of research, including drugs for erectile dysfunction, which arose from chemicals that were targeted at treatment of angina but failed to increase cardiac blood flow while unexpectedly increasing blood flow to the penis, and sleep-inducing drugs that exploit the roles of gamma aminobutyric acid (GABA) and melatonin receptors in brain sleep systems. Recent news stories on topics ranging from brain systems and drugs for memory enhancement (Rovner, 2007; Foer, 2007), appetite control (Bentivoglio and Kristensson, 2007), and sleep drugs (Saul, 2007) reflect the intense public, academic, and commercial interest in neuropsychopharmacology.

This interpretation of neuroscience research carries the additional implication that drugs can achieve or modulate not only abnormal, diseased, or disordered psychology but also normal, healthy, or optimal function. Development and utilization of drugs to treat psychiatric disorders have been accompanied by important changes in expectations of physicians, consumers, and policy makers about how drugs can and should be used (Barrett, 2007; Chatterjee, 2007; Kelly, 2007). These changes include expectations about the duration of a drug’s use by an individual and who drives the choice of whether, when, and which drugs to use; opinions on personal drug use; and ideas about which human functions can, or should, be modulated by drug use. It may be particularly important that many current prescription drugs, and possibly the majority of current prescription drugs with primarily psychological effects, are used widely off-label. 6 Such off-label use and the possibility of important placebo and nocebo effects (Lasagna et al., 1954, Beecher, 1955; Olshansky, 2007) cannot but open analysts’ eyes to the probability that drugs have heretofore undiscovered effects.

Drugs that are described and marketed as having disease-specific or diagnosis-specific effects are quite capable of producing striking psychological effects in individuals who do not suffer from the condition identified with the drug’s descriptive name or clinical indication. These psychological effects may or may not be similar to the effects in individuals with medical conditions for which the drugs are prescribed. Recent examples discussed in the public press include use of antipsychotic drugs for individuals without psychosis, beta-blockers to modulate performance anxiety in concert musicians, and steroids to enhance athletic performance. Other emerging changes in how drugs are prescribed and used include increased incidences of polypharmacy, of consumer decisions about whether and when to use drugs (as illustrated by erectile dysfunction drugs and Botox), and of consumer decisions to seek out and use available drugs for multiple effects. For example, the use of certain prescription drugs as study aids is not uncommon (White et al., 2006). Of 1,025 U.S. college students surveyed, 16.2 percent reported such use of prescription stimulants. Ninety percent of this group did not hold a legitimate prescription for stimulant drugs; 96 percent of them used Ritalin to improve attention, improve ability to participate in partying, reduce hyperactivity, and improve grades; and 15.5 percent reported using Ritalin at least two or three times per week.

Addiction and substance (or drug) abuse presents another area of change. The demarcation between abused drugs and medicines is situation dependent, as demonstrated by recent experience with OxyContin, by nicotine in over-the-counter smoking cessation aids, and by college-student exploitation of prescription stimulants. The illicit market not only provides incentives for novel drugs, manufacturing processes, and delivery systems but also poses novel risks. These factors are illustrated by episodes such as the appearance of permanent symptoms of Parkinson’s disease in opiate addicts who used the illicitly manufactured 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), a neurotoxin probably produced by improper chemical conditions in illicit manufacture of meperidine, a synthetic opioid (Langston, 1995). The incentives provided by the illicit market are also exemplified by recent U.S. experience with methamphetamine labs and by the emergence of crack cocaine (smoked, minimal manufacturing danger, sold in small quantities) to challenge freebase cocaine (smoked, risks of explosion during manufacture), both of which challenged powdered cocaine (snorted or injected, sold in large quantities) (Kleiman, 1992; Musto, 1987). Epidemio logical and clinical research on the natural history of drug abuse also provides information about the long-term effects (often) high doses of at least a subset of performance-altering drugs, which may alert analysts to dual-use possibilities. The same may be true of patterns of drug use in highly competitive industries or markets, such as the steroids, stimulants, and endurance-altering drugs used by athletes or the stimulants used by floor traders.

Cognition Enhancers

One specific area of neuropsychopharmacology that may be of considerable importance is that of cognition enhancement. Cognition enhancers can be broadly defined as drugs or other agents that have the potential to improve human functions such as attention, learning, and memory (Sarter, 2006; Sahakian and Morein-Samir, 2007 and accompanying online discussion at http://network.nature.com/forums/naturenewsandopinion/ ). Considerable research investment in the United States and other countries is directed to the discovery and development of pharmacological cognition enhancers. Often, these agents target declines in memory and cognition linked to age, dementia, or neuropsychiatric or neurological disease. It is likely, however, that agents developed for prevention or treatment of disease will alter brain processes in normal individuals. Additionally, agents already in wide medical or social use are known to alter memory and cognition, sometimes by mechanisms shared with disease-related agents and sometimes by mechanisms not so clearly linked to age or pathology. Examples of widely used agents with known or suspected ability to alter and perhaps enhance certain components of human cognition and/or performance include caffeine (found in coffee, tea, soda), nicotine (tobacco products), tacrine (energy drinks), amphetamines and methylphenidate (attention deficit hyperactivity disorder medications and certain abused drugs), propranolol (certain beta-blockers), dextroamphetamine, and modafinil.

A fairly wide range of neuropharmacological or chemical systems have been suggested as possible sources for cognition or performance enhancers (e.g., Sarter, 2006). This expanding list is illustrative of the wide range of brain processes suspected of having potential to enhance normal and/or disordered cognition. Conversely, opposite changes or disruption in such systems might disrupt cognition and/or performance. Specifically, if agonists of a particular system enhance cognition, it is mechanistically plausible that antagonists might disrupt cognition; conversely, if antagonists of a particular neurotransmitter enhance, its agonists might disrupt. Examples of the former might include dopamine agonists, which enhance attention, and dopamine antagonists, which disrupt it; examples of the latter might include the suspected cognitive enhancing effects of cannabinoid antagonists and the disrupting effects of agonists like THC.

Three areas of research in behavioral pharmacology have particular importance for analysis of enhancement and/or disruption of cognition and performance. One identifies the boundaries or limitations of cognition enhancement. Many current models of cognition incorporate ideas of processing resources and their limitations. It is an open question as to whether drug-induced enhancements in one area of cognition have a cost in other areas. Stimulant drugs are known to have rate-dependent effects, such that the same exposure regimen may enhance attentional processes that are initially occurring with low frequency but simultaneously decrease attentional processes that are occurring at high frequency (Dews and Wenger, 1977), which could result in unwanted performance decrements. A cognition enhancer that optimizes performance under a taxing attention condition might not improve performance of a task with lower attention demands (Robbins, 2005) and in fact might disrupt it. As another example, drugs that enhance working memory capacity or operation may impair capacity to simultaneously filter distractors; this could increase false alarms in a detection operation (Lavie, 2005). Public recognition that modafinil, which promotes wakefulness and increases alertness, can have small but valuable cognitive effects is an example of a change that creates opportunities for public discussion of the risks and benefits of potential cognitive enhancers.

A second relevant area of behavioral pharmacology involves efforts to identify which specific neurochemical changes actually covary with cognitive processes. Studies of neurochemistry in cholinergic brain systems, for example, suggest that drug-induced changes in neurotransmitter release activated by attentional processes are vitally different from drug-induced changes in basal release (Sarter and Bruno, 1994; Kozak et al., 2006).

A third, and related, area is research to identify agents that produce specific kinds of cognitive enhancements rather than broadly acting agents (Rovner, 2007). Additionally, the full range of effects of potential cognition- or performance-enhancing drugs warrants attention. For example, stimulants such as amphetamine can increase attention and concentration, but they also exert cardiovascular effects that can be exacerbated by physical exertion and heat, and with prolonged or high dose use, they carry risks of addiction and paranoia.

Implications for Agents That May Act to Change or Disrupt Various Aspects of Human Psychology

There is currently a widely recognized “translational gap” between preclinical research, clinical research, and development of cognition enhancers, perhaps best articulated in the area of schizophrenia (Hagan and Jones, 2005; Floresco et al., 2005). Similar translational gaps exist in most, if not all, areas of neuropsychopharmacology. Examples of recognized roadblocks to new therapeutic entities include the need for improved clinical models, the rudimentary knowledge of brain neurochemistry and function, the paucity of models to predict side effects, and poor understanding of brain diseases and disorders. One recommendation for closing these translational gaps is to improve the predictive power of animal models so that they map onto operationally defined domains of affective, cogni tive, and motivational processes (Robbins, 1998; Sarter, 2006; Barrett, 2007). The neuronal bases of cognitive function are poorly understood, and most animal models that are used to identify potentially useful therapeutics are not based on molecular- or systems-level understanding of brain processes or on functional understanding of human cognition and behavior. Improved animal models for human psychopathology (depression, anxiety, memory decline, cognitive failure) and for normal functioning (learning, affect, motivation, performance) could permit development of novel drugs and/or drugs that can be targeted to effect human cognition and performance. While animal models are useful, many effects on cognitive dimensions cannot really be tested on rats or mice.

The discovery of the probable role of the hypocretins (also called orexins) in human narcolepsy shows that once the chemistry is known and the market drivers are in place, a drug can be developed rapidly. Symptoms of narcolepsy are currently managed with amphetamine-like CNS stimulants or modafinil (for excessive daytime sleepiness) and antidepressants or sodium oxylate (for cataplexy), but none of these treatments are based on an understanding of how brain chemistry is dysregulated in affected individuals. Recent research, which arose from the discovery of narcolepsy genes in animal models, suggests that a deficiency of hypocretin/orexim is responsible for about 90 percent of human narcolepsy-cataplexy cases (Nishino, 2007a,b). This finding led directly to development of new diagnostic tests for narcolepsy and has led to the search for new therapeutic drugs for narcolepsy caused by a deficiency of hypocretin/orexim. Additionally, novel small-molecule hypocretin/orexim receptor antagonists that can be used to inhibit feeding have been identified.

Finding 2-3. Neurochemical systems modulate, and can be used to control, a wide range of human psychology. The number of neuropsychopharmacological drugs increased dramatically after the mid-1900s, along with their availability, and emerging technologies may improve the ability to harness drug effects or to produce targeted changes in human psychology. Cognitive neuroscientists do not have specific understanding of how most drugs produce their effects. Basic research in the public or private sectors that identifies the specific mechanisms of disease and of drug effects might enable rapid development of new drugs. New drugs may have unrecognized effects that emerge owing to variation in individuals, settings, or performance demands.

Nanotechnology in Medicine

In addition to bringing new drug entities or new uses for existing entities, emerging technologies might allow new pathways for drug delivery. Some observers say it is likely that the paradigm of the pharmaceutical industry will change, from “discovering” drugs by screening many compounds to the purposeful engineering of desired molecules.

Richard Feynman famously said, “There’s plenty of room at the bottom,” in a lecture in which he outlined the principle of manipulating individual atoms using larger machines to manufacture increasingly smaller machines (Feynman, 1959). Nanotechnology is a rapidly expanding, multidisciplinary field that applies engineering and manufacturing principles at a molecular level. It can be roughly divided into categories that include nanobiotechnology, biological microelectro-mechanical systems, microfluidics, biosensors, microarrays, and tissue micro-engineering (Gourley, 2005). In some sense nanotechnology is intuitive, since everything in nature is built upward from the atomic level in order to define limits and structures (Emerich and Thanos, 2006). Understanding and developing nanotechnology, therefore, depends on understanding these limits and pushing against them.

Nanomedicine (the development of effective clinical treatments based on nanotechnology) has had some successes (Freitas, 1999) and depends on several overlapping molecular technologies. These new but progressing technologies include (1) the construction of nanoscale-sized structures for diagnostics, biosensors, and local drug delivery; (2) genomics, proteomics, and nanoengineered microbes; and (3) the creation of molecular machines capable of identifying and eliminating host pathogens by replacing and repairing cells and cellular components in vivo (Emerich and Thanos, 2006). Of particular importance may be nanotechnologies that allow delivery of drugs across the blood-brain barrier in ways now impossible.

Nanotechnology for Drug Delivery

In the last decade nanotechnology and nanofabrication have significantly impacted the field of drug delivery (Emerich and Thanos, 2006). The continued development of these technologies will probably occur in conjunction with the development of pharmaceuticals targeted at the brain (Ellis-Behnke et al., 2007; Jain, 2007; Koo et al., 2006; Silva, 2006, 2007; Suri et al., 2007; Teixido and Giralt, 2008). The development of such neuropharmaceutical combinations is of great interest to the Department of Defense. Guided by the statement of task, the committee outlines in the following paragraphs nanotechnologies that may contribute to advances in the delivery of CNS drugs.

Techniques have shifted from microfabrication and micromachining (e.g., the osmotic pump) to designs ranging from secondary constructs at the nanometer scale (e.g., microspheres). The engineering of nano delivery systems for small molecules, proteins, and DNA has led to the emergence of entirely new and previously unpredicted fields. Formulation science has linked up with computer technology to create a controlled-release microchip capable of infinite modulation that would allow for the greatly improved controlled release of pharmaceutical agents (Santini et al., 1999; Grayson et al., 2003; Maloney et al., 2005; Prescott et al., 2006). Tissue engineering applications have also moved toward the development and implementation of nanometer-sized components. The creation of artificial cells with appropriate physiologic properties may provide a better understanding of normal physiological processes. Transfection systems on the nanoscale for genetic manipulation and gene delivery are being tailored using different polymers.

Nanotechnology is opening new therapeutic opportunities for agents that could not be used effectively as conventional drug formulations owing to their poor bioavailability or drug instability (Santini et al., 1999). Microsphere formulations are used to protect agents susceptible to degradation or denaturation while prolonging their duration of action by increasing systemic exposure or retention of the formulation (Hillyer and Albrecht, 2001; Hussain et al., 2001; Torche et al., 2000; Van Der Lubben et al., 2001; Varde and Pack, 2004). Nanoparticles are able to cross membrane barriers, particularly in the absorptive epithelium of the small intestine (Hillyer and Albrecht, 2001) and are being used to deliver small molecules, proteins, and other therapeutics (Dunning et al., 2004; Hamaguchi et al., 2005; Koushik et al., 2004; Panyam and Labhasetwar, 2003; Silva, 2006; Weissleder et al., 2005). Biodegradable nanospheres enhance bioavailability through uptake, followed by degradation and disappearance of the vehicle from the system.

Integration of controlled-release drug reservoirs with microchips (Santini et al., 1999) provides unlimited potential for modulating drug release. Nanotubes that have large relative internal volumes also can be functionalized on the inside surface (Martin and Kohli, 2003). One fabrication technique used self-assembling lipid microtubes to deliver testosterone in rats (Goldstein et al., 2001). Testosterone was covalently bound with an ester linkage to a glutamide core lipid, forming nanotubes that possessed an in vivo biphasic release profile characterized by an initial burst followed by a more sustained release. Another method of fabrication involves synthesizing carbon nanotubes using fullerene. These nanotubes range from one nanometer to tens of nanometers in diameter and are from several to hundreds of microns long. Drugs can be covalently attached to functional groups on the external surface of the nanotubes (Chen et al., 2001). Another drug delivery approach uses nanoshells or dielectric-metal (gold-coated silica) nanospheres. When embedded in a drug-containing polymer and then injected into the body, these nanoshells accumulate near tumor cells. When heated with an infrared laser, the polymer melts, releasing the drug at a specific site (Hirsch et al., 2005; Loo et al., 2005). This technology allows delivering drugs at very precise locations in the brain. Growing knowledge about the neural circuits underlying various functions will probably enhance the capacity to target specific effects with fewer side effects.

Improvements in drug-containing nanoparticles are already gaining regulatory approval. Abraxane, a nanoparticle form of albumin and paclitaxel, 7 elimi nates the need for toxic solvents in earlier versions of paclitaxel (Taxol) and permits more of the drug to be administered. Similarly, a micellar nanoparticle formulation of paciltaxel (NK105) is being developed to reduce toxicity while enhancing antitumor activity (Hamaguchi et al., 2005). There is broad international interest in research on nanotechnology for drug delivery. Asia, in particular, is active in this area, as evidenced by the published literature (Bosi et al., 2000; Miyata and Yamakoshi, 1997; Chen et al., 2001; Tabata et al., 1997a,b; Tsao et al., 1999, 2001; Hamaguchi et al., 2005), and there is especially strong research in drug delivery to the brain and neuropeptides (Gao et al., 2007a,b). 8

Finding 2-4. Technological advances will affect the types of neuropsychopharmacological drugs available and methods for drug delivery. For the IC, nanotechnologies that allow drugs to cross the blood-brain barrier, increase the precision of delivery, evade immune system defenses, evade metabolism, or prolong actions at cellular or downstream targets will be of particular importance. These technologies will increase the likelihood that various peptides, or other brain proteins, could ultimately be utilized as drugs. Development of antidotes or protective agents against various classes of drugs that could be used by an enemy force will also be important.

Neuropeptides and Behavior

Neuropeptides act as messengers in the brain, influencing many neurobehavioral functions (Strand, 1999). Their therapeutic use in humans has been hampered because they do not readily pass the blood-brain barrier (BBB) and they induce potent hormonelike side effects in the blood (Illum, 2000; Pardridge, 1999). To date, the results of intranasal administration testing have been mixed (Born et al., 2002; Heinrichs et al., 2003; Heinrichs et al., 2004; Merkus and van den Berg, 2007). However, nanotechnology may someday allow for quick pharmacological modifications of behaviors. Box 2-2 provides an overview of the function of the BBB.

The Blood-Brain Barrier as an Obstacle to the Delivery of Therapeutics. The blood-brain barrier (BBB) remains an obstacle to the delivery of therapeutics to the brain. It comprises an endothelial cell monolayer associated with pericytes and astrocytes. (more...)

One neuropeptide of interest is oxytocin, which—in addition to its well-known functions in milk letdown and childbirth—has a central role in positive social behavioral interactions and can increase trust behavior in human experimental subjects. Oxytocin receptors are distributed in brain regions associated with certain behaviors (Huber et al., 2005; Landgraf and Neumann, 2004) such as pair bonding, maternal care, sexual behavior, and the ability to form normal social attachments (Carter, 1998; Carter et al., 2001; Heinrichs et al., 2002; Huber et al., 2005; Insel and Young, 2001; Pedersen, 1997; Uvnäs-Moberg, 1998; Young et al., 2001). Thus oxytocin permits animals to overcome their natural avoidance of proximity and facilitates approach behavior. Given that oxytocin is believed to promote social attachment and affiliation in nonhuman mammals, researchers have hypothesized it might also promote more social behaviors—such as trust—in humans (Kosfeld et al., 2005; Zak et al., 2007; Zak and Fakhar, 2006).

• FUNCTIONAL NEUROIMAGING

Introduction

Broadly defined, functional neuroimaging is the use of neuroimaging technology to measure aspects of brain function, often with the goal of understanding the relationship between regional brain activity and specific tasks, stimuli, cognition, behaviors or neural processes. Common technologies for functional neuroimaging include multichannel electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), functional transcranial Doppler sonography (fTCDS), and magnetic resonance spectroscopy (MRS). EEG and MEG measure localized electrical or magnetic fluctuations in neuronal activity. PET, fMRI, fNIRS, and fTCDS can measure localized changes in cerebral blood flow related to neural activity. PET and MRS can measure regional modulation of brain metabolism and neurochemistry in response to neural activity or processes. These functional neuroimaging technologies are complementary, and each offers a different window onto complex neural processes. Because of this complementarity, multimodal imaging is an emerging area of great interest for research, clinical, commercial, and defense applications.

Neuroimaging technologies are likely to play an important role in endeavors to enhance cognition as well as affect and motivation over the next two decades. Predictions about future applications of technology are always speculative, but if the issues discussed above come to fruition this emergent technology might provide insight into the following areas, and others, of direct relevance to national defense: the acquisition of intelligence from captured unlawful combatants, enhanced training techniques, augmented cognition and memory enhancement of soldiers and intelligence operatives, the screening of terrorism suspects at checkpoints or ports of entry where there are no constitutional protections (i.e., airports), and soldier-machine interface devices such as are used in remotely piloted vehicles and prosthetics. Indeed, science is already beginning to see contributions of this field to clinical and battlefield medicine.

Over the next two decades, good brain-computer interfaces (BCIs) are likely to be a great of interest to the gaming industry as well as to the rehabilitation, medical, and military sectors, and neuroimaging and neurophysiology will play a central role in those endeavors. BCIs are likely to be used to enhance several areas of cognition, including memory, concentration, and emotional intelligence, among others. Indeed, this has been a focus of the Defense Advance Research Projects Agency’s (DARPA’s) Augmented Cognition program for several years. Brain prosthetics could become the new input-output devices for memory systems, allowing efficient searching and encyclopedic access to information. Sandberg and Bostrom suggest BCIs may improve better concentration by reducing working memory load and exploiting the broad attention abilities of the visual system.

Similarly, DARPA has been working on EEG and fNIRS-based BCIs that use the human visual system as the input device to a computer system to increase the speed of data processing in visual search mode. The idea is that although current technological capacities with computer vision are not even close to the speed and sophistication of the human eye, there is a lag time between the visual mental process and a motor output to the computer system. By using EEG or fNIRS to directly measure the brain’s response when it detects a target, the search process can occur more quickly than with an operator’s motor response. Given the current level of miniaturization of computer memory, a wearable computer with an efficient brain-based input/output device (a “mental mouse”) and an efficient search strategy could allow access to huge amounts of stored data in milliseconds, effectively augmenting the user’s long-term memory. Such devices could allow military troops to access visual maps and intelligence when they approach a new area or to access medical information in the field.

Sandberg and Bostrom suggest that wearable computers could also enhance emotional intelligence—that is, the ability to perceive emotions in others and respond appropriately. Such capabilities could be useful for gathering intelligence. The current prototype system was designed to help people who have difficulty in accurately assessing the emotions of other people—for example, children with Asperger syndrome—by improving their ability to interact (El Kaliouby and Robinson, 2005). The system consists of a camera to record the facial expressions of a conversational partner, facial and emotion processing software that estimates the most likely emotional state, a readout that displays a cartoon of the emotion, and suggestions for a proper response. Such systems could also help a participant in a high-stakes negotiation or interview to gauge more accurately the emotional intentions of his or her counterpart (see, for example, Ekman and O’Sullivan, 2006). Indeed, Ekman and Mark Frank (Frank and Ekman, 2004), along with others at DARPA, have been using computer-aided analysis of facial responses based on Ekman’s theories to study at-a-distance measures of deception.

Owing to their awkward size and shape and their cost, some technologies such as MEG and MRI/fMRI are likely to take a backseat in enabling cognitive enhancement, providing the underlying neuroscience upon which more fieldable technologies, such as EEG and fNIRS, can be based. It is, for example, unlikely that brain–computer interfaces based on fMRI could be loaded onto jets or spaceships in the next two decades, whereas an EEG or fNIRS-based system could very well be so deployed in that timeframe. However, the information about cognitive, affective, and motivational states that accrues from these various kinds of neuroimaging is likely to play a key role in the use of technology-based systems to enhance performance.

Another example is the use of transcranial magnetic stimulation (TMS) to facilitate neural changes that have been identified through neuroimaging. TMS is a noninvasive way to excite neurons in the brain: rapidly changing magnetic fields (electromagnetic induction) are used to induce weak electric currents in neural tissue, allowing the brain to be activated from outside with minimal discomfort. One alternative to using pharmacological agents to influence neural function is to use electrical stimulation such as TMS to excite the neuromodulatory centers that control plasticity. Experiments in the monkey have shown that electrical stimulation can result in faster cortical reorganization. In their review, Sandberg and Bostrom cite evidence that TMS can increase or decrease the excitability of the cortex, in turn changing its level of plasticity. TMS has been used to facilitate the learning of a procedural memory task by stimulating the motor cortex. Sandberg and Bostrom suggest TMS has succeeded in facilitating working memory, classification, learning motor skills such as finger tapping sequences, coordinating visuomotor tasks, and consolidating declarative memory during sleep.

Because TMS is noninvasive and able to improve performance on a variety of cognitive tasks, Sandberg and Bostrom suggest that it could be a very versatile tool for cognitive enhancement. One limitation is the short duration of its effect (minutes to an hour after stimulation), although some results suggest that coupling TMS with pharmacological manipulations of the dopaminergic system could facilitate long-term consolidation or longer effects TMS (Nitsche et al., 2006). It is worthwhile reminding the reader that the considerable interindividual variability in responses to TMS might require individual tuning of dosage, placement, and so on. Neuroimaging tools such as MRI, fMRI, or fNIRS could play a role in providing precise localization for such technology integrations.These functional neuroimaging technologies, some mature, others emergent, are commonplace in research and clinical environments and are having an impact on defense policy decisions (Peters et al., 2008). Recent advances and developments allow for functional neuroimaging capability with real-time or near-real-time data acquisition and analysis that is becoming cheaper, portable, and more user friendly. Continued refinement of these technologies is likely to lead to increased dissemination of this technology, with applications expanding well beyond the current primary fields of neuroscience research and clinical medicine. Areas where the application of advanced functional neuroimaging technology likely are business (marketing, economics, human resources), human performance, risk assessment, the field of law, and the military, all of them having great relevance to national policy and defense issues. Progress continues to be made in both functional neuroimaging and neurophysiological methods toward the holy grail of neuroimaging, namely, millisecond-level temporal resolution with precise spatial localization. No current technology affords both high temporal resolution and high spatial resolution with access to the full brain. The strengths and limitations of each technology are briefly described below.

Electroencephalography

The oldest device used to assess brain function in real time is the electroencephalograph. Many years ago it became known that electrical activity in the brain could be recorded by placing electrodes on the surface of the scalp (Berger, 1929). Such recordings represent the summated electrical signal from nominally 50,000 local neurons. Early studies focused on the spontaneous rhythmic oscillations in voltage—frequency bands that tended to shift together with changing mental status, such as alpha waves, which have frequencies between 8 and 13 Hz. Early clinical EEG was used primarily to detect and diagnose epilepsy, but today, with advances in computer technology, informative new experimental paradigms and techniques are being developed. Electroencephalographic recordings are of two main types: continuous and discrete. Continuous recordings are the traditional multitrace waveforms recorded since EEGs began and activity is classified by the frequency of the dominant waveform (0-40 Hz) on any given channel, such as alpha waves. Discrete recordings are triggered by an event, such as an external flash of light, and then the next 1 to 4 seconds of activity are recorded. In discrete recordings, the “normal” EEG waves are considered background.

Discrete, or event-related, recordings were first described by Davis (1939), who noticed that event-related changes could be seen in an ongoing electroencephalogram event-related potentials (ERPs), currently the focus of electroencephalographic research, refer to the measurement of the brain’s electrophysiological response to a particular stimulus. The brain’s response to discrete stimuli are typically relatively small (a few microvolts) compared with the ongoing background EEG activity (approximately 50 mV), and multiple stimulus presentations are averaged to distinguish the response associated with the stimulus from the background activity. When the brain response is largely automatic and dictated by the physical properties of the stimulus (say, the loudness of a sound or the brightness of a light flash) it is called an evoked potential. Evoked potentials generally occur 15-100 ms after a stimulus is presented. Later responses, which occur as early as 150 ms after a stimulus, are thought to be influenced by cognitive processes and are referred to as ERPs.

Quantitative electroencephalography (QEEG) uses postrecording computer analysis to analyze the relationship between each of the electrodes placed at the scalp. The frequency composition, amplitude, and position of each electrode is compared to the same information taken from a database of individuals without any known neurological disorder. The resulting EEG brain maps are then analyzed with sophisticated statistical techniques to reveal patterns. The results of these analyses can be presented in graphical form as topographical displays of brain electrical activity. Applications include neurofeedback, or neurotherapy, and the identification of responses to medication for certain neurological and psychiatric disorders. Neurotherapy is an experimental technique that uses a QEEG brain map to analyze psychiatric problems from attention deficit disorder to depression to schizophrenia. Patients are then subjected to a conditioning protocol to “train” the “abnormal” brain activity toward a statistically more “normal” pattern of activity. Neurotherapy can reduce aberrant symptoms of many conditions (Fox et al., 2005).

Interpretation of a scalp electroencephalogram often involves speculation as to the location inside the brain of the source of the activity recorded (Brazier, 1949; Shaw and Roth, 1955). While there is substantial PET and fMRI literature on finding the neural sources of the functional network implicated in given mental tasks (Cabeza and Nyberg, 2000), PET and fMRI are temporally limited in their ability to probe discrete neural events. The source localization capability of EEG has been used to overcome this limitation and solve the inverse problem. Even though EEG offers millisecond-level time resolution, the signals measured at the scalp do not directly indicate the location of the neurons that are generating the activity. Although the sites at which the scalp potentials are measured at any given point in time are finite, an infinite number of source configurations could account for those measurements (Plonsey, 1963; Fender, 1987). Source localization involves mathematical attempts to solve the inverse problem by introducing a priori assumptions about the generation of the EEG (or MEG) signals. The better these assumptions are, the more trustworthy the source estimations will be, and several different models have been formulated and implemented in algorithms to reach the inverse solution, each using different mathematical, biophysical, statistical, anatomical, or functional constraints (for a recent review, see Michel et al., 2004). Technological advances in the field include noncontact electrodes that use high-gain preamplifiers to mitigate the effects of the high impedence caused by the lack of contact. This arrangement could allow the “application” of a large number of electrodes in a relatively short time, at the cost of a noisier signal. As these models and constraint estimates improve, there is promise for important future developments. EEG has several advantages over other functional neuroimaging techniques, including the relatively low cost of the technology (around $15 million). Also, a single technician can produce reliable recordings with unmatched temporal resolution measured in milliseconds. A number of other countries use high-density EEG with source localization. 9

Positron Emission Tomography

The introduction of computed tomography (CT) by Sir Godfrey Hounsfield in 1973 (Petrik et al., 2006) dramatically changed the way scientists and physicians examined the brain. The development of PET (based on prior brain autoradiographic work) quickly followed, creating in vivo autoradiograms of brain function (Ter-Pogossian et al., 1975; Phelps et al., 1975) and introducing a new era of functional brain mapping. For an excellent historical review of PET and functional neuroimaging the reader is referred to Raichle (1998).

PET can be used to produce a three-dimensional image or map of functional processes in the brain. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radioisotope, which is introduced into the body on a metabolically active molecule. Images of regional metabolic activity or blood flow are then reconstructed by computer analysis. Modern versions of PET scanners are combined with CT scanning and MRI scanning capability to coregister metabolic activity with high-resolution anatomic images of the brain, creating three-dimensional metabolic/anatomic overlays.

The radionuclides utilized in PET scanning typically have short half lives; carbon-11 (~20 min), nitrogen-13 (~10 min), oxygen-15 (123), and fluorine-18 (~110 min). They are incorporated into compounds such as glucose and water. These radiotracers distribute in the brain by following the metabolic pathways of their natural analogues or by binding with specificity to the receptor proteins for which they have affinity. Due to the short half lives of most radioisotopes, the radiotracers must be produced in a cyclotron and a certified medicinal radiochemistry laboratory co-located with the PET facility. Fluorine-18, with a half life long enough to allow commercial manufacture at an offsite location and transport to an imaging center daily, is an exception.

PET has gained widespread utility in clinical medicine, particularly in oncology, where it has become the favored imaging technology for the detection, staging, and monitoring of response to treatment for many neoplasms. Clinical PET is also used in neurology, psychiatry, cardiology, and pharmacology. There is continued widespread use of PET technology to study brain metabolism and receptor ligands. Limitations of PET include its relatively low temporal resolution; spatial resolution limited to approximately 5 mm; relatively expensive equipment; requirement for an injectable, short-lived positron-emitting radioisotope that is usually produced in a cyclotron; and limits on its use for repetitive longitudinal studies and studies in certain populations owing to its emission of ionizing radiation.

However, PET remains a powerful tool for functional neuroimaging, especially with the proliferation of PET/CT and PET/MRI scanners. PET’s exquisite ability to elucidate specific receptor binding sites/activity within the brain and its ability to produce images of brain metabolism mean it is not likely to be supplanted by other neuroimaging technologies in the foreseeable future. Indeed molecular neuroimaging via PET is likely to show the most growth in functional neuroimaging research over the next decade (Hammound et al., 2007).

Functional Magnetic Resonance Imaging

MRI is widely accepted as the gold standard for anatomical neuroimaging. The most common form of functional MRI (fMRI) utilizes a blood-oxygenation-level-dependent (BOLD) contrast mechanism to distinguish areas of neural activity. Other methodologies for fMRI include dynamic contrast techniques and noncontrast techniques (e.g., arterial spin labeling). There has been explosive growth of fMRI research and clinical applications over the past decade, with research applications including brain mapping of task (motor and cognitive) dependent processes. MRI has also been employed for detection of deception, an application that has drawn the interest of various communities (ethics, defense, legal). Under controlled experimental conditions with cooperative subjects, this technology has shown initial promise (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).

Real-time data acquisition (single-trial fMRI) and near-real-time data analysis (hundreds of milliseconds delay) of complex cognitive tasks have been demonstrated and will expand the applications areas of relevance to the research, clinical, and defense communities (Posse et al., 2003; Phan et al., 2004). Real-time fMRI has been utilized to demonstrate the voluntary suppression of affective state, suggesting that it may provide insight into complex cognitive processes (Phan et al., 2005b). Whether these findings can be generalized to nonexperimental settings remains to be determined. fMRI has many advantages over other functional imaging techniques, including high spatial resolution of the activation patterns (measured in millimeters); temporal resolution (measured in a few seconds); no known risk factors in healthy subjects; 10 and, recently near-real-time analysis. Its key disadvantages include its relatively high cost; problems with data interpretation if the subject moves a few millimeters; a user-unfriendly scanning environment (noisy, small enclosed space); and the requirement for large super-conducting magnets.

Recent advances in neuroimaging technology, including high-field (3 tesla) and ultrahigh-field (7 tesla) magnetic resonance techniques for MRI, fMRI and MRS, real-time acquisition/processing, and parallel imaging, offer the potential for significant advances in spatial and temporal resolution for structural, functional and neurochemical imaging. In other words, MRI-based imaging technologies are providing faster and more detailed pictures of the human brain and brain function than ever before. These and other related technologies are moving forward rapidly, driven by clinical and research demand, and over the next two decades there are likely to be continuing significant advances in this technology, with unique applications certain to emerge (Dickerson, 2007; Ladd, 2007; Nakada, 2007).

As shown in Table 2-3 , a concerted effort at the national level in China to invest in research relating to high-field structural and functional fMRI has led to a network of coordinated laboratories and programs (Cao et al., 2006; Poo and Guo, 2007; Simon, 2007). 11

Current Neuroimaging Research in the People’s Republic of China.

Magnetic Resonance Spectroscopy

Magnetic resonance spectroscopy (MRS) provides a noninvasive window into brain chemistry. Research clinical applications include the ability to differentiate pathology (e.g., brain neoplasm) from normal or necrotic tissue (Moore, 1998); monitoring brain metabolism (glutamate, glucose levels); and monitoring neuropharmacologic treatment effects (neurotropic/neuroprotective medicines or psychoactive compounds) (Manji et al., 1999; Moore et al., 2000). While spatial resolution is on the order of 1 cc, this methodology has the potential to monitor neurochemical modulation in response to neural processes or neuropharmacologic intervention. Multiple studies have demonstrated the ability of MRS to detect biomarkers of complex neural processes, and rapid (<30 sec) neurochemical imaging becomes possible with high-field magnetic resonance technology (Phan et al., 2005c). Advances in MRS should be followed carefully because its complementarity with respect to the other functional neuroimaging technologies, particularly in the area of monitoring neuropharmacologic response, is likely to make it applicable for defense purposes. For example, as the ability to monitor neurochemistry in vivo and in near real time is developed with advanced high-field MRS and related technologies, the possibility arises of developing state-dependent neurochemical biomarkers for stress and anxiety as well as their pharmacologic modulation in a dose response fashion.

Magnetoelectroencephalography

Magnetoencephalography (MEG) is a completely noninvasive, nonhazardous technology for functional brain mapping, localizing and characterizing the electrical activity of the CNS by measuring the associated magnetic fields emanating from the brain. Every electrical current generates a magnetic field. However, unlike an electrical signal, magnetic fields are not distorted by traveling through the skull, and the source of the summated magnetic fields can be triangulated within a few millimeters. MEG provides functional mapping information on the working brain.

Modern MEG scanners use as many as 300 superconducting quantum interference device (SQUID) 12 detectors, allowing very fast acquisition and extremely high localization of the source of the electromagnetic signal. The information provided by MEG is entirely different from but complementary to the information provided by structural imaging techniques like CT or MR imaging. While MRI and CT provide excellent anatomical images, MEG measures correlates of neurological function. The advantages of MEG over fMRI and PET include the measurement of brain activity with higher temporal and spatial resolution. Its disadvantages include its greater cost than fMRI. It also requires a specialized technical team with broad expertise in the acquisition and processing of complex data and requires very precise positioning requirements.

Transcranial Ultrasonography

While transcranial ultrasonography operates on the same principle as the diagnostic ultrasound imaging of a fetus in utero, it is more difficult to obtain high-quality images of the brain because the propagation of sound waves is impaired by bone. However, the skull is thin enough in a few “monographic windows” (Duscheck and Schandry, 2003) to provide a path for the ultrasonic signal and can provide accurate real-time measurements of blood flow velocity. The transorbital window, located above the zygomatic arch (the “temple”), is used to image the posterior, anterior, and medial cerebral arteries along with a few of the branches that provide blood flow to specific areas of the brain.

Although both rely on blood flow, sonography is very different from fMRI, which measures blood oxygenation level changes with a spatial resolution of a couple of millimeters. In functional transcranial doppler sonography (fTDS), the spatial resolution is determined by the volume of the brain supplied with blood by the vessel under study. These areas can be quite large, making the spatial resolution of fTDS extremely limited. Changes in blood velocity, which are presumed to directly measure changes in resistance of the artery (i.e., the lumen diameter change), occur nearly instantaneously in an event- related experimental paradigm, giving exceptional temporal resolution.

There remain several technical problems with fTDS. Only a limited number of large arteries can be imaged. Even in the arteries that are large enough and located within sight of the few available ultrasonic windows, the angle of the ultrasonic beam can make it very difficult to accurately measure blood flow changes. However, fTDS has several advantages over other functional neuroimaging techniques including cost effectiveness; portability; continuous monitoring of blood flow activity; and excellent temporal resolution.

Functional Near-Infrared Spectroscopy

Functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging technology with several characteristics that make it a good candidate for use in military and intelligence applications. fNIRS uses light in the near infrared (700-900 nm), outside the visible spectrum, to measure changes in brain tissue that are associated with neuronal activity—in other words, it provides accurate spatial information about ongoing brain activity. Although fNIRS can measure several parameters associated with neural activity, the most common is the change in the ratio of oxygenated to deoxygenated hemoglobin in the blood, a measure analogous to fMRI’s BOLD signal. Data from Huppert et al. (2006) demonstrate the relationship between the fMRI BOLD signal and fNIRS measures of deoxyhemoglobin (HbR), oxyhemoglobin (HbO), and total hemoglobin (HbT) ( Figure 2-3 ). The design used a short-duration, event-related motor task, finger tapping, during the simultaneous recording of fMRI and fNIRS in five subjects. The results of Huppert et al. indicate that the fMRI-measured BOLD response is more highly correlated with the fNIRS measurement of deoxyhemoglobin than with the fNIRS measurement of oxyhemoglobin or total hemoglobin. This result was predicted from the theoretical basis of the BOLD response (as the BOLD response is based on changes in the concentration of HbR) and from previous publications (Toronov et al., 2001).

Averaged hemodynamic response function for simultaneously recorded fNIRS and BOLD responses during finger tapping. Maximum change was normalized to unity, and the HbR response was inverted. The error bars on plot (A) represent the standard error of each (more...)

Using a more complex cognitive paradigm, target categorization, Bunce et al. (2006) replicated the fMRI protocol of McCarthy et al. (1997) using fNIRS. The results location and time course of McCarthy et al. are displayed in Figure 2-4 , Row A. The fNIRS results were quite similar to the fMRI results reported by McCarthy et al. (1997).

Replication of the fMRI protocol of McCarthy et al. (1997) using fNIRS. SOURCE: (A) McCarthy et al. (1997). ©1997 by the American Physiological Society; reprinted with permission. (B) (Image) Digital Anatomist images are from the Neuro-anatomy (more...)

fNIRS has also been reported to measure changes in the optical properties of the cell membranes themselves that occur when a neuron fires (Gratton et al., 1995), referred to as an event-related optical signal (EROS). Although the signal-to-noise ratio is low in current technological incarnations, this latter measure is particularly interesting as it represents the holy grail of neuroimaging; high spatial resolution coupled to high temporal resolution. (Please see Appendix D for a more thorough discussion of fNIRS technology.)

Of importance in military applications, fNIRS is safe, noninvasive, and highly portable, even wireless. Subjects are able to sit upright, work on computers or perform other tasks, even walk on treadmills (Izzetoglu et al., 2004). With near-zero run-time costs, fNIRS is also inexpensive. Although current systems cost between $25,000 and $300,000, they are still largely bench-made. Continuous-wave systems could be manufactured for a few thousand dollars, and probably for less in volume, especially as the cost of manufacturing lasers and light-emitting diodes continues to fall (see Appendix D for further information on system types). Extant systems operate from a laptop computer and a 2 × 4 × 6 inch control box. Technological advances currently under development include having the entire system on a digital signal processing chip operating from a laptop computer and linked to a wireless sensor. These properties of fNIRS make neuroimaging possible where other neuroimaging technologies are impractical or impossible. Preliminary studies have been conducted with fNIRS in the backpacks of warfighters walking through virtual reality programs. Other advances currently under investigation include closed-loop human brain–computer interfaces and implantable optodes. Implantable optodes could allow realizing the holy grail of neuroimaging, the direct, noninvasive measurement of neuronal activity with millisecond-level time resolution and superior spatial resolution.

fNIRS measures relative changes in HbO and HbR, and total blood flow can be calculated from the differential equation. There are no approved clinical neuroimaging uses for this technique, but there are several advantages for experi 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 technology 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 moderate 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 cm 3 ). 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 technologies, 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 neuroimaging modalities to investigate the neural circuitry associated with deception. 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 published 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 neuroimaging 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 imagine 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 community 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; language; imaging processing for measuring workload performance; and the differences between Western and non-Western cultures.

• 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 Cognitive Neuroscience 19(2):287-295. [ PubMed : 17280517 ]
• 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. [ PubMed : 15858160 ]
• 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. [ PubMed : 7830242 ]
• Altoff, R.R., and Cohen, N.J. 1999. Eye-movement-based memory effect: Reprocessing effect in face perception . Journal of Experimental Psychology: Learning, Memory and Cognition 25: 997-1010. [ PubMed : 10439505 ]
• Beduneau, A., P. Saulnier, N. Anton, F. Hindre, C. Passirani, H. Rajerison, N. Noiret, and J.P. Benoit. 2006. Pegylated nanocapsules produced by an organic solvent-free method: Evaluation of their stealth properties . Pharmaceutical Research 23(9):2190-2199. [ PubMed : 16952009 ]
• Beecher, H.K. 1955. The powerful placebo . Journal of the American Medical Association 159:1602-1606. [ PubMed : 13271123 ]
• Bentivoglio, Marina, and Krister Kristensson. 2007. Neural–immune interactions in disorders of sleep-wakefulness organization . Trends in Neurosciences 30(12):645-652. [ PubMed : 17988749 ]
• Berger, H. 1929. Uber das elektrenkephalogramm des menschen . Archiv fur Psychiatrie und Nervenkrankheiten 87(1):527-580.
• Blascovich, J. 2000. Psychophysiological methods . In Handbook of Research Methods in Social and Personality Psychology , H.T. Reis, editor; and C.M. Judd, editor. , eds. Cambridge, UK: Cambridge University Press.
• Blascovich, J., and J. Tomaka. 1996. The biopsychosocial model of arousal regulation . In Advances in Experimental Social Psychology , M. Zanna, editor. , ed. New York, NY: Academic Press.
• Blascovich, J., and M. Seery. 2007. Visceral and somatic indexes of social psychological constructs: History, principles, propositions, and case studies . In Social Psychology: Handbook of Basic Principles , A.W. Kruglanski, editor; and E.T. Higgins, editor. , eds. New York, NY: Guilford.
• Born, Jan, Tanja Lange, Werner Kern, Gerard P. McGregor, Ulrich Bickel, and Horst L. Fehm. 2002. Sniffing neuropeptides: A transnasal approach to the human brain . Nature Neuroscience 5(6):514-516. [ PubMed : 11992114 ]
• Bosi, Susanna, Tatiana Da Ros, Sabrina Castellano, Elena Banfi, and Maurizio Prato. 2000. Antimycobacterial activity of ionic fullerene derivatives . Bioorganic and Medicinal Chemistry Letters 10(10):1043-1045. [ PubMed : 10843212 ]
• Brazier, Mary A.B. 1949. The electrical fields at the surface of the head during sleep . Electroencepha lography and Clinical Neurophysiology 1(2; May):195-204. [ PubMed : 18144877 ]
• Brightman, M.W., and T.S. Reese. 1969. Junctions between intimately apposed cell membranes in the vertebrate brain . Journal of Cell Biology 40(3):648-677. [ PMC free article : PMC2107650 ] [ PubMed : 5765759 ]
• Bunce, Scott C., Ajit Devaraj, Meltem Izzetoglu, Banu Onaral, and Kambiz Pourrezaei. 2005. Detecting deception in the brain: A functional near-infrared spectroscopy study of neural correlates of intentional deception . Proceedings of SPIE 5769:24-32.
• Bunce, Scott C., Meltem Izzetoglu, Kurtulus Izzetoglu, Banu Onaral, and Kambiz Pourrezaei. 2006. Functional near-infrared spectroscopy: An emerging neuroimaging modality . IEEE Engineering in Medicine and Biology Magazine 25(4):54-62. [ PubMed : 16898659 ]
• Cabeza, R., and L. Nyberg. 2000. Imaging cognition II: An empirical review of 275 PET and fMRI studies . Journal of Cognitive Neuroscience 12(1):1-47. [ PubMed : 10769304 ]
• Cacioppo, J.T., and L.G. Tassinary. 1990. a. Principles of Psychophysiology: Physical, Social, and Inferential Elements . New York: Cambridge University Press.
• Cacioppo, J.T., and L.G. Tassinary. 1990. b. Inferring psychological significance from physiological signals . American Psychologist 45(1):16-28. [ PubMed : 2297166 ]
• Cacioppo, J.T., R.E. Petty, M.E. Losch, and H.S. Kim. 1986. Electromyographic activity over facial muscle regions can differentiate the valence and intensity of affective reactions . Journal of Personality and Social Psychology 50(2):260-268. [ PubMed : 3701577 ]
• Cade, John F. 1949. Lithium salts in the treatment of psychotic excitement . Medical Journal of Australia 2:349-352. [ PubMed : 18142718 ]
• Cao, Cong, Richard P. Suttmeier, and Denis Fred Simon. 2006. China’s 15-year science and technology plan . Physics Today 59(12):38-43.
• Carter, C. Sue. 1998. Neuroendocrine perspectives on social attachment and love . Psychoneuro endocrinology 23(8):779-818. [ PubMed : 9924738 ]
• Carter, C. Sue, Margaret Altemus, and George P. Chrousos. 2001. Neuroendocrine and emotional changes in the post-partum period . Progress in Brain Research 133:241-249. [ PubMed : 11589134 ]
• Chen, Robert J., Yuegang Zhang, Dunwei Wang, and Hongjie Dai. 2001. Noncovalent sidewall functionalization of single-walled carbon nanotubes for protein immobilization . Journal of the American Chemical Society 123(16):3838-3839. [ PubMed : 11457124 ]
• Coyle, D., T.M. McGinnity, and G. Prasad. 2007. Identifying local ultrametricity of EEG time series for feature extraction in a brain-computer interface . Pp. 701-704 in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society . [ PubMed : 18002053 ]
• Davatzikos, C., K. Ruparel, Y. Fan, D.G. Shen, M. Acharyya, J.W. Loughead, R.C. Gur, and D.D. Langleben. 2005. Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection . NeuroImage 28(3):663-668. [ PubMed : 16169252 ]
• Davis, P.A. 1939. Effects of acoustic stimuli on the waking human brain . Journal of Neurophysiology 2:494-499.
• Dews, P.B., and G.R. Wenger. 1977. Rate-dependency of the behavioral effects of amphetamine . In Advances in Behavioral Pharmacology , Vol. 1,T. Thompson, editor; and P.B. Dews, editor. , eds. New York, NY: Academic Press.
• Dickerson, B.C. 2007. Advances in functional magnetic resonance imaging: technology and clinical applications . Neurotherapeutics 4(3):360-70. [ PMC free article : PMC7479713 ] [ PubMed : 17599702 ]
• Dickerson, Sally S. and Margaret E. Kemeny. 2004. Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research . Psychological Bulletin 130(3):355-391. [ PubMed : 15122924 ]
• Dunning, Mark D., Andras Lakatos, Louiza Loizou, Mikko Kettunen, Charles French-Constant, Kevin M. Brindle, and Robin J.M. Franklin. 2004. Superparamagnetic iron oxide-labeled Schwann cells and olfactory ensheathing cells can be traced in vivo by magnetic resonance imaging and retain functional properties after transplantation into the CNS . Journal of Neuroscience 24(44):9799-9810. [ PMC free article : PMC6730225 ] [ PubMed : 15525765 ]
• Duscheck, Stephen, and Rainer Schandry. 2003. Functional transcranial Doppler sonography as a tool in psychophysiological research . Psychophysiology 40(3):436-54. [ PubMed : 12946117 ]
• Ekman, Paul, and Maureen O’Sullivan. 2006. From flawed self-assessment to blatant whoppers: The utility of voluntary and involuntary behavior in detecting deception . Behavioral Sciences and the Law 24(5):673-686. [ PubMed : 17016820 ]
• El Kaliouby, R., and P. Robinson. 2005. The Emotional Hearing Aid: An assistive tool for children with Asperger syndrome . Universal Access in the Information Society 4(2):121-134.
• Ellis-Behnke, R.G., L.A. Teather, G.E. Schneider, and K.F. So. 2007. Using nanotechnology to design potential therapies for CNS regeneration . Current Pharmaceutical Design 13(24):2519-2528. [ PubMed : 17692020 ]
• Emerich, Dwaine F., and Christopher G. Thanos. 2006. The pinpoint promise of nanoparticle-based drug delivery and molecular diagnosis . Biomolecular Engineering 23(4):171-184. [ PubMed : 16843058 ]
• Estes, William. 2002. Traps in the route to models of memory and decision . Psychonomic Bulletin and Review 9(1):3-25. [ PubMed : 12026952 ]
• Fender, D.H. 1987. Source localization of brain electrical activity . Pp. 355-403 in Methods of Analy sis of Brain Electrical and Magnetic Signals , A.S. Gevins, editor; and A. Remond, editor. , eds. Amsterdam: Elsevier.
• Feynman, Richard P. 1959. “There is plenty of room at the bottom,” Lecture given at Annual Meeting of the American Physical Society, December 29, 1959, at California Institute of Technology, Pasadena, CA.
• Floresco, Stan B., Mark A. Geyer, Lisa H. Gold, and Anthony A. Grace. 2005. Developing predictive animal models and establishing a preclinical trials network for assessing treatment effects on cognition in schizophrenia . Schizophrenia Bulletin 31(4):888-894. [ PubMed : 16079387 ]
• Foer, Joshua. 2007. Remember this . National Geographic 212(5):32.
• Fox, Daniel J., David F. Tharp, and Lydia C. Fox. 2005. Neurofeedback: An alternative and efficacious treatment for attention deficit hyperactivity disorder . Applied Psychophysiology and Biofeedback 30(4):365-373. [ PubMed : 16385424 ]
• Frank, Mark G., and Paul Ekman. 2004. Appearing truthful generalizes across different deception situations . Journal of Personality and Social Psychology 86(3):486-495. [ PubMed : 15008651 ]
• Freitas, Robert A., Jr. 1999. Nanomedicine, Vol. 1: Basic Capabilities . Austin, TX: Landes Bioscience.
• Gamer, M., T. Bauermann, P. Stoeter, and G. Vossel. 2007. Covariations among fMRI , skin conductance , and behavioral data during processing of concealed information . Human Brain Mapping 28(12):1287-1301. [ PMC free article : PMC6871443 ] [ PubMed : 17290371 ]
• Ganis, G., S.M. Kosslyn, S. Stose, W.L. Thompson, and D.A. Yurgelun-Todd. 2003. Neural correlates of different types of deception: An fMRI investigation . Cerebral Cortex 13(8):830-836. [ PubMed : 12853369 ]
• Gao, Xiaoling, Jun Chen, Weixing Tao, Jianhua Zhu, Qizhi Zhang, Hongzhuan Chen, and Xinguo Jiang. 2007. a. UEA I-bearing nanoparticles for brain delivery following intranasal administration . International Journal of Pharmaceutics 340(1-2):207-215. [ PubMed : 17499948 ]
• Gao, Xiaoling, Bingxian Wu, Qizhi Zhang, Jun Chen, Jianhua Zhu, Weiwei Zhang, Zhengxin Rong, Hongzhuan Chen, and Xinguo Jianga. 2007. b. Brain delivery of vasoactive intestinal peptide enhanced with the nanoparticles conjugated with wheat germ agglutinin following intranasal administration . Journal of Controlled Release 121(3):156-167. [ PubMed : 17628165 ]
• Golden, Pamela L., and Gary M. Pollack. 2003. Blood-brain barrier efflux transport . Journal of Pharmaceutical Sciences 92(9):1739-1753. [ PubMed : 12949994 ]
• Goldstein, Alex S., John K. Amory, Stephanie M. Martin, Chris Vernon, Alvin Matsumoto, and Paul Yager. 2001. Testosterone delivery using glutamide-based complex high axial ratio microstructures . Bioorganic and Medicinal Chemistry Letters 9(11):2819-2825. [ PubMed : 11597462 ]
• Gourley, Paul L. 2005. Brief overview of BioMicroNano technologies . Biotechnology Progress 21(1):2-10. [ PubMed : 15903234 ]
• Grayson, Amy C.R., Insung S. Choi, Betty M. Tyler, Paul P. Wang, Henry Brem, Michael J. Cima, and Robert Langer. 2003. Multi-pulse drug delivery from a resorbable polymeric microchip device . Nature Materials 2(11):767-772. [ PubMed : 14619935 ]
• Green, D.M., and J.A. Swets. 1966. Signal Detection Theory and Psychophysics . New York, NY: Wiley.
• Guastella, Adam J., Phillip B. Mitchell, and Mark R. Dadds. 2008. Oxytocin increases gaze to the eye region of human faces . Biological Psychiatry 63(1):3-5. [ PubMed : 17888410 ]
• Guertin,W.H., and P.L. Wilhelm. 1954. A statistical analysis of the electrodermal response employed in lie detection . Journal of General Psychology 51:153-160.
• Gururangan, S., and H. Friedman. 2002. Innovations in design and delivery of chemotherapy for brain tumors . Neuroimaging Clinics of North America 12(4):583-597. [ PubMed : 12687913 ]
• Hagan, J.J., and D.N. Jones. 2005. Predicting drug efficacy for cognitive deficits in schizophrenia . Schizophrenia Bulletin 31(4):830-853. [ PubMed : 16177277 ]
• Haida, M., Y. Shinohara, Y. Ito, T. Yamamoto, F. Kawaguchi, and H. Koizumi. 2000. Brain function of an ALS patient in complete locked-in state by using optical topography . Pp. 95–97 in The Frontier of Mind-Brain Science and Its Practical Applications (II) , H. Koizumi, editor. , ed. Tokyo: Hitachi Ltd.
• Hamaguchi, T., Y. Matsumura, M. Suzuki, K. Shimizu, R. Goda, I. Nakamura, I. Nakatomi, M. Yokoyama, K. Kataoka, and T. Kakizoe. 2005. NK105, a paclitaxel-incorporating micellar nanoparticle formulation, can extend in vivo antitumour activity and reduce the neurotoxicity of paclitaxel . British Journal of Cancer 92(7):1240-1246. [ PMC free article : PMC2361981 ] [ PubMed : 15785749 ]
• Hamilton, Alice. 1915. Lead poisoning in the manufacture of storage batteries . P. 27 in Bulletin of the United States Bureau of Labor Statistics , No. 165. Washington, D.C.: Government Printing Office.
• Hammoud, D.A., J.M. Hoffman, and M.G. Pomper. 2007. Molecular neuroimaging: From conventional to emerging techniques . Radiology 245(1):21-42. [ PubMed : 17885179 ]
• Heinrichs, Markus, Inga Neumann, and Ulrike Ehlert. 2002. Lactation and stress: Protective effects of breast-feeding in humans . Stress 5(3):195-203. [ PubMed : 12186682 ]
• Heinrichs, Markus, Thomas Baumgartner, Clemens Kirschbaum, and Ulrike Ehlert. 2003. Social support and oxytocin interact to suppress cortisol and subjective responses to psychosocial stress . Biological Psychiatry 54(12):1389-1398. [ PubMed : 14675803 ]
• Heinrichs, Markus, Gunther Meinlschmidt, Werner Wippich, Ulrike Ehlert, and Dirk H. Hellhammer. 2004. Selective amnesic effects of oxytocin on human memory . Physiology and Behavior 83(1):31-38. [ PubMed : 15501488 ]
• Hillyer, Julián F., and Ralph M. Albrecht. 2001. Gastrointestinal persorption and tissue distribution of differently sized colloidal gold nanoparticles . Journal of Pharmaceutical Sciences 90(12):1927-1936. [ PubMed : 11745751 ]
• Hirsch, Lee R., Naomi J. Halas, and Jennifer L. West. 2005. Whole-blood immunoassay facilitated by gold nanoshell-conjugate antibodies . Methods in Molecular Biology 303 (April):101-111. [ PubMed : 15923678 ]
• Huber, Daniel, Pierre Veinante, and Ron Stoop. 2005. Vasopressin and oxytocin excite distinct neuronal populations in the central amygdala . Science 308(5719):245-248. [ PubMed : 15821089 ]
• Huppert, T.J., R.D. Hoge, S.G. Diamond, M.A. Franceschini, and D.A. Boas. 2006. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans . NeuroImage 29(2):368-382. [ PMC free article : PMC2692693 ] [ PubMed : 16303317 ]
• Hussain, N., V. Jaitley, and A.T. Florence. 2001. Recent advances in the understanding of uptake of microparticulates across the gastrointestinal lymphatics . Advanced Drug Delivery Reviews 50(1):107-142. [ PubMed : 11489336 ]
• Iacono, W.G. 2000. The detection of deception . Handbook of Psychophysiology , 2nd Ed., J.T. Cacioppo, editor; , L.G. Tassinary, editor; , and G.G. Berntson, editor. , eds. New York, NY: Cambridge University Press.
• Illum, Lisbeth. 2000. Transport of drugs from the nasal cavity to the central nervous system . European Journal of Pharmaceutical Sciences 11(1):1-18. [ PubMed : 10913748 ]
• Insel, Thomas R., and Larry J. Young. 2001. The neurobiology of attachment . Nature Reviews Neuro science 2(2):129-136. [ PubMed : 11252992 ]
• International Labour Office. 1934. Accumulators (storage batteries) . P. 29 in Occupation and Health: An Encyclopaedia of Hygiene, Pathology and Social Welfare , Vol. 1. Geneva, Switzerland: International Labour Office.
• Izzetoglu, K., S. Bunce, B. Onaral, K. Pourrezaei, and B. Chance. 2004. Functional optical brain imaging using NIR during cognitive tasks . International Journal of Human-Computer Interaction 17(2):211-227.
• Jain, K.K. 2007. Nanobiotechnology-based drug delivery to the central nervous system . Neuro degenerative Diseases 4(4):287-291. [ PubMed : 17627131 ]
• James, William. 1890. Principles of Psychology . Cambridge, MA: Harvard University Press.
• Jarvik, Murray. 1970. Drugs used in the treatment of psychiatric disorders . The Pharmacological Basis of Therapeutics , L. Goodman, editor; and H. Gilman, editor. , eds. New York, NY: Macmillian.
• Kleiman, M. 1992. Against Excess: Drug Policy for Results . New York, NY: Basic Books.
• Koo, Yong-Eun E., Ramachandra G. Reddy, Mahaveer Bhojani, Randy Schneider, Martin A. Philbert, Alnawaz Rehemtulla, Brian D. Ross, and Raoul Kopelman. 2006. Brain cancer diagnosis and therapy with nanoplatforms . Advanced Drug Delivery Reviews 58(14):1556-1577. [ PubMed : 17107738 ]
• Kosfeld, Michael, Markus Heinrichs, Paul J. Zak, Urs Fischbacher, and Ernst Fehr. 2005. Oxytocin increases trust in humans . Nature 435(7042):673-676. [ PubMed : 15931222 ]
• Koushik, K., D.S. Dhanda, N.P.S. Cheruvu, and U.B. Kompella. 2004. Pulmonary delivery of deslorelin: Large-porous PLGA particles and HPbetaCD complexes . Pharmaceutical Research 21(7):1119-1126. [ PubMed : 15290850 ]
• Kozak, Rouba, John P. Bruno, and Martin Sarter. 2006. Augmented prefrontal acetylcholine release during challenged attentional performance . Cerebral Cortex 16(1):9-17. [ PubMed : 15788700 ]
• Kozel, F.A., T.M. Padgett, and M.S. George. 2004. a. A replication study of the neural correlates of deception . Behavioral Neuroscience 118(4):852-856. [ PubMed : 15301611 ]
• Kozel, F.A., Letty J. Revell, Jeffrey P. Lorberbaum, Ananda Shastri, Jon D. Elhai, Michael David Horner, Adam Smith, Ziad Nahas, Daryl E. Bohning, and Mark S. George. 2004. b. A pilot study of functional magnetic resonance imaging brain correlates of deception in healthy young men . Journal of Neuropsychiatry and Clinical Neurosciences 16(3):295-305. [ PubMed : 15377736 ]
• Kozel, F.A., K.A. Johnson, Q. Mu, E.L. Grenesko, S.J. Laken, and M.S. George. 2005. Detecting deception using functional magnetic resonance imaging . Biological Psychiatry 58(8):605-613. [ PubMed : 16185668 ]
• Ladd, M.E. 2007. High-field-strength magnetic resonance: Potential and limits . Topics in Magnetic Resonance Imaging 18(2):139-152. [ PubMed : 17621228 ]
• Landgraf, Rainer, and Inga D. Neumann. 2004. Vasopressin and oxytocin release within the brain: A dynamic concept of multiple and variable modes of neuropeptide communication . Frontiers in Neuroendocrinology 25(3-4):150-176. [ PubMed : 15589267 ]
• Langleben, D.D., L. Schroeder, J.A. Maldjian, R.C. Gur, S. McDonald, J.D. Raglanda, C.P. O’Brien, and A.R. Childress. 2002. Brain activity during simulated deception: An event-related functional magnetic resonance study . Neuroimage 15(3):727-732. [ PubMed : 11848716 ]
• Langleben, D.D., James W. Loughead, Warren B. Bilker, Kosha Rupare, Anna Rose Childress, Samantha I. Busch, and Ruben C. Gur. 2005. Telling truth from lie in individual subjects with fast event-related fMRI . Human Brain Mapping 26(4):262-272. [ PMC free article : PMC6871667 ] [ PubMed : 16161128 ]
• Langston, J. William. 1995. The Case of the Frozen Addict . New York, NY: Pantheon Press.
• Lasagna L., F. Mosteller, J.M. von Felsinger, and H.K. Beecher. 1954. A study of the placebo response . American Journal of Medicine 16:770-779. [ PubMed : 13158365 ]
• Lavie, N. 2005. Distracted and confused? Selective attention under load . Trends in Cognitive Sciences 9(2):75-82. [ PubMed : 15668100 ]
• Lee, Tatia M.C., Ho-Ling Liu, Li-Hai Tan, Chetwyn C.H. Chan, Srikanth Mahankali, Ching-Mei Feng, Jinwen Hou, Peter T. Fox, and Jia-Hong Gao. 2002. Lie detection by functional magnetic resonance imaging . Human Brain Mapping 15(3):157-164. [ PMC free article : PMC6872015 ] [ PubMed : 11835606 ]
• Lee, Tatia M.C., Wen-hua Zhou, Xiao-jing Luo, Kenneth S.L. Yuen, Xin-zhong Ruan, and Xu-chu Weng. 2005. Neural activity associated with cognitive regulation in heroin users: A fMRI study . Neuroscience Letters 382(3):211-216. [ PubMed : 15925092 ]
• Loo, C., L. Hirsch, M.H. Lee, E. Chang, J. West, N. Halas, and R. Drezek. 2005. Gold nanoshell bioconjugates for molecular imaging in living cells . Optics Letters 30(9):1012-1014. [ PubMed : 15906987 ]
• Maloney, John M., Scott A. Uhland, Benjamin F. Polito, Norman F. Sheppard, Christina M. Pelta, and John T. Santini. 2005. Electrothermally activated microchips for implantable drug delivery and biosensing . Journal of Controlled Release 109(1-3):244-255. [ PubMed : 16278032 ]
• Manji, H.K., J.M. Bebchuk, G.J. Moore, D. Glitz, K. Hasanat, G. Chen. 1999. Modulation of CNS signal transduction pathways and gene expression by mood stabilizing agents: Therapeutic implications . Journal of Clinical Psychiatry 60(S2):27-39. [ PubMed : 10073385 ]
• Marchak, Frank M. 2006. Eye movement-based assessment of concealed knowledge . Journal of Credibility Assessment and Witness Psychology 7(2):148-163.
• Martin, Charles R., and Punit Kohli. 2003. The emerging field of nanotube biotechnology . Nature Reviews Drug Discovery 2(1):29-37. [ PubMed : 12509757 ]
• McCarthy, Gregory, Marie Luby, John Gore, and Patricia Goldman-Rakic. 1997. Infrequent events transiently activate human prefrontal and parietal cortex as measured by functional MRI . Journal of Neurophysiology 77(3):1630-1634. [ PubMed : 9084626 ]
• Merkus, Frans W.H.M., and Mascha P. van den Berg. 2007. Can nasal drug delivery bypass the blood-brain barrier?: Questioning the direct transport theory . Drugs in R & D 8(3):133-144. [ PubMed : 17472409 ]
• Michel, Christopher M., Micah M. Murray, Göran Lantz, Sara Gonzalez, Laurent Spinelli, and Rolando Grave de Peralta. 2004. EEG source imaging . Clinical Neurophysiology 115:2195-2222. [ PubMed : 15351361 ]
• Miller, M.B., A. Kingstone, and M.S. Gazzaniga. 2002. a. Hemispheric encoding asymmetries are more apparent than real . Journal of Cognitive Neuroscience 14(5):702-708. [ PubMed : 12167255 ]
• Miller, M.B., J. Van Horn, G.L. Wolford, T.C. Handy, M. Valsangkar-Smyth, S. Inati, S. Grafton, and M.S. Gazzaniga. 2002. b. Extensive individual differences in brain activations during episodic retrieval are reliable over time . Journal of Cognitive Neuroscience 14(8):1200-1214. [ PubMed : 12495526 ]
• Miyata, N., and T. Yamakoshi. 1997. Fullerenes: Recent advances in the chemistry and physics of fullerenes and related materials . P. 345 in Proceedings of the Symposium on Recent Advances in the Chemistry and Physics of Fullerenes and Related Materials , Vol. 5,R.S. Ruoff, editor; and K.M. Kadish, editor. , eds. Pennington, NJ: Electrochemical Society.
• Mohamed, Feroze B., Scott H. Faro, Nathan J. Gordon, Steven M. Platek, Harris Ahmad, and J. Michael Williams. 2006. Brain mapping of deception and truth telling about an ecologically valid situation: Functional MR imaging and polygraph investigation—initial experience . Radiology 238(2):679-688. [ PubMed : 16436822 ]
• Moore, Gregory J. 1998. Proton magnetic resonance spectroscopy in pediatric neuroradiology . Pediatric Radiology 28(11):805-814. [ PubMed : 9799306 ]
• Moore, Gregory J., J.M. Bebchuk, K. Hasanat, M.W. Faulk, N. Seraji-Bozorgzad, I.B. Wilds, C.L. Arfken, G. Chen, and H.K. Manji. 2000. Lithium increases N-acetyl-aspartate in the human brain: In vivo evidence in support of bcl-2’s neurotrophic effects? Biological Psychiatry 48(1):1-8. [ PubMed : 10913502 ]
• Musto, D. 1987. The American Disease: Origins of Narcotic Control . 2nd ed. New York, NY: Oxford University Press.
• Nakada, T. 2007. Clinical application of high and ultra high-field MRI . Brain and Development 29(6):325-335. [ PubMed : 17113259 ]
• Nishino, S. 2007. a. Narcolepsy: Pathophysiology and pharmacology . The Journal of Clinical Psychiatry 68(Suppl. 13):9-15. [ PubMed : 18078360 ]
• Nishino, S. 2007. b. The hypocretin/orexin receptor: Therapeutic prospective in sleep disorders . Expert Opinion on Investigational Drugs 16(11):1785-1797. [ PubMed : 17970638 ]
• Nitsche, Michael A., Christian Lampe, Andrea Antal, David Liebetanz, Nicolas Lang, Frithjof Tergau, and Walter Paulus. 2006. Dopaminergic modulation of long-lasting direct current-induced cortical excitability changes in the human motor cortex . European Journal of Neuroscience 23(6):1651-1657. [ PubMed : 16553629 ]
• NRC (National Research Council). 2003. The Polygraph and Lie Detection . Washington, DC: The National Academies Press. Available from http://www ​.nap.edu/catalog ​.php?record_id=10420 .
• Nuñez, Jennifer Maria, B.J. Casey, Tobias Egner, Todd Hare, and Joy Hirsch. 2005. Intentional false responding shares neural substrates with response conflict and cognitive control . NeuroImage 25(1):267-277. [ PubMed : 15734361 ]
• Olshansky, Brian. 2007. Placebo and nocebo in cardiovascular health. Implications for healthcare, research, and the doctor-patient relationship . Journal of the American College of Cardiology 49:415-421. Available from http://content ​.onlinejacc ​.org/cgi/reprint/49/4/415 . Last accessed June 18, 2008. [ PubMed : 17258086 ]
• OTA (Office of Technology Assessment of the U.S. Congress). 1983. Scientific Validity of Polygraph Testing: A Research Review and Evaluation . OTA-TM-H-15. Washington, DC: U.S. Congress.
• OTA. 1990. The Use of Integrity Tests for Pre-employment Screening . OTA-SET-442. Washington, DC: U.S. Congress.
• Panyam, Jayanth, and Vinod Labhasetwar. 2003. Biodegradable nanoparticles for drug and gene delivery to cells and tissue . Advanced Drug Delivery Reviews 55(3):329-347. [ PubMed : 12628320 ]
• Pardridge, William M. 1999. Blood-brain barrier biology and methodology . Journal of NeuroVirology 5(6):556-569. [ PubMed : 10602397 ]
• Pardridge, William M. 2001. BBB-genomics: Creating new openings for brain-drug targeting . Drug Discovery Today 6(8):381-383. [ PubMed : 11301273 ]
• Pedersen, Cort A. 1997. Oxytocin control of maternal behavior: Regulation by sex steroids and offspring stimuli . Annals of the New York Academy of Sciences 807:126-145. [ PubMed : 9071347 ]
• Peters, David, Richard Genik, and Christopher Green. 2008. Neuroimaging in defense policy . Pp. 1-42 in Biotechnology and the Future of America’s Military: The “New” Biological Warfare . Washington, DC: National Defense University Press.
• Petrik, Vladimir, Vinothini Apok, Juliet Britton, B. Anthony Bell, and Marios Papadopoulos. 2006. Godfrey Hounsfield and the dawn of computed tomography . Neurosurgery 58(4):780-787. [ PubMed : 16575342 ]
• Phan, K. Luan, Daniel A. Fitzgerald, Kunxiu Gao, Gregory J. Moore, Manuel E. Tancer, and Stefan Posse. 2004. Real-time fMRI of cortico-limbic brain activity during emotional processing . NeuroReport 15(3):527-532. [ PubMed : 15094517 ]
• Phan, K. Luan, Alvaro Magalhaes, Timothy J. Ziemlewicz, Daniel A. Fitzgerald, Christopher Green, and Wilbur Smith. 2005. a. Neural correlates of telling lies: A functional magnetic resonance imaging study at 4 tesla . Academic Radiology 12(2):164-172. [ PubMed : 15721593 ]
• Phan, K. Luan, Daniel A. Fitzgerald, Pradeep J. Nathan, Gregory J. Moore, Thomas W. Uhde, and Manuel E. Tancer. 2005. b. Neural substrates for voluntary suppression of negative affect: A functional magnetic resonance imaging study . Biological Psychiatry 57(3):210-219. [ PubMed : 15691521 ]
• Phan, K. Luan, Daniel A. Fitzgerald, Bernadette M. Cortese, Navid Seraji-Bozorgzad, Manuel E Tancer, and Gregory J. Moore. 2005. c. Anterior cingulate neurochemistry in social anxiety disorder: H-MRS at 4 tesla . NeuroReport 16(2):183-186. [ PubMed : 15671874 ]
• Phelps, Michael E., Edward J. Hoffman, Nizar A. Mullani, and Michel M. Ter-Pogossian. 1975. Application of annihilation coincidence detection to transaxial reconstruction tomography . Journal of Nuclear Medicin e 16(3):210-224. [ PubMed : 1113170 ]
• Plonsey, R. 1963. Reciprocity applied to volume conductors and the ECG . IEEE Transactions on Bio-Medical Engineering 10(Jan.):9-12. [ PubMed : 14059530 ]
• Poo, Mu-ming, and Aike Guo. 2007. Some recent advances in basic neuroscience research in China . Philosophical Transactions of the Royal Society 362:10. [ PMC free article : PMC2435572 ] [ PubMed : 17329228 ]
• Porges, Stephen W. 2006. The polygraph: One machine, two world views . Journal of Credibility Assessment and Witness Psychology 7:47-73.
• Posse, Stefan, Daniel A. Fitzgerald, Kunxiu Gao, Ute Habel, David Rosenberg, Gregory J. Moore, and Frank Schneider. 2003. Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness . NeuroImage 18(3):760-768. [ PubMed : 12667853 ]
• Prescott, James H., Sara Lipka, Samuel Baldwin, Norman F. Sheppard, John M. Maloney, Jonathan Coppeta, Barry Yomtov, Mark A. Staples, and John T. Santini. 2006. Chronic, programmed polypeptide delivery from an implanted, multireservoir microchip device . Nature Biotechnology 24(4):437-438. [ PubMed : 16531991 ]
• Raichle, Marcus E. 2006. Neuroscience: The brain’s dark energy . Science 314(5830):1249-1250. [ PubMed : 17124311 ]
• Reese, T.S., and Morris J. Karnovsky. 1967. Fine structural localization of a blood-brain barrier to exogenous peroxidase . Journal of Cell Biology 34(1):207-217. [ PMC free article : PMC2107213 ] [ PubMed : 6033532 ]
• Robbins, T.W. 1998. Homology in behavioural pharmacology: An approach to animal models of human cognition . Behavioural Pharmacology 9:509-519. [ PubMed : 9862075 ]
• Robbins, T.W. 2005. Chemistry of the mind: Neurochemical modulation of prefrontal cortical function . Journal of Comparative Neurology 493(1):140-146. [ PubMed : 16254988 ]
• Rovner, Sophie L. 2007. Hold that thought . Chemical and Engineering News 85(36):13-20.
• Sahankian, Barbara, and Sharon Morein-Zamir. 2007. Professor’s little helper . Nature 450:1157-1159 and accompanying discussion. Available from http://www ​.nature.com ​/nature/journal/v450 ​/n7173/full/4501157a.html . Last accessed 18 June 2008. [ PubMed : 18097378 ]
• Santini, John T., Jr., Michael J. Cima, and Robert Langer. 1999. A controlled-release microchip . Nature 397(6717):335-338. [ PubMed : 9988626 ]
• Sarter, M. 2006. Preclinical research into cognition enhancers . Trends in Pharmacological Sciences 27(11):602-608. [ PubMed : 16997388 ]
• Sarter, M., and J Bruno. 1994. Cognitive functions of cortical ACh: Lessons from studies on trans-synaptic modulation of activated efflux . Trends in Neurosciences 17(6):217-221. [ PubMed : 7521080 ]
• Saul, Stephanie. 2007. Sleep drugs found only mildly effective, but wildly popular . The New York Times , October 23, 2007.
• Shapiro, D., and A. Crider. 1969. Psychophysiological approaches in social psychology . In Handbook of Social Psychology , Vol. III . (2nd Edition), G. Lindzey, editor; and E. Aronson, editor. , eds. Reading, MA: Addison-Wesley.
• Shaw, J.C., and M. Roth. 1955. Potential distribution analysis. I. A new technique for the analysis of electrophysiological phenomena . Electroencephalography and Clinical Neurophysiology 7(2; May):273-284. [ PubMed : 13241387 ]
• Silva, Gabriel A. 2006. Neuroscience nanotechnology: Progress, opportunities and challenges . Nature Reviews Neuroscience 7(1):65-74. [ PubMed : 16371951 ]
• Silva, Gabriel A. 2007. Nanotechnology approaches for drug and small molecule delivery across the blood brain barrier . Surgical Neurology 67(2):113-116. [ PubMed : 17254859 ]
• Society for Neuroscience. 2007. Annual Meeting Statistics . Society for Neuroscience; 2007. Available from http://www ​.sfn.org/index ​.cfm?pagename=annualMeeting ​_statistics&section ​=annualMeeting . Last accessed June 18, 2008.
• Spence, S.A., T.F. Farrow, A.E. Herford, I.D. Wilkinson, Y. Zheng, and, P.W. Woodruff. 2001. Behavioural and functional anatomical correlates of deception in humans . NeuroReport 12(13):2849-2853. [ PubMed : 11588589 ]
• Spence, S.A., M.D. Hunter, T.F. Farrow, R.D. Green, D.H. Leung, C.J. Hughes, and V. Ganesan. 2004. A cognitive neurobiological account of deception: Evidence from functional neuroimaging . Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences 359(1451):1755-1762. [ PMC free article : PMC1693447 ] [ PubMed : 15590616 ]
• Strand, Fleur L. 1999. Neuropeptides: Regulators of Physiological Processes . Cambridge, MA: MIT Press.
• Suri, Sarabjeet Singh, Hicham Fenniri, and Baljit Singh. 2007. Nanotechnology-based drug delivery systems . Journal of Occupational Medicine and Toxicology 2:16. [ PMC free article : PMC2222591 ] [ PubMed : 18053152 ]
• Swazey, Judith P. 1974. Chlorpromazine in Psychiatry: A Study of Therapeutic Innovation . Cambridge, MA: MIT Press.
• Tabata, Yasuhiko, Yoshiyuki Murakami, and Yoshito Ikada. 1997. a. Photodynamic effect of polyethylene glycol-modified fullerene on tumor . Japanese Journal of Cancer Research 88(11):1108-1116. [ PMC free article : PMC5921311 ] [ PubMed : 9439687 ]
• Tabata, Yasuhiko, Yoshiyuki Murakami, and Yoshito Ikada. 1997. b. Antitumor effect of poly(ethylene glycol)-modified fullerene . Fullerenes, Nanotubes and Carbon Nanostructures 5(5):989-1007.
• Taleb, Nassim Nicholas. 2007. a. The Black Swan: The Impact of the Highly Improbable . New York, NY: Random House.
• Teixido, Meritxell, and Ernest Giralt. 2008. The role of peptides in blood-brain barrier nanotechnology . Journal of Peptide Science 14(2):163-173. [ PubMed : 18085720 ]
• Ter-Pogossian, M.M, M.E. Phelps, and E.J. Hoffman. 1975. A positron-emission transaxial tomograph for nuclear imaging (PETT) . Radiology 114(1):89-98. [ PubMed : 1208874 ]
• Torche, A.M., H. Jouan, P. Le Corre, E. Albina, R. Primault, A. Jestin, and R. Le Verge. 2000. Ex vivo and in situ PLGA microspheres uptake by pig ileal Peyer’s patch segment . International Journal of Pharmaceutics 201(1):15-27. [ PubMed : 10867261 ]
• Toronov, V.A.W., J.H. Choi, M. Wolf, A. Michalos, E. Gratton, and D. Hueber. 2001. Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging . Medical Physics 28(4):521-527. [ PubMed : 11339749 ]
• Tsao, Nina, Puthuparampil P. Kanakamma, Tien-Yau Luh, Chen-Kung Chou, and Huan-Yao Lei. 1999. Inhibition of Escherichia coli -induced meningitis by carboxyfullerene . Antimicrobial Agents and Chemotherapy 43(9):2273-2277. [ PMC free article : PMC89460 ] [ PubMed : 10471578 ]
• Tsao, Nina, Tien-Yau Luh, Chen-Kung Chou, Jiunn-Jong Wu, Yee-Shin Lin, and Huan-Yao Lei. 2001. Inhibition of group A streptococcus infection by carboxyfullerene . Antimicrobial Agents and Chemotherapy 45(6):1788-1793. [ PMC free article : PMC90546 ] [ PubMed : 11353626 ]
• Tsiamyrtzis P., J. Dowdall, D. Shastri, I.T. Pavlidis, M.G. Frank, and P. Ekman. 2007. Imaging facial physiology for the detection of deceit . International Journal of Computer Vision 71(2): 197-214.
• Uvnäs-Moberg, Kerstin. 1998. Oxytocin may mediate the benefits of positive social interaction and emotions . Psychoneuroendocrinology 23(8):819-835. [ PubMed : 9924739 ]
• Van Der Lubben, I.M., F.A. Konings, G. Borchard, J.C. Verhoef, and H.E. Junginger. 2001. In vivo uptake of chitosan microparticles by murine Peyer’s patches: Visualization studies using confocal laser scanning microscopy and immunohistochemistry . Journal of Drug Targeting 9(1):39-47. [ PubMed : 11378522 ]
• Varde, Neelesh K., and Daniel W. Pack. 2004. Microspheres for controlled release drug delivery . Expert Opinion on Biological Therapy 4(1):35-51. [ PubMed : 14680467 ]
• Weissleder, Ralph, Kimberly Kelly, Eric Yi Sun, Timur Shtatland, and Lee Josephson. 2005. Cell-specific targeting of nanoparticles by multivalent attachment of small molecules . Nature Bio technology 23(11):1418-1423. [ PubMed : 16244656 ]
• White, Barbara Prudhomme, Kathryn A. Becker-Blease, and Kathleen Grace-Bishop. 2006. Stimulant medication use, misuse, and abuse in an undergraduate and graduate student sample . Journal of American College Health 54(5):261-268. [ PubMed : 16539218 ]
• Wilson, Edward O. 1998. Consilience: The Unity of Knowledge . New York, NY: Knopf Publishing Group.
• Young, Larry J., Miranda M. Lim, Brenden Gingrich, and Thomas R. Insel. 2001. Cellular mechanisms of social attachment . Hormones and Behavior 40(2):133-138. [ PubMed : 11534973 ]
• Zak, Paul J., and Ahlam Fakhar. 2006. Neuroactive hormones and interpersonal trust: International evidence . Economics and Human Biology 4(3):412-429. [ PubMed : 16979392 ]
• Zak, Paul J., Angela A. Stanton, and Sheila Ahmadi. 2007. Oxytocin increases generosity in humans . PLoS ONE 2(11):e1128. [ PMC free article : PMC2040517 ] [ PubMed : 17987115 ]

Unpublished

• Barrett, James. “Trends and Developments in Neuropsychopharmacology: A 20-year Perspective.” Presentation to the committee on August 15, 2007.
• Chatterjee, Anjan. “Cosmetic Neurology.” Presentation to the committee on August 15, 2007.
• Dinges, David. “Optimizing Neurobehavioral Performance though Biology and Technology.” Presentation to the committee on August 15, 2007.
• Imrey, Peter. “Signal Detection Theory: How Bayes’ Theorem Constrains Accuracy of Inference from Neurophysiologic Monitoring.” Presentation to the committee on October 31, 2007.
• Kelly, Tom. “Experimental Methods in Clinical Neuropsychopharmacology.” Presentation to the committee on August 15, 2007.
• Miller, Mike. “Unique Patterns of Individual Brain Activity.” Presentation to the committee on October 31, 2007.
• Raichle, Marcus. 2007. “Two Views of Brain Function.” Presentation to the committee on October 31, 2007.
• Simon, Denis. 2007. “Scientific Innovation and Investment in China.” Presentation to the committee on August 15, 2007.
• Taleb, Nassim Nicholas. 2007. b. Nassim Nicholas Taleb’s home page 2007 [cited December 13, 2007]. Available from http://www ​.fooledbyrandomness.com/ . Last accessed on June 18, 2008.

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.

“Symmetric” means “If A then B *and* if B then A.” For example, if there is a symmetric relationship 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.

In the experimental literature, and especially in the lie detection literature, tacit assumption of 50 percent appears to guide much of the physiological indexing of psychological states. The committee notes this rate may seem otherwise high.

The term “black swan” as defined by Taleb (2007a,b) is an accepted term in the IC for unanticipated consequences. It should be noted that Taleb’s “black swan” is not related to the older term “white crow” that implies sufficiency to disprove a hypothesis.

See http://www ​.sfn.org/index ​.cfm?pagename=annualMeeting ​_statistics&section ​=annualMeeting . Last accessed December 17, 2007.

Under current FDA guidelines, manufacturers are prohibited from marketing approved drugs for off-label use.

For additional information, see http://www ​.cancer.gov . Last accessed on January 24, 2008.

Psivida Ltd. of Australia and SkyePharma of Great Britain are two examples of international interest in this area of research. For additional information, see http://www ​.psivida.com/ . Last accessed on April 10, 2008.

In Cuba, some scientists are reportedly doing very accurate localization using high-density EEG arrays to locate tumors. Personal communication to committee member Scott Bunce from Roy John.

The main injuries during MRI are caused by the magnetic field being attracted by the ferrous (i.e., magnetic) substances within the body. Proper screening of subjects by attending personnel eliminates this risk.

Personal communication between committee chair Christopher Green and Amy Kohl of Wayne State School of Medicine.

SQUID: Superconducting Quantum Interference Device, supercooled electronic component designed to detect extremely small changes in magnetic fields.

Currently, NIRs can localize hemodynamic changes within about 1 cm while the best fMRI scanner can localize changes within a few millimeters.

• Cite this Page National Research Council (US) Committee on Military and Intelligence Methodology for Emergent Neruophysiological and Cognitive/Neural Research in the Next Two Decades. Emerging Cognitive Neuroscience and Related Technologies. Washington (DC): National Academies Press (US); 2008. 2, Current Cognitive Neuroscience Research and Technology: Selected Areas of Interest.
• PDF version of this title (4.2M)

In this Page

Related information.

• PMC PubMed Central citations
• PubMed Links to PubMed

Recent Activity

• Current Cognitive Neuroscience Research and Technology: Selected Areas of Intere... Current Cognitive Neuroscience Research and Technology: Selected Areas of Interest - Emerging Cognitive Neuroscience and Related Technologies

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

Department of Cognitive Science

• Empirical Domains
• Approaches & Methods
• Linguistics at JHU

The Department of Cognitive Science at Johns Hopkins studies the mind as a computational system, with a particular focus on language and spatial representation. Our research seeks to discover the formal structure of cognition at all levels of analysis. What functions does the cognitive system compute and why? What representational formats and algorithms does the mind employ? What neural structures and patterns of neural activity realize the mind in the brain? How are these realized in the adult mind/brain, and how do they develop?

Our multidisciplinary approach to the science of the mind emphasizes intellectual cohesion and cooperation across disciplines that have traditionally been distinct. We utilize a wide range of methodologies, including traditional and experimental methods in generative linguistics and cognitive psychology, cognitive neuro-scientific studies with fMRI and other techniques, cognitive neuropsychological investigations of children and adults with brain injuries or developmental disabilities, and computational modeling.

Explore library resources related to cognitive science, or contact Cognitive Science Library Liaison Joanne Helouvry with any questions.

Topics in Cognitive Science

Subject Area and Category

• Artificial Intelligence
• Human-Computer Interaction
• Cognitive Neuroscience
• Experimental and Cognitive Psychology
• Linguistics and Language

Wiley-Blackwell

Publication type

17568757, 17568765

Information

How to publish in this journal

[email protected]

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

Leave a comment

Name * Required

Email (will not be published) * Required

* Required Cancel

The users of Scimago Journal & Country Rank have the possibility to dialogue through comments linked to a specific journal. The purpose is to have a forum in which general doubts about the processes of publication in the journal, experiences and other issues derived from the publication of papers are resolved. For topics on particular articles, maintain the dialogue through the usual channels with your editor.

Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®

Cookie settings

Cookie Policy

Legal Notice

Privacy Policy

• Frontiers in Psychology
• Cognitive Science
• Research Topics

Insights in Cognitive Science

Total Downloads

Total Views and Downloads

About this Research Topic

This Research Topic is part of the Insights in Psychology series. We are now entering the third decade of the 21st Century, and, especially in the last years, the achievements made by scientists have been exceptional, leading to major advancements in the fast-growing field of Psychology. Frontiers ...

Keywords : Cognitive Science, Psychology, Developments, Advances, Cognitive Processes, Cognitive Behavior

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Topic coordinators, recent articles, submission deadlines.

Submission closed.

Participating Journals

Total views.

• Demographics

No records found

total views article views downloads topic views

Top countries

Top referring sites, about frontiers research topics.

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

• Alzheimer's disease & dementia
• Arthritis & Rheumatism
• Attention deficit disorders
• Autism spectrum disorders
• Biomedical technology
• Diseases, Conditions, Syndromes
• Endocrinology & Metabolism
• Gastroenterology
• Gerontology & Geriatrics
• Health informatics
• Inflammatory disorders
• Medical economics
• Medical research
• Medications
• Neuroscience
• Obstetrics & gynaecology
• Oncology & Cancer
• Ophthalmology
• Overweight & Obesity
• Parkinson's & Movement disorders
• Psychology & Psychiatry
• Radiology & Imaging
• Sleep disorders
• Sports medicine & Kinesiology
• Vaccination
• Breast cancer
• Cardiovascular disease
• Chronic obstructive pulmonary disease
• Colon cancer
• Coronary artery disease
• Heart attack
• Heart disease
• High blood pressure
• Kidney disease
• Lung cancer
• Multiple sclerosis
• Myocardial infarction
• Ovarian cancer
• Post traumatic stress disorder
• Rheumatoid arthritis
• Schizophrenia
• Skin cancer
• Type 2 diabetes
• Full List »

share this!

May 8, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

New tool links Alzheimer's disease types to rate of cognitive decline

by Lynda De Widt, Mayo Clinic

Mayo Clinic researchers have discovered a series of brain changes characterized by unique clinical features and immune cell behaviors using a new corticolimbic index tool for Alzheimer's disease, a leading cause of dementia.

Their findings are published in JAMA Neurology . The tool categorizes Alzheimer's disease cases into three subtypes according to the location of brain changes and continues the team's prior work, demonstrating how these changes impact people differently. Uncovering the microscopic pathology of the disease can help researchers pinpoint biomarkers that may affect future treatments and patient care .

The new "corticolimbic index" tool assigns a score to the location of toxic tau protein tangles that damage cells in brain regions associated with Alzheimer's disease. In the study, differences in where the tangles accumulated affected the disease progression .

"Our team found striking demographic and clinical differences among sex, age at symptomatic onset and rate of cognitive decline," says Melissa E. Murray, Ph.D., a translational neuropathologist at Mayo Clinic in Florida and senior author of the study.

The team analyzed brain tissue samples from a multi-ethnic group of nearly 1,400 patients with Alzheimer's disease, donated from 1991 to 2020. The samples are part of the Florida Autopsied Multi-Ethnic (FLAME) cohort housed at the Mayo Clinic Brain Bank. The FLAME study cohort is derived from a partnership with the state of Florida's Alzheimer's Disease Initiative .

The sample population included Asian, Black/African American, Hispanic/Latino American, Native American and non-Hispanic white people who received care at memory disorder clinics in Florida and donated their brains for research.

To verify the clinical value of the tool, researchers further investigated study participants from Mayo Clinic who underwent neuroimaging while alive. In collaboration with a Mayo Clinic team led by Prashanthi Vemuri, Ph.D., the researchers found that the corticolimbic index scores were consistent with the changes in the hippocampus detected via MRI and tau positron emission tomography (tau-PET) changes in the cortex.

"By combining our expertise in the fields of neuropathology, biostatistics, neuroscience, neuroimaging and neurology to address Alzheimer's disease from all angles, we've made significant strides in understanding how it affects the brain," says Dr. Murray.

"The corticolimbic index is a score that could encourage a paradigm shift toward understanding the individuality of this complex disease and broaden our perspective. This study marks a significant step toward personalized care, offering hope for more effective future therapies."

The research team's next step is to translate the findings into clinical practice , giving radiologists and other medical specialists access to the corticolimbic index tool.

Dr. Murray says the tool could help physicians determine the progression of Alzheimer's disease in patients and enhance clinical management. The team is also planning more research using the tool to identify areas of the brain resistant to the toxic tau protein's effects.

Explore further

Feedback to editors

Study suggests two copies of APOE4 gene behind up to 20% of Alzheimer's cases

18 hours ago

Researchers show genetic variant common among Black Americans contributes to large cardiovascular disease burden

21 hours ago

First person to receive a genetically modified pig kidney transplant dies nearly 2 months later

New vaccine could protect against coronaviruses that haven't even emerged yet

May 11, 2024

Study links organization of neurotypical brains to genes involved in autism and schizophrenia

Study traces an infectious language epidemic

Visual experiences unique to early infancy provide building blocks of human vision, study finds

May 10, 2024

Study points to personalized treatment opportunities for glioblastoma

Research team introduces new tool to boost battle against childhood undernutrition

How herpes hijacks a ride into cells

Related stories, research finds ethnoracial differences in alzheimer's disease.

Feb 19, 2019

Researchers identify 'instigator' gene associated with Alzheimer's disease

Apr 19, 2021

Alzheimer's subtypes could affect future treatments, researchers find

Oct 29, 2019

Genetic association study opens up new treatment avenues for Pick's disease, a rare form of early-onset dementia

Apr 24, 2024

How Alzheimer's disease progresses faster in people with Down syndrome

Apr 17, 2024

Untangling the threads of early onset dementia

Apr 5, 2024

Recommended for you

Study suggests daily consumption of olive oil reduces chances of developing dementia

'Mathematical microscope' reveals novel, energy-efficient mechanism of working memory that works even during sleep

Study reveals that almost all individuals with two copies of the APOE4 gene develop signs of Alzheimer's

May 6, 2024

Organ transplant drug may slow Alzheimer's disease progression

May 1, 2024

Does obesity really increase your risk of dementia?

Apr 30, 2024

Let us know if there is a problem with our content

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Medical Xpress in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

Small molecule shows early-stage promise for repairing myelin sheath damage

When treated with a novel protein function inhibitor called ESI1, mice that mimic the symptoms of multiple sclerosis (MS) and lab-prepared human brain cells both demonstrated the ability to regenerate vital myelin coatings that protect healthy axon function.

This breakthrough, published May 2, 2024, in Cell , appears to overcome difficulties that have long frustrated previous attempts to reverse a form of nerve damage that robs people with MS of motor control and gradually blunts cognitive functions for many people as they age.

"Currently, there are no effective therapies to reverse myelin damage in devastating demyelinating diseases such as MS," says corresponding author Q. Richard Lu, PhD, a top brain research expert at Cincinnati Children's. "These findings are significant as they offer new pathways for treatment that potentially shift the therapeutic focus from just managing symptoms to actively promoting repair and regeneration of myelin."

Promoting healing by clearing a roadblock

A critical insight driving the new findings was observing that brain regions damaged by MS still possessed a type of cell needed to repair myelin damage, but the disease activates other cell types and signals that combine forces to silence the repair function.

These useful cells in the brain, called oligodendrocytes, are responsible for producing myelin sheaths that wrap around cable-like parts of nerve cells called axons, much like the plastic insulation around a wire. When the protective myelin gets damaged, be it by disease or the wear and tear of age, nerve signaling gets disrupted. Depending on where the damaged nerves lead, the disruptions can affect movement, vision, thinking and so on.

Essentially, the research team found a way to unsilence the silenced repair process, setting the oligodendrocytes (OLs) free to do their jobs.

Pinning down the genetic changes and signals involved in the repair silencing process and finding a small molecule compound that can reverse the silencing was a complex undertaking. The project, which spanned over five years, involved four co-first authors and 29 contributing co-authors from Cincinnati Children's, the University of Cincinnati, and 14 other institutions including universities in Australia, China, Germany, India, Singapore, and the United Kingdom.

Among the team's key findings:

Identifying the mechanism preventing myelin production in MS

Analysis of stored autopsy tissues revealed that OLs within MS lesions lacked an activating histone mark called H3K27ac, while expressing high levels of two other repressive histone marks H3K27me3 and H3K9me3 associated with silencing gene activity.

Finding a compound that can reverse the silencing

The research team scoured a library of hundreds of small molecules known to target enzymes that could modify gene expression and influence the silenced OLs. The team determined that the compound ESI1 (epigenetic-silencing-inhibitor-1) was nearly five times more powerful than any other compounds they considered.

The compound tripled the levels of the desired H3K27ac histone mark in OLs while sharply reducing levels of the two repressive histone marks. Additionally, the research reveals a new way in which ESI1 promotes the creation of special membrane-less regulatory hubs known as "biomolecular condensates" within the cell nucleus that control fat and cholesterol levels. These hubs act as central points to boost the production of essential fats and cholesterol needed to make myelin, a crucial component of nerve fibers.

Demonstrating benefits in mice and lab-grown human tissue

In both aging mice and mice mimicking MS, the ESI1 treatment prompted myelin sheath production and improved lost neurological function. Testing included tracking gene activation, measuring the microscopic new myelin sheaths surrounding axons, and observing that treated mice were quicker at navigating a water maze.

Then the team tested the treatment on lab-grown human brain cells. The team used a type of brain organoid, myelin organoids, that is far more simplified than a full brain but still produces complex myelinating cells. When the organoids were exposed to ESI1, the treatment extended the myelin sheath of myelinating cells, the study reports.

Implications and next steps

MS is the most common and best known of several major neurodegenerative diseases. The new findings may spark a new approach to stopping the degenerative effects of these conditions, Lu says.

Myelin regeneration treatment also could be helpful for people recovering from brain and spinal cord injuries.

But the most far-reaching implication of the study is the possibility of using ESI1, or similar compounds, to help slow or even reverse cognitive losses that often occur during aging. Many studies have shown that myelin loss plays a role in age-related loss of cognitive function, Lu says.

However, more research is needed to determine whether human clinical trials can be launched to evaluate ESI1 as a potential treatment. For example, the effects of ESI1 may need to be modulated by adjusting the dose, treatment duration, or using "pulsed therapy" during specific time windows. More study also is needed to determine whether even more effective compounds than ESI1 might be designed from scratch.

"This study is a beginning," Lu says. "Prior to finding ESI1, most scientists believed that remyelination failure in MS was due to the stalled development of precursors. Now we show a proof of concept that reversing the silencing activity in OLs present in the damaged brain can enable myelin regeneration."

• Nervous System
• Brain Tumor
• Healthy Aging
• Epigenetics
• Brain Injury
• Huntington's Disease
• Schizophrenia
• Multiple Sclerosis
• Illusion of control
• Sciatic nerve
• Color blindness
• MMR vaccine
• Left-handed
• Cognitive psychology
• Cognitive neuroscience
• Sensory neuron

Story Source:

Materials provided by Cincinnati Children's Hospital Medical Center . Note: Content may be edited for style and length.

Journal Reference :

• Xuezhao Liu, Dazhuan Eric Xin, Xiaowen Zhong, Chuntao Zhao, Zhidan Li, Liguo Zhang, Adam J. Dourson, Lindsay Lee, Shreya Mishra, Arman E. Bayat, Eva Nicholson, William L. Seibel, Bingfang Yan, Joel Mason, Bradley J. Turner, David G. Gonsalvez, William Ong, Sing Yian Chew, Balaram Ghosh, Sung Ok Yoon, Mei Xin, Zhigang He, Jason Tchieu, Michael Wegner, Klaus-Armin Nave, Robin J.M. Franklin, Ranjan Dutta, Bruce D. Trapp, Ming Hu, Matthew A. Smith, Michael P. Jankowski, Samantha K. Barton, Xuelian He, Q. Richard Lu. Small-molecule-induced epigenetic rejuvenation promotes SREBP condensation and overcomes barriers to CNS myelin regeneration . Cell , 2024; DOI: 10.1016/j.cell.2024.04.005

Cite This Page :

Explore More

• Controlling Shape-Shifting Soft Robots
• Brain Flexibility for a Complex World
• ONe Nova to Rule Them All
• AI Systems Are Skilled at Manipulating Humans
• Planet Glows With Molten Lava
• A Fragment of Human Brain, Mapped
• Symbiosis Solves Long-Standing Marine Mystery
• Surprising Common Ideas in Environmental ...
• 2D All-Organic Perovskites: 2D Electronics
• Generative AI That Imitates Human Motion

Trending Topics

Strange & offbeat.

Appointments at Mayo Clinic

Meditation: a simple, fast way to reduce stress.

Meditation can wipe away the day's stress, bringing with it inner peace. See how you can easily learn to practice meditation whenever you need it most.

If stress has you anxious, tense and worried, you might try meditation. Spending even a few minutes in meditation can help restore your calm and inner peace.

Anyone can practice meditation. It's simple and doesn't cost much. And you don't need any special equipment.

You can practice meditation wherever you are. You can meditate when you're out for a walk, riding the bus, waiting at the doctor's office or even in the middle of a business meeting.

Understanding meditation

Meditation has been around for thousands of years. Early meditation was meant to help deepen understanding of the sacred and mystical forces of life. These days, meditation is most often used to relax and lower stress.

Meditation is a type of mind-body complementary medicine. Meditation can help you relax deeply and calm your mind.

During meditation, you focus on one thing. You get rid of the stream of thoughts that may be crowding your mind and causing stress. This process can lead to better physical and emotional well-being.

Benefits of meditation

Meditation can give you a sense of calm, peace and balance that can benefit your emotional well-being and your overall health. You also can use it to relax and cope with stress by focusing on something that calms you. Meditation can help you learn to stay centered and keep inner peace.

These benefits don't end when your meditation session ends. Meditation can help take you more calmly through your day. And meditation may help you manage symptoms of some medical conditions.

Meditation and emotional and physical well-being

When you meditate, you may clear away the information overload that builds up every day and contributes to your stress.

The emotional and physical benefits of meditation can include:

• Giving you a new way to look at things that cause stress.
• Building skills to manage your stress.
• Making you more self-aware.
• Focusing on the present.
• Reducing negative feelings.
• Helping you be more creative.
• Helping you be more patient.
• Lowering resting heart rate.
• Lowering resting blood pressure.
• Helping you sleep better.

Meditation and illness

Meditation also might help if you have a medical condition. This is most often true if you have a condition that stress makes worse.

A lot of research shows that meditation is good for health. But some experts believe there's not enough research to prove that meditation helps.

With that in mind, some research suggests that meditation may help people manage symptoms of conditions such as:

• Chronic pain.
• Depression.
• Heart disease.
• High blood pressure.
• Irritable bowel syndrome.
• Sleep problems.
• Tension headaches.

Be sure to talk to your healthcare professional about the pros and cons of using meditation if you have any of these or other health conditions. Sometimes, meditation might worsen symptoms linked to some mental health conditions.

Meditation doesn't replace medical treatment. But it may help to add it to other treatments.

Types of meditation

Meditation is an umbrella term for the many ways to get to a relaxed state. There are many types of meditation and ways to relax that use parts of meditation. All share the same goal of gaining inner peace.

Ways to meditate can include:

Guided meditation. This is sometimes called guided imagery or visualization. With this method of meditation, you form mental images of places or things that help you relax.

You try to use as many senses as you can. These include things you can smell, see, hear and feel. You may be led through this process by a guide or teacher.

• Mantra meditation. In this type of meditation, you repeat a calming word, thought or phrase to keep out unwanted thoughts.

Mindfulness meditation. This type of meditation is based on being mindful. This means being more aware of the present.

In mindfulness meditation, you focus on one thing, such as the flow of your breath. You can notice your thoughts and feelings. But let them pass without judging them.

• Qigong. This practice most often combines meditation, relaxation, movement and breathing exercises to restore and maintain balance. Qigong (CHEE-gung) is part of Chinese medicine.
• Tai chi. This is a form of gentle Chinese martial arts training. In tai chi (TIE-CHEE), you do a series of postures or movements in a slow, graceful way. And you do deep breathing with the movements.
• Yoga. You do a series of postures with controlled breathing. This helps give you a more flexible body and a calm mind. To do the poses, you need to balance and focus. That helps you to focus less on your busy day and more on the moment.

Parts of meditation

Each type of meditation may include certain features to help you meditate. These may vary depending on whose guidance you follow or who's teaching a class. Some of the most common features in meditation include:

Focused attention. Focusing your attention is one of the most important elements of meditation.

Focusing your attention is what helps free your mind from the many things that cause stress and worry. You can focus your attention on things such as a certain object, an image, a mantra or even your breathing.

• Relaxed breathing. This technique involves deep, even-paced breathing using the muscle between your chest and your belly, called the diaphragm muscle, to expand your lungs. The purpose is to slow your breathing, take in more oxygen, and reduce the use of shoulder, neck and upper chest muscles while breathing so that you breathe better.

A quiet setting. If you're a beginner, meditation may be easier if you're in a quiet spot. Aim to have fewer things that can distract you, including no television, computers or cellphones.

As you get more skilled at meditation, you may be able to do it anywhere. This includes high-stress places, such as a traffic jam, a stressful work meeting or a long line at the grocery store. This is when you can get the most out of meditation.

• A comfortable position. You can practice meditation whether you're sitting, lying down, walking, or in other positions or activities. Just try to be comfortable so that you can get the most out of your meditation. Aim to keep good posture during meditation.
• Open attitude. Let thoughts pass through your mind without judging them.

Everyday ways to practice meditation

Don't let the thought of meditating the "right" way add to your stress. If you choose to, you can attend special meditation centers or group classes led by trained instructors. But you also can practice meditation easily on your own. There are apps to use too.

And you can make meditation as formal or informal as you like. Some people build meditation into their daily routine. For example, they may start and end each day with an hour of meditation. But all you really need is a few minutes a day for meditation.

Here are some ways you can practice meditation on your own, whenever you choose:

Breathe deeply. This is good for beginners because breathing is a natural function.

Focus all your attention on your breathing. Feel your breath and listen to it as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your mind wanders, gently return your focus to your breathing.

Scan your body. When using this technique, focus attention on each part of your body. Become aware of how your body feels. That might be pain, tension, warmth or relaxation.

Mix body scanning with breathing exercises and think about breathing heat or relaxation into and out of the parts of your body.

• Repeat a mantra. You can create your own mantra. It can be religious or not. Examples of religious mantras include the Jesus Prayer in the Christian tradition, the holy name of God in Judaism, or the om mantra of Hinduism, Buddhism and other Eastern religions.

Walk and meditate. Meditating while walking is a good and healthy way to relax. You can use this technique anywhere you're walking, such as in a forest, on a city sidewalk or at the mall.

When you use this method, slow your walking pace so that you can focus on each movement of your legs or feet. Don't focus on where you're going. Focus on your legs and feet. Repeat action words in your mind such as "lifting," "moving" and "placing" as you lift each foot, move your leg forward and place your foot on the ground. Focus on the sights, sounds and smells around you.

Pray. Prayer is the best known and most widely used type of meditation. Spoken and written prayers are found in most faith traditions.

You can pray using your own words or read prayers written by others. Check the self-help section of your local bookstore for examples. Talk with your rabbi, priest, pastor or other spiritual leader about possible resources.

Read and reflect. Many people report that they benefit from reading poems or sacred texts and taking a few moments to think about their meaning.

You also can listen to sacred music, spoken words, or any music that relaxes or inspires you. You may want to write your thoughts in a journal or discuss them with a friend or spiritual leader.

• Focus your love and kindness. In this type of meditation, you think of others with feelings of love, compassion and kindness. This can help increase how connected you feel to others.

Building your meditation skills

Don't judge how you meditate. That can increase your stress. Meditation takes practice.

It's common for your mind to wander during meditation, no matter how long you've been practicing meditation. If you're meditating to calm your mind and your mind wanders, slowly return to what you're focusing on.

Try out ways to meditate to find out what types of meditation work best for you and what you enjoy doing. Adapt meditation to your needs as you go. Remember, there's no right way or wrong way to meditate. What matters is that meditation helps you reduce your stress and feel better overall.

Related information

• Relaxation techniques: Try these steps to lower stress - Related information Relaxation techniques: Try these steps to lower stress
• Stress relievers: Tips to tame stress - Related information Stress relievers: Tips to tame stress
• Video: Need to relax? Take a break for meditation - Related information Video: Need to relax? Take a break for meditation

There is a problem with information submitted for this request. Review/update the information highlighted below and resubmit the form.

From Mayo Clinic to your inbox

Sign up for free and stay up to date on research advancements, health tips, current health topics, and expertise on managing health. Click here for an email preview.

Error Email field is required

Error Include a valid email address

To provide you with the most relevant and helpful information, and understand which information is beneficial, we may combine your email and website usage information with other information we have about you. If you are a Mayo Clinic patient, this could include protected health information. If we combine this information with your protected health information, we will treat all of that information as protected health information and will only use or disclose that information as set forth in our notice of privacy practices. You may opt-out of email communications at any time by clicking on the unsubscribe link in the e-mail.

Thank you for subscribing!

You'll soon start receiving the latest Mayo Clinic health information you requested in your inbox.

Sorry something went wrong with your subscription

Please, try again in a couple of minutes

• Meditation: In depth. National Center for Complementary and Integrative Health. https://nccih.nih.gov/health/meditation/overview.htm. Accessed Dec. 23, 2021.
• Mindfulness meditation: A research-proven way to reduce stress. American Psychological Association. https://www.apa.org/topics/mindfulness/meditation. Accessed Dec. 23, 2021.
• AskMayoExpert. Meditation. Mayo Clinic. 2021.
• Papadakis MA, et al., eds. Meditation. In: Current Medical Diagnosis & Treatment 2022. 61st ed. McGraw Hill; 2022. https://accessmedicine.mhmedical.com. Accessed Dec. 23, 2021.
• Hilton L, et al. Mindfulness meditation for chronic pain: Systematic review and meta-analysis. Annals of Behavioral Medicine. 2017; doi:10.1007/s12160-016-9844-2.
• Seaward BL. Meditation. In: Essentials of Managing Stress. 5th ed. Jones & Bartlett Learning; 2021.
• Seaward BL. Managing Stress: Principles and Strategies for Health and Well-Being. 9th ed. Burlington, Mass.: Jones & Bartlett Learning; 2018.

Products and Services

• A Book: Mayo Clinic Handbook for Happiness
• A very happy brain
• Alternative cancer treatments: 11 options to consider
• Brain tumor
• Brain Tumor
• What is a brain tumor? A Mayo Clinic expert explains
• Brain tumor FAQs
• Living with Brain Tumors
• Long Term Brain Cancer Survivor
• Mayo Clinic Minute: Meditation is good medicine
• Meditation 2.0: A new way to meditate
• Parkinson's disease
• Punk Guitarist Survives Brain Tumor
• Guided meditation video

Mayo Clinic does not endorse companies or products. Advertising revenue supports our not-for-profit mission.

• Opportunities

Mayo Clinic Press

Check out these best-sellers and special offers on books and newsletters from Mayo Clinic Press .

• Mayo Clinic on Incontinence - Mayo Clinic Press Mayo Clinic on Incontinence
• The Essential Diabetes Book - Mayo Clinic Press The Essential Diabetes Book
• Mayo Clinic on Hearing and Balance - Mayo Clinic Press Mayo Clinic on Hearing and Balance
• FREE Mayo Clinic Diet Assessment - Mayo Clinic Press FREE Mayo Clinic Diet Assessment
• Mayo Clinic Health Letter - FREE book - Mayo Clinic Press Mayo Clinic Health Letter - FREE book
• Meditation A simple fast way to reduce stress

Your gift holds great power – donate today!

Make your tax-deductible gift and be a part of the cutting-edge research and care that's changing medicine.