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What is a Sequence? The Neural Mechanisms of Perceptual, Motor, and Task Sequences Across Species and Their Interaction with Addiction  

Theresa M. Desrochers and Theresa H. McKim

Sequences permeate daily life. They can be defined as a discrete series of items or states that occur in a specific order with a beginning and end. The brain supports the perception and execution of sequences. Perceptual sequences involve tracking regularities in incoming stimuli, such as the series of sounds that make up a word in language. Executed sequences range from the series of muscle activations used by a frog to catch a fly to a chess master mapping her next moves. How the brain controls sequences must therefore scale to multiple levels of control. Investigating how the brain functions to accomplish this task spans from the study of individual cells in the brain to human cognition. Understanding the neural systems that underlie sequential control is necessary to approach the mechanistic underpinnings of complex conditions such as addiction, which may be rooted in difficult-to-extinguish sequential behaviors. Current research focuses on studies in both animal and human models and spans the levels of complexity of sequential control and the brain systems that support it.


Scientific Approaches to Mysticism  

Stephen Kaplan

The title “Scientific Approaches to Mysticism” reveals half the task and belies the other half—namely, which of the sciences and whose mysticism are to be considered. Is it Capra’s tao of physics, Bohm’s holomovement of undivided wholeness, or Saver/Rabin’s limbic correlates of mystical ecstasy? Is it Freud’s psychoanalytic oneness of nursing at the breast, or Goodall’s evolutionary biology of mystical wonder? Numerous mystics have presented us with a cornucopia of mystical experiences, and many sciences have been employed to analyze mysticism. Any effort to create a singular scientific approach to an “imagined singular mysticism” is doomed to vagueness. Specifics matter, and they matter in the scientific approaches to mysticism. A scientific study of mysticism must first clarify what mysticism means—namely, a conscious experience in which one feels that the normal subject-object boundaries manifest in waking consciousness are altered, presenting a state of unity, union, or interrelationship. This definition of mysticism is broad enough to encompass nature mysticism, theistic I–Thou mysticism, and various forms of non-dualistic mysticisms ranging from experiences of the oneness of Being to the awareness of the emptiness of becoming. Each of these broad categories of mysticism must be refined by examining the particular tradition in which it manifests. As such, the scientific study of mysticism cannot assume, for example, that all Christian mystics, proclaiming the ultimacy of a personal communion with the Trinitarian god, are uttering the same thing, nor that non-dualistic mystics from different traditions, such as Christianity and Hinduism, are saying different things. The scientific study of mysticism must immediately confront the threat of reductionism, in which “mystical experience” is reduced to some elemental explanation such as, “it is only one’s brain.” This threat of scientific reductionism has long been elicited by the knowledge, for example, that the intake of drugs is correlated with mystical experience; more recently, this threat of reductionism has been intensified by the knowledge that we have machines that measure the neural patterns associated with individuals having mystical experiences, and we have machines that can allegedly induce mystical experiences. Stepping beyond the psychological, cognitive, and neuropsychological approaches to mysticism, the connections between mystical experience and physics have also been drawn. Relativity and quantum theories have become the hermeneutical tools to analyze and interpret the declarations of all sorts of mystical experiences. These studies of mysticism tend to present parallel explanations of the world. Evolutionary theory and biology also offer different angles of approach to the study of mysticism proposing explanations, for example, which relate mystical experience to the evolutionary chain of being or to techniques for transcending present limitations.


Visual Attention  

Sabine Kastner and Timothy J. Buschman

Natural scenes are cluttered and contain many objects that cannot all be processed simultaneously. Due to this limited processing capacity, neural mechanisms are needed to selectively enhance the information that is most relevant to one’s current behavior and to filter unwanted information. We refer to these mechanisms as “selective attention.” Attention has been studied extensively at the behavioral level in a variety of paradigms, most notably, Treisman’s visual search and Posner’s paradigm. These paradigms have also provided the basis for studies directed at understanding the neural mechanisms underlying attentional selection, both in the form of neuroimaging studies in humans and intracranial electrophysiology in non-human primates. The selection of behaviorally relevant information is mediated by a large-scale network that includes regions in all major lobes as well as subcortical structures. Attending to a visual stimulus modulates processing across the visual processing hierarchy with stronger effects in higher-order areas. Current research is aimed at characterizing the functions of the different network nodes as well as the dynamics of their functional connectivity.


Attention in Early Development  

Stefania Conte and John Richards

Attention is a complex construct that shows development throughout the life span and undergoes significant changes over the first years of life. The complexity of attentional processes is described by the different systems and brain network theorized to describe the construct (i.e., alerting, orienting, executive attention, and sustained attention). Evidence of the development of attention in infancy comes from several behavioral paradigms—primarily focused on the analysis of infants’ eye gaze—physiological measures, and neuroimaging techniques. Many of the changes in attention rely upon the structural and functional development of brain areas involved in attention processes. Behavioral and physiological signs mark the development of attention and are identifiable very early in life. The investigation of the typical development of attention is pivotal for the understanding of atypical trajectories that characterize many neurodevelopmental disorders. The individuation of alterations in early visual attention processes may be utilized to guide intervention programs aimed at improving attention and other cognitive domains.


A Review and Reappraisal of the Default Network in Normal Aging and Dementia  

Jessica R. Andrews-Hanna, Matthew D. Grilli, and Muireann Irish

The brain’s default network (DN) has received considerable interest in the context of so-called “normal” and pathological aging. Findings have generally been couched in support of a pessimistic view of brain aging, marked by substantial loss of structural brain integrity accompanied by a host of impairments in brain and cognitive function. A critical look at the literature, however, reveals that the standard loss of integrity, loss of function (LILF) view in normal aging may not necessarily hold with respect to the DN and the internally guided functions it supports. Many internally guided processes subserved by the DN are preserved or enhanced in cognitively healthy older adults. Moreover, differences in motivational, contextual, and physiological factors between young and older adults likely influence the extant neuroimaging and cognitive findings. Accordingly, normal aging can be viewed as a series of possibly adaptive cognitive and DN-related alterations that bolster cognitive function and promote socioemotional well-being and stability in a stage of life noted for change. On the other hand, the available evidence reveals strong support for the LILF view of the DN in neurodegenerative disorders, whereby syndromes such as Alzheimer’s disease (AD) and semantic dementia (SD), characterized by progressive atrophy to distinct DN subsystems, display distinct aberrations in autobiographical and semantic cognition. Taken together, these findings call for more naturalistic, age-appropriate, and longitudinal paradigms when investigating neurocognitive changes in aging and to adequately assess and control for differences in non-neural factors that may obscure “true” effects of normal and pathological aging. A shift in the framework with which age-related alterations in internally guided cognition are interpreted may shed important light on the neurocognitive mechanisms differentiating healthy and pathological aging, leading to a more complete picture of the aging brain in all its complexity.


Theoretical Perspectives on Age Differences in Brain Activation: HAROLD, PASA, CRUNCH—How Do They STAC Up?  

Sara B. Festini, Laura Zahodne, and Patricia A. Reuter-Lorenz

Cognitive neuroimaging studies often report that older adults display more activation of neural networks relative to younger adults, referred to as overactivation. Greater or more widespread activity frequently involves bilateral recruitment of both cerebral hemispheres, especially the frontal cortex. In many reports, overactivation has been associated with superior cognitive performance, suggesting that this activity may reflect compensatory processes that offset age-related decline and maintain behavior. Several theories have been proposed to account for age differences in brain activation, including the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) model, the Posterior-Anterior Shift in Aging (PASA) theory, the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH), and the Scaffolding Theory of Aging and Cognition (STAC and STAC-r). Each model has a different explanatory scope with regard to compensatory processes, and each has been highly influential in the field. HAROLD contrasts the general pattern of bilateral prefrontal activation in older adults with that of more unilateral activation in younger adults. PASA describes both anterior (e.g., frontal) overactivation and posterior (e.g., occipital) underactivation in older adults relative to younger adults. CRUNCH emphasizes that the level or extent of brain activity can change in response to the level of task demand at any age. Finally, STAC and STAC-r take the broadest perspective to incorporate individual differences in brain structure, the capacity to implement functional scaffolding, and life-course neural enrichment and depletion factors to predict cognition and cognitive change across the lifespan. Extant empirical work has documented that compensatory overactivation can be observed in regions beyond the prefrontal cortex, that variations in task difficulty influence the degree of brain activation, and that younger adults can show compensatory overactivation under high mental demands. Additional research utilizing experimental designs (e.g., transcranial magnetic stimulation), longitudinal assessments, greater regional precision, both verbal and nonverbal material, and measures of individual difference factors will continue to refine our understanding of age-related activation differences and adjudicate among these various accounts of neurocognitive aging.


Visual Perception in the Human Brain: How the Brain Perceives and Understands Real-World Scenes  

Clemens G. Bartnik and Iris I. A. Groen

How humans perceive and understand real-world scenes is a long-standing question in neuroscience, cognitive psychology, and artificial intelligence. Initially, it was thought that scenes are constructed and represented by their component objects. An alternative view proposed that scene perception starts by extracting global features (e.g., spatial layout) first and individual objects in later stages. A third framework focuses on how the brain not only represents objects and layout but how this information combines to allow determining possibilities for (inter)action that the environment offers us. The discovery of scene-selective regions in the human visual system sparked interest in how scenes are represented in the brain. Experiments using functional magnetic resonance imaging show that multiple types of information are encoded in the scene-selective regions, while electroencephalography and magnetoencephalography measurements demonstrate links between the rapid extraction of different scene features and scene perception behavior. Computational models such as deep neural networks offer further insight by how training networks on different scene recognition tasks results in the computation of diagnostic features that can then be tested for their ability to predict activity in human brains when perceiving a scene. Collectively, these findings suggest that the brain flexibly and rapidly extracts a variety of information from scenes using a distributed network of brain regions.


Neurolinguistic Approaches in Morphology  

Niels O. Schiller

Neurolinguistic approaches to morphology include the main theories of morphological representation and processing in the human mind, such as full-listing, full-parsing, and hybrid dual-route models, and how the experimental evidence that has been acquired to support these theories uses different neurolinguistic paradigms (visual and auditory priming, violation, long-lag priming, picture-word interference, etc.) and methods (electroencephalography [EEG]/event-related brain potential [ERP], functional magnetic resonance imaging [fMRI], neuropsychology, and so forth).



Chelsea Ekstrand

The growing field of neuroimaging has offered exciting insights into the inner workings of the human brain in health and disease. Structural neuroimaging techniques provide detailed information about the physical properties and anatomy of the brain and nervous system, including cerebrospinal fluid, blood vessels, and different types of tissue. The most commonly used structural neuroimaging techniques are computed tomography (CT) and structural magnetic resonance imaging (MRI). CT uses X-rays to create a two-dimensional representation of neural tissue, whereas MRI quantifies differences in tissue density by manipulating molecules using magnetic fields, magnetic field gradients, and radio waves. Functional neuroimaging techniques provide a measure of when and where activity is occurring in the brain by quantifying underlying physiological processes. Functional neuroimaging techniques fall into two broad categories: measures of direct brain activity, including electroencephalography (EEG) and magnetoencephalography (MEG), and measures of indirect brain activity, such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). Different functional neuroimaging techniques can be used to examine different physiological changes, including electrical activity, magnetic field changes, metabolic and neurotransmitter activity, and indirect measures of blood flow to offer insight into cognitive processing. Structural and functional neuroimaging have made a profound impact on understanding the brain both during normal functioning and in clinical pathology. Overall, neuroimaging is a powerful tool for both research and clinical practice and offers a noninvasive window into the central nervous system of humans in both health and disease.