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The article "Bringing Humanism to California's Prisons" delves into the historical evolution of California's prison system, emphasizing a recent shift towards humanistic values. It addresses the challenges of providing healthcare in prisons and the legal standards for meeting medical needs. The text explores efforts to prioritize rehabilitation, public safety, and restorative justice over punitive measures, with Governor Newsom's "California Model" initiative leading the way. The California Department of Corrections and Rehabilitation has adopted this model to enhance successful reintegration into society, with a comprehensive measurement plan in place to evaluate progress and outcomes. The overarching goal is to move away from dehumanizing practices in prisons and embrace a more humane approach to rehabilitation. [Extracted from the article]

A foreword to "The Importance of Evolution to Understandings of Human Nature" is presented.

This article presents a brief retrospective on the Haken–Kelso–Bunz (HKB) model of certain dynamical properties of human movement. Though unanticipated, HKB introduced, and demonstrated the power of, a new vocabulary for understanding behavior, cognition and the brain, revealed through a visually compelling mathematical picture that accommodated highly reproducible experimental facts and predicted new ones. HKB stands as a harbinger of paradigm change in several scientific fields, the effects of which are still being felt. In particular, HKB constitutes the foundation of a mechanistic science of coordination called Coordination Dynamics that extends from matter to movement to mind, and beyond. [ABSTRACT FROM AUTHOR]

The human dynamic clamp (HDC) is a human–machine interface designed on the basis of coordination dynamics for studying realistic social interaction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70). Regression analyses were performed using normative modeling on global scores according to four subconditions (HDC behavior "cooperative/competitive," human task "in-phase/anti-phase," diagnosis, and age at inclusion). Children with ASD had lower scores than controls for motor skills. HDC motor coordination scores were the best candidates for stratification and diagnostic biomarkers according to exploratory analyses of hierarchical clustering and multivariate classification. Independently of phenotype, sociocognitive skills increased with developmental age while being affected by the ongoing task and HDC behavior. Weaker performance in ASD for motor skills suggests the convergent validity of the HDC for evaluating social interaction. Results provided additional evidence of a relationship between sensorimotor and sociocognitive skills. HDC may also be used as a marker of maturation of sociocognitive skills during real-time social interaction. Through its standardized and objective evaluation, the HDC not only represents a valid paradigm for the study of interpersonal synchrony but also offers a promising, clinically relevant psychometric instrument for the evaluation and stratification of sociomotor dysfunctions. [ABSTRACT FROM AUTHOR]

Team coordination—members of a group acting together rather than performing specific actions individually—is essential for success in many real-world tasks such as military missions, sports, workplace, or school interactions. However, team coordination is highly variable, which is one reason why its underlying neural processes are largely unknown. Here we used dual electroencephalography (EEG) in dyads to study the neurobehavioral dynamics of team coordination in an ecologically valid task that places intensive demands on joint performance. We present a novel conceptual framework to interpret neurobehavioral variability in terms of degeneracy , a fundamental property of complex biological systems said to enhance flexibility and robustness. We characterize degeneracy conceptually in terms of a manifold representing the geometric locus of the dynamics in the high dimensional state-space of neurobehavioral signals. The geometry and dimensionality of the manifold are determined by task constraints and team coordination requirements which restrict the manifold to trajectories that are conducive to successful task performance. Our results indicate that team coordination is associated with dimensionality reduction of the manifold as evident in increased inter-brain phase coherence of beta and gamma rhythms during critical phases of task performance where subjects exchange information. Team coordination was also found to affect the shape of the manifold manifested as a symmetry breaking of centro-parietal wavelet power patterns across subjects in trials with high team coordination. These results open a conceptual and empirical path to identifying the mechanisms underlying team performance in complex tasks. [ABSTRACT FROM AUTHOR]

Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people...) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights. [ABSTRACT FROM AUTHOR]

Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large‐scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a metastable regime of their coordination dynamics. Metastability may confer important behavioural qualities by binding distributed local areas into large‐scale neurocognitive networks. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N = 566) and comparing the metastability of the brain's large‐scale resting network architecture at rest and during the performance of several tasks. Metastability was estimated using a well‐defined collective variable capturing the level of 'phase‐locking' between large‐scale networks over time. Task‐based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving or fluid intelligence, but was less important in tasks relying on previous experience or crystallised intelligence. Crucially, subjects with resting architectures similar or 'pre‐configured' to a task‐general arrangement demonstrated superior cognitive performance. Taken together, our findings support a key linkage between the spontaneous metastability of large‐scale networks in the cerebral cortex and cognition. [ABSTRACT FROM AUTHOR]

Early influences of Michael Turvey’s work on the author’s thinking and Turvey’s long lasting impact on the field of motor behavior and related scientific disciplines are briefly described. [ABSTRACT FROM AUTHOR]

How one behaves after interacting with a friend may not be the same as before the interaction. The present study investigated which spontaneous coordination patterns formed between 2 persons and whether a remnant of the interaction remained (“social memory”). Pairs of people sat face-to-face and continuously flexed index fingers while vision between partners was manipulated to allow or prevent information exchange. Trials consisted of 3 successive 20-s intervals: without vision, with vision, and again without vision. Steady, transient, or absent phase coupling was observed during vision. In support of social memory, participants tended to remain near each other's movement frequency after the interaction ended. Furthermore, the greater the stability of interpersonal coordination, the more similar partners' postinteractional frequencies became. Proposing that social memory resulted from prior frequency adaptation, a model based on Haken-Kelso-Bunz (HKB) oscillators reproduced the experimental findings, even for patterns observed on individual trials. Parametric manipulations revealed multiple routes to social memory through the interplay of adaptation and other model parameters. The experimental results, model, and interpretation motivate potential future research and therapeutic applications. [ABSTRACT FROM AUTHOR]

Much of our knowledge of coordination comes from studies of simple, dyadic systems or systems containing large numbers of components. The huge gap ‘in between’ is seldom addressed, empirically or theoretically. We introduce a new paradigm to study the coordination dynamics of such intermediate-sized ensembles with the goal of identifying key mechanisms of interaction. Rhythmic coordination was studied in ensembles of eight people, with differences in movement frequency (‘diversity’) manipulated within the ensemble. Quantitative change in diversity led to qualitative changes in coordination, a critical value separating régimes of integration and segregation between groups. Metastable and multifrequency coordination between participants enabled communication across segregated groups within the ensemble, without destroying overall order. These novel findings reveal key factors underlying coordination in ensemble sizes previously considered too complicated or 'messy' for systematic study and supply future theoretical/computational models with new empirical checkpoints. [ABSTRACT FROM AUTHOR]

To complement experimental efforts toward understanding human social interactions at both neural and behavioral levels, two computational approaches are presented: (1) a fully parameterizable mathematical model of a social partner, the Human Dynamic Clamp which, by virtue of experimentally controlled interactions between Virtual Partners and real people, allows for emergent behaviors to be studied; and (2) a multiscale neurocomputational model of social coordination that enables exploration of social self-organization at all levels—from neuronal patterns to people interacting with each other. These complementary frameworks and the cross product of their analysis aim at understanding the fundamental principles governing social behavior. [ABSTRACT FROM AUTHOR]

The aim of the paper is to point out one way of integrating the supposedly incommensurate disciplines investigated in sports science. General, common principles can be found among apparently unrelated disciplines when the focus is put on the dynamics of sports-related phenomena. Dynamical systems approaches that have recently changed research in biological and social sciences among others, offer key concepts to create a common pluricontextual language in sport science. This common language, far from being homogenising, offers key synthesis between diverse fields, respecting and enabling the theoretical and experimental pluralism. It forms a softly integrated sports science characterised by a basic dynamic explanatory backbone as well as context-dependent theoretical flexibility. After defining the dynamic integration in living systems, unable to be captured by structural static approaches, we show the commonalities between the diversity of processes existing on different levels and time scales in biological and social entities. We justify our interpretation by drawing on some recent scientific contributions that use the same general principles and concepts, and diverse methods and techniques of data analysis, to study different types of phenomena in diverse disciplines. We show how the introduction of the dynamic framework in sport science has started to blur the boundaries between physiology, biomechanics, psychology, phenomenology and sociology. The advantages and difficulties of sport science integration and its consequences in research are also discussed. [ABSTRACT FROM PUBLISHER]

Metastability has been proposed as a new principle of behavioral and brain function and may point the way to a truly complementary neuroscience. From elementary coordination dynamics we show explicitly that metastability is a result of a symmetry breaking caused by the subtle interplay of two forces: the tendency of the components to couple together and the tendency of the components to express their intrinsic independent behavior. The metastable regime reconciles the well-known tendencies of specialized brain regions to express their autonomy (segregation) and the tendencies for those regions to work together as a synergy (integration). Integration ~ segregation is just one of the complementary pairs (denoted by the tilde (~) symbol) to emerge from the science of coordination dynamics. We discuss metastability in the brain by describing the favorable conditions existing for its emergence and by deriving some predictions for its empirical characterization in neurophysiological recordings. [ABSTRACT FROM AUTHOR]

Many researchers and clinicians in cognitive neuroscience hold to a modular view of cognitive function in which the cerebral cortex operates by the activation of areas with circumscribed elementary cognitive functions. Yet an ongoing paradigm shift to a dynamic network perspective is underway. This new viewpoint treats cortical function as arising from the coordination dynamics within and between cortical regions. Cortical coordination dynamics arises due to the unidirectional influences imposed on a cortical area by inputs from other areas that project to it, combined with the projection reciprocity that characterizes cortical connectivity and gives rise to reentrant processing. As a result, cortical dynamics exhibits both segregative and integrative tendencies and gives rise to both cooperative and competitive relations within and between cortical areas that are hypothesized to underlie the emergence of cognition in brains. [ABSTRACT FROM AUTHOR]

This study investigated interpersonal coordination tendencies in 1vs.1 dyads in rugby union, here expressed by participants' movement velocity towards or away from the sideline as they competed to score or prevent a try. We examined whether coupling tendencies of members of each dyad shaped key performance outcomes (try or successful tackle). Data on movement displacement trajectories of eight male rugby union players (aged 11–12 years) were analysed during performance in 47 trials. To assess coordination tendencies during exploratory behaviours in the dyads, analyses of performance time series data were undertaken using variable time graphs, running correlations and cross-correlations. Results revealed distinct coupling patterns characterised by shifts between synchronous coordination and asynchronous coordination tendencies and uncoordinated actions. Observed behaviours were interpreted as attempts of competing participants to create and perceive possibilities for action while seeking to achieve specific performance goals. Findings also revealed that a variety of patterned relations between participants resulted in different performance outcomes. [ABSTRACT FROM PUBLISHER]

What we know about infant learning and memory is founded largely on systematic studies by the late Carolyn Rovee-Collier (1942-2014) and her associates of a phenomenon called mobile conjugate reinforcement. Experiments show that when a ribbon is attached from a 3-month-old infant's foot to a mobile suspended overhead the baby quickly realizes it can make the mobile move. The mobile, which offers interesting sights and sounds, responds conjugately to the baby's vigorous kicks which increase in rate by a factor of 3-4. In this paper, using the concepts, methods and tools of coordination dynamics, we present a theoretical model which reproduces the experimental observations of Rovee-Collier and others and predicts a number of additional features that can be experimentally tested. The model is a dynamical system consisting of three equations, one for the baby's leg movements, one for the jiggling motion of the mobile and one for the functional coupling between the two. A key mechanism in the model is positive feedback which is shown to depend sensitively on bifurcation parameters related to the infant's level of attention and inertial properties of the mobile. The implications of our model for the dynamical (and developmental) origins of agency are discussed. [ABSTRACT FROM AUTHOR]

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities. [ABSTRACT FROM AUTHOR]

Social behavior is a complex integrative function that entails many aspects of the brain's sensory, cognitive, emotional and movement capacities. Its neural processes are seldom simultaneous but occur according to precise spatiotemporal choreographies, manifested by the coordination of their oscillations within and between brains. Methods with good temporal resolution can help to identify so-called "neuromarkers" of social function and aid in disentangling the dynamical architecture of social brains. In our ongoing research, we have used dual-electroencephalography (EEG) to study neuromarker dynamics during synchronic interactions in which pairs of subjects coordinate behavior spontaneously and intentionally (social coordination) and during diachronic transactions that require subjects to perceive or behave in turn (action observation, delayed imitation). In this paper, after outlining our dynamical approach to the neurophysiological basis of social behavior, we examine commonalities and differences in the neuromarkers that are recruited for both kinds of tasks. We find the neuromarker landscape to be taskspecific: synchronic paradigms of social coordination reveal medial mu, alpha and the phi complex as contributing neuromarkers. Diachronic tasks recruit alpha as well, in addition to lateral mu rhythms and the newly discovered nu and kappa rhythms whose functional significance is still unclear. Social coordination, observation, and delayed imitation share commonality of context: in each of our experiments, subjects exchanged information through visual perception and moved in similar ways. Nonetheless, there was little overlap between their neuromarkers, a result that hints strongly of task-specific neural mechanisms for social behavior. The only neuromarker that transcended both synchronic and diachronic social behaviors was the ubiquitous alpha rhythm, which appears to be a key signature of visually-mediated social behaviors. The present paper is both an entry point and a challenge: much work remains to determine the nature and scope of recruitment of other neuromarkers, and to create theoretical models of their within- and between-brain dynamics during social interaction. [ABSTRACT FROM AUTHOR]

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice. [ABSTRACT FROM PUBLISHER]

The article discusses the multiple scales at play when addressing the social cognition paradox. Topics covered include the role of primacy of movement in the ontogeny of social cognition, the use of sensorimotor experience in creating mirror neuron responses, and the emergence of two-body and second-person neuroscience.

To further advance our understanding of the brain, new concepts and theories are needed. In particular, the ability of the brain to create information flows must be reconciled with its propensity for synchronization and mass action. The theoretical and empirical framework of Coordination Dynamics, a key aspect of which is metastability, are presented as a starting point to study the interplay of integrative and segregative tendencies that are expressed in space and time during the normal course of brain and behavioral function. Some recent shifts in perspective are emphasized, that may ultimately lead to a better understanding of brain complexity. [ABSTRACT FROM AUTHOR]

Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the focus on the individual learner as the basic unit of analysis, in particular the quantification of predispositions and capabilities that the individual learner brings to the learning environment. Such predispositions constitute the learner's behavioral repertoire, captured here theoretically as a dynamical landscape ("intrinsic dynamics"). The learning process is demonstrated to not only lead to a relatively permanent improvement of performance in the required task-the usual outcome-but also to alter the individual's entire repertoire. Changes in the dynamical landscape due to learning are shown to result from two basic mechanisms or "routes": bifurcation and shift. Which mechanism is selected depends the initial individual repertoire before new learning begins. Both bifurcation and shift mechanisms are accommodated by a dynamical model, a relatively straightforward development of the well-established HKB model of movement coordination. Model simulations show that although environmental or task demands may be met equally well using either mechanism, the bifurcation route results in greater stabilization of the to-be-learned behavior. Thus, stability not (or not only) error is demonstrated to be the basis of selection, both of a new pattern of behavior and the path (smooth shift versus abrupt qualitative change) that learning takes. In line with these results, recent neurophysiological evidence indicates that stability is a relevant feature around which brain activity is organized while an individual performs a coordination task. Finally, we explore the consequences of the dynamical approach to learning for theories of biological change. [ABSTRACT FROM AUTHOR]

Multistable coordination dynamics exists at many levels, from multifunctional neural circuits in vertebrates and invertebrates to large-scale neural circuitry in humans. Moreover, multistability spans (at least) the domains of action and perception, and has been found to place constraints upon, even dictating the nature of, intentional change and the skill-learning process. This paper reviews some of the key evidence for multistability in the aforementioned areas, and illustrates how it has been measured, modelled and theoretically understood. It then suggests how multistability—when combined with essential aspects of coordination dynamics such as instability, transitions and (especially) metastability—provides a platform for understanding coupling and the creative dynamics of complex goal-directed systems, including the brain and the brain–behaviour relation. [ABSTRACT FROM AUTHOR]

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies. [ABSTRACT FROM PUBLISHER]

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a ''learning signal'' to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters. [ABSTRACT FROM AUTHOR]

Neural activity as measured non-invasively using electroencephalography (EEG) or magnetoencephalography (MEG) originates in the cortical gray matter. In the cortex, pyramidal cells are organized in columns and activated coherently, leading to current flow perpendicular to the cortical surface. In recent years, beamforming algorithms have been developed, which use this property as an anatomical constraint for the locations and directions of potential sources in MEG data analysis. Here, we extend this work to EEG recordings, which require a more sophisticated forward model due to the blurring of the electric current at tissue boundaries where the conductivity changes. Using CT scans, we create a realistic three-layer head model consisting of tessellated surfaces that represent the cerebrospinal fluid-skull, skull-scalp, and scalp-air boundaries. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from MRI scans. EEG beamforming is implemented on simulated sets of EEG data for three different head models: single spherical, multi-shell spherical, and multi-shell realistic. Using the same conditions for simulated EEG and MEG data, it is shown (and quantified by receiver operating characteristic analysis) that EEG beamforming detects radially oriented sources, to which MEG lacks sensitivity. By merging several techniques, such as linearly constrained minimum variance beamforming, realistic geometry forward solutions, and cortical constraints, we demonstrate it is possible to localize and estimate the dynamics of dipolar and spatially extended (distributed) sources of neural activity. [ABSTRACT FROM AUTHOR]

Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production, perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time emotional responses on a 2-dimensional rating scale (arousal and valence) as they listened to each performance. During fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to the expressive dynamics of human music performance, and both emotion and reward related activations during music listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror neuron system. These findings show that expressive music performance evokes emotion and reward related neural activations, and that music's affective impact on the brains of listeners is altered by musical training. Our observations are consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at least in part, on the perception of movement and on violations of pulse-based temporal expectancies. [ABSTRACT FROM AUTHOR]

The theoretical framework of coordination dynamics posits complementary neural mechanisms to maintain complex behavioral patterns under circumstances that may render them unstable and to voluntarily switch between behaviors if changing internal or external conditions so demand. A candidate neural structure known to play a role in both the selection and maintenance of intentional behavior is the basal ganglia. Here, we use functional magnetic resonance imaging to explore the role of basal ganglia in intentional switching between bimanual coordination patterns that are known to differ in their stability as a function of movement rate. Key measures of pattern dynamics and switching were used to map behavior onto the associated neural circuitry to determine the relation between specific behavioral variables and activated brain areas. Results show that putamen activity is highly sensitive to pattern stability: greater activity was observed in bilateral putamen when subjects were required to switch from a more to a less stable pattern than vice versa. Since putamen activity correlated with pattern stability both before and during the switching process, its role may be to select desired actions and inhibit competing ones through parametric modulation of the intrinsic dynamics. Though compatible with recent computational models of basal ganglia function, our results further suggest that pattern stability determines how the basal ganglia efficiently and successfully select among response alternatives. [ABSTRACT FROM AUTHOR]

In coordination dynamics, rate is a nonspecific control parameter that alters the stability of behavioral patterns and leads to spontaneous pattern switching. We used fMRI in conjunction with measures of effective connectivity to investigate the neural basis of behavioral dynamics by examining two coordination patterns known to be differentially stable (synchronization and syncopation) across a range of rates (0.75 to 1.75 Hz). Activity in primary auditory and motor cortices increased linearly with rate, independent of coordination pattern. On the contrary, activity in a premotor-cerebellar circuit varied directly with the stability of the collective variable (relative phase) that specifies coordinated behavioral patterns. Connectivity between premotor and motor cortices was also modulated by the stability of the behavioral pattern indicative of greater reliance on sensorimotor integration as action becomes more variable. By establishing a critical connection between behavioral and large scale brain dynamics, these findings reveal a basic principle for the neural organization underlying coordinated action. [ABSTRACT FROM AUTHOR]

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI-Virtual Partner Interaction-a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms. [ABSTRACT FROM AUTHOR]

One difference between the excitator model and other theoretical models of coordination is the mechanism of discrete movement initiation. In addition to an imperative signal common to all discrete movement initiation, the excitator model proposes that movements are initiated when a threshold element in state space, the so-called separatrix, is crossed as a consequence of stimulation or random fluctuations. The existence of a separatrix predicts that false starts will be caused by mechanical perturbations and that they depend on the perturbation's direction. The authors tested this prediction in a reaction-time task to an auditory stimulus. Participants applied perturbations in the direction of motion (i.e., index finger flexion) or opposed to the motion prior to the stimulus on 1/4 of the trials. The authors found false starts in 34% and 9% of trials following flexion perturbations and extension perturbations, respectively, as compared with only 2% of trials without perturbations, confirming a unique prediction of the excitator model. [ABSTRACT FROM AUTHOR]

The basic reciprocity between individual parts and collective organization constitutes a key scientific question spanning the biological and social sciences. Such reciprocity is accompanied by the absence of direct linkages between levels of description giving rise to what is often referred to as the aggregation or nonequivalence problem between levels of analysis. This issue is encountered both in neuroscience and economics. So far, in spite of being identified and extensively discussed in various (other) scientific fields, the problem of understanding the nature of the interactions and coordination dynamics between individual (neuron ~ agent) and collective (neural networks ~ population of humans) behaviors has received little, if any attention in the growing field of neuroeconomics. The present contribution focuses on bringing a theoretical perspective to the interpretation of experiments recently published in this field and addressing how the concepts and methods of coordination dynamics may impact future research. First, we very briefly discuss the links between biology and economics. Second, we address the nonequivalence problem between different levels of analysis and the concept of reciprocal causality. Third, neuroeconomics studies that investigate the neural underpinnings of social decision making in the context of two economic games (trust and ultimatum) are reviewed to highlight issues that arise when experimental results exist at multiple scales of observation and description. Finally, in the last two sections, we discuss how coordination dynamics might provide novel routes to studying and modelling the relation between brain activity and decision making [ABSTRACT FROM AUTHOR]

Spontaneous social coordination has been extensively described in natural settings but so far no controlled methodological approaches have been employed that systematically advance investigations into the possible self-organized nature of bond formation and dissolution between humans. We hypothesized that, under certain contexts, spontaneous synchrony—a well-described phenomenon in biological and physical settings—could emerge spontaneously between humans as a result of information exchange. Here, a new way to quantify interpersonal interactions in real time is proposed. In a simple experimental paradigm, pairs of participants facing each other were required to actively produce actions, while provided (or not) with the vision of similar actions being performed by someone else. New indices of interpersonal coordination, inspired by the theoretical framework of coordination dynamics (based on relative phase and frequency overlap between movements of individuals forming a pair) were developed and used. Results revealed that spontaneous phase synchrony (i.e., unintentional in-phase coordinated behavior) between two people emerges as soon as they exchange visual information, even if they are not explicitly instructed to coordinate with each other. Using the same tools, we also quantified the degree to which the behavior of each individual remained influenced by the social encounter even after information exchange had been removed, apparently a kind of social memory. [ABSTRACT FROM AUTHOR]

This paper examines the influence of context on the processing of category names embedded in sentences. The investigation focuses on the nature of information available immediately after such a word is heard as well as on the dynamics of adaptation to context. An on-line method (Cross Modal Lexical Priming) was used to trace how this process unfolds in time. We found that the information available immediately after a category word is presented is not altered by the sentence context in which the word is immersed. Rather, the structure of availability of particular exemplars of the category resembles the typicality structure of a conceptual representation. The adaptation to context occurs later (between 300 and 450 ms after the category word) and takes the form of a rapid reorganization of the structure rather than a gradual activation of a contextually relevant exemplar. We claim that such data is best accounted for in a dynamical framework, where a coherent global structure emerges through locally guided self-organization. [ABSTRACT FROM AUTHOR]

The primary aim of genetic association and linkage studies is to identify genetic variants that contribute to phenotypic variation within human populations. Since the overwhelming majority of human genetic variation is found within populations, these methods are expected to be effective and can likely be extrapolated from one human population to another. However, they may lack power in detecting the genetic variants that contribute to phenotypes that differ greatly between human populations. Phenotypes that show large differences between populations are expected to be associated with genomic regions exhibiting large allele frequency differences between populations. Thus, from genome-wide polymorphism data genomic regions with large allele frequency differences between populations can be identified, and evaluated as candidates for large between-population phenotypic differences. Here we use allele frequency data from ∼1.5 million SNPs from three human populations, and present an algorithm that identifies genomic regions containing SNPs with extreme Fst. We demonstrate that our candidate regions have reduced heterozygosity in Europeans and Chinese relative to African-Americans, and are likely enriched with genes that have experienced positive natural selection. We identify genes that are likely responsible for phenotypes known to differ dramatically between human populations and present several candidates worthy of future investigation. Our list of high Fst genomic regions is a first step in identifying the genetic variants that contribute to large phenotypic differences between populations, many of which have likely experienced positive natural selection. Our approach based on between population differences can compliment traditional within population linkage and association studies to uncover novel genotype-phenotype relationships. [ABSTRACT FROM AUTHOR]

Teenagers with nonverbal learning disabilities (NLD) have difficulty with fine-motor coordination, which may relate to the novelty of the task or the lack of "self-talk" to mediate action. In this study, we required two teenagers with NLD and two control group teenagers to touch the thumb of each hand firmly and accurately to the fingertips of the same hand, in an order specified by verbal or tactile instruction. Brain activity patterns (measured using functional magnetic resonance imaging) suggest that unlike control participants, the NLD participants used internalized speech to facilitate the novel task only when instructions were verbal. NLD participants also showed activity in a more widely distributed network of neural structures. These findings provide preliminary evidence for remediation strategies that encourage internal speech. [ABSTRACT FROM AUTHOR]

Many social interactions rely upon mutual information exchange: one member of a pair changes in response to the other while at the same time producing actions that alter the behavior of the other. However, little is known about how such social processes are integrated in the brain. Here, we used a specially designed dual-electroencephalogram system and the conceptual framework of coordination dynamics to identify neural signatures of effective, real-time coordination between people and its breakdown or absence. High-resolution spectral analysis of electrical brain activity before and during visually mediated social coordination revealed a marked depression in occipital alpha and rolandic mu rhythms during social interaction that was independent of whether behavior was coordinated or not. In contrast, a pair of oscillatory components (phi1 and phi2) located above right centroparietal cortex distinguished effective from ineffective coordination: increase of phi1 favored independent behavior and increase of phi2 favored coordinated behavior. The topography of the phi complex is consistent with neuroanatomical sources within the human mirror neuron system. A plausible mechanism is that the phi complex reflects the influence of the other on a person's ongoing behavior, with phi1 expressing the inhibition of the human mirror neuron system and phi2 its enhancement. [ABSTRACT FROM AUTHOR]

Very little is known about the coordination of movement in combination with stimuli such as sound and touch. The present research investigates the hypothesis that both the type of action (e.g., a flexion or extension movement) and the sensory modality (e.g., auditory or tactile) determine the stability of multimodal coordination. We performed a parametric study in which the ability to synchronize movement, touch and sound was explored over a broad range of stimulus frequencies or rates. As expected, synchronization of finger movement with external auditory and tactile stimuli was successfully established and maintained across all frequencies. In the key experimental conditions, participants were instructed to synchronize peak flexion of the index finger with touch and peak extension with sound (and vice-versa). In this situation, tactile and auditory stimuli were delivered counter-phase to each other. Two key effects were observed. First, switching between multimodal coordination patterns occurred, with transitions selecting one multimodal pattern (flexion with sound and extension with touch) more often than its partner. This finding indicates that the stability of multimodal coordination is influenced by both the type of action and the stimulus modality. Second, at higher rates, transitions from coherent to incoherent phase relations between touch, movement and sound occurred, attesting to the breakdown of multimodal coordination. Because timing errors in multimodal coordination were systematically altered when compared to unimodal control conditions we are led to consider the role played by time delays in multimodal coordination dynamics. [ABSTRACT FROM AUTHOR]

Fechner-Benham subjective color is widely believed to be governed by local interactions in early (probably retinal) mechanisms. Here we report three lines of phenomenological evidence that suggest otherwise: subjective colors seen in spatially extended stimuli (a) are dependent on global aspects of the stimuli; (b) can become multistable in position; and (c) even after being stabilized do not support the creation of McCollough's colored after-effects — a cortically based phenomenon generally thought to be more central than Fechner-Benham color. These phenomena suggest a central locus that controls perception of subjective color, characterized by pattern dependent interactions among cortical mechanisms that draw their inputs from peripheral units. [ABSTRACT FROM AUTHOR]