Coordination is something we all take for granted – at least until it breaks down under extreme stress (as it does sometimes in competition) or following an insult or disease to the brain, or even when body parts are injured or replaced. So to understand coordination scientifically, we must somehow “make the familiar strange”—akin perhaps to the proverbial falling apple that led to Newton’s great insights about gravity. Coordination is not just physics, even though we may apply physical methods to try to understand it. Nor is it just psychology, even though methods of cognitive science such as reaction time are used to assess how coordination is planned before movements are even initiated. To understand coordination is a very deep problem, maybe as deep as understanding life itself. The reason is that coordination is not just any kind of order in space and time. It concerns how very many component parts and processes on many different levels of an organization relate in an orderly fashion to produce a recognizable function or accomplish some particular task. Coordination may thus be defined as a functional ordering among interacting components in space and time. Coming in many guises, coordination represents one of the most striking features of living organisms. It’s everywhere we look. Whether in the regulatory interactions among genes that affect how cells become organs, the tumbling of a bacterium,  the coordinated responses of organisms to constantly varying environmental stimuli, the coordination among nerve cells and muscles that produce basic forms of locomotion, the coordination among cell assemblies of the brain that underlies our ability to think, decide, remember and act, the miraculous coordination between the lungs, larynx, lips and tongue that belies a child’s first word, the learned coordination among fingers and brain that allows the skilled pianist to play a concerto, the coordination of motion and emotion when making a key play in sport settings when the game is on the line, the coordination between people—like rowers in a racing eight and players in a rugby team–working together to achieve a common goal. From the micro- to the macro, from genes and cells to brains, people and society everything involves coordination.

Although the details of coordination are bound to be different at different levels of biological organization, for different organisms and for different functions, might there also be some basic principles of coordination that transcend these differences? The behavioral physiologist, Erich von Holst certainly thought this was so. In his essay “On the Nature of Order in the Central Nervous System” (1937) von Holst surveyed the wide occurrence of three basic kinds of coordination in the neural and rhythmic activities of animals, from respiration to voluntary movements, from worms to human beings. One he called absolute coordination, a long-recognized form in which component parts operate with the same frequency and with specific reciprocal phase relationships, just like a marching band or people clapping after a performance. Another, extremely rare form that von Holst didn’t give a name to, concerning the complete lack of interaction between component parts as in the locomotion of centipedes and millipedes in whom a certain number of (middle) legs had been amputated (no reference to the game of cricket intended!). Persistent practice, von Holst thought, as in playing the piano or the violin could also lead to complete independence among the fingers of the two hands. The third, possibly most important basic form, von Holst termed relative coordination. Here the activities of the individual component parts are neither completely independent of each other nor linked in a fixed mutual relationship. For example, the fins of a fish may not always oscillate at the same frequency and can flexibly slip in and out of preferred phase relationships as internal and surrounding conditions change. Relative coordination provides a glimpse of the tensions between two opposing tendencies that are present in all forms of complex coordination, the tendency of the components to keep separate (segregation) and the tendency to cooperate together (integration). Back in the days when chain reflexes were thought to govern coordinated behavior, the phenomenon of relative coordination hinted at the importance of intrinsic pattern generating processes in the central nervous system. Since these early days, great strides have been made in identifying the cellular mechanisms involved in neuronal circuits underlying the generation of rhythmic patterns of coordination. Moreover, in the last 30 years or so, a theoretical framework called Coordination Dynamics has emerged to explain all of von Holst’s basic coordination types, mixtures among them and more generally how coordination emerges, adapts, persists and changes in complex biological systems. Principles of coordination dynamics have been shown to govern patterns of coordination (a) within a moving limb and between moving limbs; (b) between the articulators during speech production; (c) between limb movements and tactile, visual and auditory stimuli; (d) between people interacting with each other spontaneously or intentionally; (e) between humans and avatars; (f) between humans and other species, as in riding a horse; and (g) within and between the neural substrates that underlie the coordinated behavior of human beings as observed using modern brain imaging methods.

Some of the key concepts that are allowing a deeper understanding of coordination are self-organization, collective variables, degeneracy, synergy, informational coupling, and intrinsic dynamics. Self-organization refers to the fact that patterns of coordinated behavior can arise solely as a result of the dynamics of the system, with no homunculus-like agent inside telling the parts what to do and when to do it.  The ‘self’ in the word comes from the fact that the system organizes itself. What is important is setting up the conditions for such self-organized pattern formation to occur. The latter is defined, not in terms of the many individual parts or degrees of freedom, but rather in terms of collective variables that arise as a result of the many interactions that are going on. Collective variables are low dimensional, hence simpler descriptions of a complex system. They are meaningful quantities for the system’s proper functioning. Collective variables are important to identify because they span different domains, such as sensory and motor, brain and body, perception and action, etc. which are usually defined as separate. Collective variables thus refer to the coupling between different things and processes. Degeneracy is an important concept in biology and means that at every conceivable level of description, the same outcome or function can be achieved in many ways using different components and different combinations among them. Thus, for example, in coordinated movements such as reaching for a cup, many different neural pathways and muscular configurations can combine to achieve the same goal. The mechanism that Nature seems to use to handle degeneracy is that it synergizes. Synergies are context-sensitive functional groupings of elements that are temporarily assembled to act as a single coherent unit. Depending on the context, synergies may accomplish different coordinative functions using some of the same components (e.g., the jaw, tongue, and teeth to speak and chew) and the same function using different components (e.g. ‘hand’ writing with a pen attached to the big toe). The hallmark of synergy is that during the course of ordinary function a perturbation to any part of the synergy is immediately compensated for by remotely linked parts in such a way as to accomplish a task or preserve functional integrity. Synergies are important because they are the functional units of coordination at all levels of biological organization. A nice example is the so-called “coxless” pair in rowing where each oarsman has a single opposing oar. The boat can only go straight across the river if each rower pulls his own weight. If one slack off, the boat will go in circles and the joint goal of the pair will not be accomplished. This kind of cooperative, mutually beneficial interdependency among the interacting parts of a coupled system to achieve a common objective is ubiquitous in nature. It is a signature of functional synergy. Relatedly, the coordination between different things (e.g. parts of the body, regions of the brain) and between different kinds of things (e.g. the organism and the environment, two people, and so forth) depends on information exchange, usually bidirectional in nature (“I talk to you, you talk to me”). Interacting components and features can thus be coupled by material forces, by light, by sound, by touch, by smell, and by intention to accomplish an objective. Such meaningful information transcends the medium through which the parts communicate; it is context-specific to the particular form that coordination takes in different task settings. In the coordinated systems of life and movement, the component parts and processes are seldom coupled purely mechanically; they are informationally coupled. Information is not lying out there as mere data, coded in some symbolic form: information is meaningful to the extent that it modifies, and is modified by the intrinsic, self-organizing dynamics.

This brings us to the important question of how new patterns of coordination are learned. Much evidence now indicates that this depends on the predispositions and capabilities of the individual learner before learning begins. This is sometimes quite difficult for scientists to quantify. Nevertheless, such predispositions constitute the learner’s behavioral repertoire at a given point in time: the learner’s intrinsic dynamics. As an inspiring coach or teacher knows, the great benefit of identifying the learner’s intrinsic dynamics is that one knows what to modify. Any new information (say a task to be learned, an intention to change behavior) has to be expressed in terms of the learner’s intrinsic dynamics, otherwise, change is not possible. Indeed, the mechanisms through which coordination changes and the nature of the change with learning itself depend crucially on the initial individual repertoire before new learning begins. In this view of coordination, information is not really information unless it modifies the intrinsic dynamics. Likewise, intrinsic dynamics is only intrinsic dynamics to the extent that it is modifiable by information.

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Key concepts:

Coordination Dynamics :
Coordination Dynamics, defined broadly as the science of coordination, describes, explains and predicts how patterns of coordination form, adapt, persist and change in living things. In coordination dynamics the parts communicate via mutual information exchange and information is meaningful and specific to the forms coordination takes. Coordination dynamics embraces both spontaneous self-organizing tendencies and the need to guide or direct them in specific ways in a single conceptual framework. Life, brain, mind, and behavior are hypothesized to be linked by virtue of sharing a common underlying coordination dynamics.

Synergies (aka coordinative structures) are functional groupings of structural elements (e. g. neurons, muscles, joints) that are temporarily constrained to act as a single coherent unit. They arise in many contexts on many levels of biological organization, from the genetic to the social. Synergies are the key to understanding biological coordination and as such are the significant units of coordination dynamics. The synergy hypothesis is a hypothesis about how Nature handles biological complexity.

The ‘self’ in the word self-organization refers to the ability of an open system that exchanges matter, energy, and information with the environment, to organize itself. Spontaneous patterns arise solely as a result of the dynamics of the system with no specific ordering influence imposed from the outside and no homunculus-like agent inside. Nonequilibrium phase transitions are the hallmark of self-organization in living things.

Collective Variables
Collective variables (aka order parameters in physics or coordination variables in coordination dynamics) are relational quantities that are created by the cooperation among the individual parts of a system. Yet they, in turn, govern the behavior of the individual parts. This is sometimes referred to as circular or reciprocal causality. In coordination dynamics, the identification of coordination variables depends on the level of description. What is “macro” at one level may be “meso” or “micro” at another?

Control Parameters
Control parameters refer to naturally occurring environmental conditions or intrinsic, endogenous factors that move the system through its repertoire of patterns and cause them to change. Experimentally, you only know for certain you have identified a control parameter if, when varied, it causes the system’s behavior to change qualitatively or discontinuously, i. e., to change its functional state.

Metastability arises due to broken symmetry in the coordination dynamics where the unstable and stable fixed points (phase- and frequency-locked states) have disappeared due to a tangent or saddle-node bifurcations leaving behind only remnants of the fixed points. Metastability is the simultaneous realization of two competing tendencies: the tendency of the components to couple together and the tendency of the components to express their intrinsic independent behavior. Metastability has been hailed as a new principle of organization in complex living systems, including the brain, reconciling apparent contraries such as individual and collective, part and whole, competition and cooperation, integration and segregation, and so forth.


The three complementary modes in our investigation: brain and behavior experiments (left) provide basic information about human’s intrinsic dynamics and their interaction; theoretical/computational models (right) incorporate discovered principles and mechanisms into equations that generate data for comparison with experiments; and (top middle) the powerful dynamic clamp called virtual partner interaction (VPI) allows a real partner to interact with an avatar is driven by a dynamical model of human social behavior thereby enabling a full exploration of the parameter space of basic social interactions.

Neural Choreography

1-3Neural circuits originate spatiotemporal signatures in the EEG called neuromarkers. These neuromarkers are transiently recruited to serve specific functions of the human brain, for instance, spatial and selective attention, somatosensation or motor coordination. This neuromarker approach has already lead to the discovery of a new set of neural oscillations related to social coordination: the two neuro markers phi1 and phi2  (See Tognoli et al., 2007for more detail). This finding was made possible by virtue of using a continuous social coordination task and a high spectral resolution. The method was then applied to other tasks such as intentional social coordination, action observation and delayed imitation (Tognoli et al., 2010; Tognoli & Kelso, submitted; Suutari at al., in prep). This pointed out the diversity of neural mechanisms contributing to different phases and facets of social behavior. This raises fundamental questions about their functional organization and quantitative properties. “How?” and “when?” they work together needs to be understood. Theory and observation suggests that neuromarkers work together in two (likely related) ways: one through phase-, frequency-locking and metastability (Bressler & Kelso, 2001; Bressler & Tognoli et al., 2006; Jensen & Colgin , 2007; Kelso & Tognoli, 2009; Tognoli & Kelso, 2009; Kelso, 2012); the other via deterministic rules of pattern sequence. Together, they form a “neuromarker choreography”.



1-4When elements of a system come to interact, their activity ceases to be solely determined by their selves: their behavior also depends on the system’s other elements and their dynamics. How are neural, behavioral and social factors coordinated in real time so as to make possible the emergence of social cognition? Our team has concentrated for years on the elaboration of the ”coordination dynamics” framework in the perspective of understanding these dynamical similarities across neurobiological, behavioral and social levels (Kelso, 1995Fuchs and Jirsa, 2008; Kelso, 2009; Kelso et al. 2012). Through the use of hyperscanning techniques, it is now possible of recording simultaneously two brains. This allows the study of the relationships between neuromarkers at the individual brain level (Tognoli et al. 2007) and inter-brain measurement (Dumas et al. 2010). Such investigation of the links between intra- and inter-brain neuromarkers could not only help in the understanding of social cognition but also of the key complementary pair of integration∼segregation (Kelso and Engstrøm, 2006).



 Memory and Learning


We demonstrated in a finger flexion study (Oullier et al., 2008) that the individual behavior of people, who have socially interacted, continues to exhibit a pattern even when visual contact is removed. This persistence of behavior indicates social memory. The goals of our new study are to provide evidence of social memory in a neurobehavioral study of social coordination, to show its neural correlates and those of its breakdown, and to identify the factors that modulate it. We instructed participants to flex their fingers continuously and at a spontaneous, comfortable pace. For twenty seconds, the participants moved alone, then for twenty seconds, they moved while watching their partner and then moved without a view of their partner for twenty seconds more. In more than half of the cases, participants spontaneously coordinated. Interestingly, we see that of those coordinating trials, their relative phase destabilized once visual contact ended, but their mutually adopted frequency remained approximately the same for some of the time. Looking at interrater agreed judgments, we see a bimodal pattern: one pattern indicating a quickly decaying memory, while the other suggests a longer than twenty-second persistence. The instant that a participant loses the social frequency is the instant where we look in the EEG recording. We are pursuing two approaches to calculating the frequency spectra of the neural oscillations. One method uses the entire post-interaction period, while the other takes small windows before, during, and after the moment of social memory loss. This allows us to compare local and global pictures of brain activity in time. Preliminary results indicate that frontocentral activity in the theta band is relevant to social memory.


We also investigate skill learning by developing strategies, conceptual tools and operational measures that enable investigations of the role of initially present patterns of behavior in individual learners. We specifically discovered that the paths that different people take to learning a new skill—whether the skill is acquired suddenly or gradually, how much attention is devoted to learning a new skill and people’s ability to recall that skill—are a function not only of improvements in accuracy of the behavioral patterns produced over the course of practice, but also, and above all, of changes in pattern stability (Kostrubiec et al. 2012).

Team Coordination


Performing a task as a team requires that team members mutually coordinate their actions. It is this coordination that distinguishes the performance of a team from the same actions performed independently. Our goal is to find behavioral and neural correlates of team coordination and team performance.
Tasks performed by teams are often complex with a high number of degrees of freedom. Such tasks are notoriously difficult to analyze because of the high variability of the associated behavioral and brain dynamics. This variability is in part due to the degeneracy of behavioral and brain dynamics. Degeneracy is ubiquitous in the brain, but rarely explicitly acknowledged. Importantly, it demands a departure from the currently prevalent quest for a single neural mechanism. We take advantage of degeneracy and pursue a conceptual and empirical framework which explains variability in geometrical terms. In our framework behavioral and brain signals are interpreted as evolving along a manifold in phase space, which reflects task constraints and team coordination.
Using this approach revealed that team coordination depends on experience, decaying quickly in novices but being maintained longer in intermediate and experienced teams (Dodel et al., 2010). Furthermore, in a study of dual EEG data from a two-member team performing a simulated combat scenario we found that dimensionality increases in the joint brain dynamics of the team members are a signature of increased task demand (Dodel et al., 2011). In another dual EEG study in which a two-member team performed an ecologically valid task, our approach revealed that team coordination is associated with increased inter-brain coherence of beta and gamma rhythms in trial segments associated with information flow between the subjects (Dodel et al., 2012).

HBBL facilities include three main integrated components, reflecting the goal of connecting brain and behavior on different spatial and temporal scales.



The 128-channel Neuroscan and 85-channel Manscan EEG systems (including dual EEG capability), and the 1.5T system available at UMRI:


The 275- and 163-channel MEG capability by virtue of ongoing collaborations at NIH Core Facility (R. Copolla, Director) and the Hospital for Sick Children, University of Toronto (D. Cheyne).

Probing cognitive and motor behavior

The VICON/OPTOTRACK lab and specialized equipment for generating displays and recording cognitive and motor behavior:

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The HBBL has also developped the Virtual Partner Interaction (VPI) paradigm, a unique Human Dynamic Clamp which allows the real-time interaction between human subjects and dynamical models.

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Empirical data only afford a partial view of a system’s spatiotemporal organization, observed dynamics being restricted to certain (often linear) domains of phase space. Dynamical modeling provides a simplified but more comprehensive view: it extends the boundaries of empirical data, exposes continuity between qualitatively different regimes, shows the paths leading from one regime to another, attempts to reveal the entire parameter space of the underlying dynamics, and may ultimately lead to uncovering first principles (Kelso, 1995; 2012; Kelso at al., 2009; in press). A complementary aspect of our approach combines extensive experimental observations with theoretical models. This continuous dialog between experiments and models (and between experimental and theoretical scientists on our team) is a key feature of our three-way plan, which adds theoretical modeling to assess the impact of specific properties or parameters such as coupling strength and directionality on dyadic interaction (de Guzman et al., 2010; Kelso, 2012; Kelso at al., in press).

The Haken-Kelso-Bunz (HKB) model

The Haken-Kelso-Bunz (HKB) model was proposed in that spirit: theoretical modeling of a system of (nonlinearly) coupled nonlinear oscillators reproduced essential properties of biological coordination (e.g. different forms of phase synchrony, instability, phase transitions) and predicted others (critical slowing, fluctuation enhancement, hysteresis, etc., Riley, et al., 2011 for recent review). With over 1200 citations, HKB is probably the best known and most extensively tested quantitative model in human movement behavior (Fuchs & Jirsa, 2008; Haken, Kelso & Bunz, 1985). More importantly, the HKB model also encompasses coordination at neural, behavioral and social scales thus offering a powerful tool for integrating different levels of brain and behavior (Kelso, 1995; 2012; Kelso, et al., in press). The extended version of the HKB (Kelso et al., 1990) investigated how the coordination dynamics is modulated when the interacting systems have different intrinsic dynamics (e.g. frequency of oscillation).


Integrating such ‘broken-symmetry’ into the model provided new insight into the phenomenon of metastability which has been proposed as a fundamental principle of brain and behavior (Bressler and Kelso, 2001; Freeman & Holmes, 2005; Friston, 1997; Kelso, 1995; 2012; Kelso & Tognoli, 2007; Perez Velazquez & Wennberg, 2004; Rabinovitch et al., 2008; Sporns, 2010; Tognoli & Kelso, 2009; Werner, 2007).

The Excitatory model

In line with the foregoing theoretical research, a further advance was to create the mathematical conditions for discrete behaviors to arise from the continuous dynamics of the system’s self-sustained oscillators, the so-called “excitator” model (Jirsa & Kelso 2005). Although it seems intuitive that continuous behavior arises from the juxtaposition of discrete actions, nature sometimes appears to proceed the other way around, using basic building blocks with self-sustained dynamics such as central pattern generators to produce the sophisticated functional mechanics of the neocortex (Yuste et al., 2005; Kelso, 2009).


The excitator model (Jirsa & Kelso 2005) shares many properties of excitable two-dimensional dynamical systems in neurobiology but is especially relevant here because it provides a tool to study both continuous and discrete dynamics. More than generalizing the HKB model, it provides an entry point to the understanding of a variety of new phenomena such as false starts (Fink, Kelso & Jirsa, 2009) and the geometry of phase space trajectories (Jirsa & Kelso, 2005).

The Virtual Partner Interaction (VPI)

1-16 A further advance made in the previous funding period was the introduction of Virtual Partner Interaction or VPI (Kelso at al., 2009), a hybrid model/experiment paradigm analog to the “dynamic clamp” of cellular neuroscience (Prinz et al., 2004). VPI consists of a human partner and its mathematical mirror (“virtual partner”), who are reciprocally coupled via the HKB equations of coordination dynamics. Human and virtual partners are provided coordination tasks to jointly accomplish and behavioral self-organization is studied (Kelso at al., 2009; de Guzman et al., 2009). The experimenter can tune parameters to assess the impact of specific properties on coupling strength and directionality (de Guzman et al., 2010; Kelso, 2012; Kelso at al., in press). The VPI technique offers access to a broad range of on-line and reciprocal interactions, including regions of the dynamical state space that cannot be easily explored during live human-human interactions. This extended parameter range opens up the possibility of systematically driving social interaction; it has already led to the discovery of novel coordination behaviors, stable patterns of social interaction, never seen before because of their location in orphaned regions of the parameter space. Because VPI left human subjects with a strong sense of agency and intentionality from their virtual partner, this work has also led to an expanded inquiry on agency and perception of cooperation (Drever et al., 2012).

 Neurocomputational model of social interaction

1-17 Finally, in order to integrate measurements from multiple levels of description into a single dynamical account (Kelso, et al., in press; Tognoli et al., 2010), we extend our approach to develop neurocomputational models of social behavior. Earlier on, we have shown theoretically how bimanual behavior relies on brain architecture, especially inter-hemispheric anatomical connectivity (Banerjee & Jirsa, 2007; Banerjee et al., 2012; Jirsa, Fuchs & Kelso, 1998). This allowed for fruitful quantitative predictions about the relationship between brain and behavior (Kelso et al., Nature, 1998). More recently, we turned the same logic to the question of social as opposed to individual behavior (Dumas et al. 2012). A novel neurocomputational model was developed to understand the relationship between brain architecture (including interbrain structural symmetries) and social interactions. The resulting social network was built from a pair of real human connectomes. Those connectomes were obtained by parcellation of brain regions combined with diffusion tensor imaging (DTI) datasets. Brain areas were mapped to self-sustained oscillators, which were coupled neurally within brain (connectomes) and between brains. At rest or combined with functional simulation, such networks further our understanding of how structure and dynamics are intertwined within and between human brains, and how they relate to social behavior. The method has already shown that the anatomical connectivity of the human brain enhances similarities of the neural dynamics and facilitates the creation of sensorimotor coupling between individuals (Dumas et al. 2012). This computational social neuroscience approach leads to insights and specific predictions about the neurobiological mechanisms underlying social behavior, including the link between structural anomalies and self-other dysfunction in pathologies such as autism and schizophrenia (Dumas, 2011).


Featured: Self-Organizing Agency


What are the roots of intentional action? This program of research investigates how infants begin to make sense of their coordinative relationship with the world and realize their ability to affect change. Detailed measurement and careful probing of infant~environment coordination dynamics will reveal an array of phenotypic paths to conscious agency which depend on intrinsic infant factors, environmental affordances, and interaction parameters.

Visit  SOAL   (Self-Organizing Agency Lab), a sub-division of HBBL, to learn more!