Draft:Coordination Dynamics

From Wikipedia, the free encyclopedia

Coordination Dynamics

Coordination dynamics is a field of scientific inquiry that examines the universal principles underlying coordination in living organisms and the systems they create, such as social systems and economies. It focuses on the spatiotemporal ordering and organization of functionally interacting components within these systems. The term "dynamics" refers to the mathematical framework of nonlinear dynamics used to describe properties such as oscillation, phase transitions, pattern formation, self-organization, multistability, and metastability.

Overview Coordination dynamics integrates methods from various disciplines, including physics, biology, psychology, cognitive science, neuroscience, nonlinear dynamics, chaos theory, kinesiology, and philosophy. This interdisciplinary approach allows researchers to investigate coordination across different levels of analysis, from genetic regulatory networks and neuronal populations to human motor movements and social interactions.

The Logic of Coordination Dynamics Coordination dynamics employs a combination of empirical observation, theoretical analysis, and computational modeling. Researchers first identify the system's order parameters, which characterize the formation and change of patterns. Control parameters, which can be endogenous or exogenous, are then identified and manipulated to observe changes in the system's behavior. The mathematical models developed help explain and predict the dynamic patterns formed by living systems.

Core Principles Collective Variables Collective variables, or order parameters, capture measurable relations between individual elements in a system. These variables reflect the global properties emerging from local interactions, such as the relative phase between oscillating neuronal populations. Control Parameters Control parameters are features of a system or its environment that, when varied, can qualitatively change the system's behavior. These changes can lead to phenomena such as bifurcations. Oscillation Oscillation refers to the repetitive back-and-forth changes in a system's state over time. It is a central feature in many systems studied by coordination dynamics, including neurons, human limbs, and predator-prey models. Degeneracy Degeneracy describes a system's ability to achieve the same function or outcome through multiple processes. This flexibility is crucial for adaptation and evolution. Synergies Synergies are functional groupings of elements that interact as a single unit to achieve various objectives. They enable degeneracy by allowing different components to accomplish the same goal. Informational Coupling Informational coupling refers to the meaningful exchange of information between components within a system. This exchange facilitates self-organization and emergence. Self-Organization and Pattern Formation Self-organization occurs in open systems that exchange energy and matter with their environment, leading to structured patterns without external influence. Phase transitions are central to this process. Metastability and Broken Symmetry Metastability involves the simultaneous coupling and autonomy of a system's components. It is particularly relevant in cortical coordination dynamics, where brain regions can coordinate their behavior while maintaining individual oscillatory activity. The Haken-Kelso-Bunz (HKB) Model The HKB model, initially developed to explain bimanual finger movements, provides a foundational mathematical description of coordination dynamics. It has been extended to include symmetry-breaking terms and stochasticity, offering insights into various forms of coordination. Applications Neurosciences Coordination dynamics principles are widely used in neuroscience to study brain oscillations, phase transitions, and self-organization. Researchers have explored how cognition emerges from the coordination dynamics of large-scale brain networks. Coordination dynamics is regularly used to study neural synchronization and the dynamic coupling of brain regions. Kinesiology and Human Movement Originally applied to inter-limb motor movements, coordination dynamics now explores complex activities such as ballet dancing and infant kicking. These studies reveal fundamental coordination patterns and the bidirectional coupling between organisms and their environment. Economics and Social Coordination Sciences Coordination dynamics provides novel approaches to modeling economic decision-making and social interactions. Researchers study how individuals and groups coordinate their behavior, often examining phenomena like social memory and collective behavior. Psychotherapy Interpersonal coordination dynamics examines the synchronized behaviors and physiological responses of therapists and patients. This field investigates how coordination influences the effectiveness of psychotherapeutic interactions. Sports Sciences Coordination dynamics is applied to sports science to analyze neural processes during performance and skill learning. Researchers also study team movement patterns as self-organizing systems.

Recent Research Recent studies have provided deeper insights into, and extended, the field of coordination dynamics: • Spatiotemporal Metastability: Research on human sensorimotor coordination reveals that metastability is a key feature of coordination dynamics. Metastability allows for the coexistence of integration and segregation in social interactions, enabling flexible and adaptive behavior. • Intermediate-Sized Ensembles: Studies on ensembles of eight people have shown that manipulating movement frequency diversity within the group leads to qualitative changes in coordination dynamics. Higher diversity leads to segregation, while lower diversity promotes integration. This has implications for understanding how group behavior emerges from individual interactions. • Cross-Frequency Communication: In conditions of high diversity, participants adopt higher-order frequency relations, enabling communication across segregated groups without losing overall order. This phenomenon has been modeled successfully, bridging the Kuramoto and Haken-Kelso-Bunz models.

See Also • Metastability in the Brain • Phi Complex • Neural Oscillation • Motor Coordination • Electroencephalogram • J.A. Scott Kelso • Complex Systems • Nonlinear Dynamics • Network Science • Self-Organization • Cognitive Modeling • Emergence • Dynamical Systems External Links • Human Brain and Behavior Laboratory • Center for Complex Systems and Brain Science

References[edit]

• Oullier, O., De Guzman, G. C., Jantzen, K. J., Lagarde, J., & Kelso, J. A. S. (2008). Social coordination dynamics: Measuring human bonding. Social Neuroscience, 3(2), 178-192. • Oullier, O., & Kelso, J. A. S. (2009). Social coordination from the perspective of coordination dynamics. In Encyclopedia of Complexity and Systems Science (pp. 8198-8213). Springer New York. • Dumas, G., De Guzman, G. C., Tognoli, E., & Kelso, J. S. (2014). The human dynamic clamp as a paradigm for social interaction. Proceedings of the National Academy of Sciences, 111(35), E3726-E3734. • Kelso, J. S. (1997). Dynamic patterns: The self-organization of brain and behavior. MIT Press. • Jirsa, V. K., & Kelso, J. S. (Eds.). (2013). Coordination dynamics: Issues and trends. Springer. • Fuchs, A., & Jirsa, V. K. (Eds.). (2007). Coordination: Neural, behavioral and social dynamics. Springer Science & Business Media. • Fuchs, A. (2014). Nonlinear dynamics in complex systems. Springer. • Zhang, M., Kelso, J. S., & Tognoli, E. (2018). Critical diversity: Divided or united states of social coordination. PLoS One, 13(4), e0193843. • Kelso, J. S., & Fuchs, A. (2016). The coordination dynamics of mobile conjugate reinforcement. Biological Cybernetics, 110(1), 41-53. • Haken, H., Kelso, J. S., & Bunz, H. (1985). A theoretical model of phase transitions in human hand movements. Biological Cybernetics, 51(5), 347-356. • Miles, L. K., Lumsden, J., Richardson, M. J., & Macrae, C. N. (2011). Do birds of a feather move together? Group membership and behavioral synchrony. Experimental Brain Research, 211(3-4), 495-503. • Kelso, J. S., & Engstrom, D. A. (2006). The complementary nature. MIT Press. • Fingelkurts, A. A., & Fingelkurts, A. A. (2004). Making complexity simpler: Multivariability and metastability in the brain. International Journal of Neuroscience, 114(7), 843-862. • Fuchs, A., & Jirsa, V. K. (2000). The HKB model revisited: How varying the degree of symmetry controls dynamics. Human Movement Science, 19(4), 425-449. • Schoner, G., & Kelso, J. A. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239(4847), 1513-1520. • Tognoli, E., Lagarde, J., DeGuzman, G. C., & Kelso, J. S. (2007). The phi complex as a neuromarker of human social coordination. Proceedings of the National Academy of Sciences, 104(19), 8190-8195. • Beek, P. J., Peper, C. E., & Daffertshofer, A. (2002). Modeling rhythmic interlimb coordination: Beyond the Haken–Kelso–Bunz model. Brain and Cognition, 48(1), 149-165. • Fuchs, A., & Kelso, J. S. (2018). Coordination dynamics and synergetics: From finger movements to brain patterns and ballet dancing. In Complexity and Synergetics (pp. 301-316). Springer Cham. • Kelso, J. S., Southard, D. L., & Goodman, D. (1979). On the coordination of two-handed movements. Journal of Experimental Psychology: Human Perception and Performance, 5(2), 229. • Kelso, J. S., & Tognoli, E. (2009). Toward a complementary neuroscience: Metastable coordination dynamics of the brain. In Downward Causation and the Neurobiology of Free Will (pp. 103-124). Springer Berlin Heidelberg. • Bressler, S. L., & Kelso, J. S. (2016). Coordination dynamics in cognitive neuroscience. Frontiers in Neuroscience, 10, 397. • Bressler, S. L., & Kelso, J. S. (2001). Cortical coordination dynamics and cognition. Trends in Cognitive Sciences, 5(1), 26-36. • Kelso, J. S. (2008). An essay on understanding the mind. Ecological Psychology, 20(2), 180-208. • Kostrubiec, V., Fuchs, A., & Kelso, J. S. (2012). Beyond the blank slate: Routes to learning new coordination patterns depend on the intrinsic dynamics of the learner—experimental evidence and theoretical model. Frontiers in Human Neuroscience, 6, 222. • Kelso, J. S., de Guzman, G. C., Reveley, C., & Tognoli, E. (2009). Virtual partner interaction (VPI): Exploring novel behaviors via coordination dynamics. PloS One, 4(6), e5749. • Jantzen, K. J., Oullier, O., & Kelso, J. S. (2008). Neuroimaging coordination dynamics in the sport sciences. Methods, 45(4), 325-335. • Thelen, E., Kelso, J. S., & Fogel, A. (1987). Self-organizing systems and infant motor development. Developmental Review, 7(1), 39-65. • Kelso, J. A. S. (2001). Self-organizing dynamical systems. • Haken, H. (1978). Synergetics: An Introduction: Nonequilibrium Phase Transition and Self-Organization in Physics, Chemistry and Biology. • Fuchs, A., Kelso, J. S., & Haken, H. (1992). Phase transitions in the human brain: Spatial mode dynamics. International Journal of Bifurcation and Chaos, 2(4), 917-939. • Tognoli, E., Zhang, M., Fuchs, A., Beetle, C., & Kelso, J. S. (2020). Coordination dynamics: A foundation for understanding social behavior. Frontiers in Human Neuroscience, 14, 317. • Wiltshire, T. J., Philipsen, J. S., Trasmundi, S. B., Jensen, T. W., & Steffensen, S. V. (2020). Interpersonal coordination dynamics in psychotherapy: A systematic review. Cognitive Therapy and Research, 44, 752-773. • Tognoli, E., Zhang, M., & Kelso, J. A. S. (2018). "On the Nature of Coordination in Nature". In J. M. Delgado-García et al. (Eds.), Advances in Cognitive Neurodynamics (VI) (pp. 375-382). Springer Nature Singapore. doi:10.1007/978-981-10-8854-4_48. • Zhang, M., Kelso, J. A. S., & Tognoli, E. (2018). "Critical diversity: Divided or united states of social coordination". PLOS ONE, 13(4), e0193843. doi:10.1371/journal.pone.0193843. • Tognoli, E., Zhang, M., Fuchs, A., Beetle, C., & Kelso, J. A. S. (2020). "Coordination dynamics: A foundation for understanding social behavior". Frontiers in Human Neuroscience, 14, 317. doi:10.3389/fnhum.2020.00317.