Cognitive science as complexity science.
complexity
emergence
nonlinearity
self-organization
universality
Journal
Wiley interdisciplinary reviews. Cognitive science
ISSN: 1939-5086
Titre abrégé: Wiley Interdiscip Rev Cogn Sci
Pays: United States
ID NLM: 101524169
Informations de publication
Date de publication:
Jul 2020
Jul 2020
Historique:
received:
19
08
2019
revised:
02
01
2020
accepted:
17
01
2020
pubmed:
12
2
2020
medline:
22
12
2020
entrez:
12
2
2020
Statut:
ppublish
Résumé
It is uncontroversial to claim that cognitive science studies many complex phenomena. What is less acknowledged are the contradictions among many traditional commitments of its investigative approaches and the nature of cognitive systems. Consider, for example, methodological tensions that arise due to the fact that like most natural systems, cognitive systems are nonlinear; and yet, traditionally cognitive science has relied on linear statistical data analyses. Cognitive science as complexity science is offered as an interdisciplinary framework for the investigation of cognition that can dissolve such contradictions and tensions. Here, cognition is treated as exhibiting the following four key features: emergence, nonlinearity, self-organization, and universality. This framework integrates concepts, methods, and theories from such disciplines as systems theory, nonlinear dynamical systems theory, and synergetics. By adopting this approach, the cognitive sciences benefit from a common set of practices to investigate, explain, and understand cognition in its varied and complex forms. This article is categorized under: Computer Science > Neural Networks Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science Neuroscience > Cognition.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1525Informations de copyright
© 2020 Wiley Periodicals, Inc.
Références
Abrahamsen, A., & Bechtel, W. (2012). History and core themes. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge handbook of cognitive science (pp. 9-28). New York, NY: Cambridge University Press.
Adams, F. (1999). Cybernetics. In R. Audi (Ed.), The Cambridge dictionary of philosophy (2nd ed., pp. 199-200). New York, NY: Cambridge University Press.
Adams, F., & Aizawa, K. (2008). The bounds of cognition. Malden, MA: Blackwell.
Agazzi, E., & Montecucco, L. (Eds.). (2002). Complexity and emergence. River Edge, NJ: World Scientific.
Aks, D. J., Zelinsky, G. J., & Sprott, J. C. (2002). Memory across eye-movements: 1/f dynamic in visual search. Nonlinear Dynamics, Psychology, and Life Sciences, 6, 1-25.
Allen, M., & Friston, K. J. (2018). From cognitivism to autopoiesis: Towards a computational framework for the embodied mind. Synthese, 195(6), 2459-2482.
Allen, P. (2001). What is complexity science? Knowledge of the limits to knowledge. Emergence, 3(1), 24-42.
Amazeen, P. G. (2018). From physics to social interactions: Scientific unification via dynamics. Cognitive Systems Research, 52, 640-657.
Amon, M. J., & Holden, J. G. (2019). The mismatch of intrinsic fluctuations and the static assumptions of linear statistics. Review of Philosophy and Psychology, 1-25. https://doi.org/10.1007/s13164-018-0428-x
Anderson, M. L. (2014). After phrenology: Neural reuse and the interactive brain. Cambridge, MA: MIT Press.
Averbeck, B. B., & Seo, M. (2008). The statistical neuroanatomy of frontal networks in the macaque. PLoS Computational Biology, 4(4), e1000050. https://doi.org/10.1371/journal.pcbi.1000050
Bak, P., Tang, C., & Wiesenfeld, K. (1988). Self-organized criticality. Physical Review A, 38, 364-374.
Ball, R., Kolokoltsov, V., & MacKay, R. S. (Eds.). (2013). Complexity science: The Warwick master's course (Vol. 408). Cambridge, MA: Cambridge University Press.
Baofu, P. (2007). The future of complexity: Conceiving a better way to understand order and chaos. Hackensack, NJ: World Scientific.
Bar-Yam, Y. (2016). From big data to important information. Complexity, 21(S2), 73-98.
Batterman, R. W. (2000). Multiple realizability and universality. British Journal for the Philosophy of Science, 51, 115-145.
Batterman, R. W. (2019). Universality and RG explanations. Perspectives on Science, 27(1), 26-45.
Bechtel, W., & Richardson, R. C. (1993/2010). Discovering complexity: Decomposition and localization as strategies in scientific research (2nd ed.). Cambridge, MA: MIT Press.
Bedau, M. A., & Humphreys, P. (2008). Emergence: Contemporary readings in philosophy and science. Cambridge, MA: MIT Press.
Beggs, J. M., & Plenz, D. (2003). Neuronal avalanches in neocortical circuits. The Journal of Neuroscience, 23, 11167-11177.
Bermudez, J. L. (2014). Cognitive science: An introduction to the science of the mind (2nd ed.). New York, NY: Cambridge University Press.
Bishop, R., & Silberstein, M. (2019). Complexity and feedback. In S. Gibb, R. F. Hendry, & T. Lancaster (Eds.), The Routledge handbook of emergence (pp. 145-156). New York, NY: Routledge.
Boccara, N. (2010). Modeling complex systems (2nd ed.). New York, NY: Springer Science+Business Media, LLC.
Boden, M. A. (2006). Mind as machine: A history of cognitive science (Vol. 1-2). New York, NY: Oxford University Press.
Bongard, J., & Lipson, H. (2007). Automated reverse engineering of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 104(24), 9943-9948.
Brigandt, I., & Love, A. (2017). Reductionism in biology. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2017 edn). Stanford, CA: Stanford University. Retrieved from https://plato.stanford.edu/archives/spr2017/entries/reduction-biology/
Brook, A. (Ed.). (2007). The prehistory of cognitive science. New York, NY: Palgrave Macmillan.
Brookes, M. J., Hall, E. L., Robson, S. E., Price, D., Palaniyappan, L., Liddle, E. B., … Morris, P. G. (2015). Complexity measures in magnetoencephalography: Measuring “disorder” in schizophrenia. PLoS One, 10(4), e0120991. https://doi.org/10.1371/journal.pone.0120991
Brown, C., & Liebovitch, L. (2010). Fractal analysis. Los Angeles, CA: Sage.
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15), 3932-3937.
Cannon, R. H., Jr. (1967). Dynamics of physical systems. Mineola, NY: Dover.
Castellani, B. (2018). Map of the complexity sciences. Art & Science Factory. Retrieved from https://www.art-sciencefactory.com/complexity-map_feb09.html
Chemero, A. (2009). Radical embodied cognitive science. Cambridge, MA: MIT Press.
Chemero, A. (2013). Radical embodied cognitive science. Review of General Psychology, 17(2), 145-150.
Chomsky, N. (1957/2002). Syntactic structures (2nd ed.). New York, NY: Mouton de Gruyter.
Chomsky, N. (2009). Cartesian linguistics. New York, NY: Cambridge University Press.
Coco, M. I., & Dale, R. (2014). Cross-recurrence quantification analysis of categorical and continuous time series: An R package. Frontiers in Psychology, 5, 510. https://doi.org/10.3389/fpsyg.2014.00510
Craven, P. (2010). Washed sand stockpile. Wikipedia. Retrieved from https://commons.wikimedia.org/wiki/File:Washed_sand_stockpile_(5081172874).jpg
Craver, C., & Tabery, J. (2019). Mechanisms in science. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Summer 2019 edn). Stanford, CA: Stanford University. Retrieved from https://plato.stanford.edu/archives/sum2019/entries/science-mechanisms/
Craver, C. F. (2005). Beyond reduction: Mechanisms, multifield integration and the unity of neuroscience. Studies in History and Philosophy of Science, Part C, 36(2), 373-395.
Crutchfield, J. P. (1994). Observing complexity and the complexity of observation. In H. Atmanspacher & G. J. Dalenoort (Eds.), Inside versus outside: Endo- and exo-concepts of observation and knowledge in physics, philosophy and cognitive science (pp. 235-272). Berlin, Heidelberg: Springer-Verlag.
Dale, R., & Bhat, H. (2018). Equations of mind: Data science for inferring nonlinear dynamics of socio-cognitive systems. Cognitive Systems Research, 52, 275-290.
Dale, R., Fusaroli, R., Duran, N. D., & Richardson, D. C. (2014). The self-organization of human interaction. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 59, pp. 43-95). Waltham, MA: Academic Press.
Daniels, B. C., & Nemenman, I. (2015). Automated adaptive inference of phenomenological dynamical models. Nature Communications, 6, 8133. https://doi.org/10.1038/ncomms9133
Davis, T. J., Brooks, T. R., & Dixon, J. A. (2016). Multi-scale interactions in interpersonal coordination. Journal of Sport and Health Science, 5(1), 25-34.
Delignieres, D., Ramdani, S., Lemoine, L., Torre, K., Fortes, M., & Ninot, G. (2006). Fractal analyses for ‘short’ time series: A re-assessment of classical methods. Journal of Mathematical Psychology, 50(6), 525-544.
Ditzinger, T., & Haken, H. (1995). A synergetic model of multistability in perception. In P. Kruse & M. Stadler (Eds.), Ambiguity in mind and nature: Multistable cognitive phenomena (pp. 255-274). Berlin, Heidelberg: Springer-Verlag.
Ducarme, F. (2018). Annella mollis, a Subergorgiidae. Wikipedia. Retrieved from https://commons.wikimedia.org/wiki/File:Gorgone_de_Mayotte.jpg
Edelman, M. (2018). Universality in systems with power-law memory and fractional dynamics. In M. Edelman, E. E. N. Macau, & M. A. F. Sanjuan (Eds.), Chaotic, fractional, and complex dynamics: New insights and perspectives (pp. 147-171). Cham, Switzerland: Springer.
Enns, R. H. (2010). It's a nonlinear world. New York, NY: Springer Science+Business Media.
Érdi, P. (2008). Complexity explained. Berlin: Springer-Verlag.
Falconer, K. (2013). Fractals: A very short introduction. Oxford, England: Oxford University Press.
Falótico, T. (2015). Stone tool use by a capuchin monkey. Wikipedia. Retrieved from https://commons.wikimedia.org/wiki/File:Stone_tool_use_by_a_capuchin_monkey.jpg
Favela, L. H. (2014). Radical embodied cognitive neuroscience: Addressing “grand challenges” of the mind sciences. Frontiers in Human Neuroscience, 8(796), 1-10. https://doi.org/10.3389/fnhum.2014.00796
Favela, L. H. (2015). Understanding cognition via complexity science. (Doctoral dissertation). University of Cincinnati, Cincinnati, OH.
Favela, L. H. (2019a). Integrated information theory as a complexity science approach to consciousness. Journal of Consciousness Studies, 26(1-2), 21-47.
Favela, L. H. (2019b). Emergence by way of dynamic interactions. Southwest Philosophy Review, 35(1), 47-57. https://doi.org/10.5840/swphilreview20193515
Favela, L. H., & Amon, M. J. (forthcoming). Enhancing the scope of predictive processing via nonlinearity. In D. Mendonça, M. Curado, & S. S. Gouveia (Eds.), The philosophy and science of predictive processing. London, England: Bloomsbury.
Favela, L. H., & Chemero, A. (2016). The animal-environment system. In Y. Coelllo & M. H. Fischer (Eds.), Foundations of embodied cognition: Perceptual and emotional embodiment (Vol. 1, pp. 59-74). New York, NY: Routledge.
Favela, L. H., Coey, C. A., Griff, E. R., & Richardson, M. J. (2016). Fractal analysis reveals subclasses of neurons and suggests an explanation of their spontaneous activity. Neuroscience Letters, 626, 54-58. https://doi.org/10.1016/j.neulet.2016.05.017
Favela, L. H., & Martin, J. (2017). “Cognition” and dynamical cognitive science. Minds and Machines, 27, 331-355. https://doi.org/10.1007/s11023-016-9411-4
Fine, J. M., Likens, A. D., Amazeen, E. L., & Amazeen, P. G. (2015). Emergent complexity matching in interpersonal coordination: Local dynamics and global variability. Journal of Experimental Psychology: Human Perception and Performance, 41(3), 723-737.
Flood, R. L., & Carson, E. R. (1993). Dealing with complexity: An introduction to the theory and application of systems science (2nd ed.). New York, NY: Springer Science+Business Media.
Floreano, D., & Mattiussi, C. (2008). Bio-inspired artificial intelligence: Theories, methods, and technologies. Cambridge, MA: MIT Press.
Fodor, J. (2009). Where is my mind. London Review of Books, 31(3), 13-15.
Fodor, J. A. (1980). Methodological solipsism considered as a research strategy in cognitive psychology. Behavioral and Brain Sciences, 3(1), 63-73.
Fodor, J. A. (1983). The modularity of mind: An essay on faculty psychology. Cambridge, MA: MIT Press.
Francescotti, R. M. (2007). Emergence. Erkenntnis, 67, 47-63.
Frank, T. D., Profeta, V. L. S., & Harrison, H. S. (2015). Interplay between order-parameter and system parameter dynamics: Considerations on perceptual-cognitive-behavioral mode-mode transitions exhibiting positive and negative hysteresis and on response times. Journal of Biological Physics, 41(3), 257-292.
Frégnac, Y. (2017). Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain? Science, 358(6362), 470-477.
Fuchs, A. (2013). Nonlinear dynamics in complex systems: Theory and applications for the life-, neuro-, and natural sciences. New York, NY: Springer-Verlag.
Gatherer, D. (2010). So what do we really mean when we say that systems biology is holistic? BMC Systems Biology, 4(22), 1-12. https://doi.org/10.1186/1752-0509-4-22
Gilden, D. L. (2001). Cognitive emissions of 1/f noise. Psychological Review, 108(1), 33-56.
Gilmore, R. (1981). Catastrophe theory for scientists and engineers. New York, NY: Dover.
Glennan, S. (2017). The new mechanical philosophy. New York, NY: Oxford University Press.
Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49-72.
Goulas, A., Bastiani, M., Bezgin, G., Uylings, H. B., Roebroeck, A., & Stiers, P. (2014). Comparative analysis of the macroscale structural connectivity in the macaque and human brain. PLoS Computational Biology, 10(3), e1003529. https://doi.org/10.1371/journal.pcbi.1003529
Guastello, S. J., Koopmans, M., & Pincus, D. (Eds.). (2011). Chaos and complexity in psychology: The theory of nonlinear dynamical systems. Cambridge, MA: Cambridge University Press.
Haken, H. (1988/2006). Information and self-organization: A macroscopic approach to complex systems (3rd ed.). New York, NY: Springer.
Haken, H. (2007). Synergetics. Scholarpedia, 2(1), 1400. https://doi.org/10.4249/scholarpedia.1400
Haken, H. (2016). The brain as a synergetic and physical system. In A. Pelster & G. Wunner (Eds.), Self-organization in complex systems: The past, present, and future of synergetics (pp. 147-163). Cham, Switzerland: Springer.
Haken, H., Kelso, J. S., & Bunz, H. (1985). A theoretical model of phase transitions in human hand movements. Biological Cybernetics, 51, 347-356.
Hammond, D. (2003). The science of synthesis: Exploring the social implications of general systems theory. Boulder, CO: University Press of Colorado.
Hausdorff, J. M., Peng, C. K., Ladin, Z., Wei, J. Y., & Goldberger, A. L. (1995). Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. Journal of Applied Physiology, 78, 349-3578.
Hawkins, J. A. (2004). Efficiency and complexity in grammars. New York, NY: Oxford University Press.
Heylighen, F., & Joslyn, C. (1999). Systems theory. In R. Audi (Ed.), The Cambridge dictionary of philosophy (2nd ed., pp. 898-899). New York, NY: Cambridge University Press.
Holden, J. G., Van Orden, G. C., & Turvey, M. T. (2009). Dispersion of response times reveals cognitive dynamics. Psychological Review, 116(2), 318-342.
Hooker, C. (Ed.). (2011a). Philosophy of complex systems. Waltham, MA: Elsevier.
Hooker, C. (2011b). Introduction to philosophy of complex systems: A. In C. Hooker (Ed.), Philosophy of complex systems (pp. 3-90). Waltham, MA: Elsevier.
Ihlen, E. A., & Vereijken, B. (2010). Interaction-dominant dynamics in human cognition: Beyond 1/ƒα fluctuation. Journal of Experimental Psychology: General, 139(3), 436-463.
Ihlen, E. A. F. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3(141), 1-18. https://doi.org/10.3389/fphys.2012.00141
Isnard, C. A., & Zeeman, E. C. (1976/2013). Some models from catastrophe theory in the social sciences. In L. Collins (Ed.), The use of models in the social sciences (pp. 44-100). Chicago, IL: Routledge.
Jensen, H. J. (1998). Self-organized criticality: Emergent complex behavior in physical and biological systems. Cambridge, MA: Cambridge University Press.
Kaneko, M., Yamaguchi, K., Eiraku, M., Sato, M., Takata, N., Kiyohara, Y., … Kengaku, M. (2011). Remodeling of monoplanar Purkinje cell dendrites during cerebellar circuit formation. PLoS One, 6(5), e20108. https://doi.org/10.1371/journal.pone.0020108
Kello, C. T., Beltz, B. C., Holden, J. G., & Van Orden, G. C. (2007). The emergent coordination of cognitive function. Journal of Experimental Psychology: General, 136, 551-568.
Kelso, J. A. S. (1997). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.
Kelso, J. A. S. (2012). Multistability and metastability: Understanding dynamic coordination in the brain. Philosophical Transactions of the Royal Society, B: Biological Sciences, 367(1591), 906-918.
Kelso, J. A. S., Tuller, B., Vatikiotis-Bateson, E., & Fowler, C. A. (1984). Functionally specific articulatory cooperation following jaw perturbations during speech: Evidence for coordinative structures. Journal of Experimental Psychology: Human Perception and Performance, 10(6), 812-832.
Kelso, J. S., Dumas, G., & Tognoli, E. (2013). Outline of a general theory of behavior and brain coordination. Neural Networks, 37, 120-131.
Kelty-Stephen, D. G., & Wallot, S. (2017). Multifractality versus (mono-)fractality as evidence of nonlinear interactions across timescales: Disentangling the belief in nonlinearity from the diagnosis of nonlinearity in empirical data. Ecological Psychology, 29(4), 259-299.
Kim, J. (2006). Emergence: Core ideas and issues. Synthese, 151, 547-559.
Klaus, A., Yu, S., & Plenz, D. (2011). Statistical analyses support power law distributions found in neuronal avalanches. PLoS One, 6(5), e19779. https://doi.org/10.1371/journal.pone.0019779
Klein, J. L. (1997). Statistical visions in time: A history of time series analysis, 1662-1938. New York, NY: Cambridge University Press.
Krishnavedala. (2014). Pendulum phase portrait. Wikipedia. Retrieved from https://commons.wikimedia.org/wiki/File:Pendulum_phase_portrait.svg
Kuhn, T. S. (1962/1996). The structure of scientific revolutions (2nd ed.). Chicago, IL: University of Chicago Press.
Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system? European Journal for Philosophy of Science, 3(1), 33-67.
Lee, J. M., Hu, J., Gao, J., Crosson, B., Peck, K. K., Wierenga, C. E., … White, K. D. (2008). Discriminating brain activity from task-related artifacts in functional MRI: Fractal scaling analysis simulation and application. NeuroImage, 40, 197-212.
Levi, P., Schanz, M., Kornienko, S., & Kornienko, O. (1999). Application of order parameter equations for the analysis and the control of nonlinear time discrete dynamical systems. International Journal of Bifurcation and Chaos, 9(8), 1618-1634.
Mainzer, K. (2007). Thinking in complexity: The computational dynamics of matter, mind, and mankind (5th ed.). New York, NY: Springer-Verlag.
Mandelbrot, B. B. (1977/1983). The fractal geometry of nature. New York, NY: W.H. Freeman.
Marmelat, V., & Delignières, D. (2012). Strong anticipation: Complexity matching in interpersonal coordination. Experimental Brain Research, 222(1-2), 137-148.
Marr, D. (1982/2010). Vision: A computational investigation into the human representation and processing of visual information. Cambridge, MA: MIT Press.
Marshall, N., Timme, N. M., Bennett, N., Ripp, M., Lautzenhiser, E., & Beggs, J. M. (2016). Analysis of power laws, shape collapses, and neural complexity: New techniques and MATLAB support via the NC toolbox. Frontiers in Physiology, 7, 250. https://doi.org/10.3389/fphys.2016.00250
Mason, M. (Ed.). (2008). Complexity theory and the philosophy of education. Malden, MA: Wiley-Blackwell.
May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459-467.
Maylor, E. A., Chater, N., & Brown, G. D. (2001). Scale invariance in the retrieval of retrospective and prospective memories. Psychonomic Bulletin & Review, 8(1), 162-167.
Mazzocchi, F. (2012). Complexity and the reductionism-holism debate in systems biology. WIREs Systems Biology and Medicine, 4(5), 413-427.
McDermott, D. (2001). Mind and mechanism. Cambridge, MA: MIT Press.
Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141-144.
Mitchell, M. (2009). Complexity: A guided tour. New York, NY: Oxford University Press.
Mobus, G. E., & Kalton, M. C. (2015). Principles of systems science. New York, NY: Springer.
Müller, S. C., Plath, P. J., Radons, G., & Fuchs, A. (Eds.). (2018). Complexity and synergetics. Cham, Switzerland: Springer.
Mustafa, N., Ahearn, T. S., Waiter, G. D., Murray, A. D., Whalley, L. J., & Staff, R. T. (2012). Brain structural complexity and life course cognitive change. NeuroImage, 61(3), 694-701.
National Science Foundation. (2011). Empowering the nation through discovery and innovation: NSF strategic plan for fiscal years (FY) 2011-2016. Retrieved from http://www.nsf.gov/news/strategicplan/nsfstrategicplan_2011_2016.pdf
Newell, A. C., Passot, T., & Lega, J. (1993). Order parameter equations for patterns. Annual Review of Fluid Mechanics, 25(1), 399-453.
Nicolis, G., & Nicolis, C. (2007). Foundations of complex systems: Nonlinear dynamics, statistical physics, information and prediction. Hackensack, NJ: World Scientific.
Ohlsson, S. (2007). The separation of thought and action in Western tradition. In A. Brook (Ed.), The prehistory of cognitive science (pp. 17-37). New York, NY: Palgrave Macmillan.
Oyama, S. (2000). The ontogeny of information: Developmental systems and evolution (2nd ed.). Durham, NC: Duke University Press.
Pease, A., Mahmoodi, K., & West, B. J. (2018). Complexity measures of music. Chaos, Solitons & Fractals, 108, 82-86.
Peng, C. K., Havlin, S., Hausdorff, J. M., Mietus, J. E., Stanley, H. E., & Goldberger, A. L. (1995). Fractal mechanisms and heart rate dynamics: Long-range correlations and their breakdown with disease. Journal of Electrocardiology, 28, 59-65.
Phelan, S. E. (2001). What is complexity science, really? Emergence, 3(1), 120-136.
Plenz, D., & Niebur, E. (Eds.). (2014). Criticality in neural systems. Weinheim, Germany: Wiley-VCH.
Poston, T., & Stewart, I. (1978). Catastrophe theory and its applications. London, England: Pitman.
Prigogine, I., & Lefever, R. (1973). Theory of dissipative structures. In H. Haken (Ed.), Synergetics: Cooperative phenomena in multi-component systems (pp. 124-135). Stuttgart: Springer-Verlag.
Pruessner, G. (2012). Self-organised criticality: Theory, models and characterisation. New York, NY: Cambridge University Press.
Pylyshyn, Z. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: MIT Press.
Ramirez-Aristizabal, A. G., Médé, B., & Kello, C. T. (2018). Complexity matching in speech: Effects of speaking rate and naturalness. Chaos, Solitons & Fractals, 111, 175-179.
Richardson, M. J., Dale, R., & Marsh, K. L. (2014). Complex dynamical systems in social and personality psychology. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 253-282). New York, NY: Cambridge University Press.
Richardson, M. J., Marsh, K. L., Isenhower, R. W., Goodman, J. R., & Schmidt, R. C. (2007). Rocking together: Dynamics of intentional and unintentional interpersonal coordination. Human Movement Science, 26, 867-891.
Riley, M. A., & Holden, J. G. (2012). Dynamics of cognition. WIREs Cognitive Science, 3, 593-606.
Riley, M. A., & van Orden, G. C. (Eds.). (2005). Tutorials in contemporary nonlinear methods for the behavioral sciences. Arlington, VA: National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2005/nsf05057/nmbs/nmbs.pdf
Roberton, M. A. (1993). New ways to think about old questions. In L. B. Smith & E. Thelen (Eds.), A dynamic systems approach to development: Applications (pp. 95-117). Cambridge, MA: MIT Press.
Sanches de Oliveira, G., Raja, V., & Chemero, A. (2019). Radical embodied cognitive science and “real cognition”. Synthese. https://doi.org/10.1007/s11229-019-02475-4
Sandu, A. L., Staff, R. T., McNeil, C. J., Mustafa, N., Ahearn, T., Whalley, L. J., & Murray, A. D. (2014). Structural brain complexity and cognitive decline in late life-A longitudinal study in the Aberdeen 1936 birth cohort. NeuroImage, 100, 558-563.
Sayama, H. (2015). Introduction to the modeling and analysis of complex systems. Geneseo, NY: Open SUNY Textbooks, Milne Library.
Scholz, J. P., Kelso, J. A. S., & Schöner, G. (1987). Nonequilibrium phase transitions in coordinated biological motion: Critical slowing down and switching time. Physics Letters A, 123(8), 390-394.
Sebastián, M. V., & Navascués, M. A. (2008). A relation between fractal dimension and Fourier transform-electroencephalographic study using spectral and fractal parameters. International Journal of Computer Mathematics, 85(3-4), 657-665.
Shaffee, T. (2015). DNA to protein or ncRNA. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/File:DNA_to_protein_or_ncRNA.svg
Sherblom, S. A. (2017). Complexity-thinking and social science: Self-organization involving human consciousness. New Ideas in Psychology, 47, 10-15.
Shine, J. M., Breakspear, M., Bell, P. T., Martens, K. A. E., Shine, R., Koyejo, O., … Poldrack, R. A. (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22(2), 289-296.
Shockley, K. (2005). Cross recurrence quantification of interpersonal postural activity. In M. A. Riley & G. C. van Orden (Eds.), Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 142-177). Arlington, VA: National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2005/nsf05057/nmbs/nmbs.pdf
Solomon, S., & Shir, B. (2003). Complexity: a science at 30. Europhysics News, 34(2), 54-57.
Spivey, M. J. (2018). Discovery in complex adaptive systems. Cognitive Systems Research, 51, 40-55.
Sporns, O. (2007). Complexity. Scholarpedia, 2(10), 1623. https://doi.org/10.4249/scholarpedia.1623
Sporns, O. (2013). Making sense of brain network data. Nature Methods, 10(6), 491-493.
Sporns, O., Tononi, G., & Edelman, G. M. (2000). Connectivity and complexity: The relationship between neuroanatomy and brain dynamics. Neural Networks, 13(8-9), 909-922.
Stanley, H. E. (1999). Scaling, universality, and renormalization: Three pillars of modern critical phenomena. Reviews of Modern Physics, 71(2), S358-S366.
Strogatz, S. H. (2015). Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering (2nd ed.). New York, NY: CRC Press.
Szary, J., Dale, R., Kello, C. T., & Rhodes, T. (2015). Patterns of interaction-dominant dynamics in individual versus collaborative memory foraging. Cognitive Processing, 16(4), 389-399.
Taborsky, P. (2014). Is complexity a scientific concept? Studies in History and Philosophy of Science, Part A, 47, 51-59.
Thagard, P. (2005). Mind: Introduction to cognitive science (2nd ed.). Cambridge, MA: MIT Press.
Thagard, P. (2019). Cognitive science. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Stanford, CA: Stanford University. Retrieved from https://plato.stanford.edu/archives/spr2019/entries/cognitive-science/
Thelen, E., & Smith, L. B. (2006). Dynamic systems theories. In W. Damon (Ed.), Handbook of child psychology: Theoretical models of human development (Vol. 1, 5th ed., pp. 563-634). New York, NY: Wiley.
Thompson, E., & Varela, F. J. (2001). Radical embodiment: Neural dynamics and consciousness. Trends in Cognitive Sciences, 5(10), 418-425.
Thouless, D. (1989). Condensed matter physics in less than three dimensions. In P. Davies (Ed.), The new physics (pp. 209-235). Cambridge, MA: Cambridge University Press.
Timme, N. M., Marshall, N. J., Bennett, N., Ripp, M., Lautzenhiser, E., & Beggs, J. M. (2016). Criticality maximizes complexity in neural tissue. Frontiers in Physiology, 7, 425. https://doi.org/10.3389/fphys.2016.00425
Tognoli, E., & Kelso, J. A. S. (2014). The metastable brain. Neuron, 81(1), 35-48.
Tomen, N., Herrmann, J. M., & Ernst, U. (Eds.). (2019). The functional role of critical dynamics in neural systems. Cham, Switzerland: Springer.
Tranquillo, J. (2019). An introduction to complex systems: Making sense of a changing world. Cham, Switzerland: Springer.
Tsuda, I. (2001). Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behavioral and Brain Sciences, 24(5), 793-810.
Tuller, B., Case, P., Ding, M., & Kelso, J. A. S. (1994). The nonlinear dynamics of speech categorization. Journal of Experimental Psychology: Human Perception and Performance, 20, 3-16.
van Geert, P. (1994). Dynamic systems of development: Change between complexity and chaos. New York, NY: Harvester Wheatsheaf.
Van Orden, G., & Stephen, D. G. (2012). Is cognitive science usefully cast as complexity science? Topics in Cognitive Science, 4, 3-6.
van Rooij, M. M. J. W., Favela, L. H., Malone, M., & Richardson, M. J. (2013). Modeling the dynamics of risky choice. Ecological Psychology, 25(3), 293-303.
Vermeer, B. (Ed.). (2014). Grip on complexity: How manageable are complex systems? Directions for future complexity research. The Hague, The Netherlands: Netherlands Organisation for Scientific Research (NWO).
von Bertalanffy, L. (1972). The history and status of general systems theory. Academy of Management Journal, 15(4), 407-426.
Von Eckardt, B. (1995). What is cognitive science? Cambridge MA: MIT Press.
Webber, C. L., Jr., & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In M. A. Riley & G. C. van Orden (Eds.), Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 26-94). Arlington, VA: National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2005/nsf05057/nmbs/nmbs.pdf
Wiener, N. (1948). Cybernetics. Scientific American, 179(5), 14-19.
Wijnants, M. L., Bosman, A. M., Hasselman, F. W., Cox, R. F., & Van Orden, G. C. (2009). 1/f scaling in movement time changes with practice in precision. Nonlinear Dynamics, Psychology, and Life Sciences, 13, 75-94.
Wilson, K. G. (1983). The renormalization group and critical phenomena. Reviews of Modern Physics, 55(3), 583-600.
Wiltshire, T. J., Butner, J. E., & Fiore, S. M. (2018). Problem-solving phase transitions during team collaboration. Cognitive Science, 42(1), 129-167.
Wittgenstein, L. (1958/1986). Philosophical investigations (3rd ed. [G. E. M. Anscombe (Trans.)]). Oxford, UK: Basil Blackwell.
Wood, B., & Bettin, H. (2019). The Planck constant for the definition and realization of the kilogram. Annalen der Physik, 531(5), 1800308. https://doi.org/10.1002/andp.201800308
Zachariou, N., Expert, P., Takayasu, M., & Christensen, K. (2015). Generalised sandpile dynamics on artificial and real-world directed networks. PLoS One, 10(11), e0142685. https://doi.org/10.1371/journal.pone.0142685