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WIREs Cogn Sci
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Cognitive science as complexity science

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Abstract 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
One way of viewing the diverse and rich foundations of complexity science. (Used with permission from Jeffrey Goldstein ())
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Universal spatial structures and temporal dynamics in nature. Coral, like the Annella mollis, giant sea fan, are organized in fractal branching structures (a). Cells, such as Purkinje neurons, are organized in fractal branching structures (b). Avalanches can follow power‐law distributions in nonbiological systems such as sandpiles (c). Avalanches can follow power‐law distributions in biological systems such as neuronal networks in the primary motor cortex. (Modified from Ducarme (). CC BY‐SA 4.0 (a), Reprinted with permission from Kaneko et al. (). Copyright 2011 Public Library of Science; CC BY 4.0 (b), Reprinted with permission from Zachariou, Expert, Takayasu, and Christensen (). Copyright 2015 Public Library of Science; CC BY 4.0 and Modified from Craven (). CC BY 2.0 (c), and Reprinted with permission from Klaus, Yu, and Plenz (). Copyright 2011 Public Library of Science; CC BY 4.0 (d))
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Recurrence quantification analysis plots. The left plot depicts the recurrence of events across 2000 s. The right plot depicts in greater detail the circled part of the left plot. By color coding data points—that is, red for α, green for β, and blue for γ—a recurrence plot is able to decipher between qualitative shifts in behavior, where each color represents a distinct and repetitive pattern that unfolds over time. (Figure created by Hana Vrzakova based on code and data from Coco & Dale, )
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Component‐dominant dynamics and interaction‐dominant dynamics. (a) A synthetic white noise time‐series. Each section depicts the localized effect of perturbations common to systems exhibiting component‐dominant dynamics. (b) A synthetic pink noise (1/f scaling) time‐series. The arrows depict the distributed, nonlocalized effects of perturbations common to scale‐free systems exhibiting interaction‐dominant dynamics. (Image inspired by Davis et al., )
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Three time‐series signal structures: white, pink, and brown. The Y‐axis refers to a value (x) such as eye movements, finger taps, or heartbeats. The X‐axis refers to temporal values (s) such as milliseconds or seconds. (a) Random white noise, which is unstructured over time. (b) Pink noise (also known as 1/f noise or 1/f scaling) is fractal in structure, specifically, the signal's structure is self‐similar at shorter and longer timescales. (c) Brown noise, which exhibits random structure at shorter timescales and more ordered structure at longer timescales, such that it is not as unordered as white noise but not as ordered as pink noise (figure created by Mary Jean Amon)
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Plots of pendulum dynamics. Time‐series plot of pendulum differential equation (top). Phase space plot of pendulum differential equation (bottom). (Reprinted with permission from Krishnavedala (). CC BY‐SA 4.0)
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Marr's three levels of analysis (Marr, ). Computational theory: What is the computation; what is it for? Typically, this is the phenomenon of interest, such as problem‐solving, for example, how to crack open a nut. (Modified from Falótico (). CC BY‐SA 4.0). Representation and algorithm: How is the computation implemented; what are the inputs and outputs that manipulate representations? For example, a connectivity diagram of a macaque monkey's brain displaying decision‐making networks connected with limbic, motor, and sensory systems. (Reprinted with permission from Averbeck and Seo (). Copyright 2008 Public Library of Science; CC BY 4.0). Hardware implementation: How are the algorithms and representations physically realized, for example, the structural connections of a macaque monkey's neuronal networks. (Reprinted with permission from Goulas et al. (). Copyright 2014 Public Library of Science; CC BY 4.0)
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An organism's functions as the product of a linear causal pathway that begins with DNA. Research in the biological sciences during the early‐ to mid‐twentieth century exemplified such reductionist and mechanistic commitments. (Adapted from Shaffee (). CC BY 4.0)
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Philosophy > Foundations of Cognitive Science
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