Search results for 'dynamical systems theory' (try it on Scholar)

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  1.  37
    Tjeerd Van De Laar (2006). Dynamical Systems Theory as an Approach to Mental Causation. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 37 (2):307-332.
    Dynamical systems theory (DST) is gaining popularity in cognitive science and philosophy of mind. Recently several authors (e.g. J.A.S. Kelso, 1995; A. Juarrero, 1999; F. Varela and E. Thompson, 2001) offered a DST approach to mental causation as an alternative for models of mental causation in the line of Jaegwon Kim (e.g. 1998). They claim that some dynamical systems exhibit a form of global to local determination or downward causation in that the large-scale, global activity (...)
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  2.  73
    Scott Hotton & Jeff Yoshimi (2011). Extending Dynamical Systems Theory to Model Embodied Cognition. Cognitive Science 35 (3):444-479.
    We define a mathematical formalism based on the concept of an ‘‘open dynamical system” and show how it can be used to model embodied cognition. This formalism extends classical dynamical systems theory by distinguishing a ‘‘total system’’ (which models an agent in an environment) and an ‘‘agent system’’ (which models an agent by itself), and it includes tools for analyzing the collections of overlapping paths that occur in an embedded agent's state space. To illustrate the way (...)
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  3.  46
    Marco Van Leeuwen (2005). Questions for the Dynamicist: The Use of Dynamical Systems Theory in the Philosophy of Cognition. [REVIEW] Minds and Machines 15 (3-4):271-333.
    The concepts and powerful mathematical tools of Dynamical Systems Theory (DST) yield illuminating methods of studying cognitive processes, and are even claimed by some to enable us to bridge the notorious explanatory gap separating mind and matter. This article includes an analysis of some of the conceptual and empirical progress Dynamical Systems Theory is claimed to accomodate. While sympathetic to the dynamicist program in principle, this article will attempt to formulate a series of problems (...)
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  4.  46
    Jeff Yoshimi (2012). Supervenience, Dynamical Systems Theory, and Non-Reductive Physicalism. British Journal for the Philosophy of Science 63 (2):373-398.
    It is often claimed (1) that levels of nature are related by supervenience, and (2) that processes occurring at particular levels of nature should be studied using dynamical systems theory. However, there has been little consideration of how these claims are related. To address the issue, I show how supervenience relations give rise to ‘supervenience functions’, and use these functions to show how dynamical systems at different levels are related to one another. I then use (...)
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  5.  54
    Roman Frigg (2004). In What Sense is the Kolmogorov-Sinai Entropy a Measure for Chaotic Behaviour?—Bridging the Gap Between Dynamical Systems Theory and Communication Theory. British Journal for the Philosophy of Science 55 (3):411 - 434.
    On an influential account, chaos is explained in terms of random behaviour; and random behaviour in turn is explained in terms of having positive Kolmogorov-Sinai entropy (KSE). Though intuitively plausible, the association of the KSE with random behaviour needs justification since the definition of the KSE does not make reference to any notion that is connected to randomness. I provide this justification for the case of Hamiltonian systems by proving that the KSE is equivalent to a generalized version of (...)
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  6.  18
    Joshua Rosaler, Theory Reduction in Physics: A Model-Based, Dynamical Systems Approach.
    In 1973, Nickles identified two senses in which the term `reduction' is used to describe the relationship between physical theories: namely, the sense based on Nagel's seminal account of reduction in the sciences, and the sense that seeks to extract one physical theory as a mathematical limit of another. These two approaches have since been the focus of most literature on the subject, as evidenced by recent work of Batterman and Butterfield, among others. In this paper, I discuss a (...)
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  7.  32
    Ralph D. Ellis (2005). Generating Predictions From a Dynamical Systems Emotion Theory. Behavioral and Brain Sciences 28 (2):202-203.
    Lewis's dynamical systems emotion theory continues a tradition including Merleau-Ponty, von Bertallanfy, and Aristotle. Understandably for a young theory, Lewis's new predictions do not follow strictly from the theory; thus their failure would not disconfirm the theory, nor their success confirm it – especially given that other self-organizational approaches to emotion (e.g., those of Ellis and of Newton) may not be inconsistent with these same predictions.
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  8. Stephen J. Guastello, Matthijs Koopmans & David Pincus (eds.) (2009). Chaos and Complexity in Psychology: The Theory of Nonlinear Dynamical Systems. Cambridge University Press.
  9.  28
    Whitney Tabor, Pyeong W. Cho & Harry Dankowicz (2013). Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task. Cognitive Science 37 (7):1193-1227.
    Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing (...)
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  10.  6
    Carmel M. Martin (2010). Complexity in Dynamical Health Systems – Transforming Science and Theory, and Knowledge and Practice. Journal of Evaluation in Clinical Practice 16 (1):209-210.
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  11.  83
    A. Carati & L. Galgani (2001). Theory of Dynamical Systems and the Relations Between Classical and Quantum Mechanics. Foundations of Physics 31 (1):69-87.
    We give a review of some works where it is shown that certain quantum-like features are exhibited by classical systems. Two kinds of problems are considered. The first one concerns the specific heat of crystals (the so called Fermi–Pasta–Ulam problem), where a glassy behavior is observed, and the energy distribution is found to be of Planck-like type. The second kind of problems concerns the self-interaction of a charged particle with the electromagnetic field, where an analog of the tunnel effect (...)
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  12.  17
    Erik Myin & Sonja Smets (2002). Could Dancing Be Coupled Oscillation? – The Interactive Approach to Linguistic Communication and Dynamical Systems Theory. Behavioral and Brain Sciences 25 (5):634-635.
    Although we applaud the interactivist approach to language and communication taken in the target article, we notice that Shanker & King (S&K) give little attention to the theoretical frameworks developed by dynamical system theorists. We point out how the dynamical idea of causality, viewed as multidirectional across multiple scales of organization, could further strengthen the position taken in the target article.
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  13. Alcibiades Malapi-Nelson (2011). Dynamical Systems Theory in Cognition: Are We Really Gaining? Gnosis 6 (1):1-23.
     
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  14.  2
    R. Frigg (2004). In What Sense is the Kolmogorov-Sinai Entropy a Measure for Chaotic Behaviour?--Bridging the Gap Between Dynamical Systems Theory and Communication Theory. British Journal for the Philosophy of Science 55 (3):411-434.
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  15.  5
    Gary Goldberg (1992). Dynamical Systems Theory and the Mobility Gradient: Information, Homology and Self-Similar Structure. Behavioral and Brain Sciences 15 (2):278-279.
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  16. Pak Kin Ho & Alcibiades Malapi-Nelson (2011). Dynamical Systems Theory, Understanding, and Explanation: Comments on Malapi-Nelson's Paper with Responses From the Author. Gnosis 6 (1).
     
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  17.  1
    Charles Tresser (1995). Aspects of Renormalization in Dynamical Systems Theory. In R. J. Russell, N. Murphy & A. R. Peacocke (eds.), Chaos and Complexity. Vatican Observatory Publications 11--19.
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  18. Pak Ho & Alcibiades Malapi-Nelson (2002). Dynamical Systems Theory, Understanding, and Explanation: Comments on Malapi-Nelson's Paper with Responses From the Author. Gnosis 6:1-9.
     
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  19.  3
    Greg Hjorth (2001). Foreman Matthew. A Descriptive View of Ergodic Theory. Descriptive Set Theory and Dynamical Systems, Edited by Foreman M., Kechris AS, Louveau A., and Weiss B., London Mathematical Society Lecture Note Series, No. 277, Cambridge University Press, Cambridge, New York, Etc., 2000, Pp. 87–171. [REVIEW] Bulletin of Symbolic Logic 7 (4):545-546.
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  20. Anita Feferman, Solomon Feferman, Robert Goldblatt, Yuri Gurevich, Klaus Grue, Sven Ove Hansson, Lauri Hella, Robert K. Meyer & Petri Mäenpää (1997). Stål Anderaa (Oslo), A Traktenbrot Inseparability Theorem for Groups. Peter Dybjer (G Öteborg), Normalization by Yoneda Embedding (Joint Work with D. Cubric and PJ Scott). Abbas Edalat (Imperial College), Dynamical Systems, Measures, Fractals, and Exact Real Number Arithmetic Via Domain Theory. [REVIEW] Bulletin of Symbolic Logic 3 (4).
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  21.  32
    Harald Atmanspacher (2006). Complementarity in Classical Dynamical Systems. Foundations of Physics 36 (2):291-306.
    The concept of complementarity, originally defined for non-commuting observables of quantum systems with states of non-vanishing dispersion, is extended to classical dynamical systems with a partitioned phase space. Interpreting partitions in terms of ensembles of epistemic states (symbols) with corresponding classical observables, it is shown that such observables are complementary to each other with respect to particular partitions unless those partitions are generating. This explains why symbolic descriptions based on an ad hoc partition of an underlying phase (...)
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  22.  43
    Marco Giunti (1992). Computers, Dynamical Systems, Phenomena, and the Mind. Dissertation, Indiana University
    This work addresses a broad range of questions which belong to four fields: computation theory, general philosophy of science, philosophy of cognitive science, and philosophy of mind. Dynamical system theory provides the framework for a unified treatment of these questions. ;The main goal of this dissertation is to propose a new view of the aims and methods of cognitive science--the dynamical approach . According to this view, the object of cognitive science is a particular set of (...)
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  23.  1
    K. Meer & M. Gori (2002). A Step Towards a Complexity Theory for Analog Systems. Mathematical Logic Quarterly 48 (S1):45-58.
    Recent years have seen an increasing interest in the study of continuous-time computational models. However, not so much has been done with respect to setting up a complexity theoretic framework for such models. The present paper intends to go a step into this direction. We consider problems over the real numbers which we try to relate to Lyapunov theory for dynamical systems: The global minimizers of particular energy functions are supposed to give solutions of the problem. The (...)
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  24. Noah Moss Brender (2013). Sense-Making and Symmetry-Breaking: Merleau-Ponty, Cognitive Science, and Dynamic Systems Theory. Symposium: The Canadian Journal of Continental Philosophy 17 (2):247-273.
    From his earliest work forward, phenomenologist Maurice Merleau-Ponty attempted to develop a new ontology of nature that would avoid the antinomies of realism and idealism by showing that nature has its own intrinsic sense which is prior to reflection. The key to this new ontology was the concept of form, which he appropriated from Gestalt psychology. However, Merleau-Ponty struggled to give a positive characterization of the phenomenon of form which would clarify its ontological status. Evan Thompson has recently taken up (...)
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  25.  50
    Esther Thelen, Gregor Schöner, Christian Scheier & Linda B. Smith (2001). The Dynamics of Embodiment: A Field Theory of Infant Perseverative Reaching. Behavioral and Brain Sciences 24 (1):1-34.
    The overall goal of this target article is to demonstrate a mechanism for an embodied cognition. The particular vehicle is a much-studied, but still widely debated phenomenon seen in 7–12 month-old-infants. In Piaget's classic “A-not-B error,” infants who have successfully uncovered a toy at location “A” continue to reach to that location even after they watch the toy hidden in a nearby location “B.” Here, we question the traditional explanations of the error as an indicator of infants' concepts of objects (...)
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  26.  16
    S. Orestis Palermos (2014). Loops, Constitution and Cognitive Extension. Cognitive Systems Research 27:25-41.
    The ‘causal-constitution’ fallacy, the ‘cognitive bloat’ worry, and the persisting theoretical confusion about the fundamental difference between the hypotheses of embedded (HEMC) and extended (HEC) cognition are three interrelated worries, whose common point—and the problem they accentuate—is the lack of a principled criterion of constitution. Attempting to address the ‘causal-constitution’ fallacy, mathematically oriented philosophers of mind have previously suggested that the presence of non-linear relations between the inner and the outer contributions is sufficient for cognitive extension. The abstract idea of (...)
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  27.  48
    Noboru Watanabe (2011). Note on Entropies of Quantum Dynamical Systems. Foundations of Physics 41 (3):549-563.
    We review some techniques and notions for quantum information theory. It is shown that the dynamical entropies is discussed and some numerical computations of these entropies are carried for several states.
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  28.  36
    Evelyn Fox Keller (2005). DDS: Dynamics of Developmental Systems. [REVIEW] Biology and Philosophy 20 (2-3):409-416.
    The acronym Developmental systems theory (DST) has been introduced into the literature on development in at least three different contexts in recent years – twice for DST, and before that, for Dynamical Systems Theory – and in all cases, to designate a new perspective for understanding development. Subtle but significant differences in argument and aims distinguish these uses, and confound the difficulty of saying just what DST is. My aim in this paper is to disambiguate (...)
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  29.  67
    Yvon Gauthier (2009). The Construction of Chaos Theory. Foundations of Science 14 (3):153-165.
    This paper aims at a logico-mathematical analysis of the concept of chaos from the point of view of a constructivist philosophy of physics. The idea of an internal logic of chaos theory is meant as an alternative to a realist conception of chaos. A brief historical overview of the theory of dynamical systems is provided in order to situate the philosophical problem in the context of probability theory. A finitary probabilistic account of chaos amounts to (...)
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  30. Charlotte Werndl (2013). Justifying Typicality Measures of Boltzmannian Statistical Mechanics and Dynamical Systems. Studies in History and Philosophy of Science Part B 44 (4):470-479.
    A popular view in contemporary Boltzmannian statistical mechanics is to interpret the measures as typicality measures. In measure-theoretic dynamical systems theory measures can similarly be interpreted as typicality measures. However, a justification why these measures are a good choice of typicality measures is missing, and the paper attempts to fill this gap. The paper first argues that Pitowsky's justification of typicality measures does not fit the bill. Then a first proposal of how to justify typicality measures is (...)
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  31. John Collier, Complexly Organised Dynamical Systems.
    Both natural and engineered systems are fundamentally dynamical in nature: their defining properties are causal, and their functional capacities are causally grounded. Among dynamical systems, an interesting and important sub-class are those that are autonomous, anticipative and adaptive (AAA). Living systems, intelligent systems, sophisticated robots and social systems belong to this class, and the use of these terms has recently spread rapidly through the scientific literature. Central to understanding these (...) systems is their complicated organisation and their consequent capacities for re- and self- organisation. But there is at present no general analysis of these capacities or of the requisite organisation involved. We define what distinguishes AAA systems from other kinds of systems by characterising their central properties in a dynamically interpreted information theory. (shrink)
     
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  32. John T. Sanders, Dynamical Systems and Scientific Method.
    Progress in the last few decades in what is widely known as “Chaos Theory” has plainly advanced understanding in the several sciences it has been applied to. But the manner in which such progress has been achieved raises important questions about scientific method and, indeed, about the very objectives and character of science. In this presentation, I hope to engage my audience in a discussion of several of these important new topics.
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  33.  48
    W. Schonbein (2005). Cognition and the Power of Continuous Dynamical Systems. Minds and Machines 15 (1):57-71.
    Traditional approaches to modeling cognitive systems are computational, based on utilizing the standard tools and concepts of the theory of computation. More recently, a number of philosophers have argued that cognition is too subtle or complex for these tools to handle. These philosophers propose an alternative based on dynamical systems theory. Proponents of this view characterize dynamical systems as (i) utilizing continuous rather than discrete mathematics, and, as a result, (ii) being computationally more (...)
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  34.  18
    Fred A. Keijzer & Sacha Bem (1996). Behavioral Systems Interpreted as Autonomous Agents and as Coupled Dynamical Systems: A Criticism. Philosophical Psychology 9 (3):323-46.
    Cognitive science's basic premises are under attack. In particular, its focus on internal cognitive processes is a target. Intelligence is increasingly interpreted, not as a matter of reclusive thought, but as successful agent-environment interaction. The critics claim that a major reorientation of the field is necessary. However, this will only occur when there is a distinct alternative conceptual framework to replace the old one. Whether or not a serious alternative is provided is not clear. Among the critics there is some (...)
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  35.  6
    Miklos Redei & Charlotte Werndl, On the History of the Isomorphism Problem of Dynamical Systems with Special Regard to von Neumann's Contribution.
    This paper reviews some major episodes in the history of the spatial isomorphism problem of dynamical systems theory. In particular, by analysing, both systematically and in historical context, a hitherto unpublished letter written in 1941 by John von Neumann to Stanislaw Ulam, this paper clarifies von Neumann's contribution to discovering the relationship between spatial isomorphism and spectral isomorphism. The main message of the paper is that von Neumann's argument described in his letter to Ulam is the very (...)
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  36.  20
    David Spurrett (2002). Information Processing and Dynamical Systems Approaches Are Complementary. Behavioral and Brain Sciences 25 (5):639-640.
    Shanker & King (S&K) trumpet the adoption of a “new paradigm” in communication studies, exemplified by ape language research. Though cautiously sympathetic, I maintain that their argument relies on a false dichotomy between “information” and “dynamical systemstheory, and that the resulting confusion prevents them from recognizing the main chance their line of thinking suggests.
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  37.  14
    Herbert Jaeger (1998). Today's Dynamical Systems Are Too Simple. Behavioral and Brain Sciences 21 (5):643-644.
    Cognitive systems are wilder than today's dynamical systems theory can handle. Cognitive systems might be tamed in principle, but the very notion of a dynamical system will change in the process.
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  38.  3
    Douglas T. Kenrick, Norman Li & Jonathan E. Butner (2000). Dynamical Systems and Mating Decision Rules. Behavioral and Brain Sciences 23 (4):607-608.
    Dynamical simulations of male and female mating strategies illustrate how traits such as restrictedness constrain, and are constrained by, local ecology. Such traits cannot be defined solely by genotype or by phenotype, but are better considered as decision rules gauged to ecological inputs. Gangestad & Simpson's work draws attention to the need for additional bridges between evolutionary psychology and dynamical systems theory.
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  39. John Collier, Functionality and Autonomy in Open Dynamical Systems.
    In Robert West’s talk last week, dynamical systems theory (DST) was applied to a specific problem involving interacting symbolic systems, without much reference to how those systems are embodied or related to other types of systems. Despite this level of abstraction, DST can yield interesting results, though one might be left wondering if it really leads to understanding, or what it all means. In particular, Robert noted problems he has in convincing referees that the (...)
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  40.  16
    Sara Green, Melinda Fagan & Johannes Jaeger (2015). Explanatory Integration Challenges in Evolutionary Systems Biology. Biological Theory 10 (1):18-35.
    Evolutionary systems biology aims to integrate methods from systems biology and evolutionary biology to go beyond the current limitations in both fields. This article clarifies some conceptual difficulties of this integration project, and shows how they can be overcome. The main challenge we consider involves the integration of evolutionary biology with developmental dynamics, illustrated with two examples. First, we examine historical tensions between efforts to define general evolutionary principles and articulation of detailed mechanistic explanations of specific traits. Next, (...)
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  41.  9
    N. Thomasson & L. Pezard (1999). Dynamical Systems and Depression: A Framework for Theoretical Perspectives. Acta Biotheoretica 47 (3-4):209-218.
    The theory of dynamical systems allows one to describe the change in a system' 's macroscopic behavior as a bifurcation in the underlying dynamics. We show here, from the example of depressive syndrome, the existence of a correspondence between clinical and electro-physiological dimensions and the association between clinical remission and brain dynamics reorganization. On the basis of this experimental study, we discuss the interest of such results concerning the question of normality versus pathology in psychiatry and the (...)
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  42. P. Van Geert (2009). Nonlinear Complex Dynamical Systems in Developmental Psychology. In Stephen J. Guastello, Matthijs Koopmans & David Pincus (eds.), Chaos and Complexity in Psychology: The Theory of Nonlinear Dynamical Systems. Cambridge University Press
     
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  43.  6
    Cliff Hooker, Conceptualising Reduction, Emergence and Self-Organisation in Complex Dynamical Systems.
    This chapter describes the application of reduction concepts in emergence and self organization of complex dynamical system. Condition-dependent laws compress and dynamical equation sets provide implicit compressed representations even when most of that information is not explicitly available without decompression. And, paradoxically, there is still the determined march of fundamental analytical dynamics expanding its compression reach toward a Theory of Everything—even while the more rapidly expanding domain of complex systems dynamics confronts its assumptions and its monolithicity. (...)
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  44.  16
    Cliff A. Hooker (1997). Dynamical Systems in Development: Review Essay of Linda V. Smith & Esther Thelen (Eds) a Dynamics Systems Approach to Development: Applications. Philosophical Psychology 10 (1):103 – 112.
    This book focuses on showing how the ideas central to the new wave oj dynamic systems studies may also form the basis for a new and distinctive theory of human development where both global order and local variability in behaviour emerge together from the same organising dynamical interactions. This also sharpens our understanding of the weaknesses of the traditional formal, structuralist theories. Conversely, dynamical models have their own matching set of problems, many of which are consiously (...)
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  45.  19
    Sumiyoshi Abe (2014). Fokker–Planck Theory of Nonequilibrium Systems Governed by Hierarchical Dynamics. Foundations of Physics 44 (2):175-182.
    Dynamics of complex systems is often hierarchically organized on different time scales. To understand the physics of such hierarchy, here Brownian motion of a particle moving through a fluctuating medium with slowly varying temperature is studied as an analytically tractable example, and a kinetic theory is formulated for describing the states of the particle. What is peculiar here is that the (inverse) temperature is treated as a dynamical variable. Dynamical hierarchy is introduced in conformity with the (...)
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  46.  74
    Tom Froese & Thomas Fuchs (2012). The Extended Body: A Case Study in the Neurophenomenology of Social Interaction. [REVIEW] Phenomenology and the Cognitive Sciences 11 (2):205-235.
    There is a growing realization in cognitive science that a theory of embodied intersubjectivity is needed to better account for social cognition. We highlight some challenges that must be addressed by attempts to interpret ‘simulation theory’ in terms of embodiment, and argue for an alternative approach that integrates phenomenology and dynamical systems theory in a mutually informing manner. Instead of ‘simulation’ we put forward the concept of the ‘extended body’, an enactive and phenomenological notion that (...)
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  47.  22
    William Bechtel (2012). Understanding Endogenously Active Mechanisms: A Scientific and Philosophical Challenge. [REVIEW] European Journal for Philosophy of Science 2 (2):233-248.
    Abstract Although noting the importance of organization in mechanisms, the new mechanistic philosophers of science have followed most biologists in focusing primarily on only the simplest mode of organization in which operations are envisaged as occurring sequentially. Increasingly, though, biologists are recognizing that the mechanisms they confront are non-sequential and the operations nonlinear. To understand how such mechanisms function through time, they are turning to computational models and tools of dynamical systems theory. Recent research on circadian rhythms (...)
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  48.  32
    Tom Froese & Shaun Gallagher (2012). Getting Interaction Theory (IT) Together: Integrating Developmental, Phenomenological, Enactive, and Dynamical Approaches to Social Interaction. Interaction Studies 13 (3):436-468.
    We argue that progress in our scientific understanding of the `social mind' is hampered by a number of unfounded assumptions. We single out the widely shared assumption that social behavior depends solely on the capacities of an individual agent. In contrast, both developmental and phenomenological studies suggest that the personal-level capacity for detached `social cognition' (conceived as a process of theorizing about and/or simulating another mind) is a secondary achievement that is dependent on more immediate processes of embodied social interaction. (...)
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  49.  49
    Joshua Rosaler (2015). Local Reduction in Physics. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 50:54-69.
    A conventional wisdom about the progress of physics holds that successive theories wholly encompass the domains of their predecessors through a process that is often called reduction. While certain influential accounts of inter-theory reduction in physics take reduction to require a single "global" derivation of one theory's laws from those of another, I show that global reductions are not available in all cases where the conventional wisdom requires reduction to hold. However, I argue that a weaker "local" form (...)
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  50.  21
    Randall D. Beer & Paul L. Williams (2015). Information Processing and Dynamics in Minimally Cognitive Agents. Cognitive Science 39 (1):1-38.
    There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system (...)
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