Dynamic Systems Theory

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Citations related to DYNAMIC SYSTEMS THEORY (works cited listed at bottom):


“In synergetics, the order parameter is created by the cooperation of the individual parts of the system, here the fluid molecules. Conversely, it governs or constrains the behavior of the individual parts. This is a strange kind of circular causality, but we will see that it is typical of all self-organizing systems. What we have here is one of the main conceptual differences between the circularly causal underpinnings of pattern formation in nonequilibrium sytems and the linear causality that underlies most of modern physiology and psychology, with its inputs and outputs, stimuli and responses.”

“Some might argue that the concept of feedback closes the loop, as it were, between input and output. This works fine in simple systems that have only two parts to be joined, each of which affects the other. But add a few more parts interlaced together and very quickly it becomes impossible to treat the system in terms of feedback circuits. In such complex systems, as W. Ross Ashby elegantly pointed out years ago, the concept of feedback is inadequate. What is more important is to realize that richly interconnected systems may exhibit both simple and complex behavioral patterns. Returning to the Bénard example, there is no reference state with which feedback can be compared and no place where comparison operations are performed. Indeed, nonequilibrium steady states emerge from the nonlinear interactions among the system’s components, but there are no feedback-regulated set points or reference values as in a thermostat.” Kelso, J. A. Scott. Dynamic Patterns: The Self-Organization of Brain and Behavior. 1995. MIT Press. Pps. 8-9.


“Dynamics gives meaning to geometrical forms while also being constrained by them. In short, the creation of structured forms such as ocular dominance columns in the cortex is activity dependent. As I said at the beginning of this chapter, the classic dichotomy between structure and function fades, and we begin to sense the intimate relation between them. Ultimately, all we are left with is dynamics self-sustaining and persisting on several space-time scales, at all levels from the single cell up.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 15.


“Thus the main picture so far is that the complexity of matter or substance with all its microscopic constituents, gives rise through the dynamical mechanism of nonequilibrium phase transitions to simpler order parameter dynamics that in turn are capable of generating enormous behavioral complexity.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 17.


“Especially in biological systems, constraints and borders are constantly being created and dissolved. Cooperativity at one level of organization may act as a parametric boundary condition on a lower level. Conversely, the former may act as an elementary or component process at higher levels of organization.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 18.


“The mechanisms underlying such behavioral complexity are generic, but nontrivial. What one always finds at the heart of the evolution of complex behavior are dynamic instabilities, bifurcations of different kinds that have to be identified.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 22.


“The sought-after oneness or globality of thought emerges, in my view, as a collective, self-organized property of the nervous system coupled, as it is, to the environment.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 25.

“The thesis here is that the human brain is fundamentally a pattern-forming, self-organized system governed by nonlinear dynamical laws. Rather than compute, our brain ‘dwells’ (at least for short times) in metastable states: it is poised on the brink of instability where it can switch flexibly and quickly.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 26.


“I refer first to the Gestalt theorist Wolfgang Köhler, who viewed psychological processes as the dynamic outcome of external constraints provided by environmental stimulation and internal constraints of brain structure and function.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 35.


“Here’s the basic two-pronged problem. The human body is a complex system in at least two senses. On the one hand, it contains roughly 102 joints, 103 muscles, 103 cell types, and 1014 neurons and neuronal connections. As Otto Rössler once said, finding a low dimension within the dynamics of such a high-dimensional system is almost a miracle. On the other hand, the human body is multifunctional and behaviorally complex. When I speak and chew, for example, I use the same set of anatomaical components, albeit in different ways, to accomplish two different functions.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. Pps 37-8.


“For Bernstein, the large number of potential degrees of freedom precluded the possibility that each is controlled individually at every point in time.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. P. 38.


“The resolution to this problem [control of a multivariable system by just a few parameters] offered by the Bernstein school contained two related parts. The first was to propose that the individual variables are organized into larger groupings called linkages or synergies. During a movement, the internal degrees of freedom are not controlled directly but are constrained to relate among themselves in a relatively fixed and autonomous fashion....”

“The second absolutely crucial aspect of the synergy concept is that it was hypothesized to be function or task specific.” Kelso, J. A. Scott. 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press. Pps. 38-9.


“The results of these demonstrations are instead consistent with the operation of a hierarchy of control systems in which upper-level systems do not tell lower-level systems what to do (that is, provide motor commands) but specify what lower-level systems should perceive. The controlled perception is that of a certain sequence of joint angles (known as proprioception) that has been associated with the perception of previously successful throwing or pounding and that will itself be adjusted by still higher-level systems depending on the perceived outcome of each trial. It is important to note that a form of associative learning is occurring here. But it is not that of associating a stimulus with a behavior. Rather, it is associating higher-level controlled perceptions with lower-level ones.” Cziko, Gary. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. 2000. MIT Press. Pp. 103-4.


“... perceptual control is a real and easily demonstrated phenomenon that cannot be understood from the traditional one-way cause-effect view of animal and human behavior, and networks of negative-feedback perceptual control systems can be fashioned into working models that behave remarkably like the purposefully behaving animals and humans that they were meant to simulate. Most important, however, is understanding that we now have a basic theory (and model) of animal and human behavior that can explain its purposeful nature in purely materialist and mechanistic terms, but which requires a rejection of the one-way cause-effect view of living behavior.” Cziko, Gary. 2000. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. MIT Press. P. 105.


“We have now seen how considering animate behavior as an organism’s means to control aspects of its environment provides a new way of addressing questions concerning the what, how, and why of behavior. From this perspective, what questions are addressed by considering the perceptual variable that an organism is controlling, keeping in mind that any given action may have many uncontrolled side effects that are of no concern to the behaving organism, and that the behavioral consequences specified in reference levels need not be static but instead can be continually changing.”

“How questions are answered by considering the subgoals, or lower-level reference levels, that must be controlled for a higher-level perceptual variable to be controlled. From this perspective, a professional golfer is able to drive her ball onto the green not because her nervous system is able to send a certain fixed sequence of motor commands to her muscles, but because she has learned to control a sequence of lower-level perceptions involving the positions and velocities of her limbs, head, and trunk, as well the relationship of these kinesthetic and proprioceptive perceptions to the visual perception of the green she is aiming at.”

“In contrast to behavioral how questions that focus our attention on lower-level control systems and their reference levels, why questions about behavior are addressed by moving up the hierarchy of control systems to find higher-level reference levels (or goals) that determine lower ones.” Cziko, Gary. 2000. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. MIT Press. Pps. 105-6.


“... we must account for how it is that new behaviors can remain adaptive under changing environmental conditions. We saw in chapter 6 that these changing conditions and the new disturbances they impose mean that learning cannot be the acquisition of invariant motor responses to stimuli. Instead, an organism’s actions must continually vary to bring about desired results. No matter how many times you may have driven your car from home to your place of work, you cannot make the trip using the same pattern of arm and leg movements that you used on any previous trip. Continually changing traffic, weather, and road conditions would make any such fixed pattern of actions ineffective in getting to work. This behavioral flexibility in the achievement of goals is not limited to humans but is characteristic of all animate behavior. We will refer to this as the behavioral flexibility problem.” Cziko, Gary. 2000. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. MIT Press. P. 207.


“... it turns out that none of the major learning theories or their variations successfully deals with the behavioral flexibility problem. This is because they all embrace simple one-way causality from stimulus to response or from stimulus to cognitive computation to response. But any theory that posits behavior as an end product (output or response) that is elicited by an input (stimulus or perception) with or without intervening cognitive processes is inherently incapable of accounting for the continuous variations in behavior that we observe in the service of achieving goals in the face of continually changing disturbances. Thus a theory that attempts to explain learning as acquisition of a repertoire of responses must fail.” Cziko, Gary. 2000. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. MIT Press. P. 209.


“In marked contrast to both Pavlov and Skinner’s stimulus-response theories of learning (and contrasting as well to stimulus-computation-response learning theories of current cognitive science), perceptual control theory sees learning as involving modification of perceptual associations, not stimulus-response associations.” Cziko, Gary. 2000. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. MIT Press. P. 211.


“By combining the extended Bernardian lesson (that organisms vary their behavior to control their perceptions) and the extended Darwinian lesson (that organisms make use of variation and selection to gain control of aspects of their environment) we arrive at a new conception of learning. Learning is no longer the association of new stimuli to old responses, or acquisition of new responses to old stimuli, but rather acquisition of new means of perceptual control by reorganizing existing perceptual control systems by within-organism variation and selection.” Cziko, Gary. 2000. The Things We Do: Using the Lessons of Bernard and Darwin to Understand the What, How, and Why of Our Behavior. MIT Press. P. 213.


“What we perceive is determined by what we do (or what we know how to do); it is determined by what we are ready to do. In ways I try to make precise, we enact our perceptual experience; we act it out.” Noë, Alva. Action in Perception. 2004. MIT Press. P. 1.


“One implication of the enactive approach is that only a creature with certain kinds of bodily skills—for example, a basic familiarity with the sensory effects of eye or hand movements, and so forth—could be a perceiver. This is because, in effect, perceiving is a kind of skillful bodily activity. It may also be that only a creature capable of at least some primitive forms of perception could be capable of self-movement.” Noë, Alva. 2004. Action in Perception. MIT Press. P.. 2.


“Although connectionist nets are often thought of as being in the brain, the same vector transformations that give them their cognitive power also govern the dynamic system that is the brain-body-world.” Rockwell, W. Teed. Neither Brain nor Ghost: A Nondualist Alternative to the Mind-Brain Identity Theory. 2005. MIT Press. P. 132.


“A dynamic system is created when conflicting forces of various kinds interact, then resolve into some kind of partly stable, partly unstable, equilibrium.” Rockwell, W. Teed. 2005. Neither Brain nor Ghost: A Nondualist Alternative to the Mind-Brain Identity Theory. MIT Press. P. 192.


“Kelso claims, along with J.J. Gibson, that coupling also explains how a perceiver interrelates to her environment. If the same process that connects neurons together also connects an organism to the world, how can we make a principled distinction between the neural network and the organism-environment network?” Rockwell, W. Teed. 2005. Neither Brain nor Ghost: A Nondualist Alternative to the Mind-Brain Identity Theory. MIT Press. Pp. 200-1.
 

"The principle of order through fluctuation which underlies all coherent evolution also requires a new information theory which is based on the complementarity of novelty and confirmation in pragmatic (i.e effective) information. The kind of information theory which has become so useful in communication technology holds only for information which consists almost totally of confirmation. In the domain of self-organizing systems, information is also capable of organizing itself; new knowledge arises....

"For the spontaneous formation of such structures in chemical reaction systems, a 'generalized' thermodynamics by Glansdorff and Prigogine stipulates precise conditions. They include openness with respect to the exchange of energy and matter with the environment, far from equilibrium conditions and auto- or crosscatalytic steps in the reaction chain....

"Whereas free energy and new reaction participants are imported, entropy and reaction end products are exported--we find here the metabolism of a system in its simplest manifestation. With the help of this energy and matter exchange with the environment, the system maintains its inner non-equilibrium, and the non-equilibrium, in turn, maintains the exchange processes. One may think of the image of a person who stumbles, loses his equilibrium and can only avoid falling on his nose by continuing to stumble forward....

"The actually unfolding process chains and the resulting process webs are unpredictable, but they obey certain rules. These rules are based on a single fundamental principle, self-consistency. Whatever comes into being has to be consistent with itself and with everything else." Jantsch, Erich. The Self-Organizing Universe, Pergamanon Press, 1980, p. 11, 31-2.
 

“For enactive theorists, information is context-dependent and agent-relative; it belongs to the coupling of a system and its environment. What counts as information is determined by the history, structure, and needs of the system acting in its environment.” Thompson, Evan. Mind in Life: Biology, Phenomenology, and the Sciences of Mind. 2007. Harvard University Press. Pp. 51-2.


“Pattee emphasizes the complementarity of the linguistic and dynamical modes of description, but also suggests that symbolic information emerges from and acts as a constraint on dynamics. This idea is important for embodied dynamicism and the enactive approach.” Thompson, Evan. Mind in Life: Biology, Phenomenology, and the Sciences of Mind. 2007. Harvard University Press. P. 55.


“In a groundbreaking paper on emotion and consciousness, neuropsycholgist Douglas Watt describes emotion as a ‘prototype whole brain event,’ a global state of the brain that recruits and holds together activities in many regions, and thus cannot have simple neural correlates. We can take this point one step further by saying that emotion is a prototype whole-organism event, for it mobilizes and coordinates virtually every aspect of the organism. Emotion involves the entire neuraxis of brain stem, limbic area, and superior cortex, as well as visceral and motor processes of the body. It encompasses psychosomatic networks of molecular communication among the nervous system, immune system, and endocrine system.” Thompson, Evan. Mind in Life: Biology, Phenomenology, and the Sciences of Mind. 2007. Harvard University Press. Pp. 262-3.
 

“Intentionality in the doctrine of Aquinas does not require consciousness, but it does require acting to create meaning instead of just thinking. This view is shared by the philosophers Martin Heidegger, Maurice Merleau-Ponty, J.J. Gibson, and the pragmatists. We sniff, move our eyes, cup an ear, and move our fingers to manipulate an object in order to optimize our relation to it for our immediate purpose. Merleau-Ponty called this dynamic action the search for maximum grip, which is the optimization of the relation of the self to the world by positioning the sense receptors toward the object intended. His conception is equivalent to Aquinas’s assimilation. John Dewey described the process as ‘acting into the stimulus’ and incorporating it into future action, as distinct from merely reacting to it. Jean Piaget based his analysis of psychological development on the concept that infants learned very early about their bodies and their environments by active exploration, which he called ‘the cycle of action, assimilation, and adaptation’ in what he identified as the ‘sensorimotor’ stage of early childhood, when infants constantly move their bodies, especially their hands and feet, and drink in the sensations they collect. Esther Thelen developed this approach in the context of dynamic systems theory. Gibson emphasized the ‘affordances’ of objects, by which he meant their utility in respect to the purposes of the perceiver. He believed that each object contained within itself the information that showed how it was to be used. This information was extracted by the brain through ‘resonance’ within brain systems, when that information ‘in-formed’ the mind. His concept is also equivalent to assimilation. He used these technical terms as metaphors, because he had no neural mechanisms in mind, but the terms convey the underlying idea of the unidirectionality of perception in a finite being coping with an infinite universe.” Freeman, Walter. How Brains Make Up Their Minds. 2000. Columbia University Press. Pp. 28-9.


“Juarrero criticizes philosophers for failing to provide coherent answers to the question of what causes intentional behavior. She advances the idea that intentional behavior, and its causes, is best characterized as a fluid, dynamic process taking shape through the interactions between brains, bodies, and their environments. Juarrero adopts the perspective of complex dynamic systems theory as a ‘theory-constitutive metaphor’ for reconceptualizing mental causation, particularly in terms of how philosophers think of the causes for intentional action.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. P. 74. Reference is from Juarrero, A. Dynamics in Action: Intentional Behavior as a Complex System. 1999. MIT Press.


“One important implication of dynamical systems theory is that the intentions one feels to purposefully yawn, or raise one’s hand to wave hello to a friend, result from a person’s self-organizing tendency. This self-organizing structure embodies a tendency for someone to want to purposefully yawn even before the desire to perform the action reaches awareness. A concrete illustration of this point is seen in the developmental work of Thelen and Smith. Thelen and Smith argued that motor development in infants is not a maturational processes determined by some hard-wired genetic code. Instead, motor development is a process of dynamical self-organization that arises from the infant’s continuous interaciton with its changing environment.

“For example, two infants started out with different inherent dynamics for reaching. One infant, Gabriel, flailed wildly and repeatedly as she reached for an object, yet another infant, Hannah, ws far less physically active and carefully assessed the situation before reaching. Both infants learned to successfully reach objects within a few weeks of one another.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. P. 74. Reference is from Thelen, E. & Smith. Dynamic Systems Approach to Development: Applications. 1994. MIT Press.


“Conceptualizing action from a dynamical system perspective explains why people need not explicitly decide something each time they act. The person’s current frame of mind automatically selects a subset from the unlimited other alternatives within her self-organized constraint-space. For instance, when your friend decides to inform you of her belief about the lecture, she does not need to explicitly formulate a decision or proximate intention about what to do. Her ‘choice’ of yawning rather than doing something else (e.g., writing a note, talking aloud to you) can be ‘decided’ by the interaction between her own dynamics and the environment as the process ‘moves downstream’. For instance, your friend knows that her being in a lecture prevents her from saying something aloud, or perhaps even whispering. None of this, however, requires that she form an explicit intention requiring explicit deliberation. She can just decide to communicate her belief about the lecture and the environmental constraints take care of the fine-grained details of how this intention is manifested in real-world behavior.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. P. 75.


“Such structuralist notions have given way to a functionalist orientation that regards emotional behavior as functioning to establish, maintain, or alter the relation between an organism and its environment. From the standpoint of a functionalist orientation, emotions are multicomponent, adaptive systems of functioning in their own right. In the midst of this zeitgeist change, applications of a dynamic systems perspective to the study of emotion and its development have become increasingly prevalent.” Witherington, David and J. Crichton. “Frameworks for Understanding Emotions and Their Development: Functionalist and Dynamic Systems Approaches.” The American Psychological Association. 2007, Vol. 7, No. 3, 628-637. P. 628.


“By the dynamic systems approach, as by the functionalist approach, emotions are complex systems involving multiple components or subsystems at multiple levels of analysis and are irreducible to these components. Each subsystem (e.g., appraisals, goals, instrumental actions) is an important part of the emotion process, but none has formative primacy, either in the generation or development of emotion.” Witherington, David and J. Crichton. “Frameworks for Understanding Emotions and Their Development: Functionalist and Dynamic Systems Approaches.” The American Psychological Association. 2007, Vol. 7, No. 3, 628-637. P. 629.


“The emotion system, like any other complex, non-linear system, self-organizes as a function of the cooperativeness of the components that comprise it. System stability is maintained through the organizational cooperation of the systems’ components.” Witherington, David and J. Crichton. “Frameworks for Understanding Emotions and Their Development: Functionalist and Dynamic Systems Approaches.” The American Psychological Association. 2007, Vol. 7, No. 3, 628-637. P. 629.


“Much of the history of emotion theory has been wrapped up in the search for a gold standard of emotion, whether it be in facial expression, autonomic signatures, or affect programs. Most modern articulations of the emotion process define emotion across multiple levels of analysis, from the levels of central and peripheral psychology and of expressive and instrumental action to the levels of goals, appraisals, subjective experience, and organism-environment relations. In effect, the emotion process is defined from multiple perspectives, prompting the realization that no single perspective fully captures emotion as a whole. The interlevel complexity of the emotion process requires an integrative orientation that can capture both the behavioral and physiological dynamics of emotion in real-time contexts and the organizational qualities of emotion at the level of organism in relation to environment. Both the dynamic systems approach and the functionalist approach have their shortcomings as far as breadth of focus, with each approach representing an incomplete vision of the full emotion process. Akin to the ancient Eastern fable of the blind men and the elephant, each metatheoretical perspective samples only part of the whole that is the emotion process in all its complexity. However, if they are considered as complements to one another, both perspectives allow for a more complete view of emotion to emerge. As such, the marriage of dynamic systems and functionalist approaches moves the field of emotion and its development forward by establishing a broader philosophical lens with which to frame our understanding of emotion and by maintaining our focus on the multilevel complexity of the emotion process. In the absence of this broader, integrative orientation, we all too easily lose sight of emotion’s complexity.

“Thus, the marriage of functionalist and dynamic systems approaches moves the field toward an increasingly integrative view and exemplifies what Overton has termed a relational developmental metatheory. Such a metatheory considers ontological differences as ‘differentiated polarities (i.e., coequals) of a unified (i.e., indissociable) inclusive matrix.’ Each approach, the functionalist and the dynamic systems, constitutes a distinct yet relationally unified line of sight or perspective. Formal and final –efficient and material – levels of causality reflect alternative perspectives and different features of the same whole. All causes are unified as alternative vantage points of the same whole, each providing a meaningful context for the others. The functionalist approach, via formal and final causality, abstracts commonality across actions and contexts, whereas the dynamic systems theorist, via efficient and material causality, works within the meaning frame established by the functionalist to explain why specific actions emerge in specific contexts.” Witherington, David and J. Crichton. “Frameworks for Understanding Emotions and Their Development: Functionalist and Dynamic Systems Approaches.” The American Psychological Association. 2007, Vol. 7, No. 3, 628-637. Pp. 635-6.
 

“It is important to keep in mind ... that the brain did not evolve merely to register representations of the world; rather, it evolved for adaptive actions and behaviors. Musculoskeletal structures coevolved with appropriate brain structures so that the entire unit must function together in an adaptive fashion ... it is the entire system of muscles, joints, and proprioceptive and kinesthetic functions and appropriate parts of the brain that evolve and function together in a unitary way.” Kelso, J. A. Scott. Dynamic Patterns: The Self-Organization of Brain and Behavior. 1995. MIT Press. P. 268. Quoted in Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. P. 9.


“A dynamical approach rejects the idea that cognition is best understood in terms of representational content , or that cognitive systems can be decomposed into inner functional subsystems or modules. Linear decomposition of cognitive performance into functional subsystems (i.e., ‘boxology’) is inadequate to understand the dynamical systems that cut across brain-body-world divisions. Most researchers working within a dynamical framework adopt the conservative strategy of seeing how far one can go in explaining various behavioral data without invoking representational explanations. Dynamical systems theory has had its most profound effect in cognitive science in the study of perception/action relations, or couplings, and in the development of situated, embodied agents, or robots, capable of minimally cognitive behavior. Although there is debate over whether dynamical approaches can ‘scale up’ to explain higher-order aspects of cognition, including language use and consciousness, I am enthusiastic about this perspective because it directly acknowledges the interaction of an agent’s physical body, its experience of its body, and the structure of the environment and social context to produce meaningful adaptive behavior.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. P. 11.


“My suggestion is that image schemas are attractors within human self-organizing systems. Attractors, such as BALANCE, SOURCE-PATH-GOAL, RESISTANCE, VERTICALITY, and PATH reflect emerging points of stability in a system as it engages in real-world interaction. New, surprising patterns encountered in the environment throw a system into momentary chaos, until the system, through its self-assembly process, reorganizes and reaches a new stability. The important point here is that attractors are not localized representations, but emerging patterns of entire systems in action (i.e., interplay of brain, body, and world). In this way, the stable properties of image schemas are not separate from sensorimotor activity. Image schemas should not be reduced to sensorimotor activity, but it is a mistake to view image schemas as mental representations that are abstracted away from experience. One implication of this dynamical view is that each construal of an image schema will have a different profile depending on the overall state of the organism involved in some activity and past basins of attractions created within the system.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. P. 115.


“The dynamical approach to development is significant because it embraces the idea that cognition is connected to bodily action. A child’s new abilities emerge through the dynamic indeterminacy of self-organization. Unlike most theories, the dynamical perspective explains development in terms of multiple causes and connections and acknowledges that even small, unexpected factors may critically shape the course of development. Moreover, dynamical systems theories recognize the importance of studying the whole system (i.e., the child) in understanding development, and not assuming that cognitive growth is based on the acquisition of isolated competencies. Although dynamical systems theory has been most successful in describing motor and perceptual development, there is an increasing body of work showing how emotional and personality development may also be characterized in dynamical terms.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. Pp. 226-7.


“Imagine that you walk down the street, come across someone you know, and smile. Why did you do this? Was your smile intentional or an automatic response to seeing someone you knew? As shown above, there is much research to demonstrate that people may strategically express emotions in the sense of intending to communicate specific messages. But are other emotional expressions, such as having sweaty palms when nervous, also intentional? This folk-level analysis does not adequately capture intentionality or the psychological dynamics of emotional expression. A dynamic systems perspective on emotional expressions, as self-organized critical states, may yield a unified view of emotional expressions as a natural consequence. Dynamic systems have a capacity for self-control whereby they reduce a set of potential actions (e.g., the large set of potential ways to greet a friend) to that which is actually expressed, such as a particular smiling demeanor. Self-organization reduces the degrees of freedom for action until a human face becomes a context-appropriate ‘special device,’ a smiling device, frowning device, or whatever will suit the singular set of circumstances in which the action is situated. This capacity is creative and exquisitely context-sensitive, in the sense that it produces a singular action tailored to a particular context.

“Under the dynamical view, emotional expressions are on a par with intentional contents. The intention one feels to purposefully smile, or raise one’s hand to wave hello, or enact some other greeting, all result from a person’s capacity for self-organization. Intentions attendant on self-organization entail a potential to purposefully smile, for example, even before the desire to smile reaches awareness. Intentional actions, such as purposefully smiling, start with the idea that self-organized dynamical structures are globally stable even though they may compose local sources of disorder. Thus a complex system can be driven toward local instabilities in the interaction of external circumstances and the system’s own internal dynamical processes.”

“For example, feeling happy when seeing a friend can precipitate local instability, not only neurologically, but also in abstract relations at cognitive and emotional ‘levels.’ By forming an intention, say to smile, when seeing a friend, a cognitive phase-change may find a locally more stable trajectory (i.e., a better match between the ‘friend bearing’ situation and the possibilities for friendly discourse). The new intention restructures (‘prunes’) the vast set of behavioral possibilities, excluding all but a potential set of friendly actions. These intentional limits on the potential set avoid the need to consider and evaluate every logical and physical possibility for action. Thus, the emergent intention to let a friend know of your happiness to see him or her prunes the set of possibilities down to the act of smiling, excluding other possibilities such as writing the person a note, shaking his or her hand, whispering to him or her, and so forth.” Gibbs, Raymond. Embodiment and Cognitive Science. 2006. Cambridge University Press. Pp. 259-60.


“Through a DST lens, spiritual development is a process of phase transitions from less to more functional organisation of the whole person, driven by system parameters, organised by attractors, and responsive to the child’s free choices. This framework can be applied across a range of naturalistic, Romantic or theistic definitions.” Cupit, C.G. “The marriage of science and spirit: dynamic systems theory and the development of spirituality.” International Journal of Children’s Spirituality. Vol. 12, No. 2, August 2007, pp. 105-116. P. 112.


“Any graph G with N nodes can be represented by a matrix encoding the topology of the network, the adjacency matrix.

“The Adjacency Matrix. The N x N adjacency matrix  has elements Aij = 1 if nodes i and j are connected and Aij = 0 if they are not connected.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 9.


“The Constant of Motion: A function F(x) on phase space x = (x1, ..., xd) is called a ‘constant of motion’ or a ‘conserved quantity’ if it is conserved under the time evolution of the dynamical system,...” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 36.


“Ergodicity: A dynamical system in which orbits come arbitrarily close to any allowed point in the phase space, irrespective of the initial condition, is called ergodic.

“All conserving systems of classical mechanics, obeying Hamiltonian dynamics, are ergodic. The ergodicity of a mechanical system is closely related to ‘Liouville’s theorem’,...

“Ergodicity holds only modulo conserved quantities, as is the case for the energy in many mechanical systems. Then, only points in the phase space having the same energy as the trajectory considered are approached arbitrarily close.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 36.


“Attractors: A bounded region in phase space to which orbits with certain initial conditions come arbitrarily close is called an attractor.

“Attractors can be isolated points (fixpoints), limiting cycles or more complex objects.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 36.


“The Basin of Attraction: The set of initial conditions that leads to orbits approaching a certain attractor arbitrarily closely is called the basin of attraction.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 36.


“It is clear that ergodicity and attractors are mutually exclusive: An ergodic system cannot have attractors and a dynamical system with one or more attractors cannot be ergodic.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 36.


“Deterministic Chaos. A deterministic dynamical system that shows exponential sensibility of the time development on the initial conditions is called chaotic.

“This means that a very small change in the initial condition can blow up even after a short time. When considering real-world applications, when models need to be determined from measurements containing inherent errors and limited accuracies, an exponential sensitivity can result in unpredictability. A well known example is the problem of long-term weather prediction.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 38.


“Near an attractor the phase space contracts.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 43.


“Dissipative and Conserving Systems. A dynamical system is dissipative, if its phase space volume contracts continuously,... The system is said to be conserving if the phase space volume is a constant of motion,...

“Mechanical systems, i.e. systems described by Hamiltonian mechanics, are all conserving in the above sense.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 44.


“A general complex system is neither fully conserving nor fully dissipative. Adaptive systems will have periods where they take up energy and periods where they give energy back to the environment. An example is the non-linear rotator ...

“In general one affiliates with the term ‘adaptive system’ the notion of complexity and adaption. Strictly speaking any dynamical system is adaptive if the [the curl ∇ of the time derivative of a space coordinate function] may take both positive and negative values. In practice, however, it is usual to reserve the term adaptive system to dynamical systems showing a certain complexity, such as emerging behavior.” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P. 47.


“Diffusive transport is characterized by transport sublinear in time in contrast to ballistic transport with x = vt...” Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems. Springer. P52.


“When scientists talk about a system’s being dynamic, what they mean is that the state of the system at the current moment is a function of the state of the system at the previous moment, and some change in between the two moments.” Beinhocker, Eric. The Origin of Wealth: The Radical Remaking of Economics and What It Means for Business and Society. 2006. Harvard Business School Press. P. 100.


“The key thing to remember is that positive feedback reinforces, accelerates, or amplifies whatever is happening, whether it is a virtuous cycle or downward spiral. Systems with positive feedback can thus exhibit exponential growth, exponential collapse, or oscillations with increasing amplitude.

“The opposite is negative feedback. Negative feedback is a dampening cycle–instead of reinforcing, it pushes in the opposite direction. While positive feedback accelerates change, negative feedback dampens change, controls things, and brings things back in line.” Beinhocker, Eric. The Origin of Wealth: The Radical Remaking of Economics and What It Means for Business and Society. 2006. Harvard Business School Press. P. 101.


“It is not difficult to see how dynamic systems can quickly become quite complex if one has multiple stocks and flows interacting via both positive and negative feedback loops. The positive feedbacks drive the system, accelerating it, but at the same time the negative feedbacks are fighting back to dampen and control it. When time delays are thrown in, the driving and damping can get out of balance, and out of synch, causing the system to oscillate in highly elaborate ways.” Beinhocker, Eric. The Origin of Wealth: The Radical Remaking of Economics and What It Means for Business and Society. 2006. Harvard Business School Press. P. 102.


“Although economists and sociologists both are concerned with emergence, they maintain distinct versions of emergence. Economists tend to believe that because social phenomena emerge from collective individual action, the best way to study those phenomena is to study the lower level of individual action from whence they emerge. This is the reading of complex dynamical systems theory that one often finds in the writings of economists: a reductionist, atomistic version, perhaps most explicitly demonstrated in multi-agent system computer models of societies. Yet this version of systems thinking is not acceptable to many sociologists because it seems to deny the reality of social phenomena like networks, symbolic interactions, and institutions. In contrast, many sociological theories of emergence argue that emergent social properties cannot be analyzed in terms of the individuals constituting society because once emergent they take on autonomous properties and seem to exert causal force over the participating individuals.” Sawyer, R. Keith. Social Emergence: Societies as Complex Systems. 2005. Cambridge University Press. P. 24.


“At this point we must be clear about how a ‘system’ is to be defined. Our first impulse is to point at the pendulum and to say, the system is that thing there. This method, however, has a fundamental disadvantage: every material object contains no less than an infinity of variables and therefore of possible systems. The real pendulum, for instance, has not only length and position; it has also mass, temperature, electric conductivity, crystalline structure, chemical impurities, some radioactivity, velocity, reflecting power, tensile strength, a surface film of moisture, bacterial contamination, an optical absorption, elasticity, shape, specific gravity, and so on and on. Any suggestion that we should study ‘all’ the facts is unrealistic, and actually the attempt is never made. What is necessary is that we should pick out and study the facts that are relevant to some main interest that is already given. The system now means not a thing, but a list of variables.” Ashby, Ross. An Introduction to Cybernetics. John Wiley. 1956. P. 39. Quoted in Erdi, Peter. Complexity Explained. Springer. 2008. P. 44.


“There are properties inherent to dynamical systems that are often responsible for the mind not quite adhering to probability theory. There is a kind of momentum that the mind develops as it travels through the state space, causing it to warp and exaggerate its deterministic influences. The mind has a tendency to gravitate closer to the nearest attractor (mental state) than warranted. That is, dynamical systems often settle toward stable states, with one attractor being almost, but not perfectly, satisfied–even when the input is unresolvably ambiguous.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. P. 16.


“What is crucial to defining a dynamical system is its balance of stability and instability.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. Pp. 17-8.


“It is my hypothesis that in more complex visual (as well as auditory, olfactory, etc.) environments, the proportion of time spent in these unstable regions of state space–that is, in the process of traveling toward an attractor basin, but not in one yet–is actually much greater than the proportion of time spent in relatively stable (or, more precisely, metastable) orbit-prone regions of state space.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. P. 22.


“Rather than the mind being composed of independent systems for perception, cognition, and action, the entire process is perhaps better conceived of as a continuous loop through perceptionlike processes, partially overlapping with cognition-like processes, and actionlike processes, producing continuous changes in the environment, which in turn, continuously influence the perceptionlike processes. In this large feedback loop, the brain itself is more of an interdependent subsystem contributing to mind than a system comprising mind. It carries out more of a subprocess than a process.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. P. 29.


“Thus we arrive at the compensatory strengths and weaknesses of dynamical systems theory and of artificial neural networks. Dynamical systems theory accommodates the genuine continuity of time and state space but says little about neurophysiology. Neural network simulations provide some approximated account of the actual neural hardware that carries out these functions, but they chop their time, and therefore their state space, into segmented periods and regions of artificial stasis.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. P. 32.


“Thus, by the time your brain state has approached a location in state space that is predominantly consistent with only one pure mental state, such as ‘I see a cat,’ changes in the environment and your own behavior will alter the brain state such that it travels back into unlabeled regions in state space, preparing for another near-settling event where it gets just close enough to a pure mental state to elicit an associated behavior and then veers off yet again. This is at the very core of the continuity of mind thesis: It means that the vast majority of the mind’s time is spent in between identifiable mental states rather than in them.

“Importantly, replacing the concept of stimulus-and-response, or perception-and-action for that matter, with the concept of a continuous trajectory in mental state space highlights the fatal flaw that behaviorism and cognitivism shared–despite their apparent opposition. Although the cognitive revolution criticized behaviorism for ignoring the intermediate processes between stimulus and response, they nonetheless embraced stimulus and response as the start and finish of a temporally bounded linear process. Therein lies the error, because most responses immediately become stimuli (i.e., we are perceiving our own actions while we are executing them). The process is not temporally bounded. It has not start and no finish. Even preparation of a response can often influence the internal processing of the incoming stimulus stream. Thus, as the continuous dynamic closed loop of sensory input and motor output makes infeasible a true discrimination of stimulus from response, so does the embedded continuous dynamic closed loop of perceptual processing and action preparation make infeasible a true discrimination of perception from action.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. Pp. 47-8.


“The animal and its environment form a system. Change in one produces change in the other, and the loop of circular causality continues over time. The animal subsystem and the environment subsystem are sufficiently coupled that it would be impossible for one to be following laws that the other does not also follow. Thus, one might perhaps include all of those parameters (neural activation patterns, muscular-skeletal kinematics, and even external objects) as dimensions in the state space that defines mind. In this view, the relevant definition of mind becomes a trajectory through the full animal-environment state space, not just the brain’s state space. And when two animals are in sufficient spatial proximity to each other that an external object (even something as mundane as a ball) is in the immediate environment of both animals, then those two minds are sharing a few dimensions of their respective state spaces. They become a system, describable by a single unified (and recurrent) trajectory–as the ball gets tossed back and forth for hours on end.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. P. 50.


“However, the radical amendment proposed by the present chapter is that this ‘trajectory through neural state space’ account of the mind is still incomplete. If you widen your scope just enough to examine that neural dynamical system as embedded inside a larger dynamical system comprising the environment and other organisms, then the self is no longer conceived of as an ivory tower in the skull and can be understood as an amalgam of interweaving influences from both internal and external sources. That is, the dimensions that define your mental trajectory are not only neural firing rates but also biomechanical variables that constrain how your body interfaces with the environment, as well as information-bearing properties of that environment itself. This larger dynamical system describes the range of trajectories exhibited by that brain-cum-environment process. And the prevailing argument throughout this chapter has been that the embedding of that neural dynamical subsystem inside the larger environmental dynamical system prevents them from being categorically separable.” Spivey, Michael. The Continuity of Mind. 2007. Oxford University Press. P. 305.


“According to Kuhn’s famous analysis of theory change in science, a field of study at any point in time is in one of three stages: it is immature, it is in the stage of normal science, or it is in a period of revolution.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 13.


“Because complicated dynamical systems have a tendency to behave like much simpler systems, one will often be able to model these systems in terms of extremely simple functions, with only a few easily observable parameters.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 88.


“In non-linearly coupled dynamical systems, one typically sees a spike in system entropy just prior to a phase transition, such as when coordination moves from out-of-phase to in-phase. As noted above, this spike in entropy is called critical fluctuation: as a system approaches a critical point, the coupling among its parts becomes highly variable.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 93.


“Affordances are opportunities for behavior. Because different animals have different abilities, affordances are relative to the behavioral abilities of the animals that perceive them.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 108.


“On the Turvey-Shaw-Mace view, an object X affords an activity Y for an organism Z just in case there are dispositional properties of object X that are complemented by dispositional properties of organism Z, and the manifestation of those dispositional properties is the occurrence of activity Y.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 110.


“The idea here is that affordances, or opportunities for behavivor, are dispositions of things in the environment to support particular behaviors, and effectivities are dispositions of animals to undertake those behaviors in the right circumstances.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 110.


“As any animal moves about its environment, the images of objects or texture elements that the animal is moving toward will expand at the animal’s eyes. This is often described by saying that optic flow is centrifugal in the direction of locomotion: texture elements radiate out from the center of your field of view as you move toward an object.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 124.


“But if the environment contains meanings, then it cannot be merely physical. This places a heavy theoretical burden on radical embodied cognitive science, a burden so severe that it may outweigh all the advantages to conceiving perception as direct. Radical embodied cognitive science requires a new ontology, one that is at odds with today’s physicalist, reductionist consensus that says the world just is the physical world, full stop. Without a coherent understanding of what the world is like, such that it can contain meanings and is not merely physical, direct perception is simply indefensible. Thus, like earlier theories that take perception to be direct (e.g., James 1912/1976; Heidegger 1927), Gibson’s ecological psychology includes an ontology, his theory of affordances.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. Pp. 135-6.


“To say that affordances are dispositional properties of the environment, then, is to say that the environment is such that in some circumstances, certain other properties will become manifest. So, for example, the affordance ‘being edible’ is a property of things in the environment only if there are animals that are capable of eating and digesting those things.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. Pp. 137-8.


“Effectivities, like affordances, are dispositions, and as such they must be complemented by properties that lead to their actualization. Effectivities are properties of animals that allow them to make use of affordances.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 138.


“Warren, in attempting to quantify affordances for stair climbing, quantified them as unitless π numbers, the ratio between leg length and riser height. The affordance climbability is then identified as this ratio. Subsequent experiments identified affordances similarly, as ratios between body scale and some bit of the environment measurable in the same units.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. Pp. 142-3. Reference is to Warren, W.H. “Perceiving affordances: Visual guidance of stair climbing.” Journal of Experimental Psychology: Human Perception and Performance. 1984. 10, 683-703.


“There will also be a nested structure of abilities, in which larger abilities will be composed of smaller-scale abilities. Each of an animal’s abilities will have a set of situations in which it can be exercised. But no larger-scale ability will be exercisable in situations in which its component smaller-scale abilities can’t be exercised; similarly no ability will be exercisable in situations in which a more basic ability on which it depends cannot be exercised.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. Pp. 147-8.


“All this said, we can define an animal’s niche as the set of situations in which one or more of its abilities can be exercised.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 148.


“But affordances do depend on the existence of some animal that could perceive them, if the right conditions were met. Because affordances, the primary perceivables according to ecological psychology, depend in this way on animals, the ontology of ecological psychology is not a simple form of realism.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 150.


“In all of this work, dynamical systems models are shown to work both in brain-only explanations and in brain-body-environment ones.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 181.


“Action-oriented representations are doubly indexical, in that they are both local and personal: they are local in that they relate to the circumstances currently surrounding an animal; they are personal in that they are related to the animal’s needs and the skills that is has.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 187.


“Recall from chapter 7 that an animal’s phenomenological-cognitive-behavioral niche is the set of affordances available to that animal; recall also that Affordances 2.0 shows the place of affordances in the ongoing developmental and behavioral unfolding of coupled animal-environment systems: an animal’s activities alter the phenomenological-cognitive-behavioral niche (i.e., the world as the animal experiences it), and these alterations to the phenomenological-cognitive-behavioral niche, in turn, affect the animal’s behavior and development of its abilities to perceive and act, which further alters the phenomenological-cognitive-behavioral niche, and so on. To see how this works, consider a case of perceptual learning by human infants. From birth, infants engage in exploratory actions that allow them to change their environment and in so doing change their experience of the world.” Chemero, Anthony. Radical Embodied Cognitive Science. 2009. MIT Press. P. 201.


“Systemism is the alternative to both individualism and holism. Presumably, it is the alternative that the historical sociologist Norbert Elias was looking for in the late 1930s when he felt dissatisfied with the conceptions of the person as the self-contained homo clausus, and of society as a black box beyond individuals. Arguably, systemism is the approach adopted by anyone who endeavors to explain the formation, maintenance, repair, or dismantling of a concrete complex thing of any kind. Notice that I use the expression ‘systemic approach,’ not ‘systems theory.’ There are two reasons for this. One is that there are nearly as many systems theories as systems theorists. The other is that the ‘systems theory’ that became popular in the 1970s was another name for old holism and got discredited because it stressed stasis at the expense of change and claimed to solve all particular problems without empirical research or serious theorizing. Systemism is just as comprehensive as holism, but unlike the latter, it invites us to analyze wholes into their constituents, and consequently it rejects the intuitionist epistemology inherent in holism.” Bunge, Mario. “How does it work? The search for explanatory mechanisms.” Philosophy of the Social Sciences. 2004. 34:182-210. Pp. 190-1. Quoted in: Pickel, Andreas. “Rethinking Systems Theory: A Programmatic Introduction.” Philosophy of the Social Sciences. 2007. 37:391-407. Pp. 399-400.


“For short, positive circuits are involved in multistationarity, whereas negative circuits are involved in homeostasis, with or without oscillations.” Thomas, R. “Circular causality.” Institution of Engineering and Technology Proc.-Syst. Biol. Vol. 153, No. 4. July 2006. Pp. 140-153. P. 141.


“The mere occurrence of circular causality is trivial and popularised by the well-known paradox of the egg and the chicken. However, we would like to insist on the fact that such situations not only exist but are extremely frequent, if not general. When one formalises a dynamical system as differential equations, the circularity of causality is in fact built in the formalism.” Thomas, R. “Circular causality.” Institution of Engineering and Technology Proc.-Syst. Biol. Vol. 153, No. 4. July 2006. Pp. 140-153. P. 142.


“Thomas proposed the very general conjecture that the presence of a positive circuit is not only involved in, but is a necessary condition of multistationarity. This statement, initially a conjecture, has been the object of a number of formal demonstrations, the more general one being that of Soule.” Thomas, R. “Circular causality.” Institution of Engineering and Technology Proc.-Syst. Biol. Vol. 153, No. 4. July 2006. Pp. 140-153. P. 143. Reference is to Soule, C. “Graphic requirements for multistationairy.” ComPlexUs, 2003,1,pp. 123-33.


“It is clear today that any ‘non-trivial’ behaviour (e.g. multistationarity, stable periodicity, deterministic chaos, etc.) requires both appropriate circuits (logical structures) and appropriate nonlinearities....”

“... In fact, nonlinearity and logical circularity are two perfectly distinct concepts. A system that comprises circuits can be linear or not and so on.” Thomas, R. “Circular causality.” Institution of Engineering and Technology Proc.-Syst. Biol. Vol. 153, No. 4. July 2006. Pp. 140-153. P. 146.


“In this emphatically interactionist view [ecological psychology] of how actors and environment relate, it is assumed that information arises as an invariant relation between actors’ dynamically changing movements and their dynamically changing perception. As a consequence, perception and movement reciprocally (co-)specify each other. In contrast to most cognitive science notions, intentions are not considered as a mental or psychological state within a person. Instead they are considered to be a property of the ecosystem arising in the interaction between organisms and their environment. Accordingly, intentions are considered to be an aspect of the physical world rather than the mental world. A key concept that illustrates this notion is ‘affordance’, which refers to ‘action possibilities’, that a particular environment provides for an organism given the organism’s particular action repertoire. A further implication of the ecological approach is that actor-object relations and actor-actor relations are considered as being governed by the same dynamical principles.” Knoblich, G. & N. Sebanz. 2008. “Evolving intentions for social interaction: from entrainment to joint action.” Philosophical Transactions of the Royal Society. B 2008 363, 2021-2031. Pp. 2022-3.


“The theoretical frameworks of computationalism and connectionism are often construed as a search for cognitive mechanisms, the specific structures and processes from which cognitive phenomena arise. In contrast, the framework of dynamicism is generally understood to be a search for principles or laws–mathematical regularities that govern the way cognitive phenomena unfold over time. In recent philosophical discourse, this difference between traditional and dynamical cognitive science has been framed as a difference in scientific explanation: whereas computationalist and connectionist explanations are mechanistic explanations, dynamical explanations take the form of covering-law explanations.” Zednik, Carlos. “The Nature of Dynamical Explanation.” April 2011. Philosophy of Science. Vol. 78, No. 2, pp. 238-263. P. 238.


“Kelso’s explanation of bimanual coordination is not in fact representative of dynamical explanation in general, and many dynamical explanations actually resemble mechanistic explanations rather than covering-law explanations.” Zednik, Carlos. “The Nature of Dynamical Explanation.” April 2011. Philosophy of Science. Vol. 78, No. 2, pp. 238-263. P. 245. Reference is to Kelso, J. 1995. Dynamical Patterns: The Self-Organization of Brain and Behavior. MIT Press.


“Thelen et al. and Beer each offer a dynamical explanation of a (minimally) cognitive phenomenon. In each case, the explanation proceeds by identifying the component parts and operations of a mechanism and by showing how the organized activity of these parts and operations produces the phenomenon being explained.” Zednik, Carlos. “The Nature of Dynamical Explanation.” April 2011. Philosophy of Science. Vol. 78, No. 2, pp. 238-263. P. 255. References are to Thelen, E., G. Schoener, C. Scheier & L. Smith. 2001. “The Dynamics of Embodiment: A Field Theory of Infant Perservative Reaching.” Behavioral and Brain Sciences. 24:1-34. Beer, R.D. 2003. “The Dynamics of Active Categorical Perception in an Evolved Model Agent.” Adaptive Behavior. 11 (4): 209-243.


“Coupling is a technical term that applies whenever two or more dynamical systems mutually influence one another’s change over time. In the philosophical literature, such mutual influence is more commonly known as continuous reciprocal causation.” Zednik, Carlos. “The Nature of Dynamical Explanation.” April 2011. Philosophy of Science. Vol. 78, No. 2, pp. 238-263. P. 258.


“The moral of the story is that the tools and concepts of dynamical systems theory can be used to describe mechanisms that exhibit continuous reciprocal causation. Although important questions do remain about the degree to which Beer’s methods will scale up to larger and increasingly realistic systems in which continuous reciprocal causation is increasingly prevalent.” Zednik, Carlos. “The Nature of Dynamical Explanation.” April 2011. Philosophy of Science. Vol. 78, No. 2, pp. 238-263. P. 260. Reference is to Beer, R.D. 2003. “The Dynamics of Active Categorical Perception in an Evolved Model Agent.” Adaptive Behavior. 11 (4): 209-243.


“... those dynamicist researchers who seek to provide mechanistic explanations rather than covering-law explanations may be steering toward reconciliation with proponents of representationalism. By describing cognitive mechanisms rather than principles or laws, these researchers describe structures that are amenable to what Chemero and Silberstein have called representation hunting–characterizing the components of a mechanism as representation producers and representation consumers and understanding their operations in terms of the transfer and manipulation of information.” Zednik, Carlos. “The Nature of Dynamical Explanation.” April 2011. Philosophy of Science. Vol. 78, No. 2, pp. 238-263. P. 261. Reference is to Chemero, A. & M. Silberstein. 2008. “After the Philosophy of Mind: Replacing Scholasticism with Science.” Philosophy of Science. 75:1-27.


Authors & Works cited in DST:

Ashby, Ross. An Introduction to Cybernetics.
Beinhocker, Eric. The Origin of Wealth: The Radical Remaking of Economics
Bunge, Mario. "How does it work? The search for explanatory mechanisms."
Chemero, Anthony. Radical Embodied Cognitive Science.
Cupit, C.G. “The marriage of science and spirit: dynamic systems theory and the development of spirituality
Cziko, Gary. The Things We Do: Using the Lessons of Bernard and
Freeman, Walter. How Brains Make Up Their Minds
Gibbs, Raymond. Embodiment and Cognitive Science
Gros, Claudius. 2008. Complex and Adaptive Dynamical Systems
Jantsch, Erich. The Self-Organizing Universe
Kelso, J. A. Scott. Dynamic Patterns: The Self-Organization of
Knoblich, G. & Sebanz. “Evolving intentions for social interaction: from entrainment to joint
Noë, Alva. Action in Perception
Rockwell, W. Teed. Neither Brain nor Ghost: A Nondualist Alternative
Sawyer, R. Keith. Social Emergence: Societies as Complex Systems
Spivey, Michael. The Continuity of Mind.
Thomas, R. “Circular causality.”
Thompson, Evan. Mind in Life: Biology, Phenomenology, and the Sciences of Mind.
Witherington, D & Crichton. Frameworks for Understanding Emotions and Their Development
Zednik, Carlos. “The Nature of Dynamical Explanation.”

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