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.
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Ashby, Ross. An
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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
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