Short works

Books : reviews

J. A. Scott Kelso.
Dynamic Patterns: the self-organization of brain and behavior.
MIT Press. 1995

rating : 2 : great stuff
review : 27 March 2004

For the past twenty years Scott Kelso’s research has focused on extending the physical concepts of self-organization and the mathematical tools of nonlinear dynamics to understand how human beings (and human brains) perceive, intend, learn, control, and coordinate complex behaviors. In this book Kelso proposes a new, general framework within which to connect brain, mind, and behavior.

Kelso’s prescription for mental life breaks dramatically with the classical computational approach that is still the operative framework for many newer psychological and neurophysiological studies. His core thesis is that the creation and evolution of patterned behavior at all levels—from neurons to mind—are governed by the generic processes of self-organization. Both human brain and behavior are shown to exhibit features of pattern-forming dynamical systems, including multistability, abrupt phase transitions, crises, and intermittency.

This marvellous book describes a wealth of biological features in terms of the structure of the underlying dynamical space, its bifurcations, and its attractors. This dynamical space is parameterised (say by the frequency of a gait), and as the value of the parameter changes, the dynamics changes, sometimes significantly (such as changing from a trot to a gallop). Moreover, the change can also depend on how the parameter varies: the system exhibits hysteresis (the frequency of changing from trot to gallop can be different from that where the gallop drops back to a trot).

The examples start off small, with oscillating finger movements, muscle flexion, animal gaits, and so on. But the ultimate aim is to describe certain cognitive processes in the same language. The behaviour is constrained by the dynamics of the embodied system. This dynamics is parameterised by "informationally meaningful quantities", which provides the essential grounding of the information. By the end of the book, one has seen very many examples of behaviours at different levels, and the underlying unifying commonalities shine through clearly.

All the examples are backed up with observational data, which makes it tough going in places (especially for one who is not a biologist), but certainly adds conviction to the thesis. There is a plea for different kinds of experiments -- experiments that manipulate the parameters in certain controlled ways to investigate and expose the underlying dynamics.

The language is that of non-linear dynamics, strange attractors, self-organisation, and emergent properties. But it isn’t some kind of "gosh wow" buzzword salad; it is solid, thoughtful stuff that uses these concepts as tools to explain and predict observations, and build rich scientific models.

This is great science, at the cutting edge of complex systems theory. Here are some quotes from the text that describe the underlying themes. They give a flavour of the approach, but read the whole book to get the full effect, including all the detailed biological backup.

p1. The mistake made by many cognitive scientists is to view symbolic contents as static, timeless entities that are independent of their origins. Symbols, like the vortices of the river, may be stable structures or patterns that persist for a long time, but they are not timeless and unchanging.

p5. All structures in animate nature are actually dynamic. We tend to think of some of them as static, but this is not really the case. Here I will adopt the view that structures and behaviors are both dynamic patterns separated only by the time scales on which they live.

p22. ... simple and complicated behaviors have been shown to emerge from the same dynamical system. Surface simplicity and surface complexity are both possible outcomes. We don’t have to posit a different mechanism for each qualitatively different behavior. Mainstream science tends to make this mistake all the time, and it leads to a huge proliferation of models. I think that this is due at least in part to a one-cause-one-effect mentality and consequent failure to explore the full range of parameter values in a given experimental system. If one only probes a few parameter values and if one sees something different in each case, one is inclined to offer separate explanations. But as we will see again and again in this book, and as illustrated in the example of the Henon map, how you move through parameter space determines what you see. And some of what you see, of course, is very fancy indeed.

p34. In self-organizing systems, contents and representations emerge from the systematic tendency of open, nonequilibrium systems to form patterns. ... a lot of action—quite fancy, complicated behaviour—can emerge from some relatively primitive arrangements given the presence of nonlinearities.

p44. All the hype about chaos and fractals tends to sweep these questions under the rug while everyone admires the nice pictures. Don’t get me wrong, I like chaos and fractals. Some of my best friends do this stuff. I also like numerical simulation and computer graphics—couldn’t do without them, in fact. They allow you to see inside a mathematical theory. But, as a scientist, I want to know what these pictures represent; I especially want to know that the mathematical equations represent (some small portion of) reality. There has to be some connection between mathematical formulae and the phenomena we are trying to understand. Without this connection, as the popular song goes, we’re "p___ing in the wind." Establishing a connection between theory and experiment is one of the canons of science that the "chaos, chaos everywhere" crowd seems to ignore.

p52. ... to understand coordinated behavior as self-organized, new quantities have to be introduced beyond the ones typical of the individual components. Also, we need a variable that captures not only the observed patterns but transitions between them.

p53. Some people say that point attractors are boring and nonbiological; others say that the only biological systems that contain point attractors are dead ones. That is sheer nonsense from a theoretic modeling point of view, as it ignores the crucial issue of what fixed points refer to. When I talk about fixed points here it will be in the context of collective variable dynamics of some biological system, not some analogy to mechanical springs or pendula.

p.70. Coordination dynamics is not ordinary physics. It deals with the dynamics of informationally meaningful quantities. Coupling in biological systems must reflect functional, not merely mechanical constraints if behavior is to be adaptive and successful.

p81. Such findings suggest that when the many subsystems are assembled by the central nervous system into coordinated patterns of behavior, a higher priority is placed on direction of movement than particular muscle groupings. The cerebral cortex, it seems, does not plan its actions on the level of muscles. The self-assembly process appears to be spatially determined and hence far more abstract than the language of muscles.

p109. Why should a biological system occupy the strategic position near boundaries of mode-locked states rather than residing inside them? ... by residing near the edge, the system possesses both flexibility and metastability. There is attraction (the ghost of the fixed point), but no longer any attractor.

p123. where the system lives in parameter space dictates the complexity of its behavior

p138. Without knowledge of spontaneous coordination tendencies and their dynamics, it is difficult to understand what is modifiable by the environment, by learning or ... by intention.

p139. Living things are open, nonequilibrium systems. ... ordinary matter under open, nonequilibrium conditions exhibits self-organization, the creation and evolution of patterned structures. But in biology, at least so far, processes of self-organization in open systems have received short shrift. ... For mainstream biology, the chief source of biological organization is not its openness, but the fact that organisms are controlled by a program. ... how the program originated is quite irrelevant.

p140. Far from being equated with a program, a set of instructions controlling development, the gene, at least to me, looks more and more like a self-organized, dynamical system. ... In contrast to artificial machines, the gene is far more likely to be a self-organized, functional unit, a metastable, dynamical form that relies on the system’s openness for its creation, integrity, and self-maintenance.

p141. an intention is conceived as specific information acting on the dynamics, attracting the system toward the intended pattern. This means that intentions are an intrinsic aspect of the pattern dynamics, stabilizing or destabilizing the organization that is already there.

p156. I have often asked myself why a biological system should have to climb over a barrier in order to switch state. The transitions I have been talking about in this book do not involve much energy at all. ... switching is informationally based. The couplings between things are informational, not force mediated in the conventional sense. ... parameters (nonspecific and specific) act to deform or raise and lower basins of attraction surrounding (nonequilibrium) steady states. The formulation is not of the quantum tunneling type, but the effects are the same.

p161. the entire attractor layout is modified and restructured, sometimes drastically, as a given task is learned. Learning doesn’t just strengthen the memory trace or the synaptic connections between inputs and outputs; it changes the whole system.

p163. organisms acquire new forms of skilled behavior on the background of already existing capacities. The initial state of the organism never corresponds to a disordered random network, but is already ordered to some degree. Thus, it is very likely that learning involves the passage from one organized state of the system to another, rather than from disorder to order.

p173. the attractor layout may change qualitatively with learning. This means that learning can take the form of a phase transition. ... [learning persists because] the learned pattern has become a stable attractive state of the underlying coordination dynamics.

p217. In the low-level feature space, these prototype patterns constitute attractors. And, because there are potentially many prototypes, the entire system is multistable. When a pattern is presented to the system, its state vector is pulled to the attractor to which it comes closest. Once the specific attractor is reached, the pattern is recognized.

p223. Intermittency means that the perceptual system (and the brain itself?) is intrinsically metastable, living at the edge of instability where it can switch spontaneously among collective states. Rather than requiring active processes to destabilize and switch from one stable state to another (e.g., through changes in parameter(s), increases in fluctuations), here intermittency appears to be an inherent built-in feature of the neural machinery that supports perception.

p234. what is the relationship between the geometry of the neuron and its dynamics? Might the fractal hierarchy of time scales observed in the switching behavior between states be related to a similar fractal hierarchy in space? Has nature chosen to couple nonlinear dynamic function with non-Euclidean geometrical structure? ... most current neural network models, real or artificial, completely ignore the morphology of the neuron. ... Fractal geometry and dynamics imply that learning processes are distributed over many space and time scales and are not simplistically represented as a single scalar coupling strength quantity.

p284. If the brain is intrinsically chaotic, possessing, by definition, an infinite number of unstable periodic orbits, it has the capacity to match an equally unpredictable environment. Being chaotic at rest allows the brain access to any of these unstable orbits to satisfy functional requirements. Thus, when a cognitive, emotional or environmental demand is made on the organism, an appropriate orbit or sequence of orbits is selected and then stabilized through a kind of chaotic synchronization mechanism.

J. A. Scott Kelso, David A. Engstrøm.
The Complementary Nature.
MIT Press. 2006

Why do we divide our world into contraries? Why do we perceive and interpret so many of life’s contraries as mutually exclusive, either/or dichotomies such as individual~collective. self~other, body~mind, nature~nurture, cooperation~competition? Throughout history, many have recognized that truth may well lie in between such polar opposites. In The Complementary Nature, Scott Kelso and David Engstrøm contend that ubiquitous contraries are complementary and propose a comprehensive, empirically based scientific theory of how the polarized world and the world in between can be reconciled. They nominate the tilde, or squiggle (~), as the symbolic punctuation for reconciled complementary pairs.

Experiments show that the human brain is capable of displaying two apparently contradictory, mutually exclusive behaviors at the same time. Coordination dynamics—a mathematically expressed theory that reconciles the scientific language of “states” with the novel dynamical language of “tendencies”—attests to the complementary nature inherent in human brains and behavior. It may explain, Kelso and Engstrøm argue, why we (and nature) appear to partition things, events, and ideas into pairs. Kelso and Engstrom’s account is not just metaphorical; the reconciliations they describe are grounded in the principles and mathematical language of the theory of coordination dynamics. The Complementary Nature provides a clear-cut methodology for this evolving theory of brain and behavior that can also be applied to areas and developments outside the neurosciences, hence aiding reconciliations within and between disparate fields.