This is a paper I wrote for my course in Order, Chaos and Complexity (given by the wonderfully -talented but stingy on the grade Dr. Yoav Ben-Dov, who I wholeheartedly recommend studying with if you ever have the chance). I figured since it was in English, I may as well post it here for posterity. Enjoy

Introduction

Consciousness is notoriously difficult to define, though as some have pointed out, we all know what it is from direct experience. Throughout the history of cognitive science and the philosophy of mind, there have been many attempts at deciphering the mystery. Others have asked whether there is any mystery at all. Should consciousness be explained or rather explained away¹?

The fields of Chaos and Complexity theories have opened a new window to looking at the natural and social sciences. With the proliferation of personal computers, mathematic research in the behaviour of nonlinear dynamic systems has quite literally blurred the boundaries of what science can and cannot do.

I will attempt in this essay to present how certain approaches to both cognitive science and philosophy of mind fit well within the scientific and philosophical framework suggested by Chaos and Complexity theories.

The Problem of Consciousness

As mentioned in the introduction, consciousness is a slippery term to define. Ned Block once called it a ‘mongrel concept’², a mix-up mess of several ideas. Generally speaking, it is a quality of the mind, and has to do with states of apperception, introspection, self-awareness, sentience, and sapience.

Block distinguishes between what he calls “Access Consciousness” (or A-Consciousness), the phenomenon associated with memory, control of behaviour, reasoning, linguistic functions, and information processing, in contrast with “Phenomenal Consciousness” (or P-Consciousness), the phenomenon associated with the subjective experience itself, independent of any impact on behaviour, commonly known as ‘Qualia’.

David Chalmers³ coined the term ‘The Hard Problem of Consciousness” and distinguishes it from “The Easy Problem”. According to Chalmers, The Hard Problem of Consciousness is how to explain a state of P-Consciousness in terms of its neurological basis. Why do we experience Qualia at all? If some neural state N is the physiological basis of the sensation of red, why is N the basis of that experience rather than some other experience or none at all? These sorts of formulations are distinct of The Hard Problem. The “Easy” Problem, according to Chalmers, is formed of questions dealing with explaining phenomena related to A-Consciousness: Pattern Recognition, Integration of Information, Focus of Attention, etc.

Chalmers’ further analysis leads to some provocative arguments for a type of Dualism: The idea that Mind and Body are comprised of distinct substances or “stuff”, the Physical, which is what we see around us and is described by the natural sciences, and the Mental, the realm of our consciousness and thoughts, distinctly non-physical. This view is further supported by such thinkers as Saul Kripke⁴ and Frank Jackson⁵.

Other thinkers disagree. Daniel Dennett¹ considers the distinction between A- and P- Consciousness mistaken, and argues that the entirety of the conscious experience can be explained in terms of A-Consciousness. Joseph Levine⁶ on the other hand argues that while Dualist arguments fail at their main goal, they do point out an inadequacy in the purely-Physical account, which he calls the “Explanatory Gap”. An adequate explanation of the mind must include complete explanation (of the nominal-deductive type, according to Levin) of qualitative subjective experience. The various physico-functionalist approaches (the first of which, “Machine-State Functionalism” will be discussed in further detail in the following section) have been most fruitful in describing the mental, but appear to be not robust enough to explain qualia.

Some philosophers, such as Colin McGinn or Yeshayahu Leibowitz, suggest that The Mind-Body Problem is fundamentally insoluble, as the human mind lacks the principle faculties required to comprehend itself entirely (a position sometimes called “New Mysterianism⁷“)

Others have suggested radical proto-scientific theories to fill the Explanatory Gap, such as J. McFadden’s CEMI Theory⁸ or Penrose-Hameroff’s Orch OR model⁹ of quantum consciousness.

In later sections I will introduce a type of Functionalism as an example of the predominant position held towards the mind today, it’s relation to the empirical field of neurocognitive science, and how Chaos and Complexity theories might fill in the gap.

Mind as Computer

During the first half of the 20^th century, the field of (mostly American) psychology was dominated by a school of thought known as Behaviorism. Behaviorism’s philosophical antecedents, Logical Positivism, gave rise to an ideal of science which rejected any non-verifiable “facts” as nonsense. Thus, Behaviorists such as John B. Watson posit that the study of psychology should be restricted to the empirical study of relations between observable stimuli and overt behavior responses, without recourse to inner mental states.

This strong materialist position came crashing down in 1959 when Noam Chomsky¹⁰ published an influential critique of behaviorist B.F. Skinner’s work “Verbal Behavior” (1959). I will not go into any great details over Chomsky’s move, but suffice to say that it is widely regarded as one of the stepping-stones towards the Cognitive Revolution.

The newly formed Cognitive Sciences, an interdisciplinary effort to scientifically explore the fields of mind and intelligence, embracing philosophy, psychology, artificial intelligence, mathematics, neuroscience, linguistics, and anthropology.
The first approach to the field, what is now known as the “classical,” “symbolic,” or “computationalist” approach, attempt to develop theories of mind based on complex representations and computational procedures.

At the time, primitive computers had been around for only a few years, but pioneers such as John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon were founding the field of artificial intelligence, based heavily on the works of British mathematician Alan Turing¹¹. To the classical view, the brain is likened to a computer, and the mind, as the analogy goes, is the software running on it.

In the field of philosophy, a 1967 paper by Hilary Putnam¹² swiftly replaced Mind-Brain Type Physicalism with a new dominant theory: Functionalism¹³. What Functionalism posits is that mental states can be considered as causal-functional states, i.e. they serve a causal role. The functionalist fills the black box in-between the Behaviorists Stimuli and Response with a computational algorithm of cause leading to effect. For example, to the Functionalist view, pain can be considered as the activation of a tissue-damage detector. The central idea of Machine-State functionalism relies heavily on the ideas of Alan Turing’s computational machine. What it is for something to have mentality is for it to be a physically realized Turing machine of appropriate complexity, with it’s mental states identified with the internal states of the machine table. More formally, functionalism says that “mental states are constituted by their causal relations to one another and to sensory inputs and behavioural outputs” (Block¹⁴, 1996).

This functionalist view has been criticized not only by modern dualists such as Chalmers and Kripke, but also by materialists who do not accept the position that what the mind does can be explained in terms of algorithmic computations. These arguments against “Strong AI” (The position that states that if mental properties are merely computational states, they can, in principle, be realized by a powerful computer) such as presented by John Searle¹⁵ and Sir Roger Penrose¹⁶, turn mostly to intuition and for now, functionalism remains the predominant theory of mind.

The Connectionist Twist

The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures. However, there is much disagreement about the nature of the representations and computations that constitute thinking.

Connectionist theories (also known as Parallel-Distributed Processing, PDP, or Neural-Network Models) model thinking using artificial neural networks. It is a framework consisting of a set of methods for modeling cognition through the interaction of a number of simple neuron-like processing elements, known as units¹⁷.

Most work in cognitive science assumes that the mind has mental representations analogous to computer data structures, and computational procedures similar to computational algorithms. Cognitive theorists have proposed that the mind contains such mental representations as logical propositions, rules, concepts, images, and analogies, and that it uses mental procedures such as deduction, search, matching, rotating, and retrieval. The dominant mind computer analogy in cognitive science has taken on a novel twist from the use of another analog, the brain.

Connectionists have proposed novel ideas about representation and computation that use neurons and their connections as inspirations for data structures, and neuron firing and spreading activation as inspirations for algorithms. Connectionist cognitive science then works with a complex 3-way analogy among the mind, the brain, and computers. Mind, brain, and computation can each be used to suggest new ideas about the others. There is no single computational model of mind, since different kinds of computers and programming approaches suggest different ways in which the mind might work. The computers that most of us work with today are serial processors, performing one instruction at a time, but it is theorized that the brain as well as some recently developed computers are parallel processors, capable of doing many operations at once.

Emergence of Consciousness

According to a materialist Cognitive Scientist, consciousness can be understood in terms of simple associative interactions among a large number of small simple computing elements. Thus, the problem of consciousness reduces down to understanding the behavior of the individual elements and the interactions between them. This is a reductionist approach, but it is a different kind of reductionism in the sense that the nature and scope of the “modules” is very different.

In contrast, a different approach would be to suppose that consciousness is an emergent phenomenon. Emergence is defined as a state or process in which higher-order or macroscopic properties are irreducible to lower-level properties. Emergent properties are generally unpredictable they are the properties which make something greater than the sum of its parts. This fits well within the Cognitive Science framework. Few scientists believe that a single neuron is intelligent, though somehow the sum of neurons in our brain in their particular order and alignment provide the basis of intelligent behaviour.

Where does emergence come from? It’s hard to say precisely, but emergent properties are characteristic of Complex Networks¹⁸. When a number of simple agents mutually interact in an open environment, circular causal interactions can occur between the group as a whole and the individual agents, and between the individual agents themselves. These further dynamic feedback-loops lead to constant growth and adaptability in the system. The network becomes organic. Because they are nonlinear, complex systems are more than the sum of their parts. Another distinct feature of Complex Networks is that they are usually very flexible in terms of exhibiting qualitatively different behaviours at different times. In dynamical systems terminology, such a qualitative change is known as a bifurcation of the attractor; Splitting from one attractor to another.

There are three kinds of attractors. There are fixed-point attractors, i.e. equilibrium systems, in which a system does not change over time. There are periodic attractors, i.e. cyclic systems. And lastly, there are strange attractors, covering systems that are neither unchanging nor periodic. Strange attractors are often chaotic, in that, once a system is locked into a strange attractor, its behaviour cannot be predicted in any detail. But, nevertheless, strange attractors need not be “random”; they can be intricately structured. Strange attractors can have sections or “wings” representing system states that a system moves between according to certain statistical rules.

In the next section I will try and present how states of consciousness can be thought of as attractors of neural complex-dynamical systems, and thus, present my position that Chaos & Complexity theories may help us understand consciousness.

Chaos, Complexity and Psychology

While researching for this essay, I found surprisingly few papers on how mentality can be understood through Chaos & Complexity theories (one particularly noteworthy exception is Van Gelder’s Dynamical Hypothesis¹⁹, though there are others). This is still a new field, and I suspect not all the players have left the locker-room yet. Thus, this section will have fewer citations, but will rather be comprised of ideas, such ideas which brought me to write about this subject in the first place.

I will begin with citing some motivations for accepting the Complex-System model to mentality. Going back to the Cognitive Science approach, if consciousness can be understood in terms of interactions among a large number of computing agents, can it possibly be better understood in terms of Complexity theory? Van Gelder (cited above) argues precisely that. Not only are cognitive agents dynamic, but they can be understood by us as such, with use of the mathematical models of dynamic systems. Furthermore, there is some evidence that psychological processes, as well as the neurophysiologic events that sustain them, are at least partially chaotic²⁰. They appear to be deterministic and nonlinear, exhibiting globally predictable patterns of behaviour which never precisely repeat themselves, and are not predictable in fine detail. Another characteristic phenomenon of complex-networks which recent studies²¹ show is apparent in neurology is small-world phenomenon²², where every node on a large complex-network can be reached in a logarithmically small number of steps. Hence, I believe there is reason to postulate that neurological and psychological processes can be modeled as chaotic attractors.

From this it seems reasonable that consciousness itself, as a whole underlying fabric, can be understood as a Complex Network comprised of chaotic psychological processes, or states of consciousness²³.

These states of consciousness can be theorized as strange attractors of complex-systems of interacting, inter-producing neural processes. Each process in the mind acts on other processes, producing new processes and stabilizing old ones. The mind can move back and forth between different “wings”, perhaps with these wings corresponding to certain states of mind within a state of consciousness. The transition between one state of consciousness and another is represented as a jolt which knocks the system out of its attractor, and leads it along a trajectory toward another attractor. This model predicts that transition between states of consciousness should be a sudden and dramatic process – a discrete shift rather than a continuous gradation. And indeed, the discrete nature of shifts between states of consciousness has long been established in the consciousness literature²⁴. For example, while falling asleep or under the intoxicating effect of certain drugs may seem gradual, they are in fact accompanied by an abrupt shift of awareness, or a distinct moment in which one feels they have become intoxicated. Interestingly, this fits well William James view of mentality as a stream of consciousness²⁵, in constant flux.

States of consciousness can be viewed as self-organizing, or autopoietic²⁶. Tendencies lean towards further tendencies in a snowballing effect, for example a good mood facilitates happy memories, smiling, and social behaviour, which in turn, support a positive disposition. This can also be seen in self-regulating, self-generating (and sometimes logically incoherent) justifications of beliefs, in cases of cognitive dissonance²⁷ and anxiety.

But is the mind purely chaotic? This may appear counterintuitive. But, in fact, this does not constitute a problem with the model; Even a relatively simple chaotic function such as the Lorenz attractor does not necessarily appear chaotic upon brief observation. In Chaos theory, instability is characteristic of bifurcation points, when an existing attractor breaks up and disappears. Psychological processes may seem to move back and forth between relatively chaotic patterns of activity and more cyclic or even static ones. For example, moods can oscillate in a regular and predictable rhythm over the course of the day. Such a pattern, however, could not last indefinitely without perturbations contributing to more chaotic periods of activity. Mood swings are an obvious example, but there is no apparent reason to assume that cognitive functions such as memory, thought, one’s sense of self, dreams, and general arousal do not also exhibit periods of constant or rhythmic activity, and other periods of more chaotic behaviour.

If this is correct, then the overall process fabric of consciousness would likewise be expected to exhibit periods of calm, periods of more or less regular oscillation, and pronounced periods chaotic activity. This can be understood as a system moving in and out of chaos. Such systems are referred to as systems on the edge of chaos. They are characterized by maximal adaptability²⁸, flexibility, and evolutionary reaction to a complex environment. The ability of a system to move in and out of chaos gives it a creative advantage. It is capable of shifting from a steady or cyclic rhythm to one that generates novel emergent properties, whether those be original ideas or perceptions, new patterns of behaviour, or novel emotional responses. Moreover, there is a tenacious resilience to a chaotic regimen that is absent in routine behaviour. Chaos can be seen as the antidote to stasis and stagnation.

The dynamical concepts of creativity, autopoiesis, and self-control bring an interesting twist to the long-standing philosophical debate surrounding Determinism vs. Free-Will. What we learn from Chaos Theory is that even in a Laplacian “clockwork” world, in practice, you may not be able to predict the future. It can be deterministic in principle, but not in practice. Consequentially, chance and determinism become intertwined. They are both a part of the same complex reality. Psychological behaviour is globally predictable, in the sense that if a person is hungry, and thinks there is a tasty meal waiting for him on the table, and he has no other apparent duties, it is very safe to assume that that person will head towards the table. However, the precise route, timing, possible spontaneous behaviour and other such fine details, while governed by deterministic rule, will never be practically predictable. To rework Albert Einstein’s famous letter to Max Born, it is not whether God plays dice that matters, it’s how.

Conclusions and filling the gap

When I first set out to write this paper, I hoped to achieve a greater understanding of the respective fields of Chaos and Mind by working out how they could possibly be viewed in coherence. I set out to see if the so-called Explanatory Gap could be filled with use of Chaos & Complexity. Approaching the endpoint, I think I finally realize that if there is an explanation at all, it is a fuzzy one.

This isn’t a glitch, or a skeptic solution, but rather a feature of Chaos. When I see the image of an intricate fractal pattern created by successive crossings of a chaotic trajectory across a Lorenz plane, I cannot help but wonder, are these not the same shifting patterns I see when I close my eyes at night? What is the Pantha Rehi nature of our consciousness if not the essence of unpredictability?

In this sense, I believe I have reached some success in both accounts. It is my belief that consciousness is a higher-order emergent phenomenon of our complex-dynamic physical brain. It stems from the “creative” adaptability race of natural selection. It serves a function, though that function may never be fully apparent to us. It is in constant flux, its boundaries hard to define, literally seated on the edge of chaos. To answer questions of why our phenomenal world is the way it is, would be prudent, though it seems likely the answer may very well be “by chance.”

This account may not be perfect, but it may be the best I ever see. Though, somehow, I don’t think I will ever stop wondering.

References

1. Daniel C. Dennett, Consciousness Explained (1991). [↩]
2. Ned Block, ‘On a Confusion about a Function of Consciousness’, Behavioural and Brain Sciences, (1995). [↩]
3. David Chalmers, The Conscious Mind (1996). [↩]
4. Saul Kripke, Naming & Necessity (1980). [↩]
5. Frank Jackson, “What Mary Didn’t Know”, in The Journal of Philosophy LXXXIII, (May 1986). [↩]
6. Joseph Levin, On Leaving Out What It’s Like, The Nature of Consciousness Philosophical Debates edited by Ned Block et al., (1997). [↩]
7. Owen Flanagan, Science of the Mind (1991). [↩]
8. Johnjoe McFadden, The conscious electromagnetic field theory: the Hard Problem made easy. Journal of Consciousness Studies 9 (8):45-60, (2002). [↩]
9. Roger Penrose and Stuart Hameroff, Orchestrated Objective Reduction of Quantum Coherence in Brain Microtubules: The “Orch OR” Model for Consciousness Toward a Science of Consciousness, edited by S Hameroff, A Kaszniak, A Scott (1996). [↩]
10. Noam Chomsky, A Review of B. F. Skinner’s Verbal Behavior. Language (1)35pp. 26-58 (1959). [↩]
11. Alan M. Turing On Computable Numbers, with an Application to the Entscheidungsproblem (1936) and Computing Machinery and Intelligence, Mind (1950). [↩]
12. Hilary Putnam Psychological Predicates, Art, Philosophy, and Religion (1967). [↩]
13. See Jaegwon Kim, Philosophy of Mind, 2nd ed. (2006) Chapter 4: Mind as Computer: Machine Functionalism for a deeper exploration of the history of the idea. [↩]
14. Ned Block, What is Functionalism? The Encyclopaedia of Philosophy Supplement (1996). [↩]
15. John Searle’s Chinese Room thought-experiment in Minds, Brains, and Programs, Behavioral and Brain Sciences 3 (1980). [↩]
16. Roger Penrose The Emperor’s New Mind (1989). [↩]
17. This is somewhat reminiscent to the works of both Friedrich Hayek’s The Sensory Order (1952) and Marvin Minsky’s Society of Mind (1988 – though this is a late date, it was published many years after Minsky’s research on the subject). [↩]
18. Albert-László Barabási, Linked: How Everything is Connected to Everything Else (2004). [↩]
19. Tim Van Gelder, The dynamical hypothesis in cognitive science. Behav. Brain Sci. 21, 615-665 (1998). [↩]
20. Frederick Abraham. and Albert Gilgen, Chaos Theory in Psychology (1994). [↩]
21. Radmila Manev and Hari Manev, The meaning of mammalian adult neurogenesis and the function of newly added neurons: the “small-world” network, Med. Hypotheses 64(1):114-7 (2005). [↩]
22. Stanley Milgram, The Small World Problem, Psychology Today 2 (1967). [↩]
23. Allan Combs Radiance of Being (1995). [↩]
24. Charles Tart, States of Consciousness (1975). [↩]
25. William James, The Principles of Psychology (1950). [↩]
26. Maturana, Humberto & Varela, Francisco, Autopoiesis and Cognition: the Realization of the Living (1973). [↩]
27. Leon Festinger, A theory of cognitive dissonance (1957). [↩]
28. Stuart Kauffman, Origins of Order: Self-Organization and Selection in Evolution (1993). [↩]

Tagged as adaptability, AI, Alan Turing, Albert-László Barabási, autopoiesis, behaviorism, bifurcation, chaos, cognitive science, cognitive turn, Complex Networks, complexity, computationalism, connectionism, Consciousness, constunt flux, david chalmers, determinism, dualism, dynamic systems, edge of chaos, emergentism, explanatory gap, fractals, Frank Jackson, functionalism, instability, joseph levine, linguistics, Mind, mind-body dualism, Mind-Body Problem, Ned Block, neural network, Noam Chomsky, nonlinear, pantha rehi, Parallel-Distributed Processing, PDP, Philosophy, physico-functionalism, predictability, psychophysical problem, Roger Penrose, Saul Kripke, Shay Brog, small world phenomenon, strange atractor, stream of consciousness, Telecart, The Problem of Consciousness, Thomas Nagel, שי ברוג, שי טלכרט + Categorized as Philosophy, Psychology, Science/Technology