World Views, Paradigms, Metaphors, and the Pre Verbal Core

Each of us views the world in a somewhat unique way. This "world view" is informed by a number of conscious and subconscious paradigms. What is a paradigm? It is a model of some aspect of reality. When large numbers of people share the same paradigms -- or models of reality -- we refer to "shared paradigms." Here are three examples of shared scientific paradigms:

• Quantum Mechanics: the extent and precision of confirmed predictions proves the basic theory (though "cutting edge" hypotheses are still up in the air, and the loonier interpretations are demonstrably false). Ultimately it will be derivable, as exact or approximate, from a deeper theory.
• Big Bang Theory of the origin of the universe: the best explanation of available facts, but not enough facts to prove it. May be confirmed and refined, or may suffer a complete paradigm shift.
• Evolution of living things: the fact of evolution and the important mechanisms of mutation and natural selection are true, but further as yet unknown mechanisms cannot be ruled out.

_Doubt and Certainty

Leah Johnston

Paradigms may explain many aspects of reality -- but they can just as easily obscure and distort aspects of reality. The reliability of a paradigm has nothing to do with the number of people who share that paradigm. Frequently, the number of people who share a paradigm is inversely related to the reliability of that paradigm. Each one must be continuously tested -- without mercy or restraint.

A paradigm is a framework of perceiving, thinking and acting. It is a cognitive structure composed of aggregated concepts, values, beliefs and assumptions that organizes how we perceive, how we think and how we act — by, consciously or unconsciously, supporting rule-governed behavior. _ProcessParadigm

Where do paradigms come from? They come from more basic mental constructs, known as conceptual metaphors. It is possible for a paradigm to be assembled from thousands of distinct metaphors, in the same way that a complex computer model may be constructed using thousands of different equations.

Then what are conceptual metaphors? On the most basic level, metaphors are the language of unconscious thought -- low level mental constructs that influence every single thought that we have ever had, or that we will have in the future.

Linguist George Lakoff is one of the modern pioneers of conceptual metaphor theory. (PDF) Lakoff and frequent collaborator Mark Johnson have described a basic model of conceptual metaphor, which operates on the subconscious level to influence our every thought and argument. Conceptual metaphors also explain much of the power of modern conversational hypnotic techniques.

But even deeper than the idea of the "conceptual metaphor" as described by Johnson and Lakoff, is the idea of the pre-verbal metaphor. Conceptual metaphors can be described and labeled with words. Pre-verbal metaphors do not enjoy such advantages or luxuries.

Psychoanalysts such as Allan Schore have attempted to describe the process of early childhood development of pre-verbal metaphors -- although the phenomenon is not described in those terms.

But psychoanalysis is not always held in the highest regard these days, at least not by mainstream cognitive science. Science needs better ways of measuring and describing unconscious cognition. Not just in adolescents and adults, but in children -- particularly very young children whose unconscious cognitive processes are still in the formative stages.

What we describe as consciousness is only the barest tip of the cognitive iceberg. Beneath the waves exists a primal and dynamic menagerie of sea creatures, beyond the powers of modern science to capture, describe, or catalog. We can only detect faint and phosphorescent hints of their wakes as they swim by.

Is this really all that important? Not unless you want to better understand the basic flux of reality -- both individual, societal, and scientific.

Mass movements, for example, are based upon shared paradigms. These shared paradigms may or may not be able to survive a harsh test of reason and reality. But the movement itself may acquire sufficient momentum so that its underlying rationality loses importance -- in terms of the short and intermediate term success and victory of the mass movement. In the long run, of course, all mass movements are dead.

The best place to start for a basic understanding of the nature of scientific paradigms and scientific revolutions, is The Structure of Scientific Revolutions, by Thomas Kuhn. (PDF)

Learning more about pre-verbal metaphors is somewhat more difficult, although I would not be betraying a trust by revealing that such a concept is among one of the most important proprietary ingredients of The Dangerous Child Method of childhood education -- specifically in the infant years.

Not everyone really wants to understand "what makes them tick." Not everyone wants to understand how easily a broad scientific consensus can turn out to be wrong -- even absurdly wrong. Not everyone wants to understand why mass movements and governments go so badly awry, so frequently.

But I am assuming that you -- by virtue of reading this blog -- are not just anyone. At the least, you will possess curiosity and some level of reading comprehension.

Applications of these ideas to The Dangerous Child Method of childhood education will be pursued mainly at Al Fin, the Next Level.


More:

Non-verbal and Multi-modal Metaphors in a Cognitivist Framework (PDF)

Also recommended: Marvin Minsky's Society of Mind . . .

Mental Models and Metaphors: Creative Thinking

The human mind is built upon models and metaphor. This is true from the pre-natal moment that enough nerve cells come together to co-oscillate, and it is true for the most advanced explorations into the cutting edge of science and technology. Cognitive scientist Nancy J. Nersessian has discovered the importance of model building in science:

Designing, building, and experimenting with physical simulation models are central problem-solving practices in the engineering sciences. Model-based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of "distributed cognition"... _TopicsCognitiveScience

More from Nersessian:

To develop an understanding of the system under investigation, scientists build real-world models and make predictions with them. The models are tentative at first, but over time they are revised and refined, and can lead the community to novel problem solutions. Models, thus, play a big role in the creative thinking processes of scientists. _SD

This is a very basic understanding of the work of invention and creativity at the borders of science and technology. But what Nersessian is doing is to make the process more explicit, in order to bring creativity to a wider range of activities -- including the classroom.

Model-building and metaphor is basic to the thinking process. But as the domain of thinking and creativity grows more complex -- as at the cutting edge of science and engineering -- the models and metaphors used will grow more intricate.

Einstein made wonderful use of mental models in his pursuit of new science, as did Feynman and other great scientists. As better methods of building physical and computing models are developed, the creative process is augmented by a form of "distributed cognition", as Nersessian terms it. Once the model exits the mind of the scientist and exists in the outer world, other minds can grasp it and tweak it -- making the tool of cognition distributed.

Models are only tools to help discover reality, however. They are not the reality. That is the error that climate scientists too often make: they forsake scientific observation and data confirmation and pursue computer models as if the models were the reality.

Models sit at the crux between data and theory. Without data, theory is mere confabulation. But without theory, data is simply noise. The human mind is always attempting to create order out of chaos, model out of data. This process is unconscious, and begins to occur long before the mind acquires language. It leads to optical illusions, common delusions, and mass confusion when conclusions are jumped to without adequately testing the models ("carbon climate catastrophe").

But since it is how we think, we need to know how to make the most of it -- while also keeping things "real."More: The above image is a computer mapping of shipping traffic near Rotterdam. The human mind is capable of creating similar maps mentally, but as a phenomenon grows more complex, computer mappings and visualisations become more helpful

A map (or complex visualisation) is similar to a model, in that the map takes a large mass of data, and connects the data in such a way that the mind can grasp it more easily -- in all its complexity.

How is Higher Reasoning Like a Bowel Movement?

Or, Why Computers Can Never Think, Since They Have No Guts

The crux of the matter is the type of reasoning that people use, compared to the type of reasoning that computers use. Computers use deductive reasoning almost exclusively. Humans, on the other hand, use a wide array of reasoning strategies -- including deduction. Humans unconsciously use inference and induction to build their beliefs, prejudices, and modes of operation. Humans use their entire bodies to think, not just their brains.

From their first breath (and earlier), humans use their body sensations to build inferences about the world around them. At first, the child is aware only of internal sensations and the feeling of body functions.

Lakoff and Nunez (1997, 2000) proposed a theory of embodied learning in mathematics where cognition is situated in the mind and developed through psychological and biological processes. Grounding and linking metaphors support the development of schema. These are influenced both by the body and the environment and develop understanding of mathematical ideas..... Mathematics is viewed as human imagination where mathematical reasoning is based on bodily experiences (Johnson, 1987). _CarolMurphy U of Exeter PDF

Human thought is "grounded" to body sensations and "gut feelings" via "grounding metaphors." These metaphors are not based upon language, since they are laid down long before the infant has begun to learn language. Later, observations of his own and others' movements are integrated into the child's inferential data base. Then, as he learns language, the unconscious metaphors take on a more formal aspect which can be analysed using linguistic theory.

You may be starting to see how radically different basic human reasoning is from the formal deductive logic that computer codes and machines incorporate and use.

Let's look at inductive reasoning.

How do humans reason in situations that are complicated or ill-defined? Modern psychology tells us that as humans we are only moderately good at deductive logic, and we make only moderate use of it. But we are superb at seeing or recognizing or matching patterns—behaviors that confer obvious evolutionary benefits. In problems of complication then, we look for patterns; and we simplify the problem by using these to construct temporary internal models or hypotheses or schemata to work with.1 We carry out localized deductions based on our current hypotheses and act on them. And, as feedback from the environment comes in, we may strengthen or weaken our beliefs in our current hypotheses, discarding some when they cease to perform, and replacing them as needed with new ones. In other words, where we cannot fully reason or lack full definition of the problem, we use simple models to fill the gaps in our understanding. Such behavior is inductive. _ Inductive Reasoning Arthur Santa Fe InstPDF _ via _SimoleonSense

Some modern efforts to emulate human cognition in machines are making use of "pattern matching" algorithms and neural nets, with some limited success. Pattern matching is very basic thinking strategy, that is utilised in a closely related strategy -- analogy. Computers can use these primitive strategies to "self-expand" and "self-organise" a data base.

These simple strategies can be augmented with Bayesian inference modules, in an attempt to maintain inferential discipline, as it were.

But in the absence of "grounding metaphors", imaginative and intuitive computers will simply not know when to stop. Available memory will fill up with junk, and the system will crash. Computer programmers and designers simply cannot take that chance, for now.

Humans have bodily needs and bodily functions. If these needs and functions are not accommodated, the system may crash. This is why body states are monitored so closely, and take on such a central importance from the earliest moment of a human being's existence. Human cognition must be grounded in the body, or the body may stop functioning. The "grounding metaphors" are hardwired into the system, and everything else is kludged around them.

Artificial Intelligence researchers sometimes imagine that they can dispense with all the "legacy microcode" and create a cognition from original principles. Back in the 1950s, they predicted human-level computation within a matter of a decade or two. By the 1980s and 1990s, AI people were beginning to acquire a bit of sensible caution -- although by and large they had still not figured out what was frustrating their goals. They figured out that pure logic computing, eg Prolog, wasn't going to work alone, but why in blazes couldn't they make more progress with LISP?

Then along came the connectionism and neural nets, and then genetic algorithms and fuzzy logic. All very useful, and all finding places in the overall strategy of understanding natural and artificial cognition. Neural nets could learn to match patterns, genetic algorithms could learn to optimise designs and strategies, and fuzzy logic could function effortlessly in environments that would crash formal deductive systems.

So what is the problem? If Henry Markram can say with a straight face that the human brain could be replicated within 10 years, given enough funding, why are investors not beating a path to the door of the Brain Mind Institute?

Because these "10 year" predictions are very easy to make. But most of them do not pan out. And because Markram is the only one making such grandiose claims. Jeff Hawkins thinks he can achieve great things within 10 years, but he hasn't claimed the ability to replicate the human brain. Of course, if Markram could "replicate" a rat brain within 10 years, his fame and fortune would be assured. Providing, of course, the resulting "brain" occupied a space no larger than an iPod. ;-)

Machine intelligence projects have created some amazing software. You can expect to experience much more amazement from that direction. But will computers actually think in the way we think of thinking?

Yes. But it will require a central grounding mechanism that forms a "sticky core" to which the different "thinking strategies" can link. The thinking machine will need better ways of keeping things real. It needs to have gut feelings.

The machine does not need a real body, but it can learn to feel as if it has a body. Its body might be an airplane or a train. Or it might be a neighborhood or an entire city. Or perhaps an electrical grid or a space elevator or space station. Any of these dynamic systems could play the part of an intelligent machine's body for the purposes of grounding. A complex simulation is the most logical starting point.

At that point, the thinking machine would have ways to test its models and inferences against "real" consequences.

Mind The Enchantment

The human mind is subject to various forms of enchantment. Not a magical enchantment, but more like a trance, sometimes pleasant, sometimes not.

Because our minds are "self organised", they are subject to falling into distinctly different states, at particular "bifurcations."An illustration of this phenomenan is the "bistability" of particular images. Following the series of images above, can you say exactly where the transition occurs? What if you saw only that one image?

But the deeper you dive into the mechanisms of consciousness, the larger the number of possible mind states, so that bistability becomes tristability and so on. Just the single topic of synaptic plasticity quickly acquires a complexity to confound most scientists.

Hypnosis takes advantage of the inherent ambiguity of consciousness, and "adjusts the weighting" of various competing states of mind. Since mind is inherently a self-organizing, ongoing trance-like process, it is often likened to "riding the wave," or staying on the "bucking bronco." From the moment of waking to the release of sleep, that "blinking cursor" of consciousness compels us to provide answers and solutions, even to unknown or nonexistent problems.

For anyone who is curious about some of the underlying neurophilosophy of consciousness, I suggests looking over this article by Edelman and Tononi--two prolific and respected students of consciousness. Or look over this overview of Models of Consciousness from Scholarpedia.

Understanding human consciousness is difficult enough. But a lot of people wish to create intelligence in machines. This dream goes back hundreds, if not thousands, of years. But since the computer age beginning in the 1940s, multiple generations of ingenious scientists of mind and computation have dashed their skulls against the wall of computational complexity (not to mention a lack of understanding of the complexity of human cognition or intentionality).

Each person experiences a consciousness of an enchanted mind. Not a mind of equations and computations. Rather a mind of metaphor and narrative. An entranced mind where real world expediencies intrude on waking dreams. Complex trances of strange attractors and slippery bistable conscious surfaces.

There would be no point in trying to emulate all of that in a machine. Not unless that is the only way we can find to create a conscious machine. Perhaps it is better to settle for machines that only seem conscious or intelligent, as viewed by a simple Turing test. After all, we are only looking for help in making better decisions and devising a better world for smarter, healthier, longer-lived people.

We may be entranced, but why burden our machines with all of that? It is our trance that we wish to enjoy far into the future, not the trance of a machine.

Brain Calibration

Baseball pitchers warm up with practice pitches before every appearance. Basketball players shoot practice hoops before games. Actors practice emoting before acting, singers practice intervals and runs, surgeons go through the sequence of a major surgery before scrubbing. All of these preliminaries are examples of mental calibration.

Calibrating the brain is a priming operation. The brain can be prepped for specific types of performance. But it is not just professional performers and practitioners of high risk procedures who need to calibrate. Every morning when the brain wakes up, it falls into a new dynamic state. If the brain has to achieve a certain level of performance each day, it must be calibrated for that performance. Otherwise, the level of achievement from day to day is left to chance. That is how most people live, by chance. When has popular culture ever offered anything else?

In Palestine the children are calibrated daily to hate the Jew, as they are in Hizballah controlled Lebanon, and much of the arab muslim lands. Hatred of the proper enemy is too important to be left to chance. If a society wants a ready supply of suicide bombers and guerilla fighters, it must begin calibrating minds at an early age.

Christian fundamentalists begin calibrating the minds of their children quite early as well. Sunday School and church, religious schools, camps, bible schools, and so on. Maintaining the proper religious way of thinking is too important to be left to chance. Early morning mass, confession, liturgy, prayer meetings, revivals. If a group wants the next generation to carry on the traditions of the current one, it must begin calibrating minds very early.

But then, those examples are not really the same thing as calibrating for top performance. They are more like brainwashing. But why do so many parents leave the cultivation of the minds of their children to chance and a largely indifferent culture? Affluent societies so often breed listless and goalless children. Decadence is what happens when one generation leaves the minds of the next generations to chance.

People think in basic metaphoric "thought concepts", and combinations of concepts called "thought patterns."

A thought-pattern is always subjectively created, and successively "objectified" by different calibration procedures. The state of consensus (= collective agreement on validity) of any though-pattern is represented by its corresponding calibration history. Since the thought-patterns presented here lack an attempt to describe this history, they are to be considered basically my own. That does not mean, however, that I consider them all as having originated with me. On the contrary, I have often tried to express how I conceive the thought-patterns of others. This is of course an important part of the group-consensus forming calibration process.

Source.

Brain calibration can take many forms--from meditation to mental imaging to neuromuscular practice to estimating distances or calculations, then checking the estimates. More complex forms of calibration are needed for more complex thought patterns. Unfortunately, modern educational practices have not kept up with current findings in neuroscience, but have instead regressed to conform with current whims of "political correctness."

Many professors of educational trends are willing to destroy a society in order to see their methods of social engineering "tried out" on new generations of hapless students. Fortunately, there are others who pay attention to scientific findings, who are outside the reach of the fashionable trendsetting PC social engineers.

There is a lot of need for reform in education, thanks to the PC professors who currently rule the universities and schools of education. Whether they like it or not, this reform is coming.