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 . . .

Post-Normal Science and the Climate Apocalypse

The following is a brief excerpt from an essay by Steven Mosher, on the difference between "normal science" and "post normal science." What makes the full essay such poignant reading is that Mosher himself has often fallen into the "post normal trap" when arguing the science of climate change with more sceptical fellow scientists -- both amateur and "professional."

This is a particularly important topic. If ordinary people allow science to become post normal -- both heavily politicised while also being placed beyond debate or question -- they will soon be marching well down the path, on the road to serfdom.

Science has changed. More precisely, in post normal conditions the behavior of people doing science has changed. Ravetz describes a post normal situation by the following criteria:

1. Facts are uncertain
2. Values are in conflict
3. Stakes are high
4. Immediate action is required

The difference between Kuhnian normal science, or the behavior of those doing science under normal conditions, and post normal science is best illustrated by example. We can use the recent discovery of the Higgs Boson as an example. Facts were uncertain–they always are to a degree; no values were in conflict; the stakes were not high; and, immediate action was not required. What we see in that situation is those doing science acting as we expect them to, according to our vague ideal of science. Because facts are uncertain, they listen to various conflicting theories. They try to put those theories to a test. They face a shared uncertainity and in good faith accept the questions and doubts of others interested in the same field. Their participation in politics is limited to asking for money. Because values are not in conflict no theorist takes the time to investigate his opponent’s views on evolution or smoking or taxation. Because the field of personal values is never in play, personal attacks are minimized. Personal pride may be at stake, but values rarely are. The stakes for humanity in the discovery of the Higgs are low: at least no one argues that our future depends upon the outcome. No scientist straps himself to the collider and demands that it be shut down. And finally, immediate action is not required; under no theory is the settling of the uncertainty so important as to rush the result. In normal science, according to Kuhn, we can view the behavior of those doing science as puzzle solving. The details of a paradigm are filled out slowly and deliberately.

The situation in climate science are close to the polar opposite of this. That does not mean and should not be construed as a criticism of climate science or its claims. The simple point is this: in a PNS situation, the behavior of those doing science changes. To be sure much of their behavior remains the same. They formulate theories; they collect data, and they test their theories against the data. They don’t stop doing what we notional describe as science. But, as foreshadowed above in the description of how high energy particle physicists behave, one can see how that behavior changes in a PNS situation. There is uncertainty, but the good faith that exists in normal science, the faith that other people are asking questions because they actually want the answer is gone. Asking questions, raising doubts, asking to see proof becomes suspect in and of itself. And those doing science are faced with a question that science cannot answer: Does this person really want the answer or are they amerchant of doubt? Such a question never gets asked in normal science. Normal science doesn’t ask this question because science cannot answer it.

_Steven Mosher via The GWPF

Read the full essay at Judith Curry's climate blog

Complexity, Causation, and Crucial Failures of Science

Khan Academy Video: Correlation and Causality
The confusion of correlation with causation is a common mistake among journalists, celebrities, academics, and political activists -- not to mention ordinary people. It is difficult to blame one for making this mistake, since modern media -- even much of "scientific media" -- is drowning in this error.

If you do not have a grip on the distinction between correlation and causation, then you do not have a prayer of understanding the deeper issues that will be touched on here. Therefore, we will take a look at "Hill's Criteria of Causation," which are applied to possible causal links in the field of medicine and public health.

1. Temporal Relationship:

Exposure always precedes the outcome. If factor "A" is believed to cause a disease, then it is clear that factor "A" must necessarily always precede the occurrence of the disease. This is the only absolutely essential criterion. This criterion negates the validity of all functional explanations used in the social sciences, including the functionalist explanations that dominated British social anthropology for so many years and the ecological functionalism that pervades much American cultural ecology.


2. Strength:

This is defined by the size of the association as measured by appropriate statistical tests. The stronger the association, the more likely it is that the relation of "A" to "B" is causal. For example, the more highly correlated hypertension is with a high sodium diet, the stronger is the relation between sodium and hypertension. Similarly, the higher the correlation between patrilocal residence and the practice of male circumcision, the stronger is the relation between the two social practices.


3. Dose-Response Relationship:

An increasing amount of exposure increases the risk. If a dose-response relationship is present, it is strong evidence for a causal relationship. However, as with specificity (see below), the absence of a dose-response relationship does not rule out a causal relationship. A threshold may exist above which a relationship may develop. At the same time, if a specific factor is the cause of a disease, the incidence of the disease should decline when exposure to the factor is reduced or eliminated. An anthropological example of this would be the relationship between population growth and agricultural intensification. If population growth is a cause of agricultural intensification, then an increase in the size of a population within a given area should result in a commensurate increase in the amount of energy and resources invested in agricultural production. Conversely, when a population decrease occurs, we should see a commensurate reduction in the investment of energy and resources per acre. This is precisely what happened in Europe before and after the Black Plague. The same analogy can be applied to global temperatures. If increasing levels of CO2 in the atmosphere causes increasing global temperatures, then "other things being equal", we should see both a commensurate increase and a commensurate decrease in global temperatures following an increase or decrease respectively in CO2 levels in the atmosphere.


4. Consistency:

The association is consistent when results are replicated in studies in different settings using different methods. That is, if a relationship is causal, we would expect to find it consistently in different studies and among different populations. This is why numerous experiments have to be done before meaningful statements can be made about the causal relationship between two or more factors. For example, it required thousands of highly technical studies of the relationship between cigarette smoking and cancer before a definitive conclusion could be made that cigarette smoking increases the risk of (but does not cause) cancer. Similarly, it would require numerous studies of the difference between male and female performance of specific behaviors by a number of different researchers and under a variety of different circumstances before a conclusion could be made regarding whether a gender difference exists in the performance of such behaviors.


5. Plausibility:

The association agrees with currently accepted understanding of pathological processes. In other words, there needs to be some theoretical basis for positing an association between a vector and disease, or one social phenomenon and another. One may, by chance, discover a correlation between the price of bananas and the election of dog catchers in a particular community, but there is not likely to be any logical connection between the two phenomena. On the other hand, the discovery of a correlation between population growth and the incidence of warfare among Yanomamo villages would fit well with ecological theories of conflict under conditions of increasing competition over resources. At the same time, research that disagrees with established theory is not necessarily false; it may, in fact, force a reconsideration of accepted beliefs and principles.


6. Consideration of Alternate Explanations:

In judging whether a reported association is causal, it is necessary to determine the extent to which researchers have taken other possible explanations into account and have effectively ruled out such alternate explanations. In other words, it is always necessary to consider multiple hypotheses before making conclusions about the causal relationship between any two items under investigation.


7. Experiment:

The condition can be altered (prevented or ameliorated) by an appropriate experimental regimen.


8. Specificity:

This is established when a single putative cause produces a specific effect. This is considered by some to be the weakest of all the criteria. The diseases attributed to cigarette smoking, for example, do not meet this criteria. When specificity of an association is found, it provides additional support for a causal relationship. However, absence of specificity in no way negates a causal relationship. Because outcomes (be they the spread of a disease, the incidence of a specific human social behavior or changes in global temperature) are likely to have multiple factors influencing them, it is highly unlikely that we will find a one-to-one cause-effect relationship between two phenomena. Causality is most often multiple. Therefore, it is necessary to examine specific causal relationships within a larger systemic perspective.


9. Coherence:

The association should be compatible with existing theory and knowledge. In other words, it is necessary to evaluate claims of causality within the context of the current state of knowledge within a given field and in related fields. What do we have to sacrifice about what we currently know in order to accept a particular claim of causality. What, for example, do we have to reject regarding our current knowledge in geography, physics, biology and anthropology in order to accept the Creationist claim that the world was created as described in the Bible a few thousand years ago? Similarly, how consistent are racist and sexist theories of intelligence with our current understanding of how genes work and how they are inherited from one generation to the next? However, as with the issue of plausibility, research that disagrees with established theory and knowledge are not automatically false. They may, in fact, force a reconsideration of accepted beliefs and principles. All currently accepted theories, including Evolution, Relativity and non-Malthusian population ecology, were at one time new ideas that challenged orthodoxy. Thomas Kuhn has referred to such changes in accepted theories as "Paradigm Shifts". _Hill's Criteria of Causation

This is basic stuff which most basic and clinical scientists and physicians studied in the early stages of their training. But there is little evidence that many science journalists have given these criteria any thought, to judge by what they write.

All of the preceding is by way of introduction to the phenomenon where science gets bogged down by complexity and by a confusion of logical levels -- or a failure to recognise "emergent phenomena." (PDF)

An example of this type of science failure is presented in a Wired.com article written about a cholesterol drug which ended up making the heart disease in patients worse, rather than better -- even to the point of killing some of them. This happens sometimes in medicine, where all logic and data suggests that a treatment is most likely to be highly beneficial -- but it ends up being worthless or worse.

The story of torcetrapib is a tale of mistaken causation. Pfizer was operating on the assumption that raising levels of HDL cholesterol and lowering LDL would lead to a predictable outcome: Improved cardiovascular health. Less arterial plaque. Cleaner pipes. But that didn’t happen.

Such failures occur all the time in the drug industry. (According to one recent analysis, more than 40 percent of drugs fail Phase III clinical trials.) And yet there is something particularly disturbing about the failure of torcetrapib. After all, a bet on this compound wasn’t supposed to be risky. For Pfizer, torcetrapib was the payoff for decades of research. Little wonder that the company was so confident about its clinical trials, which involved a total of 25,000 volunteers. Pfizer invested more than $1 billion in the development of the drug and $90 million to expand the factory that would manufacture the compound. Because scientists understood the individual steps of the cholesterol pathway at such a precise level, they assumed they also understood how it worked as a whole.

This assumption—that understanding a system’s constituent parts means we also understand the causes within the system—is not limited to the pharmaceutical industry or even to biology. It defines modern science. In general, we believe that the so-called problem of causation can be cured by more information, by our ceaseless accumulation of facts. Scientists refer to this process as reductionism. By breaking down a process, we can see how everything fits together; the complex mystery is distilled into a list of ingredients. And so the question of cholesterol—what is its relationship to heart disease?—becomes a predictable loop of proteins tweaking proteins, acronyms altering one another. Modern medicine is particularly reliant on this approach. Every year, nearly $100 billion is invested in biomedical research in the US, all of it aimed at teasing apart the invisible bits of the body. We assume that these new details will finally reveal the causes of illness, pinning our maladies on small molecules and errant snippets of DNA. Once we find the cause, of course, we can begin working on a cure.

...The truth is, our stories about causation are shadowed by all sorts of mental shortcuts. Most of the time, these shortcuts work well enough. They allow us to hit fastballs, discover the law of gravity, and design wondrous technologies. However, when it comes to reasoning about complex systems—say, the human body—these shortcuts go from being slickly efficient to outright misleading.

Consider a set of classic experiments designed by Belgian psychologist Albert Michotte, first conducted in the 1940s. The research featured a series of short films about a blue ball and a red ball. In the first film, the red ball races across the screen, touches the blue ball, and then stops. The blue ball, meanwhile, begins moving in the same basic direction as the red ball. When Michotte asked people to describe the film, they automatically lapsed into the language of causation. The red ball hit the blue ball, which caused it to move.

This is known as the launching effect, and it’s a universal property of visual perception. Although there was nothing about causation in the two-second film—it was just a montage of animated images—people couldn’t help but tell a story about what had happened. They translated their perceptions into causal beliefs.

...There are two lessons to be learned from these experiments. The first is that our theories about a particular cause and effect are inherently perceptual, infected by all the sensory cheats of vision. (Michotte compared causal beliefs to color perception: We apprehend what we perceive as a cause as automatically as we identify that a ball is red.) While Hume was right that causes are never seen, only inferred, the blunt truth is that we can’t tell the difference. And so we look at moving balls and automatically see causes, a melodrama of taps and collisions, chasing and fleeing.

The second lesson is that causal explanations are oversimplifications. This is what makes them useful—they help us grasp the world at a glance. For instance, after watching the short films, people immediately settled on the most straightforward explanation for the ricocheting objects. Although this account felt true, the brain wasn’t seeking the literal truth—it just wanted a plausible story that didn’t contradict observation.

This mental approach to causality is often effective, which is why it’s so deeply embedded in the brain. However, those same shortcuts get us into serious trouble in the modern world when we use our perceptual habits to explain events that we can’t perceive or easily understand. Rather than accept the complexity of a situation—say, that snarl of causal interactions in the cholesterol pathway—we persist in pretending that we’re staring at a blue ball and a red ball bouncing off each other. There’s a fundamental mismatch between how the world works and how we think about the world.

...Although modern pharmaceuticals are supposed to represent the practical payoff of basic research, the R&D to discover a promising new compound now costs about 100 times more (in inflation-adjusted dollars) than it did in 1950. (It also takes nearly three times as long.) This trend shows no sign of letting up: Industry forecasts suggest that once failures are taken into account, the average cost per approved molecule will top $3.8 billion by 2015. What’s worse, even these “successful” compounds don’t seem to be worth the investment. According to one internal estimate, approximately 85 percent of new prescription drugs approved by European regulators provide little to no new benefit. We are witnessing Moore’s law in reverse.

...Given the increasing difficulty of identifying and treating the causes of illness, it’s not surprising that some companies have responded by abandoning entire fields of research. Most recently, two leading drug firms, AstraZeneca and GlaxoSmithKline, announced that they were scaling back research into the brain. The organ is simply too complicated, too full of networks we don’t comprehend. _Wired

The author of the Wired article, Jonah Lehrer, provides other examples where medical science has foundered on the rocks of complexity, in the full article. He also alludes to philosophical theories of causation -- particularly the ideas of David Hume -- to help the reader to get a better idea of the scale of the problem.

Most people have not thought too deeply about cause and effect. For most lives, such deep thinking is completely unnecessary -- and probably counter-productive. But if one wants to better understand what is happening when science butts its head against the wall -- as in the examples given by Jonah Lehrer -- such thinking becomes unavoidable. Here are a couple of web-based overviews which you may wish to look at after browsing through the Wikipedia entry "Causality:"

A brief overview of philosophical idas about causality from informationphilosopher.com

A look at the metaphysics of causation from Stanford Encyclopedia of Philosophy

Here is the problem that human science faces, as I see it: We do not truly understand the mechanisms of what is happening within us and around us at any scale, but we wish to. In order to understand these underlying mechanisms -- in the absence of a valid overarching theory -- we are forced to collect a large amount of data, which we can only correlate in fairly crude ways.

Even as our computational machines improve along with our methods of correlation, we must still face up to the fact that "correlation is not causation." And even as we approach theories, hypotheses, and explanations which appear to be valid on one logical level, we are liable to be completely stymied when these explanations fail on higher and more emergent levels.

The intractability of many problems in science forced the reluctant, back-door acceptance by part of mainstream science, of some of the ideas of complexity, chaos, and paradoxical causality.

But most "bad science" of today is merely the failure of scientists to scrupulously stick to the rules of the scientific method. In other words, modern climate science is not unreliable and untrustworthy due to the chaotic nature of climate. Modern climate science is untrustworthy because the most powerful and best-connected of the group are willing to lie, obscure, strong-arm, and cover up the many weaknesses of their arguments and theories in order to enlarge their influence and power. That has everything to do with human weakness, greed, and immorality, and nothing to do with deep level difficulties in science.

Many observers of science -- and even many scientists -- feel that philosophy has been superceded by the power of modern science. But that is not actually true. In fact, the more powerful the science, the more it needs a sound philosophical underpinning. But that is easier said than done.

More on this topic -- including an attempt to clarify many of the most critical ideas in simpler language -- at a later date.

Making a Habit of Blowing One's Monkey Mind

Most humans never rise above "First Order Thinking" -- basic goal oriented behaviours, reading, computing, memorising, and fitting acquired comprehensions within a pre-existing, culturally inherited mental frame. At the most, a typical human will achieve "Metacognition," where he monitors his progress in first order thinking, and grades his progress. It is the unusual human who breaks through to "Transformative Learning."

The capacity for transformative learning develops in middle to late adolescence, although few persons truly experience it, and fewer still achieve mastery of the transformative process. Transformational learning involves breaking through cultural and paradigmatic constraints, to richer conceptual fields beyond.

The academic theory of transformative learning is almost entirely restricted to the field of "adult learning", because that is the field of learning where the theory originated and developed, for the most part. And perhaps those who teach adolescents and young adults in conventional institutions of education are just a bit too happy and complacent with the way things are in conventional education.

Transformational learning is not easy or comfortable -- for either teacher or student. But it is necessary, and those who care about the fate of humans in this part of the galaxy will begin to take it seriously.

Personal transformations often follow  the following phases:

1. Experiencing a disorientating dilemma, paradox, enigma or anomaly
2. Feelings of fear, anger, guilt, or shame
3. Questioning one's assumptions
4. Recognising the need for personal transformation
5. Exploring new roles, relationships and actions
6. Planning a course of action
7. Acquiring new knowledge and skills
8. Provisional trying of new roles
9. Building confidence in new roles and relationships
10. A re-integration of a new perspective into one's life

Ref. Mezirow, Jack et al. (2000) Learning as Transformation

_Hent.org

Most people do not take kindly to being "disoriented." Feelings of "guilt, shame, anger" etc. are not the half of it, for most ordinary monkey-minds. The full play of emotions can enter the dynamic process of transformative learning. Not exactly how you remember school? Join the club.

When a fundamentally disconfirming experience or a disorienting dilemma challenges our frame of reference, we are presented with the opportunity to dive to the deepest level of learning in an effort to make meaning of the catastrophic experience. So powerful is this kind of learning that Mezirow described it as "emancipation from libidinal, linguistic, epistemic, institutional or environmental forces that limit our options and our rational control over our lives, but have been taken for granted or seen as beyond human control." This experience can cause us to critically reflect on our beliefs and presuppositions, resulting in either a transformed way of pattern recognition, or, at the deepest level, a totally transformed framework. _ Learning to Think Strategically

Emancipation? Yes, and much more. Release from constraints plus the motive power to go beyond previous limitations.

Many animals can be trained to remain within a limited perimeter of physical or behavioural space. Then, when the real constraints are surreptitiously removed without the animal's awareness, the animal continues to limit itself as if the constraints remained. Monkey men are just like those animals. Transformative learning wakes them up.

For learners to change their "meaning schemes (specific beliefs, attitudes, and emotional reactions)," they must engage in critical reflection on their experiences, which in turn leads to a perspective transformation (Mezirow 1991, p. 167). "Perspective transformation is the process of becoming critically aware of how and why our assumptions have come to constrain the way we perceive, understand, and feel about our world; changing these structures of habitual expectation to make possible a more inclusive, discriminating, and integrating perspective; and, finally, making choices or otherwise acting upon these new understandings" (ibid.).

...Transformative learning has two layers that at times seem to be in conflict: the cognitive, rational, and objective and the intuitive, imaginative, and subjective (Grabov 1997). Both the rational and the affective play a role in transformative learning. Although the emphasis has been on transformative learning as a rational process, teachers need to consider how they can help students connect the rational and the affective by using feelings and emotions both in critical reflection and as a means of reflection (Taylor 1998). _ericdigests

Very few theorists have come to understand the centrality of grief to the process of learning and growing. Manfred Clynes is one such researcher. Robert Boyd is another. To understand why meaningful learning and transformation should involve grief, is to begin to appreciate how profoundly important being a human (as opposed to being a monkey) truly is.

There is an innate drive in all humans to understand and make meaning of their experiences. It is through established belief systems that adults construct meaning of what happens in their lives...Developing more reliable beliefs about the world, exploring and validating their dependability, and making decisions based upon an informed basis, is central to the adult learning process. _PraegerHandbookVol2

Most pseudo-intellectuals in academia, media, law, politics, and environmentalism are perfectly content to forego the exhausting work of transformation. Their time is filled with a daily routine of monkey games, which is as satisfying as they seem to require.

But those who wish to step beyond the ordinary shite fight and mudslinging of monkey play, into the larger universe of human, trans-human, and "posthuman" possibilities, it's going to take a lot of work, pain, and grief. Over and over, as long as you continue wanting to go farther.

Good little self-satisfied monkeys would never tolerate anything like that.

This is important: The crucially important and helpful techniques of transformative learning can be twisted -- are being twisted every day -- by professors, workshop leaders, propagandists, politicians, and others who shape the minds of others for their own benefit, rather than helping the person shape his own mind for purposes of personal and professional growth. This perversion of the transformative process is called brainwashing, or sometimes "consciousness raising." The goal of brainwashing is to confine the mind -- the opposite of liberation or emancipation.

Anyone who has experienced genuinely liberating and empowering transformation will detect the attempt at false and perverted "transformation" fairly easily. And it will make him very, very angry to see it.

More on this topic later.

Why Asians Can't Think (Outside the Box)

The higher average intelligence of East Asians compared to Europeans is well documented. The question is: why do East Asians -- despite their intelligence -- lag behind Europeans in measures of creativity, particularly over the past millenium?

Dennis Mangan recently looked at differences between thought styles of Asians and Europeans in this posting. Satoshi Kanazawa of the London School of Economics and Political Science provides the grist for discussion:

The first four Euro-American nations are overrepresented among the Nobel laureates by a factor of 5 to 10; Switzerland is overrepresented by a factor of 28! In sharp contrast, all Asian nations are underrepresented among the Nobel laureates. Japan, for example, has been a major geopolitical and economic power for most of the 20th century (Small and Singer, 1982). Yet it has produced only 12 Nobel laureates, the same number as Austria, which has one-sixteenth of Japan's population.

This problem has long been known to East Asian specialists as the "creativity problem" (Eberts and Eberts, 1995, pp. 123-127; Taylor, 1983, pp. 92-123; van Wolferen, 1989, pp. 89-90). Some argue that the ideographic Asian languages curb abstract thinking and creativity among Asians (Hannas, 2003).....Whatever the reason, it is evident from Table 1 that some combinations of cultural, social, and institutional factors combine to stifle basic science in Asia.
_Kanazawa (PDF)

A similar story is told in Charles Murray's classic compilation "Human Accomplishment." While Murray went to great lengths to include as strong an Asian componentas possible in the history of human accomplishment, the cumulative list of Asian accomplishments up to the present fell short.

Q. You pay a surprising amount of attention to Asian culture. Does that stem from the six years you lived in Asia beginning as a Peace Corps volunteer?

A. Put it this way: There are aspects of Asian culture as it is lived that I still prefer to Western culture, 30 years after I last lived in Thailand. Two of my children are half-Asian. Apart from those personal aspects, I have always thought that the Chinese and Japanese civilizations had elements that represented the apex of human accomplishment in certain domains.

When I began the book, I actually hoped to give Asian accomplishment a still larger place than it wound up getting.

Q. Why did you end up with mostly Dead White European Males in your inventory of 4,002 significant figures?

A. That's what happens when you employ the methods I used. And as I spend many pages in the book describing in perhaps excessive detail, those methods are not skewed by Western sources that are unfairly oblivious to non-Western accomplishment. _Charles Murray Interview

So, how does one explain the lagging of East Asians behind Europeans in the creativity race? La Griffe du Lion suggests that East Asians have such high visuospatial ability, that their overall IQ score is lifted higher than all others except for Ashkenazi Jews. But while visuospatial / mathematical ability is quite important in many fields of hard science, mathematics, and engineering / technology, deep creativity and radical innovation appear to spring from yet other parts of the cognitive neural assemblage beyond mere visuospatial ability.

Part of the reason why Asians cannot think for themselves and make original and creative contributions to science is because they are too conformist. One of the factors that Miller identifies as a possible obstacle to the Asian future of evolutionary psychology ("academic conservatism") is actually fatal. Scientific revolutions happen by challenging the established paradigms. No conformists have ever brought about a scientific revolution. _Kanazawa PDF

A conformist culture will certainly lend toward an anti-innovative conservatism, which can leave life-long imprints in the brain of a growing child. On the other hand, culture does not spring out of nothing. Culture is strongly influenced by the genetic complement of a population. For example, communist totalitarian conformity was forced onto several nations of Eastern Europe at roughly the same time that China was forced into communism by the victory of Mao's PLA. But communism did not last in most of the European populations, whereas in China the CCP is still the locus of one-party rule.

Long ago, inventors in China devised gunpowder, printing, paper money, the magnetic compass, and probably other wonders now lost to history. But even millenia ago, entrenched Chinese conservatism prevented the constructive uses of most of these inventions. It was left to Europeans to expand and innovate on these ancient inventions around the time of the "renaissance." Has there been a fatal "lack of curiousity" in Chinese culture?

This lack of curiosity extended into science. While ancient China was in many ways more technologically advanced than ancient Greece, knowledge for its own sake was never valued. The ancient Greeks in contrast wrote and debated tirelessly about abstract ideas that had no connection to the real world. _HBDBooks

Similarly, Hindu mathematicians devised advanced arithmetic notation and algebraic logic, as well as other advanced mathematical concepts for the times. These ideas moved along routes of trade and conquest to Islamic centers of thought in Persia, Iraq, Egypt, Syria, Andalusia ... where the Hindu ideas were combined with ideas from ancient Greek mathematics, and a synthesis of sorts was created. But it was left to Europeans to take the Hindu - Islamic - Greek synthesis along with rediscovered Greek ideas, and turn them into the modern mathematics upon which modern technology is based.

East Asian scientists and technologists certainly have the brilliance to maintain and advance the modern technologies that Europeans are bequeathing to them. The question remains: what will be the pattern of advance? Will we see a plodding, step by step elaboration and revising of science and technology centered on current fields of study, from the new "Asian renaissance?" Or will we see the sort of radical creation of entirely new foci of science and technology of the sort we have become accustomed to over the past few centuries, from European inventors and researchers?

There is much to be learned about the cultural -- and genetic -- reasons why different populations seen to have different habits of thought. Rather than shying away from such research as somehow "racist" or "not PC", we should get busy understanding all aspects of this universe we live in.

Bandwidth of Consciousness

Anyone who has read the book "The User Illusion" by Tor Norretranders must have been struck by the low estimate of human consciousness bandwidth--approximately 10 to 20 bits per second. A few moments of observing drivers trying to drive and talk on the cellphone at the same time will probably lead you to reluctantly agree with Norretranders.

Now, thanks to Chris Chatham at Develintel, we have better scientific explanations for the low bandwidth of consciousness--the attentional bottleneck, and the visual working memory bottleneck.

To investigate whether attention does cause this limitation, the authors used a dual-task design in which subjects must remember the location & color of three circles (the VWM task) during the time they are performing a multiple-object tracking (MOT) task. According to their logic, if the capacity limitation is purely due to attentional constraints, then the MOT task should interfere with the VWM task just as much as a second, concurrent VWM task would, assuming that the MOT and VWM tasks were equated for their attentional demands. On the other hand, if the capacity limitation results even in part from content-specific processes, as opposed to solely resulting from an amodal and content-general pool of attentional resources, then the MOT task should interfere with the VWM task less than a second concurrent VWM task would.

The details of the methodology and analysis of results are all in italics, as follows:

40 subjects participated in this task, in which their VWM capacity was calculated via Cowan's K (N*[hit rate + correct rejection rate - 1], based on their performance in remembering the color & location of displays containing three circles each. During the retention interval between display and test, subjects had to track either 1 (low load) or 3 (high load) white circles as they moved randomly throughout a display containing many identical white circles. To prevent participants from using their phonological system to store information, participants performed articulatory suppression, in which they repeated the word "the" 2 times per second throughout each trial.

MOT and VWM tasks showed mutual interference, in that performance on both tasks was lower than on either task independently. Furthermore, the amount of interference increased between the low- and high-load MOT tasks, indicating that there's not simply a constant level of "performance cost" incurred by the dual tasking - instead, that the additional storage demands resulting from the high memory load causes additional interference between the tasks.

Subsequent experiments showed two VWM tasks interfere with each other even more than VWM and MOT mutually interfere. The authors found a level of interference between a simple verbal task and the VWM that was equivalent to that found between VWM and MOT, but less than between two VWM tasks. This shows that the VWM-MOT interference is likely due to a central attentional bottleneck, and not due to a more specific shared process.

The final three experiments indicated that this "central source" of interference was not related to the similarities of the features used between the two tasks (such as color or location), nor was it related to the degree to which each task was spatial in nature, nor was it affected by the use of a rapid serial visual presentation task instead of multiple object tracking. In other words, no dual task paradigm was found that could create the same level of interference as a VWM-VWM dual task, whereas a variety of other tasks showed the same level of interference as the original VWM-MOT dual task.

These experiments strongly support the idea that attention and visual working memory have distinct capacity limits, and contribute jointly to observed capacity limitations. Although it's possible that equivalent levels of interference can occur for different reasons in different dual-task paradigms, it seems unlikely that the precise amount of interference would be so similar among so many different dual-tasks, unless that interference originates from a single, central source.

More at Develintel.

This does not mean that humans are forever stuck with these limitations. But like Clint Eastwood always says, "a man's got to know his limitations." We have to start from where we are now, if we are going to get to the next level.