Characteristics and Problems of the Gifted neural propagation depth and flow motivation as a model of intelligence and creativity

I only read this paper because it was recommended to me from a reader. I don’t think it’s particularly good, although it is somewhat interesting in its attempt to combine neuroscience with intelligence research into something that seems alright. Since I haven’t heard of this model before and it’s been years since it was published, it apparently have gained much ground.

For our present purposes, the exact implementation of concepts at the neural level

is not so important. What counts is the way concepts are associated in such a way that

the activation of one concept may trigger the activation of other concepts. All

connectionist models agree that the weights of connections develop by reinforcement:

the more often a connection is (successfully) used, the stronger it will become. The

simplest learning algorithm, which is reflected in the actual dynamics of synapses, is

the Hebbian rule, which states that a connection between units is strengthened each time

both units are co-activated. This means basically that concepts will develop an

association whenever the one is encountered simultaneously with, or shortly after, the

other one. The corresponding process for neurons is the long-term potentiation of the

connecting synapses. For example, regularly seeing a baby in a cradle, will create a

strong association between the concepts “baby” and “cradle”. Conversely, concepts that

are rarely or never encountered together will not develop any associations. Thus, few

people would associate the concepts “baby” and “fish”.

Who complains about stereotypes?

In spite of this strength or feeling, the present model does not make any a priori

assumptions about GPs being more neurotic or emotionally unstable than others.

According to an entrenched cliche, genius and madness are closely related [cf.

Simonton, 2001; Eysenck, 1995]. This is illustrated by many accounts of exceptionally

gifted people, such as Newton, Van Gogh or Mozart, who also had exceptional

emotional problems. On the other hand, Maslow’s [1970] study of self-actualizing

personalities, who are supposed to be the epitome of mental health and emotional

stability, included many renowned GPs, such as Einstein and Eleanor Roosevelt. A

review of the empirical literature [Neihart, 1999] confirms this inconsistent picture:

most studies of gifted children find that they are somewhat better adjusted than their

peers, while a few point to particular problems of alienation characteristic especially of

the exceptionally gifted; some studies of creative artists, on the other hand, find a higher

than normal level of neuroses.

This would seem to indicate that it is not intelligence or genes associated with intelligence itself that cause these emotional problems, but rather that the loneliness from lack of adequate companions at the high levels of giftedness cause the emotional problems. See also:

Some examples of these multiple talents and cross-disciplinary achievements

exhibited by the truly gifted are Leonardo Da Vinci, who was both a most imaginative

engineer and an artist, and closer to us, the 20th century scientists John von Neumann

(1903-1957) [Macrame, 2000] and Herbert Simon (1915-2001) [Simon, 1991], The

mathematician von Neumann was not only one of the founders of the modem

computing paradigm, but also laid the groundwork for the physical theories of quantum

mechanics, quantum logic and ergodic theory, the economic theory of games, and the

recently fashionable modelling paradigm of cellular automata. Among colleagues, he

was notorious for the fact that you could ask him about any complex mathematical

problem that you had unsuccessfully been struggling with, and within an hour or so he

would come up with a solution. Simon received a Nobel price in economics for his

concept of bounded rationality and equivalent honors in computer science as one of the

founders of artificial intelligence and in psychology for his investigation of human

problem-solving. In addition he made various revolutionary contributions to the theory

of organizations, complexity, and philosophy of science. Note that although Simon and

von Neumann were arguably more talented than Albert Einstein, they have not reached

anything comparable to Einstein’s level of recognition, probably because their

contributions cannot be pinholed to a recognized domain of expertise, such as

theoretical physics, but rather opened up a slew of new problem areas in between the


There is one fact, and one fact only one needs to know about von Neumann. If one looks at his Wikipedia profile, Wikipedia has to hide his list of notable ideas per default because it is so long! I have not come across this with any other person. Seriously, go look at it. His list of notable ideas is 80 items long. 80!

But von Neumann was more productive than he was a polymath. For the most extreme polymath ever, look no further than Francis Galton, Darwin’s half-cousin. Wikipedia describes him as:

Sir Francis Galton, FRS (/ˈfrɑːnsɪs ˈɡɔːltən/; 16 February 1822 – 17 January 1911), cousin of Douglas Strutt Galton, cousin of Charles Darwin, was an English Victorian polymath: anthropologist, eugenicist, tropical explorer, geographer, inventor, meteorologist, proto-geneticist, psychometrician, and statistician. He was knighted in 1909.”

I can mention that he also invented fingerprinting for criminal justice systems, twin studies, studied the power of prayer (found no effect), invented the dog flute, and various other things. Surely his Wikipedia article deserves a longer list of notable ideas.

The problem may be exacerbated by the fact that GPs tend to have unrealistic

appraisals of other people, expecting them to understand or tackle problems that they

themselves would have little difficulty with, but that are simply above the head of the

average person. Therefore, they will tend to underestimate the difficulty of projects that

involve others, even when they have a realistic estimate of their own capabilities. This

brings us to the most problematic area of gifted psychology: their relations with others.

Reminds me of primary school where I would declare every problem or assignment to be easy, apparently not realizing it wasn’t easy for others.

We started this paper by noting that giftedness is a very valuable resource that we

should try to optimally exploit. One strategy is to increase the overall level of giftedness

in the population. Given the strong biological component of giftedness this may seem

unrealistic in the present state of science. Yet, the Flynn effect is the well-confirmed

observation that average IQs, and in particular the g-components of IQ, have been

steadily increasing over the past century, with some 3 points per decade [Flynn, 1987;

Neisser, 1998; Jensen, 1998]. While there is as yet no generally accepted explanation

for this phenomenon, plausible causes are on-going advances in general health,

nutrition, education, and cognitive stimulation by an increasingly complex environment

[Neisser, 1998]. Further research into the physiological bases of what we have called

neural propagation efficiency—e.g. examining the roles of essential fatty acids in

myelination, of antioxidants in improving cerebral blood circulation, or of cognitive

stimulation in creating “synaptic shortcuts”—may help us to understand the most

effective ways to further increase the general level of intelligence.

This author has got it backwards. The Flynn effect is not g-loaded, and hence not an increase in intelligence at all. Most people agree about this now a days I think.

In the meantime, the concept of propagation depth will need to be further

developed and tested to ascertain its value as an explanatory model for the brain

mechanisms underlying intelligence and creativity. Empirical tests of the model are not

obvious, given that our methods of observing brain processes are still not sufficiently

refined to follow individual thoughts as they propagate between neuronal assemblies. It

may be possible to design more indirect tests by extending traditional methods such as

measurement of divergent thinking skills, free association, or priming. For example, a

testable prediction deriving from the model would be that more intelligent people,

having higher propagation depths, can be primed more easily via indirect associations,

like in the example where the word “lion” via its association to “tiger” primes the mind

to more quickly recognize the word “striped”.

What kind of weak priming is that? It primed me for “liger”, much coolor. :p