Paper: Characteristics and Problems of the Gifted neural propagation depth and flow motivation as a model of intelligence and creativity
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  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: http://hiqnews.megafoundation.org/Children_Above_180_IQ.htm
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