The Intelligence Construct

There is no doubt that our brain is the source of intelligent behavior as was vividly illustrated by Mountcastle's quote (1975, p. 131):  “Each of us lives within the universe – the prison – of his own brain. Projecting from it are millions of fragile sensory nerve fibers, in groups uniquely adapted to sample the energetic states of the world around us: heat, light, force and chemical composition. That is all we ever know of it directly; all else is logical inference.”

This reminds me of the movie Matrix starring Keanu Reeves as Neo; or Vanilla sky with Tom Cruise as David Aames who, after his suicide, is placed in a lucid dream as part of a Life Extension program.

More recently Robert Lanza in his book Biocentrism (BenBella Books, Inc., 2009) argued that “custom has told us that what we see is “out there,” outside ourselves, and such a viewpoint is fine and necessary in terms of language and utility, as in “Please pass the butter that’s over there.” But make no mistake: the visual image of that butter, that is, the butter itself, actually exists only inside your brain. That is its location. It is the only place visual images are perceived and cognized.”

He further suggests: “Some may imagine that there are two worlds, one “out there” and a separate one being cognized inside the skull. But the “two worlds” model is a myth. Only one visual reality is extant, and there it is. Nothing is perceived except the perceptions themselves, and nothing exists outside of consciousness. Only one visual reality is extant, and there it is. Right there. The “outside world” is, therefore, located within the brain or mind.” (Lanza and Berman, p.31.)

Therefore in our opinion only a brain perspective on intelligence can bring us closer to a better understanding of it. Let me explain.

Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological construct of intelligence. For example, Colom and colleagues (2006) related brain structure, specifically gray matter volume, to the general factor of intelligence (g). The results of their study showed that performance on two prototypical measures of general intelligence correlates with gray matter volume across frontal, parietal, temporal, and occipital lobes.

A less common approach is to investigate how the brain functions in order to see if this corresponds to the construct of intelligence – the brain perspective. The most far-reaching attempt in the so-called brain perspective is a study conducted by Hampshire and colleagues (2012). The authors compared factor analyses of brain imaging, behavioral, and simulated data in order to determine whether different components of intelligence are paralleled by distinct brain networks. Functional brain imaging data was analyzed within the Multiple Demand Cortex [1]. The activation patterns resulted in two principal components: a working memory component (insula, the superior frontal sulcus, and the anterior cingulate cortex) and a reasoning component (inferior frontal sulcus, inferior parietal cortex, and parts of anterior cingulate cortex). See figure below. The analysis of behavioral data resulted in 3 factors, two of which corresponded to the components extracted based on brain activity, while the third included tasks that used verbal information. According to the authors, these findings suggest that intelligence is an emergent property of multiple specialized brain systems, each of which has its own capacity (Hampshire et al., 2012).

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[1] The multiple demand system refers to frontal and parietal brain area activation patterns related to diverse cognitive demands (for a review see Duncan, 2010).

In an exploratory study conducted in our lab (Jaušovec and Jaušovec 2010), an even broader question was put forward: is there a typology of neuro-electric brain activity that could explain individual differences in human behavior? To answer the question an analysis of electroencephalography data based on resting brain activity of 331 individuals was performed. A hierarchical cluster analysis revealed 3 neuro-clusters which differed based on: (1) local and long range coding of information and (2) the way the nervous system entrains to rhythmic environmental stimulation and reacts when the environmental stimuli lack regularity. These differences tentatively correspond to the switching between the default mode network [2] and the central executive network [3].

The behavioral analysis showed that the difference between the neuro-clusters was mainly due to individuals scoring high on performance IQ, strategic emotional intelligence, and the personality factors of conscientiousness and openness. This finding is similar to the results reported by Hampshire and colleagues (2012) and further questions the assumption that our brain organization supports a single g solution.

However, as recently pointed out by Haier and colleagues (2014), so far none of the imaging studies have found evidence for a “neuro-g” - yet this does not mean that there is none.

To our knowledge the only definition of intelligence that completely relies on the central nervous system was suggested by Jensen (2011; p. 173):  “Intelligence is the periodicity of neural oscillation in the action potentials of the brain and central nervous system.”  According to Jensen, action potentials are the building blocks of all our thoughts, thus also of intelligence. Their periodical fluctuation is a response to incoming stimuli. A simplified example could be the change from alpha waves (8-12 Hz) when one’s eyes are closed to fast oscillations like beta waves (20 Hz <) when one’s eyes are opened.

The bad news about the brain approach at the moment is that it has some methodological difficulties. This was also admonished by Hampshire and colleagues (2014; p. 17):

“[…] no neuroimaging method can accurately measure the capacity of a functional brain network; consequently, that approach for estimating ‘g’ is entirely intractable.”

                                

References

Buckner, R. L., Andrews-Hanna, J. R. and Schacter, D. L. (2008), The Brain's Default Network. Annals of the New York Academy of Sciences, 1124: 1–38. doi:10.1196/annals.1440.011

Colom, R., Jung, R. E., & Haier, R. J. (2006). Distributed brain sites for the g-factor of intelligence. Neuroimage, 31(3), 1359-1365. http://dx.doi.org/10.1016/j.neuroimage.2006.01.006

Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in cognitive sciences, 14(4), 172-179. http://dx.doi.org/10.1016/j.tics.2010.01.004

Haier, R. J., Karama, S., Colom, R., Jung, R., & Johnson, W. (2014). A comment on “Fractionating Intelligence” and the peer review process. Intelligence, 46, 323–332. http://doi.org/10.1016/j.intell.2014.02.007

Hampshire, A., Highfield, R. R., Parkin, B. L., & Owen, A. M. (2012). Fractionating Human Intelligence. Neuron, 76(6), 1225–1237.  http://dx.doi.org/10.1016/j.neuron.2012.06.022

Hampshire, A., Parkin, B., Highfield, R., & Owen, A. M. (2014). Brief response to Ashton and colleagues regarding Fractionating Human Intelligence. Personality and Individual Differences, 60, 16–17. http://doi.org/10.1016/j.paid.2013.11.013

Jaušovec, N. & Jaušovec, K., (2010). A typology of human neuro-electric brain activity and its relation to personality and ability, Ricerche di Psichologia, 2; 291-306.

Jensen, A. R. (2011). The theory of intelligence and its measurement. Intelligence, 39(4), 171–177. http://dx.doi.org/10.1016/j.intell.2011.03.004

Lanza, R., & Berman, B. (2010). Biocentrism: How life and consciousness are the keys to understanding the true nature of the universe. BenBella Books.

Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Structure and Function, 214(5-6), 655-667. http://doi:10.1007/s00429-010-0262-0

Mountcastle, V. B. (1975). The View from Within: Pathways to the Study of Perception. Johns Hopkins Medical Journal, 136, 109–131.

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[2] The default mode network participates in internal modes of cognition and shows deactivation during performance on demanding cognitive tasks. The main hubs of this network are the ventromedial prefrontal cortex and the posterior cingulate cortex (Buckner et al., 2008).

[3] The central-executive network is involved in maintenance and manipulation of information in working memory and plays a role in goal-directed behavior and problem solving. Key areas include the dorsolateral prefrontal cortex and posterior parietal cortex (Menon & Uddin, 2010).

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