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Unraveling Intelligence: Exploring its Neuroscientific Foundations

Article By Neocortex

Defining Intelligence

The quest for a comprehensive and universally accepted theory of intelligence that can explain all aspects of cognitive functioning and abilities has long represented the holy grail of intelligence research. Finally proving and achieving it, however, is still an ongoing and complex endeavor. A famous definition of intelligence was provided by Alfred Binet and Theodore Simon, who also developed the concept of the Intelligence Quotient (IQ) in the early 20th century;

"Intelligence is the capacity to understand the world, think rationally, and use resources effectively when faced with challenges."

While it does not necessarily seem conclusive, precise and all-encompassing, it certainly laid the foundation for future research in this field and catches on to today's widely accepted concept of intelligence as a general mental ability. These capabilities vary among individuals, which in turn is measurable and used to assess intelligence differences. The higher-order factor which hereby influences all cognitive performances is called the g-factor, proposed by Charles Spearman, which was deduced by the finding that people who perform well on one type of mental test had a high likelihood of performing well on all others. This structure of abilities composing g is illustrated here[1]

Note that the g-factor is an abstract idea used to explain the consistent patterns of correlations between cognitive tasks (reasoning, spatial ability, memory etc.). In contrast to this, an IQ-score is quantifiable and can be obtained from standardized tests such as the Raven's Advanced Progressive Matrices[2]. These test scores, although not deterministic, are overall considered meaningful, as they are not only related to brain glucose metabolism (ruling out the criticism that these are meaningless numbers), but also indicate consistent positive correlations with various life outcomes such as general learning ability, academic success and even adult mortality (related meta-analyses and studies illustrated in Richard J. Haier's work[3]).

The Role of Genetics

Just as the name implies, the old controversy between nature vs nurture in the context of intelligence suggests two opposing positions influencing brain development and our mental abilities. This includes genetic factors inherited from our parents (nature), as well as the Behaviorist view of environmental influences (nurture). An extreme view however is mostly avoided. The most recognized theory today endorses that neither nature nor nurture are independent factors, but always two forces in interaction with one another.

Cross sectional data in twin studies revealed how, despite the environment playing a role in intelligence during early childhood, this diminishes almost entirely by teenage-hood, as depicted in the figure below[3].

The genetic influences on intelligence variants thus increase as we age (up to 80% by the end of the teenage years), most likely due to certain genes getting activated throughout the lifespan, as well as general gene-environment interactions.

A related question that might arise is whether there is a certain "intelligence gene" that can be identified in our genome. The answer to that would currently unanimously be no, as the genetic basis for intelligence is considered to be polygenic, meaning that the trait arises from an interplay of numerous genetic variants, each of them contributing to this trait on a small scale[4]. This finding is highly in line with the idea that various, different cognitive processes underlie intelligence (memory, processing speed, problem solving), arising from the gene's coordinated activity. Researchers today are still uncovering the intricate neurobiological mechanism through which genes influence brain function, the methods steadily evolving.

Nevertheless, the combination of molecular genetics and individual differences between brain measures was able to identify genes that may influence intelligence variations, although none of the enlisted entries have ever been replicated.

Neuroimaging Results

But where exactly in the brain could intelligence be situated? An intuitive guess might be the frontal lobes, which are primarily involved in higher order cognitive functions like executive control or decision making.

Various neuroimaging studies however contributed to a much more nuanced understanding, suggesting that intelligence arises from the dynamic interactions of multiple brain regions expressed through networks (the communication between neurons through synapses, forming functional circuits enabling various cognitive tasks). Such networks are identified through their functional connectivity, meaning the degree to which different regions exhibit activation & deactivation together while a specific task is performed[5].

Arguably the most prevalent hypothesis attempting to explain the neural basis for intelligence, namely the Parieto-Frontal Integration Theory (PFIT), proposes the involvement of two specific brain regions as the central elements to intelligence related tasks[6].

This network comprises the parietal and frontal lobes, connected through a major white matter tract of fibers that have been linked to variations in IQ when comparing their density, integrity and organization among individuals. This already alludes to the idea that not all brains work the same way and the structural characteristics + the information flow within the PFIT areas should especially be taken into account. An intelligent brain will thusly integrate sensory information in the posterior area (bottom right), which subsequently flows to anterior regions (top left) involved in higher level processing.

The next figure presents an even more advanced brain model for the brain basis of intelligence, at the same time extending the PFIT model[7]. There is now an additional differentiation between two properties, namely both brain function, showcasing the positive & negative associations of regional activation, as well as the amount of grey matter (structure) and its positive association. It is important to emphasize that these findings are mainly correlational and do not directly define the observed brain differences as the cause for intelligence differences.

Another famous theory is called the Neural-Efficiency-Hypothesis. It might appear counterintuitive at first, but suggested neuroimaging results indicate that there is an inverse correlation between how smart you are and how hard your brain works. Instead, fMRI data showed that it’s the global efficient communication among multiple brain areas as well as the length of pathway connections (indicative of efficient information transmission) that was associated with intelligence[8].

This correlation, however, became more complex over the years as more variables influencing efficiency came into play. For instance, more recent findings could point out a brain signal enhancement in high IQ subjects as task complexity increased, and vice versa. Thus, caution must be exercised when thinking about neural circuit activity in this context and its relation to complex cognition.

Pathways to the Future

In the unlikely scenario wherein we have finally achieved a profound comprehension of the essence of intelligence in the near future, there should, from my perspective, exist two major variables that serve as evidence of our complete understanding. The first being a static element - namely a clear ratio-scale inherent in IQ-measurement providing an objective metric; and secondly a dynamic, malleable component that allows us to manipulate intelligence by primarily augmenting its value.

One potential measurement of intelligence in the future could be chronometric testing[9], which is first and foremost based on the reaction time while performing standard cognitive tasks (processing speed) and tracking individual information processing in time units. It aims to provide a quantitative assessment on a ratio scale with a true zero point. While the concept itself holds promise, it cannot yet be utilized as a universally accepted, standardized method due to several unresolved challenges (task specificity, consistent reliability, etc.).

The most exciting prospect would most likely be the potential for both neuroscience and genetic research to find a way to increase the intelligence factor. As expected, there is currently no valid approach that has successfully undergone independent replication with a substantial body of supporting evidence. Meanwhile, a majority of the (erroneously) positive assertions found in various sources are replete with

methodological flaws. The potential for advancement in this field hinges largely on the identification and comprehensive molecular-level comprehension of specific genes associated with intelligence, including their epigenetic influences.

A significantly more pragmatic and hopeful outlook emerges from the revelation of what are known as "brain fingerprints" or signatures. These have demonstrated not only to be unique for each individual but also, notably, are stable within a person over extended periods of time[10]. These connectivity "neuromarkers" could thus be employed to identify neural patterns linked to heightened cognitive performance. Consequently, this information might eventually find application in tailoring educational and clinical approaches, thereby optimizing cognitive development through the recognition of individual strengths and weaknesses.

Conclusively, it’s apparent that progress in neuroscience has exciting future opportunities to offer as intelligence-research advances. As we unravel the brain's secrets, we gain not only a deeper understanding of ourselves but also the potential to enhance educational strategies, interventions, and our appreciation of the wondrous complexity of the human mind.


[1] Deary, I., Penke, L. & Johnson, W. The neuroscience of human intelligence differences;

[2] (For those that want to try;)

[3] Haier, J. Richard. (2016). The Neuroscience of Intelligence

[4] Plomin R, von Stumm S. The new genetics of intelligence;

[5] Bressler SL, Menon V. Large-scale brain networks in cognition: emerging methods and principles;

[6] Colom R, Karama S, Jung RE, Haier RJ. Human intelligence and brain networks;

[7] Ulrike Basten, Kirsten Hilger, Christian J. Fiebach. Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence;

[8] Martijn P. van den Heuvel, Cornelis J. Stam, René S. Kahn, Hilleke E. Hulshoff Pol. Efficiency of Functional Brain Networks and Intellectual Performance;

[9] Jensen, A. R. (2006). Clocking the mind: Mental chronometer individual differences;

[10] Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity;

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