Neuroscience of the “Smart” Brain

Tthis article has been written by Kaveh Farrokh (Ph.D.), Counsellor & Learning Specialist at Langara College Counselling Department.


This article summarizes many of the key points raised in 2015 in the Scientific American Mind Edition by Richard J. Haier (Professor Emeritus, School of Medicine, University of California, Irvine) on the neuroscience of intelligence (see Reference section). Note that this article will also discuss other aspects of pertinent research that are not discussed in the original 2015 article.

An Overview of Intelligence or “IQ”

The traditional methods for assessing a person’s intelligence or “IQ” has been through the use of paper-pencil tests developed since a century ago. While psychologists continue to utilize statistical techniques to quantify various aspects of IQ, it is important to note that these aspects have been, up to very recently, based on theoretical parameters and that aspects of IQ can be changed over the human lifespan. Theoretically, all IQ tests of mental abilities often have statistically positive relationships (despite content differences) between them. For example, a person who achieves a high score on a particular test will also tend to score highly on another (different) test. The theoretical implication of this is that all tests of IQ are measuring a common General Factor of Intelligence, also known as the “G” Factor.

Tied into the notion of a “G” factor is are the notions of Fluid Intelligence and Crystallized Intelligence. Fluid Intelligence in general entails a person’s capacity for global reasoning, problem-solving and abstract thinking skills, one’s ability to learn new things, as well as verbal, spatial and numerical factors. Crystallized Intelligence, which is said to improve with age, pertains to the person’s past learning and experience, as well as information of facts.

Another paradigm of IQ is Gardner’s Theory of Multiple Intelligences. These are as follows (see also accompanying Handout in pdf):

  • LINGUISTIC: Ability to read, write, communicate with words.
  • LOGICAL-MATHEMATICAL: Ability to reason, calculate, use logic.
  • VISUAL-SPATIAL: Thinking in pictures, visualizing, sense of direction and space.
  • MUSICAL: Ability to keep rhythm, appreciate, compose, and make music.
  • BODILY (KINESTHETIC): Ability to create physical products, present ideas and emotions, and to use body to solve problems
  • INTERPERSONAL (SOCIAL): Relate to people, have empathy and understanding, notice their goals and motivations.
  • INTRAPERSONAL: Self-assessment, self-analysis, reflection, make plans, set goals.
  • EXISTENTIAL: the “spiritual” questioning of why we are born, live, and pass away.
  • NATURALIST: our harmony with nature and living beings

Smart Brain: Research Challenges

A key challenge with IQ test scores is that these cannot locate the brain structures associated with “G” and other (theoretical) aspects of Intelligence. In addition, brain structures associated with “G” and other theoretical aspects of IQ cannot be inferred from persons with brain damage either.

Persons vary with one other in two broad ways: (1) Intelligence and (2) how one engages and performs in particular tasks or skills (how fast one can do mental math). As noted by Haier (2015, pp. 18-25) the research allows us now to challenge the notion that all brains somehow work the same way. Contrary to conventional wisdom, recent research reveals that each brain is as unique as the person, especially with respect to problem solving.

While it is known that very specific tasks can be correlated with localized areas in the cerebral cortex, however two questions in this regard are: (1) What does a “Smart” brain look like?  (2) How does the Brain as a whole, integrate activities between its different areas?

Research on “G” and Brain Efficiency

New neuroscience technologies developed over the course of the last two and half decades now offer us new opportunities for research. Neuroimaging in particular provides us a primary tool required for objectively defining a person’s characteristics based on the properties of their brain.

An interesting study by Haier et al. in 1988 used PET (Positron Emission Tomography) for producing images of the metabolism of the brain by detecting the amount of (low-level radioactive) glucose utilized by neurons as they fire. The researchers studied the participants’ brain energy consumption as they solved the RAPM [Raven’s Advanced Progressive Matrices] which is a task for solving non-verbal abstract reasoning problems. The RAPM is considered as a good indicator of “G”. The primary aim of the study was to see where General Intelligence arises by observing which brain regions show increased activation when solving problems. The findings as reported directly by Haier (2015, p.21) are as follows:

“To our surprise, greater energy use (that is, increased glucose metabolism) was associated with poorer test performance. Smarter people were using less energy to solve the problems – their brains were more efficient.

Four years later in 1992, Haier et al. again used PET (Positron Emission Tomography). This study observed students before and after they learned TETRIS (a fast computer game for visuo-spatial puzzles). There were two important findings in this study. The first finding was that participants engaged in less energy use in several brain regions after 50 days of practice and enhanced skills. In this regard the researchers found greater energy use to be associated with poorer test performance. The second finding in the study was that individuals with high “G” scores were found to have even more brain efficiency after practice in comparison to individuals with lower “G” scores. Extrapolating from these results, Haier theorizes that the brain learns which areas are unnecessary for improved performance. This leads to less activities in those (unnecessary areas) which in turn leads to enhanced overall brain efficiency.

Brain Function and Math Reasoning Skills

A third study by Haier et al. (1995) using PET (Positron Emission Tomography) was used to examine the Brain Function of students as they solve Math Reasoning problems. The study selected two groups of students: one group with very High SAT (Scholastic Aptitude Test) scores and the other with Average SAT scores.

The results of this study were remarkable. People with High Math Skills used More Brain energy in the Temporal Lobes – BUT: this was true mainly for Males and Not for Females, even as both genders performed at a similar level on the tasks. The researchers concluded that there are differences in the way Female and Male brains Function with respect to Math Reasoning tasks.

As will be seen later in this article, studies have found individual and Group Differences in Brain activity when engaged in problem-solving. The notion of “Efficiency” evidently requires fine-tuning in order to accommodate the fact that this depends on (1) the Type of Task involved and (2) the Difficulty level with respect to the Task involved.

Brain Structure and Networks

Starting in 2004 a series of studies used MRI Scans to investigate correlations between Gray and White Matter and IQ test scores. Gray Matter is composed of Neural Cell Bodies that engage in the computational work of the brain. White Matter are the Axons of those neurons that enable communication among the regions of gray matter. The MRI research have revealed a Network system in the brain: there is a Network of brain regions where More Gray Matter or More White Matter is associated with Higher IQ scores.

Brain Structure and Networks: Adult Females & Males

Differences in Brain Structure (Network Regions or Areas) between adult males and females appear to play a significant role in Gender Differences with respect to Cognition functioning. Despite these differences, Men and Women have equivalent IQ results on tasks. Females in general have More Gray Matter and White Matter in their Frontal Areas (especially those associated with Language) which in turn is correlated with Higher IQ scores. Men in general have More Gray Matter in their Frontal Areas and Posterior Areas assimilating sensory information. This type of brain architecture may explain (at least in part) why Males are often better at visuo-spatial abilities.

Children show different developmental patterns with respect to gender. This was investigated by Neuroscientist V.J. Schmithorst et al. who conducted a series of imaging studies with large samples in 2006-2008. Their findings can be summarized as follows:

  • As Girls age, they exhibit increasing brain organization (i.e. well defined pathways between distinct regions) in the Right hemisphere.
  • As Boys age, they exhibit increasing brain organization (i.e. well defined pathways between distinct regions) in the Left hemisphere.

Nevertheless no specific data is available at this time with respect to how these findings are related to Learning and behavioral Differences.

Passive Mental Activity without Assigned Task: Functional Variations

Haier et al investigated mental activity without assigned tasks in 2003 by using PET scans. Two groups were investigated: Group A (hi score on Raven’s Test) and Group B (average score on Raven’s Test). Both groups watched the same videos in a passive fashion; there was no problem solving or other types of task demands. Despite this there were differences between the two groups with respect to brain activation.

Group A (high score on Raven Test) exhibited different brain activations in their Posterior Visual processing Areas in comparison to Group B (average score on Raven’s Test). The data of this study would suggest that persons with higher IQ are more engaged in the early stages of Information processing. One possibility is that the persons with the high Raven scores (Group A) are not watching the videos “passively” but are “actively” processing what they see.

Mapping out “G” at Last: Frontal & Parietal (P-FIT)

R.E. Jung & R.J. Haier (2007) examined 37 neuroimaging studies on IQ existing up to 2007. They discovered that the seat of IQ is not just in the Frontal Lobes as has been traditionally believed. Instead they found that there are fourteen brain areas distributed throughout the brain with respect to IQ. Especially notable was the finding that sections of the Parietal Lobes (known for their role in sensory integration) are as involved in IQ as the Frontal Lobes.

This has led to a new definition of IQ which sees IQ as higher in proportion to: (a) amount of available Gray Matter in P-FIT areas AND (b) Rate of Information Flow between the Gray Areas. Put another way, General IQ or “G” can be seen as proportional to the amount of Gray Matter volume concomitant with the amount of White Matter connections between the critical Gray Matter regions.

More Efficient connections between Gray Matter regions allow for more Information to Flow Faster between them. Thus quick processing times are also associated with High IQ. This is a notable observation as Restak had proposed as far back as 1986 that one of the indicators of intelligence pertains to the speed of connections between neurons.

A seminal Study by Haier, Roberto Colom (Autonomous University of Madrid), et al in 2009 discovered correlations between specific IQ factors and Gray Matter in certain brain regions. In this study 100 subjects completed 9 cognitive tests which tested different IQ factors including “G”, Fluid IQ, Crystallized (factual) IQ, and Spatial factors. The researchers discovered the following:

  • Positive Correlations between G factor scores and the Amount of Grey Matter in several regions as predicted by the P-FIT model.
  • Once G had been accounted for, certain Gray Matter Volume in specific brain regions were Correlated with specific IQ factors.

As noted by Haier:

Everyone is Unique …IQ scores do not tell the whole story, not even close” (2015, p.23).

Intelligence arises from various combinations of the brain’s P-FIT regions, however these combinations are different and/or unique in different persons. This helps explain every person’s strengths & weaknesses.

A typical example of the uniqueness is the case of Daniel Tammer, an Autistic Savant with Hi IQ who sees numbers as colors and shapes allowing him memorize pi up to 22,514 digits. Tammer for example was able to converse fluently in Icelandic just days after instruction.

The implications of the discussion in this section raises the possibility of matching a person’s  Gray and White Matter with their G and other IQ Factors. Brain tissue in P-FIT Regions may help predict a person’s:

  • (unique) strength patterns across a range of cognitive capabilities
  • (unique) weaknesses across a range of cognitive capabilities

A structural MRI Study by Jan Glascher in 2009 of 241 patients with Brain lesions discovered that the site of each lesion is correlated with specific factor scores.

For example, when the Right parietal lobe is damaged, patients have trouble comprehending raw sensory information, thus their perceptual organization suffers as a result.

 “Everyone is Unique

The P-FIT data from the studies cited thus far may be able to help explain why often persons with similar IQ scores will show different cognitive abilities.

As seen in the graph below, a person in the Madrid Study with the highest G-score showed far more Gray Matter than the Group’s average amount in several P-FIT regions. Two people with identical G-scores of 100 (average for the group tested in the study) showed different cognitive profiles, which would suggest them having different cognitive strengths and weaknesses.

Smart Brain: Synopsis of Research Results

Haier has summarized three overall research results with imaging technology (2015, pp.20-23):

1] “Brain structure and metabolic efficiency may underlie individual differences in intelligence and imaging research is pinpointing which regions are key players” (2015, p.20). Essentially individual differences in neural structure and function are related to differences in IQ. It is also notable that not all brains work in the same way as one another.

2] “Smart brains work in many different ways. Women and men who have the same IQ show different underlying brain structures” (2015, p.20). The same process is evident with respect to older and younger persons having similar IQ scores. These also show average differences on neuroimaging measures. People with the same IQ may often solve the same problem with the same speed and accuracy, but will often rely on different combinations of brain regions working with each other.

3] “The latest research suggests that an individual’s pattern of gray and white matter might underlie his or her specific cognitive strengths and weaknesses” (2015, p.20).

4] “Every individual uses some combination of intelligence-related brain areas in a unique way” (2015, p.23).

As regional Gray Matter is correlated with a person’s IQ, can we now also suggest Specific Training towards Specific Brain Regions to elevate Specific IQs? This question requires Research Studies that will examine the relationships between Neurology, IQ and Education. More specifically, this query breaks down into four distinct questions:

1] Investigate whether: specific strategies produce specific brain changes

2] Investigate whether: students selected on the basis of their unique Brain profiles are more likely to maximize their learning in a specific subject with one educational strategy versus another

3] Investigate How: Specific Brain characteristics are Developed

4] Investigate How: the Brain (and its characteristics) may be Influenced

A number of Studies already suggest that it is Possible to Improve Brain Performance:

Neuroimaging may one day act as supplement (or even substitute) for traditional paper-pencil (or on-line) IQ testing, allowing us to even arrive at an individual brain profile.

A New Definition of Intelligence

The studies cited in this article suggest that key individual differences with respect to IQ are related to Brain Structure and Function. Interestingly, Restak had theorized in the 1980s that intelligence is based on:

1] Speed of Connections between Neurons

2] Quantity or Numbers of Connections between Neurons

3] Strength of Connections between Neurons

Haier’s new definition of Intelligence has notable parallels with Restak’s theory decades past:

1] Size of select Brain regions … and …

2] How efficiently Information flows between the select Brain regions. That flow of information between the brain regions in particular may be theorized as consistent with Restak’s original proposals

Brain Research and Emergent Technologies: Implications for Counselling and Learning

Haier has noted the profound implications of neuroimaging research with respect to Counselling (2015, p.20):

“…Brain scans may soon be able to reveal an individual’s aptitude for certain academic subjects or jobs, enabling accurate and useful education and career counselling. As we learn more about intelligence, we will better understand how to help individuals fulfill or perhaps enhance their intellectual potential and success (p.20) … a learning program could be tailored for an individual student, at any age, based on that student’s brain characteristics. Perhaps vocational success could also be predicted – are there patterns of gray matter across some areas, for example, fighter pilots, engineers or tennis players? People who are seeking a better life with vocational and career consultation certainly will want the choice of having a brain assessment if there are data to support its usefulness (p.24) …

Essentially, we are arriving at a stage where Counselling will soon be provided with the tool of brain scans (neuroimaging Information on the student’s Brain). This provides counsellors with a scientific tool to assist students with respect to their educational and career goals, which are also key adaptive components for the client’s mental health.

Dr. Daniel Amen discusses in a 2013 Ted Talks program how advances in Brain imaging (83,000 scans) can assist in better therapies for persons suffering from psychological, psychiatric and behavioral challenges (Source: TedX Orange Coast).

It must also be noted that the Brain is not a Fixed entity: it is in fact Flexible, Plastic and Changes during the person’s life cycle. Therefore, a “Brain profile” may be used as a GUIDE (NOT as a PRESCRIPTION) in order to enhance the Counselling process. The AIM is to Better counsel the Client for Education, Careers and especially Activities that she/he is most interested in.

Technology is advancing at a breathtaking speed to seen usher in new technologies, again with serious implications for counselling and all professionals working in the field of mental wellness (therapists, psychiatrists, psychologists, counsellors, etc.). A critical, yet little noticed, article in the Special Edition of Popular Science entitled “Your New Brain: When Humans and Computers Merge” in 2018 outlined the following upcoming technologies:

  • Remote monitoring of the Brain (pp.40-41)
  • “Upgrading” of the brain with artificial neurons (pp.53)
  • “Implanting” the brain with “new experiences” as well as “editing” the brain by “erasing” negative memories (pp.54-57)
  • Brain implants for management of maladaptive behaviours (pp.78-81)

These new and emergent technologies are not theoretical: they are arriving and are already “in the pipeline”. This was acknowledged in the World Economic Forum Conference at Davos in 2020 entitled “When Humans Become Cyborgs”. The panel of experts discuss a number of topics including the promotion of mental wellness, students using these technologies to achieve higher academic success, etc. See the below video of that conference discussing these technologies, the challenges of their implementation as well as ethical, commercial and privacy questions – note that 35 minutes and 5 seconds into the video the participants discuss smart wrist-band technologies that (among many capabilities) will be able to track one’s emotions:

A panel of experts at the 2020 Davos Conference engaged in discussion of the topic of “When Humans Become Cyborgs” The experts discuss recent advances in brain-computer interfaces that are blurring the lines between mind and machine. The panel examines the steps that need to be taken at present to ensure the ethical and responsible application of human enhancement (Source: YouTube – When Humans Become Cyborgs | DAVOS 2020).


Haier, R.J. (2015). What does a smart brain look like? Scientific American Mind: Mysteries of the Mind (Special Collector’s Edition), Volume 23, Number 4, Winter, pp. 18-25.

Baggalley, K., Chodosh, S., Choi, C.Q., Cole, S., Eschner, K., Feltman, R., Letzter, R., Maldarelli, C., Mayeda, C., Ossola, A., Pierre-Luois, Piore, A., & Sofge, E. (2018). Your New Brain: When Humans and Computers Merge. Popular Science Special Edition. New York, NY: Time Inc. Books.

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