Part 1 here
Richard Haier begins his fascinating 2017 work The Neuroscience of Intelligence with a question: Why are some people smarter than others? From this he brings the reader up to speed on what neuroscientists have discovered about the genetic and physiological underpinnings of intelligence. This seems like a vast topic, but it really isn’t given how many neuroscientists shy away from the “controversial” topic of intelligence. Thankfully, Haier does not, and in this slender yet tightly-packed volume he presents the broad scope of intelligence research from its pencil-and-paper origins in psychometrics over a century ago to the latest in brain imaging and mapping technology.
Most importantly, Haier does not pay homage to political correctness. He recognizes that intelligence differences among individuals coupled with the well-established heritability of intelligence leads inevitably to differences in average intelligence among races. He draws no moralistic conclusions from this and focuses more on with whether such a conclusion is valid based on the data. And, as he demonstrates, it certainly is. This above all else makes The Neuroscience of Intelligence highly valuable for the Dissident Right.
Psychometricians knew about the reality of IQ well before neuroscientists could prove it. As far back as 1904, Charles Spearman recognized that high scores in one mental ability correlate positively with high scores in other mental abilities. From this he conceived of g, otherwise known as the g-factor, which is a person’s general intelligence best estimated from a battery of tests through a statistical method known as factor analysis. These tests measure reasoning, spatial ability, memory, and other mental abilities. g captures what is believed to be the biological essence of intelligence, from which we derive IQ and other, broader intelligence factors. This is why tests such as the Raven’s Advanced Progressive Matrices (RAPM), which eschew language, may get to g better than IQ tests. In these tests, a 3×3 matrix of shapes follow a left-to-right, top-to-bottom rule of progression, with the bottom right entry missing. The subject must then select the shape which completes this progression from a list of distractors.
Here is an example of a RAPM item (scroll to the bottom of this essay for the answer):
The use in measuring concepts such as g and IQ comes in their predictive value. Like in the film Moneyball, if one can distill such predictive power into a single number, one can make fairly accurate decisions in real life. In the late 1960s, researcher Arthur Jensen proved that IQ and SAT scores were excellent predictors of academic success at the university level — regardless of socioeconomic status, age, sex, and race. Haier cites various studies which show how IQ predicts job performance, especially in jobs which require a great deal of training and problem-solving. Various longitudinal studies also demonstrate how high math SAT scores can reliably predict career success in STEM fields. In one shocking study from the 1990s, the Unites States Air Force showed that g accounted for nearly all the variance in pilot performance.
Many studies have shown IQ’s predictive power when it comes to everyday life:
Consider some statistics comparing low and high IQ groups (low=75-90; high=110-125) on relative risk of several life events. For example, the odds of being a high school dropout are 133 times more likely if you’re in the low group. People in the low group are 10 times more at risk for being a chronic welfare recipient. The risk is 7.5 times greater in the low group for incarceration, and 6.2 times more for living in poverty. Unemployment and even divorce are a bit more likely in the low group. IQ even predicts traffic accidents. In the high IQ group, the death rate from traffic accidents is about 51 per 100,000 drivers, but in the low IQ group, this almost triples to about 147.
Haier also reveals how no amount of environmental pressure, education, or social engineering has been found to raise IQ. In fact, individual genes have become associated with cognitive ability. A 1978 Polish study shows that despite the communists’ efforts to “allocate dwellings, school, and health facilities without regard to social class,” in Warsaw RAPM scores among children still correlated most with parental occupation and education. Haier even describes how multiple studies have now refuted the “10,000 hours” notion, which Malcolm Gladwell made fashionable in his 2008 book Outliers. The environmental impact on intelligence has been shown to be negligible — and this, coupled with mountains of psychometric data as well as numerous studies correlating intelligence variance with genetic similarity, has led Haier to assert that “intelligence is 100% biological.”
But how to prove it?
Prior to the days of neuroimaging, such assertions could be countered with claims of test bias, poverty, racism, and the like. With the brain and the human genome essentially being black boxes, who could know for sure? Further, after psychometrics trailblazers such as Arthur Jenson and Richard Herrnstein received horrendous criticism and abuse in the 1970s for postulating a genetic component of intelligence, few researchers wanted to find out. Fewer still could find the funding to do so.
As for intelligence differences between the races, Haier does not deny it, nor does he dwell on it. He addresses it best when discussing Arthur Jensen:
Jensen was once asked directly if he was a racist. His answer was, “I’ve thought about this a lot and I have come to the conclusion that it’s irrelevant.” . . . I knew Jensen for many years and I understand his point was that his interpretation of data, even if it was motivated by unconscious racism, was testable and falsifiable by objective scientific methods. He was confident that future research could potentially refute any of his hypotheses.
After his chapters on psychometrics and genetics, Haier dedicates the remainder of The Neuroscience of Intelligence to neuroimaging and how this relatively new technology can pinpoint physiological functions and structures which correspond directly with pencil-and-paper intelligence test scores. The human brain is no longer a black box, and the claims made by Arthur Jensen and others many decades ago have now been vindicated.
Haier begins with Positron Emission Tomography (PET) studies, which use radioactive tracers to measure blood flow and glucose metabolism in certain areas of the brain while a subject is performing various mental exercises. Developed in the 1980s, this technology demonstrates the counterintuitive notion of brain efficiency to explain why subjects with higher RAPM scores showed less brain activity in crucial areas while performing mental tasks. One of these studies required subjects to play Tetris over a 50-day period (something I would have gladly signed up for as an undergraduate!).
Another thing PET studies reveal is how men and women process information differently. Here is an amusing paragraph to which I am sure many husbands and wives can relate:
In the 22 men, statistical analysis showed that high math ability went with greater activity in the temporal lobes . . . during problem solving. This was just the opposite of efficiency. In the 22 women, we found no systematic statistical relationship between mathematical reasoning ability and brain activity. How the brains in the high SAT-Math women were working to solve the problems could not be determined, even though they were solving the same problems as the men equally well. And the men showed the opposite of what we expected. And that is how research often goes.
This leads us to the following joke: Neuroscience has now proven that smart men often get flustered, and that the minds of women are a complete mystery! Perhaps the men and women were taking their exams in the same room simultaneously and could barely keep their eyes off each other.
Haier moves on to Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI). When used in conjunction with computer technology called voxel-based morphometry (VBM), three-dimensional images of the brain can be created and studied. A voxel is essentially a 3D pixel, and lowering the voxel level within regions of interest in the brain allows researchers to accurately correlate test scores with physical structures. Here is a brief list of brain structures which correspond directly with IQ:
- Cortical thickness and surface area (which correlates with the number of neurons in a major part of the brain associated with memories and reasoning).
- White matter in the parietal lobe (which corresponds to the speed of signals sent between brain cells).
- Gray matter in the anterior cingulate cortex (which is associated with attention allocation and impulse control).
- N-acetylaspartate measurements (a marker of neuron density and viability).
- Shorter path length of frontal-parietal connections (a measure of communication efficiency between crucial parts of the brain).
- Inter-hemispheric connectivity between parallel brain structures (an inverse relationship with IQ).
- Basal ganglia volumes (subcortical nuclei associated with cognition and learning).
- Volume of the thalamus (“an important hub of brain circuit connectivity”).
In other words, ignoring all this empirical evidence and linking IQ to environmental factors such as racism, socioeconomic status, and historical oppression would be like saying a featherweight journeyman boxer would be able to defeat the heavyweight champion if not for his parents’ low income and his ancestors’ status as slaves. We know this because, as Haier states,
[a]ll these early MRI studies of gray and white matter were exciting because they found correlations between various psychometric test scores of intelligence and quantifiable brain characteristics both in specific locations and in the connections among them. This increased optimism for the potential of discovering not only “where” in the brain was intelligence, but also “how” intelligence is related to brain function.
And just when you think the horse is already dead, Haier proceeds to keep beating it just to remove any scintilla of doubt the reader might still have. By the time you’re done with the book, you almost feel sorry for the horse.
Haier goes on to report findings from even more cutting edge technologies such as electroencephalogram (EEG) measurements, magneto-encephalogram (MEG) imaging, and diffusion tensor imaging (DTI). He describes his Parieto-Frontal Integration Theory (PFIT) and how it has been exonerated, with only minor adjustments, over the past two decades. He also communicates the excitement and optimism that many neuroscientists who study intelligence are feeling right now. Yes, The Neuroscience of Intelligence is highly technical and sometimes a challenge for the lay person to get through. One finds acronyms everywhere in the field of neuroscience, it seems. Yet, it is not as dry as all that. Haier’s enthusiasm for his subject matter is infectious, which will make the work engrossing for anyone with more than just a passing interest in the field. “We are light years past earlier controversies about whether there is a role for genetics for understanding individual differences in intelligence,” he writes confidently. How could anyone not be excited by this?
So confident is Haier in the correlation between g and IQ, on one hand, and brain structures and function on the other that he even proposes that one day, brain imaging could completely replace standardized testing. He argues this would ultimately be more objective, since imaging can rule out minor environmental factors such as motivation, anxiety, or someone simply having a bad day. It would be less expensive as well. His goal in neuroscience is to completely reverse what is happening now. Instead of using IQ to predict brain structure and function, he wishes to eventually use brain structure and function to predict IQ, and also find the holy grail of neuroscience: the ability to “manipulate brain variables to enhance IQ.”
When and if this ever happens, we will find ourselves in a science-fiction alternate reality where everything we understand about human nature will be tossed on its head. Enhancing IQ will likely enable us to reduce IQ as well. Think about it. Haier also includes the following chilling detail:
On a final note, genetic studies are logistically complex and expensive, especially when large samples are involved. DNA sequencing machines alone, for example, cost about $1-2 million each. Reportedly, in 2012 a single research institute in China, the Behavioral Genetics Institute, had 128 of them, along with super computers. Finding intelligence genes is a high priority. This one institute has over 4,000 scientists and technicians working there and a poster on the wall reportedly says: “Genes build the future.”
Until this dystopian future arrives, however, cutting-edge neuroscience has completely vindicated what race realists and racial identitarians have always known to be true: IQ is both real and heritable. From this, it is only a small step to conclude that IQ differences across individuals, groups, and races are also real and heritable — a fact Richard Haier does not deny. Given the dire circumstances of today’s white populations with regard to widespread, low-IQ, non-white immigration, the crystal clear messaging from The Neuroscience of Intelligence should be spread far and wide — lest the blunder of ignoring such messaging one day destroys the very civilization which produced neuroscience to begin with.
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 The answer is 5. Each row and column contains twelve individual shapes, and no individual shape is repeated across entries. For example, the curved, vertical line appears in only one entry per row and in one entry per column. The correct answer must contain five individual shapes, all of which cannot already appear on the bottom row or on the rightmost column. This leads us to distractor 5.
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