Thich, Quentin, and I have spent the past few months buried in academic articles in attempt to fully elucidate a claim that we now unabashedly support: Intelligence enhancement is one of the most important causes of our time. This research has culminated in a 30,000-word, 300-citation literature review that aims to answer three fundamental questions: What is intelligence? How do we measure it? And how do we most effectively improve it?
The topic of intelligence is shrouded with contention. One need only look to the life and times of Charles Murray for evidence of this. But as our review attempts to prove, the ferocity and offense that conversations about intelligence elicit have stymied progress in a field that possesses the potential to make an incredibly positive impact upon the world.
Lulled by popular opinion and headlines, we too began our research mildly convinced that IQ was a faulty metric, cognitive abilities were malleable, and education substantially affected intelligence. Yet as the field began to unfold, these priors fell to pieces. Leading theorists and researchers have largely resolved most of the debates that continue to haunt our notions of intelligence. IQ correlates significantly with desirable outcomes ranging from longevity and prosocial behavior to occupational attainment and economic development. The heritability of intelligence is one of the top ten most replicated findings in behavioral genetics. And while education plays an important role in shaping children’s interactions with society, the effect sizes of its direct influence upon intelligence are negligible.
If these findings are shocking, they are also motivating. Did you know that something as simple as iodized salt, costing roughly $0.05 per person per year, can save an infant from being born with almost a full standard deviation fewer IQ points? Did you know that serious advances in genomic analysis have increasingly allowed us to identify genes that predict a significant degree of variance in intelligence?
We didn’t a year ago, but we do now. In sharing this paper, we aim to spark further understanding of the importance of intelligence to everyday life, encourage the consideration of reverse causality in the explanation of current phenomena, and drive thought beyond traditional education to determine the ways in which we may help everyone to become smarter.
If you have any thoughts or questions, please do not hesitate to contact us. The field of intelligence demands further research into our ability to both measure and improve the very real constructs of human potential. Our hope is that this paper is only the beginning.
As complex as it is interesting, the phenomenon of intelligence has proven to be the center of much thought and argument. What truly is intelligence? How can we measure it? What are the ways in which we might effectively improve it? Each of these questions possesses a rich and interesting history, which too often undermines the scientific consensus that has been reached in the field and thwarts thorough examinations of the cognitive abilities that set humans apart from other species and each other. This paper will first establish a definition of intelligence and its primary facets. It will then explore the significance of intelligence in relation to various societal and individual outcomes. Finally, past, present and future interventions into these different components of human development and consequent intelligence will be explained and assessed. In short, this literature review will hope to provide readers with a solid foundational understanding of the nature and enhancement of intelligence before providing a good starting point for possible enhancements that can be pursued and specific frameworks for thinking about them.
Table of Contents:
It has been said that Socrates argued true intelligence was the knowledge that you know nothing. Einstein asserted that intelligence was not knowledge, but imagination. Stephen Hawking swore that intelligence was simply the capacity to “adapt to change”. Which of these very intelligent men was correct? Over the years, theorists, popular figures, and laymen alike have described intelligence as diverse abilities ranging from powers of reasoning or quick thinking to social skills or an excellent memory. Even as recently as 1987, asking two dozen leading theorists to describe their conception of intelligence was likely to result in “two dozen somewhat different definitions”(Neisser et al., 1996). It is fitting that a phenomenon as influential as intelligence has been the center of so much debate. Yet amongst the many myths that have arisen around intelligence, perhaps the most unfortunate is that intelligence itself is a “concept too amorphous and ill defined for scientific study” (Haier, 2017, p.18) . Hawking has not been alone in linking intelligence with an abstract ability to adapt to new information and new environments. On the whole, it is widely agreed that intelligence involves general mental ability. Thus a few of the historical definitions of intelligence as wisdom, books smarts, or imagination are right in part – they simply fail to capture the full range general mental abilities that intelligence represents. Most established and respected definitions now bear discernable resemblance to a formal statement of consensus on the nature of intelligence issued by the American Psychological Association in 2012:
[Intelligence] . . . involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather it reflects a broader and deeper capability for comprehending our surroundings: “catching on,” “making sense” of things, or “figuring out” what to do. (p. 13)
Not merely theoretical, this definition of intelligence as general mental ability has proven to be one of the most replicable findings in the field of intelligence and currently forms the basis of nearly all modern research (Plomin, 2016). Those who disagree with this description chiefly complain of its “limited” scope or the limited utility of traditional intelligence assessments (Gardner, 1987). In order to understand the nuance of these debates and the consensus that has been reached, it is helpful to look in more detail at the specific conception of general intelligence and the arguments of its detractors.
Support for the idea that intelligence may be defined as general mental ability is wide ranging. Remembering specific details, performing mental calculations, providing novel solutions to problems: experiences in everyday life attest to the fact that cognitive abilities associated with intelligence come in a variety of forms. While very few people exhibit an equal degree of each ability, different mental abilities are not independent.
Pioneering intelligence researcher Charles Spearman was one of the first to note that individuals who possess one ability associated with intelligence normally possess many other abilities associated with intelligence as well (Spearman, 1904). In his development of the “Two Factor Theory”, Spearman analyzed the rank ordering of student’s performance in their different classes (Spearman, 1904). He found that while the performance of a student in, for example, Math was different to French, all of the different performances were correlated with one another. This led him to the belief that each test measures its own factor, but that underlying all of these was a general factor of intelligence common to all tests. Therefore, Spearman proposed that there was a general factor of intelligence, referred to as g, and another factor which acts as an umbrella for all of the unique ability one has in say, Math, that fails to translate over to French (Mackintosh, 2011). It has been found that this general factor, g, accounts for roughly 50% of variance between the different broad factors of intelligence not only in humans but also non-human populations including primates and rodents (Reader, Hager & Laland, 2011) (Plomin R., 2001).
Spearman’s g-factor, the measurable trait also known as general intelligence, is commonly depicted as sitting at the zenith of a hierarchy of mental abilities. Below g, lie a number of levels which have been grouped together and categorized within the hierarchy after years of rigorous experimentation. The diagram below is of the Cattell-Horn-Carroll Theory (CHC), which is the most prominent and influential variation of this analysis. It organizes its three strata to represent narrow, broad, and general cognitive ability and then further divides the tasks at each level based upon whether they assess skill or speed (Willis, Dumont& Kaufman, 2011). Typical representations of CHC resemble the image below:
Taken from Bates T., 2013. Wikimedia Commons.
As can be seen, the general factor of intelligence, called g, is at the top, with 8 broad factors grouped into Stratum II just below, and then roughly 50 sub factors below these in Stratum I. The 8 broad factors (in order) are: fluid intelligence; crystallized intelligence; general memory and learning; broad visual perception; broad auditory perception; broad retrieval ability; broad cognitive speediness; and processing speed (Willis, Dumont & Kaufman, 2011).
The CHC theory is called a “factor model”, as certain independent “factors” have arisen and developed symbiotically through the statistical technique of factor analysis over many iterations of testing different human abilities. Depending upon the nature of the test and the questions employed (examples of questions are given later), other factor models for intelligence exist. However, CHC is the most widely accepted and adopted due to the amount of empirical analysis by which it is supported. The CHC theory was the merger of the Cattell-Horn theory produced in 1963 by Raymond Cattell and John Horn and the Carroll Three-stratum theory which was made in 1993 after he performed a “truly staggering reanalysis” (Willis, Dumont & Kaufman, 2011, p. 44) on 461 data sets narrowed down from ~1,500 in total (Carroll, 1993, pp. 631-655). Since the creation of the CHC model, it has been espoused as the backbone of many IQ tests in terms of the different types of questions asked, as well as how these questions are grouped and weighted to produce an overall IQ score.
While Spearman may have been wrong in some of his initial mathematical calculations and the existence of only “two factors” to explain the correlations present in intelligence tests, recent evidence beyond just high correlations supports the existence of g as a phenomenon essential to the operation of human intelligence, rather than merely a statistical artifact of similar intelligence tests. Two different studies found that when three, and then five, completely separate batteries of multiple diverse intelligence tests were given to randomized groups, the g extracted from the eight different tests all had correlations between 0.95 and 1.00 with one another in all but one study. These results, in combination with the nucleus of Spearman’s original findings, have provided substantial evidence for the existence of g.
Interestingly, a portion of recent research in the field has been targeted toward narrowing down upon what exactly g is, finding that it correlates highly with working memory. A major meta-analysis of 86 studies found the average correlation between g and working memory to vary depending on the specific type of working memory test (whether it was spatial, verbal or analytical). However, on average it was estimated to be r=0.5 (Ackerman, Beier & Boyle, 2005). It is important to note that g also correlates to a lesser degree (r <= 0.3) with everything from one’s reaction time, to the speed at which information travels to the visual cortex of the brain (Fox, Roring & Mitchum, 2009), how efficiently one’s brain consumes glucose (Haier et al., 1998) (Mackintosh, 2011), and even overall body symmetry (Bates, 2007). In sum, g appears to be “a reflection of a more general fitness factor influencing the growth and maintenance of all bodily systems, with brain function being an especially sensitive indicator of this fitness factor.” (Bates, 2007)
The two next most significant factors of the CHC model, which exist in the second tier and which have the highest contributions to g are Gf, known as fluid intelligence, and Gc, crystallized intelligence. Gf is the ability to reason and spot patterns; this requires working memory, processing speed, and analytical abilities. The highest correlation measure of Gf is Raven’s Progressive Matrices, which is a test where one must identify a pattern and guess the next part of the sequence. Other tests of sequences and pattern matching are likewise good measures of Gf.
What number would come next in this sequence?
-2, 5, -4, 3, -6…
Graphic found through (Carpenter, Just, & Shell, 1990)
On the other hand, Gc, or crystallized intelligence, measures the accumulation of knowledge, which requires a robust memory, the ability for accurate recall, and a work ethic that accumulates the information to be remembered in the first place. Gc tends to be tested through one’s knowledge of vocabulary and verbal ability. One of the ways this can be done is through the “C-test” which requires completing the second half of every second word in a passage with a point awarded for each correct word (Baghaei & Tabatabaee, 2015, p. 48) or other tests of vocabulary.
“If you were to ask most people who Charles Darwin was, many of them would reply that he was the man who said that we were descended from monkeys. They wo___ be wr___. Darwin d___ no mo___ than sug___ the possi___. What h___ said, a___ proved b___ thousands o___ examples, w___ that ov___ millions o___ years ani___ and pla___ have cha___. This he called evolution.”
What fragment, when added before the first dash and after the second will form two recognizable words?
-pet, par- (snip) -mill, chain- (saw) -son, pa- (per)
Analysis of correlations between these factors and g can vary, but a recent confirmatory analysis found that Gf correlated as high as 1.00 with g and Gc 0.79 with g. Both of these correlations can be lower or, as will be shown, change with age (McGrew & Knopik, 2005). Aside from the close correlation with g, Gf and Gc are important is because they fit well into a developmental framework. Our Gf ability peaks in our 20s before declining with age (Horn, Donaldson, & Engstrom, 1981). Fortunately, as our Gf declines our Gc rises with the more information we accumulate over our lifetime. Also reassuring are theories that while our Gf declines, we gain expertise in domains that can help compensate (Horn & Blankson, 2005). For example, we are able to chunk information in a way that allows our working memory to be more efficient and allows us to think about issues in a more sophisticated and accurate way (Davidson & Kemp, 2011).
The graphic below represents the relative correlations between different intelligence test components. Those tests placed in or near the center of the concentric circles have the highest correlation with each other, and uncoincidentally, are also the most complex. It is important to note that Raven’s Matrices and other clear tests of Gf are extremely central, a fact which supports their importance to and close relationship with general intelligence. Anagrams and word completion tasks, like those associated with Gc may be found closer to the circle’s periphery. The dotted lines crossing through the circle show rough distinctions for the verbal, arithmetic, and spatial domains while the outer circles show lower levels of correlation and complexity the further away they are from the center.
Graphic found through (Carpenter, Just, & Shell, 1990) and taken originally from (Snow, Kyllonen, & Marshalek, 1984, p. 92)
It is telling that though Gf and Gc are some of the most accepted and utilized measures of intelligence, even they contain unresolved issues and questions (Willis, Dumont, & Kaufman, 2011). One issue is their causality, as information has to be taken up and utilized by Gf before it crystallizes in our long term memory. Gf is integral to Gc. This obviously causes difficulty in making a clear distinction between the two concepts and elicits further questions about the cognitive psychological basis of Gf and Gc. For example, there is significant debate around what Gc actually measures: is it measuring declarative knowledge or verbal ability? (Schipolowski, Wilhelm, & Schroeders, 2014) It is useful to both acknowledge and accept these critiques. Psychometricians develop and refine tests in more of an empirical than a theoretical way, by designing a test and then seeing what sticks. Their tests are measuring something that is stable and unique, but answering what exactly that stable and unique trait is and how it came to be is another matter entirely. Those questions cannot be understood in the same trial and error manner. What it is exactly and how it comes to be are questions that cannot be answered in the same way.
Critiques of general intelligence often stem from discontent with the limitations it imposes. Though evidence for g is well established, a number of theorists remain averse to the thought that intelligence may be defined as one “integrative power of mind” (Haier, 2016). This idea that there may be multiple “intelligences”, which manifest themselves in different ways in different people, is best embodied in the writings of developmental psychologist Howard Gardner. In his book Frames of Mind, Gardner posits that definitions of intelligence should extend beyond traditional analytical reasoning and tests that only measure what can be answered with pen and paper (Gardner, 1987). Consequently, his theory of Multiple Intelligences, or MI, incorporated a holistic range of mental abilities. The seven original forms of intelligence he defined included intrapersonal, interpersonal, bodily-kinesthetic, musical-rhythmic, visual-spatial, verbal-linguistic and logical-mathematical abilities.
Since then Gardner has added naturalistic intelligence, hypothesized that existential intelligence might exist, and that a spiritual intelligence (which he initially considered) does not exist (Gardner, 1999). In this light, an “intelligent” individual may be one who excels in math or music or even athletics. While this sweeping definition of intelligence holds intuitive appeal, it quickly becomes problematic. Gardner defines intelligence as an individual’s unique abilities in a range of entirely isolated domains. Yet, as has been shown, this idea does not survive empirical analysis.
For as different his concept of intelligence is, Gardner’s definition remains quite similar to that given by most theorists in the field. He says that intelligence is “a biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture.” (1999, pp. 33-34) The differences between his definition and that given for g, or general intelligence, exists only in the cultural emphasis that Gardner provides, in line with his more inclusive and subjective definition of what constitutes intelligence.
Gardner’s theories have unsurprisingly come under fire from other intelligence researchers. (There is in fact a book by the very title of “Howard Gardner Under Fire”…) The primary problem with Multiple Intelligences is the difficulty of objective assessment for things like bodily-kinesthetic abilities. In fact, Gardner even admits “I have not devoted energies to the devising of tasks that purport to assess MI” (Gardner, 2006). This did not stop a research group in 2006 from testing the validity of his categorizations and whether or not they were already encompassed by the CHC model (Visser, Ashton, & Vernon, 2006a) (Visser, Ashton, & Vernon, 2006b). The paper found that all of the cognitively related categories – linguistic, logical-mathematical, spatial, naturalistic, and interpersonal were highly correlated with g, while the other three abilities had low correlations, especially bodily-kinesthetic. The authors then explain that the aspects of MI not measured by g, are not related to either general brain function or general significance life outcomes and as a result should not be referred to as intelligence but instead “special talents”. See (Almeidaa, et al., 2010) and (Castejon, Perez, & Gilar, 2010) for replicated findings.
With general intelligence anchoring one side of the intelligence inclusiveness spectrum and Gardner’s Multiple Intelligences anchoring the other, a theory that attempts to occupy middle ground is Robert Sternberg’s triarchic model of analytical, creative and practical intelligences. There are a number of different evolutions of Sternberg’s theories of intelligence that are beyond the scope of this paper. However, a significant contribution has been Sternberg’s theory of practical intelligence, or common sense, and its measurement through “tacit knowledge” (Sternberg, Wagner, Williams, & Horvath, 1995). This theory and its practice stemmed from the acknowledgement that there is more to intelligence than just the analytical reasoning dominated by IQ tests and that the correlation between IQ and job performance is only r=0.5 leaving plenty of variance unexplained.
Tacit knowledge has three requirements:
1. It is procedural knowledge which answers “how” rather than declarative “what” questions. For example, there is a big difference between knowing the fact that “a good leader has to understand people” and actually understanding them.
2. It is directly relevant to the individual and their goals.
3. It is learned implicitly by the individual without instruction.
Sternberg explains how all of these components are interrelated. Procedural knowledge is often related to our goals and is often much harder to teach and communicate to others than declarative facts are. Moreover, when it comes to predicting future life outcomes, due to the difficult independent learning inherent to tacit knowledge, there is a comparative advantage in knowing what others do not (Sternberg, Wagner, Williams, & Horvath, 1995). Tacit knowledge is in the same spirit of Oscar Wilde’s quote, “nothing that is worth knowing can be taught” (1894).
The empirical evidence for the existence of tacit knowledge is significant. Firstly, it was found that like with g for IQ tests, there is a principal factor behind all forms of tacit knowledge testing. When controlling for test unreliability, tacit knowledge explained 76% of variance in a single tacit knowledge domain (in this case it was tacit knowledge for business managers) and there was a correlation of r=0.58 for the total score across domains, for example, between business and academia (Wagner, 1987). In another study managers were given an extensive battery of different psychometric tests including the “Tacit Knowledge Inventory for Managers” and their management skills were rated by psychologists through team game simulations (Wagner & Sternberg, 1990). When finding what test in particular was the best predictor of outcomes, “the best predictors of the criterion score of managerial performance were tacit knowledge (r = —.61, p < .001) and IQ (r = .38, p < .001).” And “the correlation between tacit knowledge and IQ was not significantly different from 0 (r = -.14,p > .05).” (Sternberg, Wagner, Williams, & Horvath, 1995) Therefore, given the small and non-statistically significant correlation between IQ and tacit knowledge, we can conclude that tacit knowledge is a stable and independent concept. We can also conclude that it has predictive validity, however, the extent of this predictive validity is unclear due to the fact that the above example used idiosyncratic simulations and scoring mechanisms. The predictive validity of tacit knowledge was much lower given more objective summary metrics of success. For example in another Sternberg study, with a regression analysis “predicting maximum compensation”, tacit knowledge came in 3rd place accounting for only 4% of the variance with years of education and management experience mattering far more (1995, p. 992). Moreover, it was not clear if IQ was controlled for as this could be one of the latent confounding variables.
As evidenced by the lack of “Tacit Knowledge Inventory” tests when compared to IQ tests in society, it is clear that the practical intelligence theory and measurement still needs much time for development and refinement, it is after all roughly 90 years younger than the first IQ tests. However, by acknowledging the significance of IQ and trying to augment its gaps, Sternberg has certainly provided a valuable contribution to the psychometric field.
As important as it is to acknowledge what is encompassed within the scope of intelligence, it is equally important to acknowledge what is not. Beyond Sternberg’s practical intelligence, this includes personality types, narrow talents and broad success. Besides being a very complex term in general, because we semantically associate intelligence with “good”, there is the temptation to describe any and every trait as intelligence. For example, one could say that the fact someone works hard is “intelligent” even though this would in fact be referring to their levels of conscientiousness as measured by the Big Five personality types (more on this later), which have very little correlation with measures of IQ. We must resist the urge to label things as intelligence that in reality are not intelligence, for something that defines everything defines nothing.
Another remaining question is whether to define only underlying ability or also accomplishments as intelligence. Consensus on this issue is that intelligence should be defined as one’s underlying ability (Davidson & Kemp, 2011). Accomplishments can be good indicators of intelligence but are not proof of intelligence in themselves and intelligence can exist through ability without any concrete accomplishments. Of course, one could see high performance on an IQ test as an “accomplishment”, but there is a clear distinction between accomplishments like this which are designed to solely measure innate ability, and those achievements which often require a much larger assortment of other variables such as skills, people, and luck. Therefore, when considering the extent to which one’s accomplishment is obtained through innate abilities rather than external influences, a rule of thumb should be to separate abilities from achievements.
Drawing this exploration of intelligence theories to a close and coming full circle, intelligence is a complex construct that emerges from a number of factors. Yet these factors all in some way fit under the umbrellas of information processing, problem solving, memory, and awareness. For the remainder of this paper, it is safe to assume that any reference to intelligence is grounded in reference to general intelligence, or g as proposed by Spearman and accepted by the vast majority of intelligence theorists. IQ, a concept further explored in the succeeding section, will be utilized as the main metric for intelligence. Being the most reliable and established form of measurement for intelligence, IQ truly is measuring something important. As will be explained later, it is highly correlated with everything from academic achievement to life outcomes and even one’s likelihood of committing a crime and longevity. Intelligence likely applies to many domains of one’s life and we see this occurring with measurements of IQ.
While IQ will be used as the primary metric for analysis, we hope that by having explored other concepts of intelligence the reader will be aware that IQ is certainly not the end-all-be-all. When in the third section of this paper we explore different ways to enhance intelligence, other putative facets of intelligence that are independent of IQ, such as unstructured problem solving, will still be considered if and when they are relevant.
The first successful intelligence test was developed by Alfred Binet. Commissioned by the French Ministry of Education to find a way identify less able students and place them in special schools, Binet devised a strategy to calculate a student’s mental age through a series of 30 subtests on the interconnected areas of memory, attention, perception, verbal comprehension, and reasoning (Urbina, 2011). The mental age assigned to the child was the age of the group of students who received the same score on average. This mental age was then divided by their chronological age and multiplied by 100 to form what would be known as their ‘intelligence quotient’, or IQ. By this logic, average students, whose mental age exactly matched their chronological age, possessed an average IQ of 100. (Binet & Simon, 1911). What became called the Binet-Simon intelligence test was brought to the US in 1908 before becoming widespread after World War I, when Army recruitment used a version of the test identify which soldiers should be trained as officers (Mackintosh, 2011). The IQ test was standardized for Americans by Lewis Terman, in what he named the Stanford-Binet Intelligence Test.
As much confusion in the field stems from misuse and misunderstanding of the terms intelligence, IQ, g, and mental ability, the image above details their relationship. g is a component of IQ or “intelligence quotient”, which is the index derived from intelligence tests. Thus IQ is merely a component of intelligence, which is a generalizable subset of mental abilities.
The original Stanford-Binet test’s use of the intelligence quotient calculation restricted its scalability. Variance in mental and chronological age is much more significant in children than adults. For this reason, the test’s validity declined dramatically when administered to individuals after the age of 25, when intelligence stops rising. For example, if a 50 year old were to perform at the same level as a 25 year old, their intelligence quotient would equal 50 IQ points, classifying them as mentally retarded. This clearly does not make any sense.
Noting these limitations, psychologist David Wechsler created a new way to analyze the results of the Stanford-Binet IQ test. Rather than divide mental age by chronological age, Wechsler decided to derive IQ by comparing an individual’s performance on the tests of intellectual ability to the performance of representative group (Urbina, 2011). Raw scores for a chosen sample are converted into a normal distribution of scores with a mean of 100 and a standard deviation of 15, depicted in the graph below.
Normal Distribution of IQ, taken from Haier (2017).
As with any normal distribution, 68% of the scores will fall within one standard deviation above or below the mean, 90% will fall within two standard deviations, and 99% will fall within three standard deviations. In this light, 99% of the population possess an IQ in the range of 75-120, and 68% possess a score in the range of 85 to 115. Those who possess an IQ over 145 are typically deemed “genius” (Urbina, 2011). Yet it is important to remember that an IQ score is not a measure of an absolute quantity. An individual’s score is only meaningful when compared to the scores of his or her peers.
It is important to acknowledge that IQ scores use relative interval scales rather than ratio scales. Unlike inches or pounds where 200 units is double the size of 100 units, for IQ a score of 130 does not make someone 30% smarter than someone with 100. A score of 130 puts them in the highest 2% while a score of 100 is at the 50% mark and it is hard to quantify how much “smarter” they are (Haier, 2014). Therefore, IQ scores are only valuable in relation to the scores of others and they currently lack a ratio scale metric with which to compare them not only to other humans in a more objective way, but also to other biological and artificial systems, something that should be researched further and addressed.
There are just over a dozen intelligence tests currently in use, including more updated versions of traditional tests like the Stanford-Binet assessment. Fewer tests now employ the word intelligence in the title, though each use Wechsler’s method to calculate an IQ score through deviation and most make use of the CHC model to organize the cognitive abilities being assessed. There is no one definitive measure of intelligence, though some tests and forms of questions are used much more frequently than others. Specific tests have been developed for different developmental periods, ranging from the Bayley Scales of Infant Development to the Wechsler Adult Intelligence Scale. Different tests of intelligence may also use different types of questions and sometimes place different weightings on the two areas that are said to comprise general mental ability (Neisser et al., 1996). The Wechsler Adult Intelligence Scale claims to measure fluid intelligence on the performance scale and crystallized on the verbal scale, combining these scores to tally an individual’s IQ score (Wechsler, 2008). However, many assessments, including Raven’s Progressive Matrices, only claim to test fluid intelligence, seeing crystallized intelligence as an inherent component of that. Though tests like the SAT, GRE, and MCAT are highly predictive of IQ and correlate with g at r = .72, they do not explicitly test intelligence (Frey & Detterman, 2004). Administered by practicing psychologists on an individual basis and often lasting multiple hours, true intelligence tests measure samples of behavior that are relevant to cognitive abilities. This is done through batteries of varied tasks, that may include picture completion, block design, vocabulary, comprehension, arithmetic, matrix reasoning or digit span (the previous questions presented were taken from IQ tests).
Individual differences in intelligence arise because of differences amongst both people’s environments and their genetics. To what extent it is environment versus genetics, or “nurture versus nature”, is highly debated in public discourse, but less so empirically within the research community.
One of the most counter-intuitive findings we have discovered since engaging with the field of intelligence involves the extent of intelligence heritability. As a child, approximately 30% of IQ depends upon heritability, while only 40% depends upon “shared environment”, and the remaining 30% depends upon “non-shared environment”. (Shared environment is the environment you have that is shared with others, the largest component of which is your home with how many books you have, how often your parents read to you, etc. Non-shared environment is a trickier concept, including both an individual’s unique, unpredictable interactions with the broader world and measurement error.) By the time children reach adulthood, however, IQ is 60-80% heritable with the remaining 10-20% being “non-shared environment” and the other 10-20% being “shared environment” (Neisser, 1996). The graph below was taken from the paper Top 10 Replicated Findings From Behavioral Genetics which finds that one of these de facto “laws” of the field is the heritability of IQ increasing with age (Plomin, DeFries, Knopik, & Neiderhiser, 2016).
It is worth noting that values for shared environment tend to be smaller than 10-20% in adults, with many meta-analyses giving all of the non-heritable weight solely to non-shared environment (Neisser, 1996) (Plomin & Deary, 2015). Variations in heritability between samples are often due to the sample size, the diversity of the sample, and the reliability of the IQ data. If any of these factors are poor, then the heritability score will be too. It should also be noted that the significant influence of heritability on adult IQ has been accepted by a majority consensus in the field of psychology, even before 1996 when the American Psychological Association gathered a diverse committee of experts to publish a paper on the topic titled Intelligence: Knowns and Unknowns, in order to give a definitive response to Herrnstein and Murray’s contentious publication, The Bell Curve (1994).
To provide further information regarding this significant heritability finding: the most basic equation for heritability is calculated as where (Kempthorne, 1957) (Wikipedia, 2017). Readers should know that there are far more complex equations for calculating heritability and that some of these have at times come under significant and deserved critique (Schönemann, 1997). However, these concerns about the validity of a few heritability equations used prior to 1996 do not apply to more modern heritability studies, especially when one considers the sheer number and diverse range of studies that have been performed across identical twins, siblings, and adoptees (Plomin, DeFries, Knopik, & Neiderhiser, 2016). Moreover, rather than using a heritability equation that requires estimating the amount of shared genetic material on average without actually knowing what is expressed, new genetic analysis techniques such as GCTA (Genome Wide Complex Trait Analysis) and GWAS (Genome Wide Association Studies) have repeatedly replicated the same historic findings.
GCTA involves comparing phenotypic similarity (in this case IQ scores) against genetic similarities between different, unrelated people. Doing this over even a small sample allows for specific SNPs, or single nucleotide polymorphisms, to stand out as being shared and therefore contributing in some way to the phenotype (Davies, et al., 2011). Because of small sample sizes, GCTAs only try to identify what SNPs and areas of the genome seem to matter, not to what extent they contribute or possess any complex non-additive interactions. This provides a floor for the total amount of heritability that can exist. GWASs build off of GCTAs by performing the same sort of analysis but with a much larger sample size and sequencing more of the genome to analyse more data. This allows for the control of any phenotypic difference and the analysis of the variation between not just SNPs, but also non-additive clusters in order to determine what is significant and to what extent. The results for this have been promising inasmuch as they support the heritability findings made prior to their new and sophisticated forms of analysis. For example, Hill (2017) reported that when GCTA analysis was applied to the common DNA segments, a 23% heritability value was found. However, when the technique was also expanded to rarer genetic variants shared within families, another 31% of heritability was found. Combined, these figures vouchsafe a heritability score of 54%, which uncoincidentally was the heritability score estimated for this sample using the traditional equations. Previously individual SNPs in the genome were found to account for 1% of variance at most. New papers continue to be published in this space frequently (see Plomin & Stumm, 2018).
An understanding of the basis of intelligence is incomplete without the knowledge that the term “non-shared environment” can be very misleading. In truth, it is a rubbish pail for anything that is not easily directly measurable in the “shared environment” category, which includes one’s family, parents, and home. Non-shared environment includes (Alexander, 2016):
- Measurement error – random variation due to people guessing correctly on an IQ test
- Luck of the draw – one person gets a lucky break and is far more successful than others who are equally talented
- Biological random noise including:
- Genetic differences (even between identical twins due to mutations, it is estimated that there are on average 359 different SNPs (Ghose, 2012)).
- The immune system – if one twin gets ill early on in life this could change their development and outcomes, the most extreme example being a parasitic infection.
- Random noise – a unique environment but not due to nurture. For example, people having different freckle positions, birthmarks, left handedness etc.
- Actual nurture
All of this seems to suggest that the impact of actual nurture really does not contribute much to the formation of intelligence. Moreover, because the majority of the easily measured environmental metrics are grouped into shared environment, if non-shared interventions do play a part they are all unique idiosyncratic experiences that cannot simply be recreated at scale.
While findings regarding heritability are very surprising, they make sense. In the same way that mental disorders are on spectrums and that these arise due to biological differences between our brains, then it is not strange to find that there are also innate differences in degrees of intelligence. In other words, complex phenotypes arise from complex genotypes. When one thinks of the intricacies of human biology, and the number of different proteins, enzymes, and metabolic pathways that are required for its proper functioning, there is a huge degree of room for variation in how our brains grow, function and are maintained. It follows that some brains will inevitably function better or worse than others. And as with most phenomena in our high entropy world, there are infinitely more ways in which something can be broken than in which it can be perfect. Moreover, as proposed by Dickens & Flynn (2001), our initial genetic differences can experience positive reinforcement and compound over time to allow for the ultimate fulfillment of individual genetic potential. The example given is of a child, who is biologically inclined to be tall. If he is at first naturally good at basketball, he may then be encouraged to train and get better, while growing ever taller, and thus a cycle of positive reinforcement occurs.
On one hand, this finding on the magnitude of heritability has very significant implications. Given a normative child rearing environment, an individual’s final adult IQ is to a large degree independent of the specifics surrounding how they were raised, for example, whether or not their parents read to them before bedtime. This calls into question the impact of traditional educational interventions upon intelligence. More broadly, it opens up the idea that reverse causality may be at play in many scenarios in which it has been assumed that environment takes the lead in shaping character and societal position. One’s genes may play a more significant role in determining outcome and environment, rather than the other way around. A particular example of this concerns the relationship between child abuse and IQ. It was previously thought child abuse lowered IQ, noting that many abused children were found to be below average in intelligence when their IQs were measured after abuse. However, a new study managed to collect data on children who had an IQ test taken before their abuse and again afterwards. The result found was that abuse does not lead to a lower IQ. Rather those children with a lower IQ were more likely to end up in situations where abuse was more likely (Danese, et al., 2016). Beyond IQ changes, Dinkler et al. (2017) showed that child abuse doesn’t increase risk for ADHD, autism, or learning disorders. Schulz-Heik et al. (2010) found that child abuse did not increase risk of broadly defined “conduct problems”, and Berenz et al. (2013) found child trauma only correlated r=0.10 to personality disorders.
The same sort of reverse causality is likely present for achievement gaps and poverty whereby those less fortunate in their genetic makeup end up in worse environments and pass genes associated with lower intelligence, disease risk, or personality disorder on to their children. To be very clear, this is not necessarily the case and it is most likely that the complex outcomes observed depend upon a complex set of factors and their interaction. Nonetheless, the ability to consider reversed causality for issues in any domain should increase the chance that one is able to find the real causal direction and address it fully.
Of course, there is undoubtedly more to life outcomes than IQ scores. Events like abuse lead to negative side effects outside of IQ scores such as increasing the likelihood of PTSD (Metzger et al., 2008). Moreover, while IQ depends largely upon one’s genetic makeup, the correct environment is needed to actualize one’s potential – intelligence arises by nature through nurture. An example of particular importance is nutrition, which can be crucial for healthy brain development and functioning. For every degree of intelligence, certain genes are necessary, but not sufficient.
The influence of the environment on our genetics stretches beyond its ability to exercise short term influence on our genetic potential. Long term environmental trends can create evolutionary pressures which alter the genomes of entire populations. For example, Ashkenazi Jews are of Eastern European origin and have a mean IQ of roughly 115 points, a whole standard deviation higher than the mean of the general population (Backman, 1972; Lynn R. , 2004). Due to the bell curve distribution of IQ, having a mean that is 15 points higher increases the percentage of the population with an IQ above 145, by a factor of almost 17. This exponential increase in the number of very high IQ individuals is demonstrated empirically by the fact that “During the 20th century, [Ashkenazi Jews] made up about 3% of the US population, but won over 27% of the US Nobel science prizes and 25% of the Turing Awards [in computer science]. They account for more than half of world chess champions.” (Cochran, Hardy, & Harpending, 2006, p. 662).
One smoking gun for the cause of this higher intelligence is that the Ashkenazi population is significantly more likely to have a range of sphingolipid and DNA repair disorders which often lead to an early and painful death, but are associated with increased neuronal growth and higher IQs. For example, the Ashkenazi Jewish population is at least 100 times more likely to suffer from Gaucher’s disease (Orphanet, 2018) (Zimran et al., 1991) and 100 times more likely to suffer from Tay-Sachs (Tay-Sachs Disease, n.d.) with many more examples of higher frequency disorders centered around brain development and function, in the Cochran, 2006 paper. This implies that Ashkenazi Jews may benefit from a “heterozygous advantage”, where having one of the genes that lead to these diseases is good, but having two of them is bad. The most well-known example of heterozygous advantage exists for sickled cells. Heterozygosity has protective effects against malaria, but homozygosity leads to sickle cell anemia (Cochran, Hardy, & Harpending, 2006) not only outline the selection pressures that have to do with the persecution of Ashkenazi jews between 800-1600 AD, which selected for their higher intelligence, but also refute other possible explanations such as a population bottleneck or genetic drift. This population reveals the intricate entwinement of the environment and genetics, demonstrating that unique circumstances and a specific people adapted in such a way as to ultimately have higher genetic potential for intelligence. While these claims may be considered controversial, the facts surrounding the connection between disorders associated with greater neuron growth and intelligence are both undeniable and thought-provoking (Alexander, 2017). Moreover, no viable evidence has arisen to disprove them.
Understandably, facts concerning the heritability of intelligence have generated much unease amongst those who shy away from the determinism inherent to any suggestion that intelligence is largely decided by an individual’s genetic makeup. Nevertheless, it is unproductive not to acknowledge the heritability of IQ and consider the ways in which this fact shapes not only research into the improvement of intelligence, such as through education, but also outcomes in one’s personal life and society more broadly. It is essential to any attempt to address persistent issues concerning cognitive development and success.
A quick survey of popular opinion reveals numerous media headlines claiming measures of IQ to be “fundamentally flawed”, “too simplistic”, full of “disadvantages”, or even “meaningless” (McDermott, 2016). Though these claims are not supported by the scientific community at large, they reveal the prevalence of incomplete and misled opinions toward intelligence. A serious point of contention and misconception about IQ has stemmed from accusations that it is culturally and/or racially biased. The 1996 APA (American Psychological Association) paper, Intelligence: Knowns and Unknowns states:
“Such a bias would exist if African American performance on the criterion variables (school achievement, college GPA, etc.) were systematically higher than the same subjects’ test scores would predict. This is not the case. The actual regression lines (which show the mean criterion performance for individuals who got various scores on the predictor) for Blacks do not lie above those for Whites; there is even a slight tendency in the other direction (Jensen, 1980) (Reynolds & Brown, 1984). Considered as predictors of future performance, the tests do not seem to be biased against African Americans.” (Neisser, 1996, p. 93). This means that IQ tests predict future important outcomes (such as income, longevity and many others that will be outlined in detail in the next section) equally well for African Americans as for White Americans.
It should be noted that this paper was published in response to the controversial book, The Bell Curve, where the American Psychological Association gathered a diverse panel of academics in the field to determine what about intelligence was in fact known and unknown. We are confident that the APA’s 1996 report is the most credible of its kind, particularly in contrast to just how contentious research around racial differences in intelligence is. Any reader keen to explore this topic further should note that many superficial and weakly supported claims exist in contradiction to the APA report. Their official report on this issue, written in the midst of the furor that followed The Bell Curve, should not be dismissed off-hand.
Amongst the plethora of different IQ tests, there are those which have been designed specifically to be culturally and knowledge independent. An example of a test that achieves this purpose without being specifically designed for such a use case is the aforementioned Raven’s Progressive Matrices. Utilizing only puzzles and pattern matching, it has even been administered to entirely illiterate populations. A rebuttal to this is that no test can be free of cultural influences. For example, it has been argued that the Raven’s Matrices is biased in that it utilizes a matrix of rows and columns in displaying information (Benson, 2003). Children with schooling will have had more exposure to this form of organizing information than those without and thus bias the results.
It is true that intelligence tests are fundamentally limited by the fact that they must be somewhat complex in order to measure intelligence. This will always cause some suspicion that some artifact of the test is culture specific, be it shape, arrangement, color, etc. Such suspicions are unavoidable. However, Raven’s Progressive Matrices are some of the simplest nonverbal tests of intelligence that exist. As such, it seems that any cultural differences will have insignificant effects on IQ; the claim that culture has some small influence will remain unfalsifiable. Moreover, it is interesting to consider that as the world becomes more culturally homogenous, any cultural differences that do have an effect will fade, leaving measures of intelligence only more accurate in the future.
Rebuttal to this equality of measurement is that different cultures place value, emphasis and investment upon different types of intelligence, which would lead naturally to differences in performance. Yet it is hard to think of a culturally desirable trait that would not directly benefit from a higher level of general intelligence. As will be described below, there is good reason to believe that many correlates of intelligence, such as longevity and prosociality, are related to intelligence by virtue of biology and not culture. As such, IQ will likely predict outcomes that will be valued across cultures.
How Significant is IQ?
Given public skepticism about the validity of aptitude tests in general and IQ tests in particular, it is striking just how significant IQ is as a metric for a dramatic swathe of socially desirable traits and outcomes. These range from the likelihood of obesity (Kanazawa S., 2013) to longevity (Whalley & Deary, 2001), height (Harris, Brett, Deary, & Starr, 2016 ), the chance one commits a crime (Lynam, Moffitt, & Stouthamer-Loeber, 1993), and even prosocial behavior (Proto, Rustichini, & Sofianos, 2014).
Before going into specific findings around a number of traits, we have provided two descriptive tables here that outline a number of significant IQ correlations:
Anders Sandberg provides a descriptive table expanded from (Gottfredson L. S., 2003), itself adapted from (Gottfredson, 1997) (Branwen, 2017).
“Table 25.1 Relationship between intelligence and measures of success (Results from meta-analyses) r correlation between intelligence and the measure of success, k number of studies included in the meta-analysis, N number of individuals included in the meta-analysis” (Goldstein, 2015) (Branwen, 2017)
|Measure of success||r||k||N||Source|
|Academic performance in primary education||0.58||4||1791||Poropat (2009)|
|Educational attainment||0.56||59||84828||Strenze (2007)|
|Job performance (supervisory rating)||0.53||425||32124||Hunter and Hunter (1984)|
|Occupational attainment||0.43||45||72290||Strenze (2007)|
|Job performance (work sample)||0.38||36||16480||Roth et al. (2005)|
|Skill acquisition in work training||0.38||17||6713||Colquitt et al. (2000)|
|Degree attainment speed in graduate school||0.35||5||1700||Kuncel et al. (2004)|
|Group leadership success (group productivity)||0.33||14||Judge et al. (2004)|
|Promotions at work||0.28||9||21290||Schmitt et al. (1984)|
|Interview success (interviewer rating of applicant)||0.27||40||11317||Berry et al. (2007)|
|Reading performance among problem children||0.26||8||944||Nelson et al. (2003)|
|Becoming a leader in group||0.25||65||Judge et al. (2004)|
|Academic performance in secondary education||0.24||17||12606||Poropat (2009)|
|Academic performance in tertiary education||0.23||26||17588||Poropat (2009)|
|Having anorexia nervosa||0.20||16||484||Lopez et al. (2010)|
|Research productivity in graduate school||0.19||4||314||Kuncel et al. (2004)|
|Participation in group activities||0.18||36||Mann (1959)|
|Group leadership success (group member rating)||0.17||64||Judge et al. (2004)|
|Popularity among group members||0.10||38||Mann (1959)|
|Happiness||0.05||19||2546||DeNeve and Cooper (1998)|
|Procrastination (needless delay of action)||0.03||14||2151||Steel (2007)|
|Changing jobs||0.01||7||6062||Griffeth et al. (2000)|
|Physical attractiveness||-0.04||31||3497||Feingold (1992)|
|Recidivism (repeated criminal behavior)||-0.07||32||21369||Gendreau et al. (1996)|
|Number of children||-0.11||3||Lynn (1996)|
|Traffic accident involvement||-0.12||10||1020||Arthur et al. (1991)|
|Conformity to persuasion||-0.12||7||Rhodes and Wood (1992)|
|Communication anxiety||-0.13||8||2548||Bourhis and Allen (1992)|
|Having schizophrenia||-0.26||18||Woodberry et al. (2008)|
The numerous correlations above paint a picture that having a higher IQ is good not only for society as a whole, but also for the individual.
It is beyond the scope of this paper to explain how correlation values are calculated and the nuances of interpreting them. However, for those unfamiliar with statistics, a brief explanation is that when looking at correlation values we square them to find the percentage of “variance explained”. For example, the correlation between IQ and the SAT is roughly 0.7 (Frey & Detterman, 2004). This means that the variance explained equates to 0.49. In other words, if an individual’s SAT score rises by 10% then we can predict that their IQ has risen by roughly 5%, or nearly half of the 10% gain in SAT score. See Magnusson (n.d.) for a helpful visualization of this.
Another important point is that correlation values only consider how closely data points fit a line going through all of the data. This provides no indication as to the slope of the line. To make this concrete, the correlation between income and IQ is at most 0.4 meaning 0.16 of variance explained (Neisser, 1996). When one hears that a 10% increase in IQ leads to only a 1.6% increase in income, their response may be quite different to their response upon realizing that an individual’s rise from the bottom 10% to the top 10% of income distribution would account for approximately $350,000 extra dollars over one’s working lifetime.
Graph taken from (Gensowski, Heckman, & Savelyev, 2014).
What would happen if the IQ of the general population shifted by only three points? In 1994, Herrnstein and Murray utilized this thought experiment as the basis for a nationwide study on the significance of intelligence. For those who deemed IQ nothing more than a meaningless number, their results were shocking:
“For starters, [in America] the poverty rate falls by 25%. So does the proportion of males ever interviewed in jail. High school dropouts fall by 28%. Children living without their parents fall by 20%. Welfare recipiency, both temporary and chronic, falls by 18%. Children born out of wedlock drop by 15%. The incidence of low-weight births drops by 12%. Children in the bottom decile of home environments drop by 13%. Children who live in poverty for the first three years of their lives drop by 20%.” (Herrnstein & Murray, 1994, p. 365)
Though seemingly incredible, these results have been replicated by countless studies: the practical importance of general intelligence is too often underestimated. The most important findings regarding IQs contribution to society stem from the book IQ and the Wealth of Nations, which estimated the IQs of 81 different countries by amassing testing results and explored the correlation between IQ and GDP per capita (Lynn & Vanhanen, 2002). Since its publication in 2002, the IQ estimates have been revised and expanded twice. A study running an exponential regression model found that the correlation between IQ and GDP per capita to be r = 0.83 or 70% variance explained, which implies that a 10 IQ mean increase in a country predicts the doubling of its GDP per capita (Dickerson, 2006).
Similar analysis done by Rindermann et al. (2009) has drawn from these studies in an attempt to explain global developmental patterns, positing the Smart Fraction Theory, which claims that there exists an IQ threshold below which countries are not able to generate the civic foundations, inclusive of complex social networks and democratic engagement, upon which a thriving economy is built. The real GDP per capita of 185 nations is plotted against their mean IQs in the graph below.
A finding with this degree of societal importance and with a correlation value higher than almost any seen in the social sciences provides ample justification for skepticism. However, questioning the validity of the IQ estimations, three follow-up analyses breaking down and cross-referencing their estimations have found them to be legitimate (Whetzel & McDaniel, 2006) (Jones & Schneider, 2006) (Hunt & Wittmann, 2008) (Sandberg & Savulescu, 2011). A similar relationship can also be found between the economic performance of US states and their estimated IQ score averages (Kanazawa S., 2006).
Even with the existence of accurate data, the most prominent rebuttal to studies concerning IQ and the wealth of nations is that correlation does not equal causation. In other words, it is often asserted that reverse causation may be to blame whereby having a lower GDP per capita led to a lower IQ. Facts relating to the heritability of IQ weaken these claims to a degree, but not entirely. Thus Christainsen (2013) decided to intensify its analysis and employ GDP per capita, under 5 mortality rate, malnutrition, average years of schooling, parasitic load, and ancestral origin to try and predict the IQs of the currently adult population in across countries when they were born and developing. Using non-linear regression this study found that when malnutrition was measured, all of the other environmental conditions no longer statistically significant while all of the dummy variables regarding ancestral origin remained not only significant, but also possessed large coefficients. For example, when looking at the difference in IQ between Switzerland and Nigeria, the model found that just 4 points of the 22 point difference could be accounted for by malnutrition. The regression model was also overall very accurate in its ability to explain 86% of variance (r=0.93) (Christainsen, 2013).
It seems clear from these findings that the genetic and cultural implications of a country’s region of ancestry are the chief influence in regression analyses of IQ and the national wealth. Taking this fact and the scientific consensus surrounding heritability of IQ into account, the pattern of causality claimed by Smart Fraction Theory is evidentially sound. It is most likely that the mechanization relates to the influence of intelligence on the interrelated phenomena of innovation, accomplishment, and national development. The positive relationship between intelligence (g) and educational attainment and occupational success has been demonstrated repeatedly. The correlation values (r) are roughly .56 and .53 respectively, indicating that differences in intelligence account for an average of 30% of the variance in outcome typically observed in these areas (Strenze, 2007; Hunter & Hunter, 1984). Examining representative samples (N = 2254) of CEOs, billionaires, senators, and federal judges, intensive studies of the American elite vouchsafe that one-third to one-half of those in positions of power fall in the top 1% of the nation’s cognitive ability distribution (Wai, 2013). Those in both elected and unelected positions draw from this bracket. Moreover, the results of longitudinal studies of young individuals who demonstrate extreme intellectual talent further support the notion that these types of individuals consistently possess a serious capacity to contribute to society through innovation and excellence in fields ranging from the natural sciences to the creative arts: “constituting the far edge of a population whose continued success will be further emphasized – globally – for the foreseeable future” (Kell, Lubinski & Benbow, 2013).
Beyond tangible metrics of societal well-being, it has been shown that higher IQ leads to more prosocial action and societal cohesion, both of which are crucial to the formation of stable, productive societies (Fehr, Fischbacher, & Gatchter, 2002).
In 2014, Proto, Rustichini, et al. conducted a study in which a group of participants were divided into two groups based upon their score on an administered Raven’s Progressive Matrices assessment and then asked to participate in an iterative series of prisoner’s dilemma games with legitimate monetary payoff. Even after controlling for personality type, risk aversion, and other possible confounders, their results showed significant differences between the performance of the high scoring Raven’s group and the low scoring Raven’s group. The graph below displays the cooperation trends for both groups, charting the high-scoring Raven’s group in blue and the low scoring Raven’s group in red with 95% confidence intervals (Proto, Rustichini, et al., 2014).
It is clear that the average cooperation for the high-scoring Raven’s group was significantly higher than that of the low-scoring Raven’s group. Much more likely to engage in reciprocal cooperation, the higher-scoring Raven’s group consistently utilized more effectively strategies: “Low Raven subjects play Always Defect with probability above 50 per cent, in stark contrast with high Raven subjects who play this strategy with probability statistically equal to 0. Instead, the probability for the high Raven to play more cooperative strategies (Grim and Tit for Tat) is about 67 per cent, while for the low Raven this is lower (around 45 per cent).” (Proto, Rustichini, et al., 2014, p.14) This finding has extended and replicated earlier findings (Al-Ubaydli, Jones, & Weel, 2016; Jones, 2008).
In the same vein, cognitive ability has been shown to anticipate a vast array of antisocial and criminal behaviors across populations. In fact, apart from age and sex, intelligence has proven to be one of the best predictors of this behavior. It has been found and replicated that those who display delinquent behaviors have a mean IQ of 92, independent of race, social class and gender (Lynam, Moffitt, & Stouthamer-Loeber, 1993; Short & Strodtbeck, 1965; Wolfgang, Figlio, & Sellin, 1987; Stattin & Magnusson, 1990). This correlation exists on a more macro level with the mean IQ of different US states (Bartels et al., 2010). Recent studies have also examined the relationship between county-level IQ and county-level crime analysis, as well as state-level IQ and state-level crime analysis (Beaver & Wright, 2011). Significant negative associations were found to exist between these intelligence averages and rates of larceny, aggravated assault, burglary, motor-vehicle theft, violent crime, property crime, and robbery. This propensity for criminal and antisocial behavior correlated with IQ also manifests itself in a heightened susceptibility to drug and alcohol abuse. The hazard ratio for a one standard deviation decrease in intelligence is 1.29, meaning even a moderate dip in cognitive ability results in a 29% increase in risk for substance misuse (Latvala et al., 2016).
As rigorous twin and intergenerational studies suggest, a significant portion of the effect is genetic (Latvala et al., 2016; Latvala et al., 2014). Controlling for poverty, race, or “concentrated disadvantage” does not attenuate the strength of the correlation between criminality and IQ (Koenen et al., 2015). The relationship is even evident during early childhood. Lower cognitive ability predicts the persistency of antisocial behavior. And life-course persistent antisocial individuals present the highest risk of succumbing to a range of undesirable and even criminal behaviors, including “substance abuse, drug-related violent crime, and violent crime against women and children (Moffitt et al., 2002). Though a number of researchers have recently attempted to prove that there is a significant “variation in the IQ-offending association across subscales of intelligence”, these studies often focus not upon intelligence, but upon the “executive functioning”, which they divide into an unorthodox set of abilities including: crystallized intelligence, fluid intelligence, “shifting”, and “inhibition” (Herrero, Escorial, & Colom, 2010; Herrero, Escorial , & Colom, 2010; Isen, 2010) While IQ and executive functioning as a whole do not exhibit extremely significant correlations, their results still clearly indicate an inverse relationship between both subsets of intelligence and criminality: “inmates score lower than controls on crystallized intelligence tests”, there is “worse [inmate] performance on updating processes” that are most closely “related to intelligence”, and there exists a “significant difference between offenders and controls on the administered fluid intelligence measure” (Herrero, Escorial, & Colom, 2010). More efforts should always be made to control for confounders, but it is noteworthy that studies like these produce the results that they do, though they take pains to account for the influence of socioeconomic status and educational attainment.
Replicated by numerous other studies, findings of this nature call into question the genius fallacy’s clichéd image of the highly intelligent individual – unadjusted and awkward, alone with their books in the library. Not only are the intelligent socially competent, they are able to perform considerably better in situations, like the prisoner’s dilemma above, that require an extreme degree of intrapersonal skill. It is worth mentioning that intelligence correlates significantly with other politically desirable effects as well. Examining the connection between cognitive ability, prejudice, and political orientation, a number of interesting studies have found that lower measured levels of general intelligence (g) or verbal intelligence in childhood predict racism in adulthood (Hodson & Busseri, 2012). Authoritarian leanings and aggressive tendencies toward social dominance have likewise been linked with low general intelligence (Heaven, Ciarrochi & Leeson, 2011). In light of the extensive and well-documented relationship between cognitive ability, antisocial disorders, these facts should not be extremely surprising. What is perhaps more surprising, is the significant correlations that have been found to exist between intelligence and height, as well as intelligence and attractiveness, further undermining the genius fallacy (Sundet et al., 2005)
IQ also correlates with creativity, another crucial component of innovative and flourishing societies. Peter Thiel in Zero to One outlines the power of creative innovation as a way to use things around us more efficiently and create multiplier effects (Thiel & Masters, 2014). This drives economic growth and improves quality of life. For example, just consider the way economic growth, driven by globalization and technology, has reduced global absolute poverty from 42% in 1981 to below 10% now (Economist, 2017).
Findings surrounding the correlation between intelligence and creativity are messy due to the difficulty of testing creativity and the range of different measurements for it, not to mention the different ways of measuring IQ. An important distinction should be made between creative potential – what one can achieve in a test environment – and creative achievement – what creative accomplishments one makes in the real world. Starting with accomplishments, it is largely agreed that creative achievements correlates linearly with intelligence (Kaufman & Plucker, 2011). Across three different studies, researchers were able to find that, using Math and Verbal SAT scores of 13 year-old children, who scored in the top 1% on these tests, they were able to predict creative accomplishments 20 years later. Even among these high performers, they found high odds ratios across domains. For example, between the top and bottom quintiles of these top performers in SAT math, those in the top quintile were 4.8 times more likely to have a patent (Park, Lubinski, & Benbow, 2007) (Park, Lubinski, & Benbow, 2008) (Wai, Lubinski, & Benbow, 2005). Park and others have used similar data to demonstrate that early “manifestations of [cognitive] abilities foreshadow the emergence of exceptional achievement and creativity in the world of work”. The graph below shows the bivariate means for age-13 SAT math (x-axis) and SAT verbal (y-axis) scores within “creative accomplishment” categories when participants were only 38 years old.
Creative potential is more highly debated due to the many ways in which it can be measured. An extensive meta-analysis of 21 studies around creative potential found the mean correlation to be r=0.17 and failed to confirm the threshold model whereby “intelligence is a necessary but not a sufficient condition of creativity” (Kaufman & Plucker, 2011, p. 772) (Kim, 2005). However, this study noted the heterogeneous results emergent from different forms of tests. The study is also questionable in that many of the studies used were over 30 years old and employed antiquated IQ tests (Kaufman & Plucker, 2011). A more recent study which cited this meta-analysis did find the threshold model for creative potential while confirming a linear model for achievement (Jauk et al., 2013). This paper discovered that having an IQ above 89 was necessary for the quantity of ideas one can generate, 104 for the quality of one’s best ideas, and 119 for the average quality of one’s ideas. Above these thresholds of IQ, personality factors were found to matter far more with the particular personality traits relevant depending upon the creative domain.
Turning to the effect that IQ has on the individual level, a higher IQ does not just reduce one’s chances of being unemployed, in jail, or homeless. A seminal paper in 2001 found that “a person with IQ 115 (85th percentile) is 20% more likely to survive to age 76 than an average person with IQ 100.” (Whalley & Deary, 2001).
Graph taken from Whalley & Deary, 2001, showing the differences in longevity.
This finding sparked a flurry of replication studies including a 2010 meta-analysis of 16 papers, which found that IQ in fact had an even higher predictive power at 24% and that a significant amount could not be controlled for by educational attainment or socio-economic status (Calvin et al., 2011). A 2015 paper looked at monozygotic (identical) and dizygotic (fraternal) twins intelligence versus life expectancy and found that the overall heritability of life expectancy, independent of IQ in particular, was 0.28, while the estimated correlation between intelligence and lifespan was r=0.32, or ~9% variance explained (similar to the finding from the meta-analysis when controlling for education and SES). Their model also found that 95% of this correlation was due to genetics (Arden et al., 2016).
It has also been found that the health and efficiency of one’s nervous system matters even more than IQ in affecting lifespan. Measuring reaction times to two different tasks, one study found that these tasks were more predictive of life span than IQ but that both IQ and reaction times were significant influencers of life expectancy even after controlling for smoking, education, and adult SES. Using forwards and backwards regression, the model picked only choice and simple reaction time which, when one standard deviation faster than the mean, gave 28% and 18% higher chances of dying at a given age, respectively. Taking into account these two variables made the IQ effect become no longer statistically significant. The reaction time variables were chosen instead of IQ because its effect size was slightly larger and because the tasks correlated with IQ at 0.49 and 0.31. These findings help explain how IQ and the genetics associated with it matter for longevity through having a more efficient nervous system that can better process information and has superior “physiological integrity”. The paper also acknowledged that the correlation between lower IQ and earlier death is especially strong in the lowest IQ quartile (Deary & Der, 2005).
Looking at other significant health outcomes in association with intelligence, an examination of hazard ratios associated with common diseases after controlling for likely confounders, including socioeconomic status, reveals a similar pattern: a one standard deviation advantage in intelligence is associated with a 30% reduction in risk of respiratory diseases, a 27% reduction of risk for coronary heart disease, and a 21% reduction of risk for strokes (Calvin et al., 2017). Moreover, increases in levels of intelligence are associated with decreases in instances of death by injury, as well as decreases in smoking-related cancers, type II diabetes, and digestive diseases (Gottfredson & Deary, 2004; Gottfredson, 2004). Meta-analyses find that low-intelligence correlated negatively with individual health outcomes in every stage of life, anticipating afflictions from obesity in childhood to dementia in old age (Arden, Gottfredson, & Miller, 2009; Batty et al., 2008; Belsky et al., 2013; Der, Batty, & Deary, 2009; Wrulich et al., 2013).
An inverse association has also been reported between mental health and intelligence, with a number of psychiatric disorders correlating inversely and significantly with measured cognitive ability. It has been shown that even after adjusting for a wide range of confounders, lower intelligence is prevalent among those with bipolar disorder, conduct disorder, and specific phobia: Adolescents suffering from past-year disorders possess a mean intelligence that is roughly one standard deviation below the mean intelligence of healthy individuals (Konen, 2009). Moreover, across the spectrum of disorders, higher severity is closely linked with lower levels of fluid intelligence (Keyes, 2017). The utilization of GWAS has even revealed associations between cognitive impairments and the polygenic architecture of certain diseases, like Schizophrenia (McIntosh et al., 2013). It is worth noting that there are some exceptions to the generally positive correlation between health and intelligence. Karpinski et al. (2018) recently conducted a study of members of the American Mensa Association, providing a survey through which these high-IQ individuals (98+ percentile) were asked to self-report the “prevalence of both diagnosed and/or suspected mood and anxiety disorders, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and physiological diseases that include environmental and food allergies, asthma, and autoimmune disease” (Karpinski et al., 2018) When compared to the national average, the results of this survey revealed relatively high odds-ratios that were statistically significant. These findings form the basis of a preliminary “hyper-mind, hyper-body” hypothesis, that suggests extremely intelligent individuals possess mental “overexcitability” that possesses the potential to manifest negatively as anxiety and related disorders. Nevertheless, as Karpinski notes, “the hyper brain/hyper body theory is new and as such a number of studies will need to be carried out to better understand its strengths and limitations” (Karpinski et al., 2018)
Summary and Assessment of the Significance of IQ:
In light of these findings, it should be clear that the typical concept of a “genius”, someone who we picture as being a very smart but lonely super nerd confined to their parents basement, is simply misguided (Miles & Terman, 1926). Yet it should not be assumed that IQ, for all its significance, is a flawless metric. Before looking at ways in which IQ can be enhanced, it is important to highlight some of its significant flaws. Normally IQ is used to predict things, like career attainment, however, a study instead did the reverse by taking career attainment of Chinese Americans and using it to reverse predict IQ. Based on their careers they should have had mean IQs of 120. In fact their IQ mean was only 99 leaving a 21 point accomplishment gap (Flynn, 1991, Tables 4.4 and 4.5).
Further analysis by Flynn of this gap found that Chinese Americans could gain the same academic credentials needed for elite jobs with seven fewer IQ points. The remaining fourteen points of the gap were from Chinese Americans capitalizing better on the opportunities that they had at 78% versus 60% for white Americans. As Flynn poetically states, “take an Irish American and a Chinese American, both of whom have qualified for Stanford Law School, and both of whom have a fiancee who wants them to stay at home. The Irish American is more likely to stay at home, whereas the Chinese American is more likely to get a new fiancee.” (Flynn, 1999, p. 11) This all suggests that cultural factors and personality type have found a way to augment the achievement levels predicted by the average IQ of Chinese-Americans (Neisser, 1996).
There are also a number of other cases where those in very different cultures or environments display intelligent characteristics but still perform poorly on measurements of IQ. There are examples in which adaptability of the human mind allows for those who perform low on intelligence tests to still display impressive cognitive abilities. For example, a popular study found that Brazilian children who ran businesses in the favelas were able to perform basic business accounting but failed when tested on similar material in math class (Ceci & Roazzi, 1994). Another study asked adults in the Kenyan Kpelle tribe to sort objects. In Western countries it is found that the more intelligent sorted taxonomically and less intelligent functionally. “The researchers were unable to get the Kpelle to sort taxonomically (i.e., supposedly more maturely), until they asked the Kpelle to sort in the way a foolish person would. The Kpelle then had no trouble sorting taxonomically.” (Cole et al., 1971; Glick, 1968) (Robert J. Sternberg, 2001) These examples are all good reminders of the shortcomings of our interpretations of intelligence tests and their implications. However, it is unclear to what extent these displays of talent are meeting ecological needs and are dichotomous to the general factor of intelligence which can be applied across every cognitive task and environment. Diversity of thought is certainly valuable and it is implausible that “Western” thought is optimal. However, the aforementioned findings around GDP per capita depending upon IQ to such a large degree begs the question of whether or not there are objectively better ways of thinking to achieve traditional metrics of success, such as freedom from absolute poverty and disease.
Looking beyond flaws in the measurement of IQ and its significance, the next most significant and independent construct that contributes to outcomes is the “Big Five” model of personality traits. The five components (with subcomponents inside each) are: Extraversion, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience. Across the Big Five, heritability has been found to be 0.50 +- 0.1 (Bouchard & Loehlin, 2001). Yet, when measurement error is reduced by taking personality scores from multiple different sources, it has been found that as much as 80% or more of one’s personality type is heritable (Riemann & Kandler, 2010). The extent to which they change over time decreases as one ages and their personality becomes fixed. However, while there is change over time there is little evidence around interventions that can reliably alter one’s personality in a targeted way. For example, Eisenberger has done research on increasing conscientiousness, but it seems to be somewhat of a chicken and egg problem. This is in the sense that the way to increase conscientiousness is through positive reinforcement by giving rewards, and yet, the only way to give these rewards and encourage future conscientiousness is if the participant is conscientious in the first place (Eisenberger & Aselage, 2008).
Fathering the oldest longitudinal study in America, Lewis Terman followed a sample of hundreds of empirically “gifted” children with IQs above 130 beginning in the twentieth century. As another example of how personality types can influence outcomes, a study looking at Terman’s data of the highly gifted untangled the influences of IQ, education, and Big Five Personality type on income (Gensowski, Heckman, & Savelyev, 2014). Below is a series of graphs from the paper showing non-discounted cumulative income for individual’s lifetime from the age of 18 – 75, depending on their relative personality where all other variables for each graph other than the one being compared are held constant. It is measured in thousands of 2008 US dollars.
Further exploration of personality type is beyond this paper, however, it is clear that the “Big Five” are an important component of one’s life. Understanding not only the ways in which these traits may be measured, but also the ways in which we might improve them would be extremely worthwhile. For more on the Big Five, a good starting point is (Caspi, Roberts, & Shiner, 2005).
The past few decades have seen cognitive enhancement transformed into a subject of serious debate. As rapid progress in medicine and technology extends the ways in which individuals may improve themselves, opinions on the ethical implications of these improvements have grown stronger, and more polarized. In the words of Nick Bostrom, though the debate is relatively young, a “biopolitical fault line” may easily be discerned (Bostrom & Savulescu, 2009). On one side stands those who are adamantly pro-enhancement – the transhumanists, who hold that enhancement technologies should be developed and made readily accessible. Those aptly deemed “bioconservatives” stand on the other side of the debate, opposing enhancement with the conviction that drastic changes to our natural biological state are undesirable as well as unethical. As Japanese bioconservative Ryuichi Ida claims, their position is often simply one of “respect for Nature and the natural state.” (Bostrom & Savelescu, 2009) Arguments on both sides are nuanced and tend to vary in intensity based upon the type of enhancement strategy under consideration; a fact which is supported by the simple observation that talk of gene editing or embryonic selection typically elicits much more criticism and ire than talk of education reform or nutritional supplementation. It is important to note, however, that all four of these practices may accurately be defined and compared as “cognitive enhancements”, as they each operate with the aim of making individuals smarter.
This disagreement over the definition of enhancement itself lies at the core of any discussion of the debate that exists in the field. Bioconservatives’ arguments revolve around the idea of that “enhancements” somehow jeopardize innate human integrity, stripping individuals of their unique identities. While questions of integrity and identity are pertinent and important, defining enhancements as unnatural in this way is extremely misleading. Every individual’s life is permeated with attempts to “enhance” themselves. Is it wrong to wear glasses? Have lasik surgery? Roughly 60% of the world possesses some sort of vision impairment; and most would agree that denying them access to adequate vision correction technology would be criminal (WHO Global Data on Vision Impairment, 2010). Yet it is technically an enhancement – something that improves an individual’s natural state. Considering enhancement in this light, it is not unnatural at all. It’s the linchpin of evolution and the end goal of activities as innocuous as drinking a cup of coffee, wearing a winter coat, or exercising.
Moreover, the argument that technologies like genetic engineering are separated from these more acceptable enhancement strategies by their potential to inspire misuse or engender negative ramifications should note that even the enhancement technologies deemed acceptable are not without potential trade-offs. Most modern citizens use mental artifacts like GPS and calculators that enhance their natural abilities. Would they be able to perform three-digit multiplication in their heads and read traditional road maps if they were not allowed to use these forms of enhancement? Perhaps. Perhaps not. But either way, most hail both of these technological advancements as just that – highly desirable advancements. If divested of all such enhancements, would humanity remain recognizably human? The desire and ability to improve is embedded within the human identity.
Remaining convinced that this desire to improve is not ubiquitously or stably desirable, some have attempted to draw a line between treatment and enhancement. When considering broad cases of competitive necessity, this division certainly should be invoked. The patient with a tumor or a neurodegenerative disease deserves care before the person who simply wants to improve their powers of recollection. Nevertheless, the division blurs when examined in cases of a less extreme nature. An increased knowledge of genetics allows for the “medicalization” of attributes, like height and intelligence, that were formerly only deemed products of normal variance (Daniels, 2010). When genes may engender the same unideal consequence in a variety of ways, complexity arises in deciding which deserves to be fixed via methodologies deemed “treatment” and which deserve to be ignored as disparities that only claim need of “enhancement”. Advancements in cognitive enhancement continue to testify to the controversy inherent to this distinction between treatment and enhancement. Scientists have been successful in genetically altering memory capacity since the mid-1990’s when Tully, Dubnau, et al. (2010) “managed to manipulate a memory-linked gene in fruit flies, creating photographic memories.” Headlines accompanying the public release of their findings typically extolled the “broad implications seen for treating Alzheimer’s and other human diseases.” Do the 81 million Americans of the Baby Boomer generation, who are currently suffering from the memory loss that “naturally” accompanies age not qualify for intervention as well? (Bostrom & Savelescu, 2009) Should doctors, whose vocational efficacy and impact would skyrocket with memory-related cognitive enhancement, be isolated from such benefits as they do not technically need treatment? 10% of patient deaths and 17% of hospital accidents in the United States stem from misdiagnosis (Newman-Toker & Provanost, 2009). Though these incidents would not be eradicated perforce by “enhancements”, it is not unreasonable to think that many could be averted through perfect recall of diagnostic information and previous case details, let alone other advancements in information synthesis and problem solving that could accompany successful strategies to make humans smarter. A number of other issues related to efficacy and aging amplify the complications that arise in dividing treatment and enhancement. In many respects, cognitive enhancement may even itself be considered a form of treatment, as it is more than likely that increases in IQ and mental functioning will contribute to progressive innovations in not only clinical practice, but also medical research. Additionally, these increases in IQ and mental functioning can be preventative of illness themselves, and likewise protect against health degeneration in the long term.
Questions over the definition of enhancement aside, other criticisms of cognitive enhancement generally fall into one of two categories: human rights and inequality. Certain cognitive enhancements are often contested because of the potential threat to the “open future” of the individual (Sandel, 2004). Genetic engineering, implemented prenatally, must be undertaken without the consent of the child. Some view this as an inherent violation of that child’s rights, seeing as he or she will be unwittingly and thus unwillingly predisposed to certain traits and behaviors. Yet this argument begs an important question. Whose future is truly open? It could very well be that the child who is born with an IQ of 80 is actually less free than the child who is born with an IQ of 130. In addition to this, unease regarding potential human rights violations is really only relevant to a small and extreme fraction of potential strategies for cognitive enhancement.
The same may be said of unease related to cognitive enhancement’s relationship with inequality. Some technologies do possess the potential to entrench modern divisions, inasmuch as they may possess a number of barriers to access: requiring social or monetary capital, as well as a baseline level of comfort with new technologies that may not be evenly distributed (Bostrom & Savelescu, 2009). Assessments of these concerns are often hyperpolarized. Disparities in intelligence clearly exist already. And the majority considers an IQ of 75, a just under two standard deviations from the average, to be the threshold for functionality in the modern world. It could be suggested that the individuals clustered around this threshold may stand to benefit the most. A good number of cognitive enhancement technologies seem to present an “inverted U effect”, whereby “the degree of intelligence enhancement increases as intelligence levels deviate further below the mean” (Dunlop, 2014). Current examinations of the relationship between rates of cognitive decline due to nutritional deficits and measured economic output in the developing world provide further insight into these issues. All in all, there is no doubt that intensive technologies like gene editing or embryonic selection should be approached with a high degree of caution. Yet this does not mean that these technologies should not be approached at all. To admit that inequality is of concern is to admit that some could stand to benefit greatly from cognitive enhancement.
A myriad of strategies and mechanisms have been proposed to enhance intelligence. Some like educational interventions and nutritional supplementation are familiar and widely pursued, others like brain machine interfaces and genetic engineering utilize technological advancements and ideas that are apt to resemble nothing as much as a science fiction novel. While each of these enhancements are united in their commitment to making humans smarter, they are also united in their exploitation of evolutionary gaps to accomplish that goal. This fact is elucidated by the Algernon Argument, an influential theory fittingly named after Daniel Keyes’s 1959 sci-fi novella Flowers for Algernon. Chronicling the life of a man who undergoes a risky enhancement, Keyes’ tacitly asserts that interventions in individual intelligence may possess side effects that outweigh their benefits. The Algernon Argument encapsulates the moral of this story: If the proposed intervention would result in enhancement, why have we not already evolved to be that way? (Bostrom & Sandberg, 2008) Over countless years of evolution, certainly any simple shifts that would result in dramatic intelligence gains and no negative side effects would have been selected for.
The natural conclusion of this argument is that intelligence arises from a system of complicated biological and environmental interactions, in which “any simple major enhancement to human intelligence is a net evolutionary disadvantage” (Yudkowsky, 1999). Tradeoffs form the root of the reasoning behind the Algernon Argument. The first of these tradeoffs is in the form of resource constraints. The human brain takes up roughly 5% of our body mass, yet 25% of its energy consumption (Swaminathan, 2008). For a newborn infant, 87% of energy consumption is used by the brain (Zimmer, 2011). To put this into perspective, the adult brain “in terms of energy consumption…is equal to the rate of energy consumed by leg muscles of a marathon runner when running” (Attwell & Laughlin, 2001). Not only does our current energy consumption strain caloric intake, but any energy and development that goes to the brain is energy not going to the other parts of the body or immune system.
Secondly, there are neural space and organizational constraints. A study modelled the information processing abilities of the brain and found that it is 20-30% below the level that it could optimally operate at if the brain was double its existing size. Yet, “any further enhancement of human brain power would require a simultaneous improvement of neural organization, signal processing, and thermodynamics. Such a scenario, however, is an unrealistic biological option and must be discarded because of the trade-off that exists between these factors.” (Hofman, 2014) (Hofman, 2015). Here, tradeoffs strike again.
Finally, there are constraints with mutational load. To quote Steven Pinker:
“New mutations creep into the genome faster than natural selection can weed them out…Intelligence depends on a large network of brain areas, and it thrives in a body that is properly nourished and free of diseases and defects…Mutations in general are far more likely to be harmful than helpful, and the large, helpful ones were low-hanging fruit that were picked long ago in our evolutionary history and entrenched in the species…But as the barrel gets closer to the target, smaller and smaller tweaks are needed to bring any further improvement…Though we know that genes for intelligence must exist, each is likely to be small in effect, found in only a few people, or both” (Pinker, 2009, p. 46)
Moreover, even for those mutations that do increase intelligence, we have already seen with the Ashkenazi Jews that these enhancements can come with steep costs in the form of cognitive diseases.
This biological zero-sum game for intelligence enhancement is interesting in light of the fact that it is not typically found in other complex systems. Even a simple dose of liquid nitrogen can double the processing speed of any computer. To quote the internet blogger Gwern “We remain the same. It is as if scientists and doctors, after studying cars for centuries, shamefacedly had to admit that their thousands of experimental cars all still had their speed throttles stuck on 25-30kph – but the good news was that this new oil additive might make a few of the cars run 0.1kph faster! This is not the usual state of affairs for even extremely complex systems. This raises the question of why all these cars are so uniformly stuck at a certain top speed and how they got to be so optimized; why are we like these fantastical cars, and not computer processors?” (Branwen, 2010) The great minds of history really do not seem to be inferior to the great minds of the last century. Without a doubt we have seen a rise in the collective intelligence of society, but it is not readily apparent that we have seen the same rise for its individuals.
Therefore, noting the Algernon Argument’s compelling evidence, any successful enhancement to be must consider loopholes it can exploit. These loopholes fit into three broad categories (Bostrom & Sandberg, 2008):
· Changed tradeoffs – The environment in which modern humans exist is vastly different from that in which their distant ancestors dwelt. Thus, the types of demands imposed upon our biological system may differ as well. As an example, caloric intake is no longer an area of significant concern for a large portion of the developed world and so the energy consumptions of enlarged brains is not costly.
· Value discordance – Evolution’s interests are not always aligned with our own, as evidenced by rates of population growth and obesity, among other things. It may be that intelligence modifications thwart one of evolution’s less beneficial effects, while achieving human goals. Birth control is a prime example of enhancements that would fall into this category.
Evolutionary restrictions – Modifications within a population occur slowly, as one mutations achieve fixation in succession. It is plausible that human enhancements could involve a mechanism whose complexity is beyond the reach of evolution’s steady rate of change. Prosthetic limbs fill a void that evolution only could by allowing for the regeneration of bodily extremities.
While these loopholes present opportunities and lenses through which interventions should be considered, any intervention operating within our biological framework can not be expected to have dramatic gains. On a societal level, as mentioned before, small mean improvements can have a dramatic influence, but we should be very skeptical of any proclaimed panaceas. It may be that the only way to achieve far larger gains is if we “play God” by escaping our evolutionary biological framework. However, these gains will likewise be hard fought, not for evolutionary, but technological and ethical reasons. The three main categories of intelligence enhancement that will be considered are: Education, Nutrition, and Genetics. Each of these vectors for enhancement, in order to be effective, must work within the context of the Algernon Argument.
One example of an evolutionary loophole for intelligence enhancement that has already been exploited is the “Flynn effect”. The Flynn effect was named after James Flynn, who first observed that in 14 different developed countries and across a range of different IQ tests, there was a mean increase of roughly 3 IQ points throughout the population per decade beginning in the 1940s (Flynn, 1987) (Neisser, 1996). For decades this discovery was hidden by the fact that every time IQ tests are revised they are renormalized to have a mean of 100.
The existence of the Flynn effect has been refined and confirmed over time (Trahan et al., 2014). There have been much smaller gains in crystallized intelligence than in fluid intelligence, with the largest gains seen in Raven’s Progressive Matrices specifically. Almost all of the original developed countries that underwent the Flynn effect have now stopped seeing gains. These countries include many in Western Europe, North America, and parts of Asia (Dutton & Lynn, 2015) (Sundet, Barlaug, & Torjussen, 2004)(Teasdale & Owen, 2008)(Pietschnig & Gittler, 2015). Over the course of the Flynn effect’s existence, these countries have seen a total increase of roughly 15 IQ points or a whole standard deviation. Many developing countries are now beginning to experience Flynn effect gains including Kenya (Daley et al., 2003), Brazil (Colom, Flores-Mendoza, & Abad, 2007), Turkey (Kagitcibasi & Biricik, 2011; Rindermann, Schott, & Baumeister, 2013), and others (Armstrong & Woodley, 2014). There has been conflicting evidence as to whether or not the Flynn effect has influenced people of all ability ranges in the population or just a subset. However, the meta-analysis across countries experiencing the Flynn effect revealed that all groups have seemed to benefit the same amount, therefore this conflict has been resolved on the empirical level (Trahan et al., 2014, pp. 9-10).
Theories for why the Flynn effect has occurred include but are not limited to: improvements in child rearing (Elley, 1969) and education (Tuddenham, 1948); increased environmental complexity (Schooler, 1998), test sophistication (Tuddenham, 1948), and test-taking confidence (Brand, 1987); the effects of genetics (Jensen, 1998) and the individual and social multiplier phenomena (Dickens & Flynn, 2001a; Dickens & Flynn, 2001b); and nutritional improvements (Lynn, 1990) (Lynn, 2009). A summary of the literature on the Flynn effect seems to indicate that all of these proposed influences make some form of contribution to the overall increase in IQ witnessed. Yet there remains a large degree of confusion around untangling the sizes of each effect and what forms of intelligence they have influenced.
Current theorists in the field seem to have reached consensus that the Flynn effect is a cultural rather than biological phenomenon, influencing how the brain thinks rather than how efficiently it actually works. To use a computer analogy, the Flynn effect has altered the software of the brain so that it can solve the problems of today (and in IQ tests) more effectively, but it has not changed brain’s underlying hardware. It has affected the strategies that our brains use to think rather than the rate at which our neurons fire or branch off.
For readers who recall the distinction between IQ and g (general intelligence), this means that our IQ scores have gone up as we have developed the skills to solve abstract problems better, but our general ability to solve problems across any cognitive domain and the rate at which we learn new information and adapt to it has not changed (Flynn, 2011) (Armstrong & Woodley, 2014) (Jensen, 1998).
We know that the Flynn effect has raised IQ, but not g, because we have seen far larger gains in fluid intelligence than in crystallized intelligence. Moreover, of those gains in fluid intelligence, test items on Raven’s Matrices that have the lowest g loadings have demonstrated the largest increases. To quote Armstrong & Woodley (2014), “the preponderance of studies indicate that the [Flynn] effect is either uncorrelated or mildly negatively correlated with subtest g loadings (Jensen, 1998a; Must, Must, & Raudik, 2003a, 2003b; Rushton, 1999; te Nijenhuis, 2013, te Nijenhuis & van der Flier, 2007; Woodley & Meisenberg, 2013b). A recent meta-analytic study of over 17,000 individuals revealed that the Flynn effect is in fact a statistically significant anti-Jensen [non-g] effect (rho = −.38; te Nijenhuis & van der Flier, in press)”. The astute reader will have noticed that the above claim of Raven’s Matrices performance have seen increases and yet these have not been increases in g is contradictory. After all, are Raven’s Matrices not a good way to measure g? While Raven’s Matrices is one of the best measures of g that we have, among the individual matrices in the test (such as the example shown earlier in this paper) these vary in the amount they correlate with g. Those matrices with the lowest g loadings have seen the largest gains suggesting that the gains made are superficial.
Moreover, there have been almost non-existent gains in educational achievement over this time (NAEP, 2017) and as Flynn himself puts it “the data imply that dozens of nations should now be in the midst of a cultural renaissance too great to be overlooked.” (Flynn, 1987, p. 187). This gain in Gf but not Gc makes sense because increases in g (improvements in one’s biological functioning) stem from genetic differences which set the blueprint for how the body and brain develop, or nutritional interventions which allow for the body’s blueprint to be successfully followed. The gains witnessed from the Flynn effect are too fast and large for any form of genetic selection to be the cause (Flynn, 2011). Any increase in g due to the Flynn effect may most likely be explained by the nutritional improvements that have occurred, but these improvements still fail to explain the majority of the Flynn effect’s rise in IQ.
In light of all this, the two largest drivers behind the Flynn effect that are not encompassed by small nutritional gains appear to be: 1. Cultural cultivation of abstract thinking 2. Better test taking performance and strategies.
Flynn creates a compelling argument for how the world in the last century has changed dramatically with regards to the visual stimuli and demands for abstract and symbolic, rather than concrete, reasoning (2011). Examples given include modern video games such as Tetris and Grand Theft Auto (Johnson, 2005) and TV shows such as 24, which revolves around 21 different characters, each with their own background and story. Consider how before the age of the computer schools trained students to be “human computers” that could perform rote memorization tasks (Mitra, 2013).
It is also possible that gains in fluid intelligence are merely the product of better test taking strategies, which are specific to the test and do not show real world applicability. Hayes, Petrov, & Sederber, (2015), using sophisticated new eye tracking software, found that 1/3 of the variance in performance increases on Raven’s Progressive Matrices can be accounted for by learnt strategies for solving the problems. After this variance was accounted for, the residual gains were no longer statistically significant. Armstrong & Woodley (2014) provide a new analysis for how the hardest Raven’s problems are those that best represent g and it is these harder problems that are most likely to be guessed. Given a rise in the amount of guessing that has occurred over time due to test taking strategies taught in modern schools, the authors were also able to account for 1/3 of performance increases. As a caveat, analysis of eye movements and guessing patterns are both new domains that must be better developed, however, the analysis provided by the papers is compelling in helping explain how we have seen such large IQ increases only in fluid intelligence and why increases of this magnitude have not transferred towards increases in educational performance (NAEP, 2017) (Flynn, 2011) and society as a whole (Neisser, 1996).
Overall, it seems that roughly 3 – 5 points of crystallized gains can be accounted for by nutrition, while the 15 point fluid gains are majorly due to better abstract thinking and minorly due to better visuospatial and test taking strategies that have inflated IQ scores. Regardless of the cause, the Flynn effect fits the Algernon Argument requirements. It is a significant, but still small increase in intelligence, and low hanging fruit that has either already been used up or is in the process of being digested by populations across the world.
Earlier it was mentioned that the Flynn effect could not be caused by genetic changes due to the rate at which it has occurred. This is true, but does not hold true of intelligence decline as opposed to enhancement. [e4] It has been found that dysgenics has been occurring since the 19th century. Dysgenics is the inverse of eugenics and entails a gradual decline in intelligence. Dysgenics has arisen for two reasons. [e5] Firstly, advancements in medicine and living standards have removed evolutionary selection pressures allowing more people who, in a hunter-gatherer society would have died, to instead survive and reproduce. Secondly, there has been an inverse relationship between intelligence and reproductive rates. These factors combined imply that genes associated with lower intelligence are spreading at a faster rate through populations, which will lower the overall fitness of the gene pool and thus lower mean genetically based intelligence. Therefore, we have seen an increase in IQ scores over the last half century because the cultural factors raising intelligence via the Flynn effect have been larger than the genetic factors lowering intelligence that have been masked. However, in countries where the Flynn effect has now stopped, there is a worrying decline in intelligence, as dysgenics has now become apparent. Lynn and Harvey find that there is a correlation of r= -0.73 between intelligence and fertility across nations, estimate that there has been a 0.86 IQ point decline from the years 1950-2000, and forecast a larger decline in the future (Lynn & Harvey, 2008). In another recent study it was found that “the average decline in g across cohorts may be equivalent to around − 1.16 points per decade, or − 13.35 points between 1889 and 2004,” when looking at reaction times slowing (Woodley & Meisenberg, 2013) (Silverman, Simple reaction time: it is not what it used to be., 2010) (Woodley, Nijenhuis, Must, & Must, 2014).
However, there have been a number of critiques to these finding primarily questioning the strength of the correlation between reaction time measures and g and thus at what rate g is actually declining (Flynn J. , 2013) (Dodonova & Dodonov, 2013) (Nettelbeck, 2014) (Silverman, 2013). Yet it seems to be more empirically clear that there is in fact a decline occurring in a number of countries where the Flynn effect has now stalled: Reversal of the flynn effect and signs of dysgenics in Germany (Pietschnig & Gittler, 2015); In Norway (Sundet, Barlaug, & Torjussen, 2004); Denmark (Teasdale & Owen, 2008);
France (Dutton & Lynn, 2015); and the Netherlands (Woodley & Meisenberg, 2013).
Moving away from the macro-trends of intelligence, we will now consider a number of different categories of enhancements that one may consider as tools to enhance intelligence. The three general buckets and the mechanisms that will be explored in each are:
- Education: Fadeout Effects, Early Childhood, Barriers to Success
- Nutrition: Iodine, DHA, Folic Acid, Vitamin D
- Genetics: Embryonic Selection, Gene Editing, Gene Synthesis
Given that one’s adult IQ is highly heritable and relies very little on shared environment, it should not come as a surprise that better education in the form of better schooling does not provide reliable IQ gains. Duncan and Magnuson produced the below graph showing that:
“the distribution of 84 program-average treatment effect sizes for cognitive and achievement outcomes, measured at the end of each program’s treatment period, by the calendar year in which the program began.” (2013, p. 4) It is important to note that “Taken as a whole, the simple average effects size for early childhood education on cognitive and achievement scores was .35 standard deviations at the end of the treatment periods, an amount equal to nearly half of race differences in the kindergarten achievement gap (Duncan and Magnuson 2011). However, as can be seen…average effect sizes vary substantially and studies with the largest effect sizes tended to have the fewest subjects. When weighted by the inverse of the squared standard errors of the estimates, the average drops to .21 standard deviations.”
Reflecting their approximate contributions to weighted results, “bubble” sizes are proportional to the inverse of the squared standard error of the estimated program impact. The figure differentiates between evaluations of Head Start and other early childhood education programs and also includes a weighted regression line of effect size by calendar year.
This graphic shows us that the largest IQ gains at the end of treatment were for those studies which had the smallest sample sizes. While this graph measures “cognitive and achievement” outcomes rather than IQ specifically (which should be harder to raise than school knowledge), the 0.21 effect size would translate into a 3 IQ point raise. Yet the graphic does not show us is the fade-out effect that these gains undergo.
A different study performed original calculations “using information from a meta-analytic database of the evaluations of 67 high-quality early childhood education (ECE) interventions published between 1960 and 2007” (Bailey, Duncan, Odgers, & Yu, 2016). It found that within two to four years after the intervention, the effect size drops 0.05 or roughly 0.75 IQ points and ceases to remain statistically significant.
“The meta-analytic database is the product of the National Forum on Early Childhood Policy and Programs (Harvard, 2017) based on a comprehensive search of the literature from 1960 to 2007, when the coding project began. Studies had to have a treatment and control/comparison group, rather than simply assessing the growth of one group of children over time. Early childhood education programs were defined as structured, center-based early childhood education classes, day care with some educational component, or center-based child care.” (Bailey, Duncan, Odgers, & Yu, 2016, p. 15)
From these analyses it should be clear that fadeout effects exist across almost all interventions. Yet in order to further solidify their findings, the authors of the aforementioned paper performed even on rigorous analysis on two very well known and intensive early intervention study programs: the Perry Preschool Project and the Abecedarian Program. Their effect sizes on IQ specifically are shown in the figure below.
The Perry Project, while starting off with a very high effect size, shows a rapid geometric decline like the meta-analysis. Meanwhile, the Abecedarian Program, while also showing fadeout effects, seems to successfully maintain a ~3.5 point IQ gain. The Abecedarian program was unique in its duration, scope, and quality. As summarized by (Duncan & Magnuson, 2013, p. 6):
“The Abecedarian program, which served 57 low-income, mostly African American families from Chapel Hill, North Carolina, provided even more-intensive services than Perry Preschool. Beginning in 1972, children assigned to the Abecedarian “treatment” received year-round, full-time center-based care for five years, starting in the child’s first year of life. The Abecedarian preschool program included transportation, individualized educational activities that changed as the children grew older, and low child–teacher ratios of 3:1 for the youngest children and up to 6:1 for older children. Abecedarian teachers followed a curriculum that focused on language development and explained to teachers the importance of each task as well as how to teach it. High-quality health care, additional social services, and nutritional supplements were also provided to participating families (Ramey and Campbell 1979; Campbell, Ramey, Pungello, Sparkling, and Miller-Johnson 2002).”
While clearly very well resourced, the Abecedarian Program has not proven to be scalable. There remains much doubt as to whether or not the effect size was small enough that the resultant gains from the program were an anomaly or that they were achieved due to a biased group of participants (Bacharach & Baumeister, 2000) Spitz (1986, 1992, 1993, 1999). In fact, the control group randomization was actively compromised when seven families assigned to the experimental group and one family assigned to the control group, upon learning about their randomized assignment, dropped out of the study (Campbell & Ramey, 1994). Without randomization, the children over time could have seen IQ increases in line with their underlying genetic potential, which gradually overtakes shared environmental influences during childhood. Even if the gains we see are real, and if we ignore the cost and scalability issues, it is still not clear to what extent the educational components of the program had the largest contribution rather than the nutritional support. For example, Herman Spitz asked (1999, p. 283) “What happened during those first 1.6 months at the day care centre to produce an effect worth 6 points, whereas an additional 4 1/2 years of massive intervention ended with virtually no effect? It seems to me that it is not unreasonable to infer that nothing happened, but rather, some initial difference in the control and intervention groups had (by chance) escaped randomisation, and revealed itself at six months of age.” From the above figure we can only see effect sizes starting at 3 years, but even here we see a decline rather than gains, suggesting that not all components of the intervention were effective.
If these fadeout effects across programs are not disheartening enough, bear in mind that Head Start, providing preschool support for low income families, has cost of $9.25 billion in 2017 alone to serve ~920,000 children at roughly $10,000 per child per year (Head Start, 2017) (Start, 2016). Eyeballing the graph below showing federal spending on Head Start since its inception, roughly $160 billion has been spent in total (Head Start, 2016).
This is a large sum given very conflicted evidence about the effectiveness of the program in either the short or long term. Aside from the meta-analysis fadeout effects from the earlier figures, Puma et al. (2012), found that for a “random assignment of 4,442 children to a national sample of Head Start centers…while noteworthy impacts were observed at the end of the Head Start year, virtually no statistically significant impacts on any cognitive or non-cognitive measure persisted over the next several years.” (Bailey, Duncan, Odgers, & Yu, 2016). See (Bernardy, 2004) for a similar finding.
Bailey et al., after acknowledging these fadeout effects, try to explain their persistence through three different possibilities and, in a way similar to the Algernon Argument, present loopholes that interventions could pursue to thwart fadeout effects:
- Trifecta skills – these are skills “that are malleable, fundamental and would not have developed in the absence of the intervention”. It is noted that this requirement is “particularly problematic for interventions that build early literacy, math or executive function (EF) skills because most children are likely to eventually acquire these skills.” For example, if an intervention teaches children basic arithmetic sooner than their peers have learnt it, they will perform better on the mental arithmetic parts of the IQ test showing an effect size, until this arithmetic is inevitably taught to all of their peers at a later date at which point the gains disappear.
- Sustaining environments – this requirement “recognizes the importance of interventions that build important skills and capacities, but views the quality of environments subsequent to the completion of the intervention as crucial for maintaining initial skill advantages.” In other words, some interventions are only successful in as much as creating a better environmental framework for one to succeed in, rather than providing any ingrained skills. Once this environment disappears, so do the gains.
- Foot-in-the-door – Any intervention that is not teaching a trifecta skill, or creating a sustaining environment, should aim to temporarily “equip a child with the right skills or capacities at the right time to avoid imminent risks (e.g., grade failure, teen drinking or teen childbearing) or to seize emerging opportunities (e.g., entry into honors classes, SAT prep). The skill or capacity boosts need not be permanent, as with SAT prep that boosts chances of acceptance into a higher-resourced college.” This intervention strategy acknowledges that there are key moments in one’s life where nudging their trajectory one way or another will determine a large part of their future.
While fadeout effects exist for a large majority of interventions trying to raise a child’s cognitive abilities, looking beyond IQ, schooling is clearly important for not only the provision of knowledge – determining how one applies their IQ to the real world – but also for teaching other life skills, such as social interaction, that are important to function in society. In order to try and calculate these gains from education versus IQ, a recent study (mentioned earlier in this paper to outline the importance of personality types) used male data from Terman’s Termites longitudinal study to accurately disentangle the separate effects of education, IQ, and personality type as contributors to one’s income (Gensowski, Heckman, & Savelyev, 2014). The study found that “for the males, the returns to education beyond high school are sizeable. For example, the
IRR [internal rate of return] for obtaining a bachelor’s degree over a high school diploma is 11.1%, and for a doctoral degree over a bachelor’s degree it is 6.7%.” This shows that even for the top 1% of those in the IQ bell curve, education does increase earnings. It is hypothesized that this is for both the actual knowledge imparted and also the positive signaling effect that education has. The paper also notes that, contrary to claims made in the popular book Outliers by Malcolm Gladwell, IQ continued to contribute to income in a linear fashion rather than ceasing to matter beyond a certain threshold.
A final interesting and important area for future research in the early educational intervention space is the long term effects of the previously faded out interventions. This has befuddled researchers (Bailey, Duncan, Odgers, & Yu, 2016), (Duncan & Magnuson, 2013) (Chetty, Friedman, Hilger, & Saez, 2011) (Ludwig & Phillips, 2008).
For example, it is noted “While Perry’s large impacts on IQ at the point of school entry had all but disappeared by third grade (Schweinhart, Montie, Xiang, Barnett, Belfield, and Nores 2005), the program produced lasting improvements through age 40 on employment rates and substantially reduced the likelihood that participants had been arrested. Heckman, Moon, Pinto, Savelyev, and Yavitz (2010) estimate that the program generated about $152,000 in benefits over the life course, boosting individuals’ earnings, reducing use of welfare programs, and, most importantly for the benefit calculation, reducing criminal activity. These financial benefits produced a social rate of return between 7 and 10 percent…Moreover, for both genders a substantial share of the program impacts on adult outcomes is not explained by any of the observed early program impacts. Other programs provide little evidence of program impacts on children’s behavior. Deming’s (2009) analysis of Head Start found no short-run effects of Head Start on parental reports of children’s behavior problems. Haskins (1985) reported that the Abecedarian program had the unexpected effect of increasing teacher reports of children’s aggressiveness in the early school years, although these effects appeared to fade with time.” (Duncan & Magnuson, 2013, p. 6).
It is crucial to note that all of the aforementioned interventions are in the developed world. They consider gains that can be had on top of existing schooling, the same of which cannot be said for proportions of the developing world which lack access to quality schooling. It may be the case that the very act of performing basic cognitive tasks is important in sustaining ones skills with respect to IQ. For example, in Norway when the government increased the mandatory age at which one must attend school from fourteen to sixteen years of age, the average increase in educational attainment for the population was 0.16 years and the average increase in IQ was 0.6 points (Brinch & Galloway, 2011). It should be noted that it is unclear whether or not this 0.6 point increase contributed to the Flynn Effect seen in Norway or was confounded by it. An assessment of later life educational interventions, sadly cannot be covered in further detail considering the scope of this paper.
To summarize the educational interventions findings: Many interventions fail to teach trifecta skills and experience fadeout. Gains can be achieved in other areas, but more research is needed to determine what the mechanisms behind these observations are. Moreover, the effect sizes are still small. Expenses, duration of interventions, and difficulties in maintaining high quality support must also be taken into consideration and weighed against the effect sizes of any intervention. Future studies must be done to better disentangle what works and what doesn’t and it is crucial that these studies do not stop the moment the intervention does, but instead are longitudinal to measure not only short term fadeout effects after the programs completion, but also potential much longer term benefits.
From a summary of the literature on nutrition, there are significant nutritional deficits present today that have known, affordable solutions, which societies have unforgivably failed to implement.
The intervention with the most potential to benefit cognition specifically is iodine supplementation. Iodine is a mineral that is essential to the production of the thyroid hormones that “ensure the normal development of the brain and nervous system during gestation and early life”, it is required for normal neuronal migration, myelination, and synaptic transmission and plasticity during early fetal and postnatal periods. Severe iodine deficiencies during pregnancy are the root cause of maternal and fetal hypothyroxinenima, a condition which results in brain damage with mental retardation and neurologic abnormalities through various combinations of neurologic and myxedematous cretinism. This hypothryoxinenima has been deemed the world’s most frequent cause of preventable mental retardation in later life, because of both the irreversible nature of the negative effects in engenders and, as will be shown, the potential ease of iodine deficiency remediation.
Over the past two decades a number of studies have attempted to determine the potential effects of iodine supplementation during pregnancy upon the cognitive development of offspring. Quian et al. (2005) analyzed 37 studies (N = 12,291) in a recent meta analysis to conclude that children living in iodine sufficient communities possessed an IQ 12.45 points higher than those living in areas with no supplementation, 12.3 points higher than those living in areas with inadequate supplementation, and 4.8 points higher than those whose mother’s received supplementation before and during pregnancy. The total effect size was an increase of about 8.7 IQ points in the group that received iodine supplementation during pregnancy. Moreover, for those born more 3.5 years after the introduction of iodine supplementation, 12 to 17.5 point increases in IQ were demonstrated on the Binet and Raven Scale respectively. (Quian, 2005, p.33) These results are consistent with earlier meta-analyses, including Bleichrodt and Born (1994) and Scrimshaw (1998). Intervention trials in areas as remote as Papua New Guinea, Peru, Zaire, and rural China have demonstrated the efficacy of iodine treatments in severely deficient populations, with Pharoah and Connolly (1987) showing sharp reductions in endemic cretinism and Cao, Jiang, Dao, et al. (1994) reporting scores up to 10-20% higher in young children born to mothers that underwent treatment. (Zimmerman, 2008)In summary, A pregnant mother, depending on the extent of their deficiency, can cause their child to lose up as many as 13 IQ points due to their brain failing to develop the way that it should (Bleichrodt & Born, 1994) (Qian, et al., 2005).
The World Health Organization estimated in 2007 that “two billion individuals or over 28% of the global population possess “an insufficient iodine intake, including 1/3 of all school-age children” (Benoist, McLean, Andersson, & Rogers, 2008). In fact, when iodine deficiency was first addressed in the US, it is estimated that deficient areas saw IQs rise by an average of 10 IQ points translating into an overall rise for the entire US population 3.5 (Feyrer, Politi, & Weil, 2013).
Using table salt as an efficient distribution mechanism, iodine supplementation of a population can be achieved for $0.02 to $0.05 per person per year (Horton, Mannar, & Wesley, 2008). It has been calculated that the cost benefit ratio for addressing cognitive losses is 30:1 (Horton, Mannar, & Wesley, 2008) and losses to general health with a ratio of 70:1 (Horton, 2006; Bostrom & Roache, Cognitive Enhancement and the Public Interest, 2011; Horton, The Economic Impact of Micronutrient Deficiencies, 2004). “Looked at in another way, prior to widespread salt iodization, the annual potential losses attributable to ID [iodine deficiency] in the developing world have been estimated to be US$35.7 billion as compared with an estimated US$0.5 billion annual cost for salt iodization” (Zimmerman, 2008; Horton 2006). These figures are using 1998 dollars and are rough estimates using GDP as a proxy (Horton, 2004, p. 193) which fails to take into account the benefits that higher IQs have to society which have been outlined previously such as reduced crime levels, increased innovation, and better health. The Global Health Data Exchange (2017) calculates this to equate to roughly 3,240,585.56 DALYs, or years lost to to premature death and disability, over the past 15 years of alone.
This map shows iodine deficiency in pregnant women across the world. Pay particular attention to Europe, noting that while there is not adequate data for some countries shown (in white), it is highly likely they are also iodine deficient and would be displayed in purple given more knowledge about their nutritional condition:
Taken from (Iodine Global Network, 2017).
This map (below), showing the legislation status for iodized salt in different countries, helps to explain some of the reasons that may underlie a country’s possession of an iodine deficiency. It is both concerning and perplexing that the United States has mandatory supplementation requirements for 8 different nutrients including for iron, calcium, and riboflavin, yet no requirement for iodine (Global Fortification Data Exchange, 2017).
This image is taken from: (Global Fortification Data Exchange, 2017)
This lack of awareness concerning a solution to iodine deficiency as inexpensive and easy as salt iodization is particularly concerning. When summarizing research on iodine, Bostrom and Roache (2011) asserted: “it is morally and prudentially scandalous that this problem has not already been solved.” (p. 146). We would add to this list of accusations that it is baffling so much of the developed world is deficient. For example, a number of studies of ordinary people found high or moderate deficiency at rates from 67% in British school girls (Vanderpump, et al., 2011) to 73% in Australian mothers (Gunton, Hams, Fiegert, & McElduff, 1999) (Clifton, et al., 2013) and roughly similar proportions looking at a large American study (Hollowell & Haddow, 2007). In a study looking at British mothers it was found that mild iodine deficiency lead to a loss of 3 IQ points in total (Bath, Steer, Golding, Emmett, & Rayman, 2013).
It has even been found that the introduction of iodized salt in the first half of the twentieth century not only resulted in the decreased incidence of diseases and cognitive impairments associated with iodine deficiency, but also produced changes in school attendance, occupation choice, occupation performance, and even voting patterns. Noting the relationship between IQ and positive life outcomes, these findings are unsurprising. Nevertheless, Monahan, Boelart, et al. (2015) estimate that even negating the earnings-benefits associated with increases in IQ and utilizing more expensive versions of supplementation, iodine interventions save the United Kingdom roughly 4,476 pounds (roughly $7000 dollars) per pregnancy.
Beyond the lack of legislation for mandatory supplementation that allows salt producers to not have to bother or have poor quality control, a limited number of foods which supply iodine naturally, the war on salt in America, the rise of sea salt in popularity, and improper storage leading to the evaporation of iodine, all lead to deficiency.
This figure has been taken from (Bath, Steer, Golding, Emmett, & Rayman, 2013).
There are good reasons to suspect that the iodine deficiency estimates provided for pregnant mothers (whom sufficient iodine matters most) are underestimates. This is true for a number of reasons: pregnant mothers need almost double the amount of iodine as non-pregnant adults because of the nutritional demands of their foetus (see table below); iodine is only passively absorbed by the body; and iodine is most commonly detected by measuring urine iodine concentration. During pregnancy, the glomerular filtration rate (the rate at which your kidneys remove waste and excess nutrients to be excreted) can increase by up to 50% (Koutras, 2000). This means that not only is less iodine being absorbed by the body, but also this iodine is instead being excreted into the urine, artificially raising urine iodine concentrations. This combined with the already higher need for iodine in pregnant mother’s means that deficiency rates should be higher than they already appear to be. In the spirit of Lord Kelvin’s quote, “if you cannot measure it, you cannot improve it” (1883) there would seem to be significant benefits to better measuring iodine levels in pregnant mothers and knowing the real extent of their deficiency to motivate corrective action.
Table taken from (Zimmerman, 2008) showing iodine intake recommendations for different subsets of the population.
Aside from iodine, there are a range of other compelling nutritional interventions that should also be provided to the general population and pregnant mothers in particular.
Vitamin D is crucial for healthy bones, brain development, strong skulls, and healthy pregnancy. A deficiency has even been linked to more likely having schizophrenia (Mackay-Sim, et al., 2004). There are very limited sources of vitamin D, with fish, eggs, and sunlight being the best sources but not sufficient unless in consumed in large amounts (Calvo, Whiting, & Barton, 2005). Moreover, it has been found that “supplement use contributed 6%–47% of the average vitamin D intake in some countries” (Morse, 2012). One billion people worldwide are estimated to be vitamin D deficient “with people living in Europe, the Middle East, China and Japan at particular risk” (Vieth, et al., 2007).
Deficiency is more common in women than men (9.2% vs. 6.6%) and pregnancy is known to represent a particularly high-risk situation (Hyppönen & Boucher, 2010). In addition, pregnant women with darker skin pigmentation are at even greater risk of low vitamin D status as compared to pregnant women with lighter skin pigmentation (Liu & Hewison, 2011).
Folic acid (a B vitamin) is crucial for cell division and amino acid synthesis, processes that form the backbone of life and, in particular foetal growth of the spine, brain and skull during the first four weeks of pregnancy (Antony, 2007) (Morse, 2012). Roughly thirty years ago, Smithells et al. analyzed the diets and postpartum blood of the mothers of infants born with neural tube defects to conclude that they were each linked by their lack of folate. Using the Global Health Data Exchange, it is estimated that these defects alone have been responsible for 5.1 million disability adjusted life years lost and 3.4 million years of life lost (GHDx, 2017). Another literature review notes that, “globally, it is estimated that approximately 300,000 babies are born each year with NTDs , resulting in approximately 88,000 deaths and 8.6 million disability adjusted life years [2, 3]” (Christianson, Howson & Modell, 2006; World Health Organization, 2015a; World Health Organization, 2015b). If the emotional and physical burdens of neural tube defects were not already high enough, it is estimated that medical costs for those affected exceeds over 81 million dollars per year in the state of California alone.
As folic acid is so important during the earliest stages of pregnancy, there is even a need to have it in in sufficient quantities over 3 months before pregnancy. This is of course difficult considering that many pregnancies occur unplanned and that even after a month women can be unaware they are pregnant (Morse, 2012). An extensive meta-analysis looking at 1083 trials found “an overall effect on reduction of neural tube defects or an effect on harms associated with folic acid containing supplements.” (Wolff, Witkop, Miller & Syed, 2009) finding a 40 – 80% reduction in risk when taken before or very early into pregnancy. The amount of reduction depending largely upon the sample’s preexisting nutritional status.Sadly, it is noted that “Even though knowledge pertaining to the benefits of folic acid supplementation to prevent neural tube defects has been known for decades, a 2009 study reported that only 23%–38% of women met UK recommendations for folate through dietary sources” (Derbyshire, Davies, Costarelli & Dettmar, 2009)
Fatty acids, long chain polyunsaturated fatty acids in particular, are “critical for proper brain, nervous system and eye development and function”. DHA (docosahexaenoic acid) and the omega-6 arachidonic acid (AA) are the most crucial as they have been shown to be “highly concentrated in membrane phospholipids of the retina and brain, where they accumulate rapidly during foetal and infant growth spurts.” McCann & Ames, 2005; Rapoport, Chang & Spector, 2001) This makes sense considering that “About 60% of the dry weight of brain tissue is fat.” (Morse, 2012).
As an example of the importance of DHA, a study found that controlling for 28 different variables to try and just looking at seafood consumption amounts in almost 12,000 women in Bristol, UK (Hibbeln, et al., 2007). “the verbal intelligence quotient (IQ) scores for children from mothers with no seafood intake were found to be 50% more likely to be in the group with the lowest IQ. Overall, low seafood intake during pregnancy was directly associated with suboptimal outcomes in the offspring for prosocial behavior, fine motor coordination, communication and social development.”
Some shadows have been cast over the importance of DHA by study results in which it was found that DHA suffered from fadeout effects by the age of eight (Helland, et al., 2008). Yet it is important to note that fadeout effects for iodine, folic acid, and vitamin D have not been discovered as far as this literature review is aware. While there is still very clearly low hanging fruit in the form of IQ gains through addressing nutritional deficiencies, challenges that lie in the way of implementation and sustainability still exist and must be careful studied for any entrepreneurs considering entering this space.
Using folic acid as an example for the difficulty of supplemental interventions, a study found that: “Even with wide spread recognition of the need for folic acid to prevent neural tube defects, it is still not widely used in the general population globally. For example, in 2008 a systematic review of relevant research from 1989 to May 2006 in Europe, the USA, Canada, Australia and New Zealand was used to make recommendations to improve folic acid supplement use in the UK, particularly among low-income and young women. It included 26 systematic reviews and/or meta-analyses identified from the wider public health literature, and 18 studies on the effectiveness of preconception interventions. The results showed that even high-quality public relations campaigns that increase use result in under half of women in the target group taking supplements” (Stockley & Lund, 2008).
The specifics of genetic engineering for intelligence enhancement are beyond the scope of this paper, both because of the amount of detail required and because of the lack of research in this space due to the novelty of genetic techniques such as Crispr-Cas9 and the ethical implications of such work.
However, given these caveats, with the rate of research into genetics and our new tools to precisely cut and paste parts of any genome, or even artificially synthesize entirely new ones, research in this field and its ethics is only set to accelerate.
As an example of the type of genetic selection for intelligence that could occur even without any form of direct genetic engineering, Nick Bostrom in Superintelligence outlines the idea of “iterative embryonic selection” whereby, using in vitro fertilization (IVF) to produce ~9 embryos, one could screen for the most intelligent one and implant it (2014). Given a normal distribution of the intelligence of the embryos, this could produce a maximum IQ gain of 11.5 points. (Bostrom, 2014, pp. 44-45). Doing this generation after generation would lead to very large gains over time. A non-peer reviewed, but thorough analysis of this claim finds that, given our existing genetic screen abilities for genes related to intelligence and given the success rate of implanting embryos using IVF, the average gain we could expect today would be around a 0.5 IQ point gain (Branwen, 2017).
However, two other strategies readily available today could dramatically surpass this small 0.5 IQ point gain. Firstly, rather than relying upon random genetic recombination of a parent’s genetic material for a small IQ gain, Crispr-Cas9 could be used to directly edit base pairs in the genome. Through genome wide association studies (GWAS) a number of gene variations have been found to contribute ~0.5 IQ points to ones intelligence (Rietveld, Medland, & Koellinger, 2013) (Davies & Armstrong, 2015) with most of these having a 50% frequency in the population. Therefore, given this frequency with only 30 Crispr-Cas9 edits, we could expect to gain 6.5 IQ points.
Secondly, rather than editing the existing genome, artificially synthesize an entirely new one. Non-peer reviewed but meta-analytic extrapolations of the cost of genome synthesis estimate that at today’s prices it would cost $700 million to synthesise an entire human genome from scratch (Branwen, 2017). This is a small price in comparison to the three billion dollar cost of the Human Genome Project just under two decades ago (National Human Genome Research Institute, 2017). With synthesis rather than editing, it has been pointed out that every desirable edit could be made at once including every single edit for intelligence leading to uncalculatable IQ gains. Beyond just intelligence, leading geneticist George Church has found ten different rare genetic variants that convey advantages from extra strong bones to lower risk of diabetes, cancer, and Alzheimer’s (Knoepfler, 2015). In addition to this list, it has been observed that there is a particular gene which allows one to sleep roughly two hours less every night (Konnikova, 2014) (He, Jones, & Fu, 2009) (Pellegrino, Kavakli, & Pack, 2014). If this edit alone was implemented this would translate into spending roughly 11 more years of one’s life awake.
Much more must be known before genetic engineering could be used as a mechanism of cognitive enhancement, though these technologies have been used to successfully alter a variety of a creatures and even a number of embryos in early 2017 (one difficulty is the folding of synthetic chromosomes of this size). Yet if Algernon’s Argument is to be believed, genetic engineering may be the most promising enhancement strategy in existence because it replaces evolution itself. Debates concerning the ethical nature of genetic engineering are already being forced to shed their theoretical skins, as technologies that once seemed far off have become a reality. In this light, further examination of the significance of gene editing in the context of cognitive enhancement should be undertaken.
In conclusion, though humanity’s understanding of intelligence has grown and changed dramatically since the cognitive revolution that occurred nearly 70,000 years ago, the importance of our idiosyncratic mental abilities has not. Intelligence sets homo sapiens apart from other species on the plains of Africa and continues to hold a very significant relationship with most modern notions of success. It is thus unsurprising that society devotes countless resources to the fundamental goal of making humans smarter. However, as scientific advancements alter our ability to define, measure, and enhance intelligence, traditional strategies for accomplishing this goal must continually be reevaluated. There remains an abundance of research and work to be done in the field of intelligence.
This literature review has attempted to examine the definition and significance of intelligence, as well as the methods by which we might go about improving it. Noting that the field of intelligence is as important as it is contentious, it is often difficult to discern signal from noise and understand the direction in which the evidence lies. Yet, from this literature review alone, it should be clear to the reader that there is more consensus in the field than there is in public discourse, in particular around the topics of heritability and test bias.
Any planned enhancements of intelligence must answer to the Algernon Argument, however, even within this constraint there remains a large state space of possible interventions that can be pursued. Unfortunately, Brain Machine Interfaces (BMI) were also beyond the scope of this literature review. Given that our brain is the way that it is for good reasons, BMIs will have trouble passing the tradeoff tests of Algernon’s Argument. 
Looking towards the future, we believe that the intelligence field demands further research in two key areas: finding new, better measurements of human potential and better exploring different enhancement mechanisms beyond traditional education. If this literature review causes more people to understand the importance of intelligence in everyday life, consider reverse causality that may underlie any number of events around us, and think beyond traditional education to determine the ways in which we might help everyone to become smarter, then this literature review will have fulfilled its intended purpose, forming a significant contribution to both science and society.
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- The different tests given include the Comprehensive Ability Battery (CAB), the Hawaii Battery with Raven (HBR), and the Wechsler Adult Intelligence Scale (WAIS). These tests have different factor models and components. For example, the CAB has a number of memory recall and verbal fluency tasks, the HBR has a significant number of mental rotation tasks and the WAIS more of a range of both and arithmetic abilities. ↑
- There was only one exception that averaged a correlation of 0.85 (Johnson, Bouchard & Krueger, 2004) (Johnson, te Nijenhuis & Bouchard, 2008) (Mackintosh, 2011). ↑
- Here the negative score for tacit knowledge should be considered positive because of the deviation scoring system used. ↑
- It should also go without saying that people do not always perform to the limits of their abilities. ↑
- For example, few would argue that autism, schizophrenia, and any other number of psychological diseases are merely environmental in origin. ↑
- Do not forget that one’s adult IQ is currently the best predictor we have for educational and income attainment (Sternberg, Wagner, Williams, & Horvath, 1995). ↑
- Estimated to be the average IQ of major Nobel Prize breakthrough winners (Size, 2014). ↑
- Moreover, the chart below uses data from a gifted and talented study where the lowest IQ scores are 135, typically the threshold for the term “genius”. It is probably safe to assume that IQ’s influence on income at such high levels has diminishing returns. ↑
- Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own is another book that provides explanations for why we see the effects we do beyond what has already been explained through prosocial behavior and creativity (Jones, 2015). ↑
- Moreover, in the same spirit as this paper, we cannot assume that those 4 points related to malnutrition were influencing IQ rather than vice versa. ↑
- This last finding was not statistically significant like the former two due to the smaller sample size above 119. ↑
- There is also evidence that some countries experienced large gains even before the 1940s such as the US. (Flynn, 2011) ↑
- Implying that the gains calculated are too good to be true. ↑
- For a detailed explanation of nutritional interventions changing over time and their effect on outcomes related to intelligence see (Lynn R. , 2009). ↑
- Note that these figures do not adjust for inflation where the real value of money 30 years ago would be much larger in today’s terms. ↑
- See Bailey et al. for coverage of specific interventions that have been successful at meeting one of these loopholes (2016). ↑
- Return on Investment (ROI) – is shows the overall cumulative return based on initial money invested. Internal Rate of Return (IRR) is the annualized return on investment. It finds the discount rate at which the initial investment would not be worthwhile based upon all future earnings. ↑
- This does not mean that one can extrapolate larger increases in schooling with larger IQ gains. The average rose due to those who otherwise would have dropped out of school not doing so and them alone. ↑
- Another example of the extent to which we fail to know what works and what doesn’t is with regards to teacher quality. Value-added-modelling (VAM), is the current gold standard of measuring teacher quality and yet its rating of the teacher has between 0.18 and 0.38 reliability between classes in the same year taking the same class. See (Haertel, 2013) for a more in depth analysis of the failures around our best measurement mechanism. ↑
- In an analysis of 88 different samples of salt from US producers, 47 fell below USFDA recommended level and their labeled level (Dasgupta, Liu, & Dyke, 2008). ↑
- This is predominantly seafood, some fruits vegetables and some diary products (American Thyroid Association, 2017) ↑
- For example, when cooking meat, 60% of the iodine in iodized salt is lost (Wang, Zhou, Wang, Shi, & Sun, 1999). ↑
- For a more detailed coverage of the importance of fatty acids, folic acid, and vitamin D see (Morse, 2012) for a good introduction and overview. ↑
- For an interesting example of these tradeoffs, a series of papers by the same researcher found that by using transcranial magnetic stimulation (TMS), savant like abilities could be activated in participants (Snyder, et al., 2003; Snyder A. , 2009). More research into the field of BMIs is certainly warranted. ↑