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Job performance is supposedly one measure that validates the construct of IQ tests since they correlate so highly with IQ tests (Schmidt et al, 1986). However, there are problems with the methods used to get the high correlations (sometimes doubling correlations, there are also questions to the robusticity of the studies meta-analyzed); corrections used have to make a number of assumptions; uncertainty of the interpretation of what the supposed IQ and job performance correlations mean; other non-cognitive factors may also explain differences in job performance. Most surprisingly, intelligence test scores did not predict promotion to senior doctor and intelligence does not predict careers.
Job performance and IQ
Does IQ really correlate around .5 with job performance like is so commonly stated? There are a number of problems citing such the commonly used meta-analyses for evidence that IQ does indeed predict job performance.
Richardson and Norgate (2015) show that one should use caution when interpreting the results of IQ and job performance on the basis of numerous criteria. It is important to note that job performance is rated by supervisors, which is, of course, a problem since supervisors tend to be subjective in their ratings. Further, supervisor ratings have low correlations with work performance, while work knowledge has a correlation of around .3 (Richardson and Norgate 2015; Richardson, 2002). So, one of the main things that the correlation hinges upon is strongly subjective.
However, one of the most important things to note here is that the validation of IQ tests is relied on with correlations with other tests. For instance, blood alcohol and level of consumption are valid constructs. The higher your blood alcohol is, the more alcohol you consumed. There is no such validity for the construct of IQ—except correlations with other tests—which is a huge problem. This goes back to the fact that there is no individual theory of intelligence differences (Deary, 2001: 14) and no neurophysiological theory of g (Jensen, 1998: 257).
So IQ tests don’t have the same construct validity that other models that describe biologic/physiologic functions do; hundreds of studies before the 70s showed low correlations between IQ and job performance; corrections for error make a lot of assumptions; the common claim that the IQ/job performance correlation increases with more complex jobs is not observed in more recent studies; and there is great uncertainty in the interpretation of the IQ and job performance correlation, due to the fact that there is no construct validity to IQ tests. This goes back to the question: What is it that IQ tests test (Richardson, 2002)? Is it the ever-elusive general factor of intelligence? I’m skeptical there.
Richardson (2017) writes:
The committee described the differences as “puzzling and somewhat worrisome.” But they noted how the quality of the data might explain it. For example, the 264 newer studies have much greater numbers of participants, on average (146 versus 75). It was shown how the larger samples produced much lower sampling error and less range restriction, also requiring less correction (with much less possibility of a false boost to observed correlations). And there was no need to devise estimates to cover for missing data. So, even by 1989, these more recent results are indicative of the unreliability of those usually cited. But it is the earlier test results that are still being cited by IQ testers. (pg. 89)
IQ and job performance correlations are also substantially weaker in other parts of the world, such as the Middle East and China, where motivation and effort explain school and work performance and not cognitive ability (Byington and Felps, 2010). So, again, caution is to be taken when interpreting any IQ and job performance correlation, as well as—most importantly—asserting that higher IQ means better job performance.
In his 2015 book Intelligence in the Flesh, Guy Claxton wrote:
We saw earlier that Google is not impressed by people’s track records of success, but is equally sceptical of high IQs. Laszlo Bock, the senior vice-president in charge of ‘people operations’ – the head of HR – says: ‘For every job the No. 1 thing we look for is general cognitive ability, and it’s not I.Q. It’s learning agility. It’s the ability to process on the fly.‘ Behind the ability to learn quickly lies what Bock calls ‘intellectual humility.’ You have to be able to give up the knowledge and expertise you thought would see you through, and look with fresh eyes. People with a high IQ ofen have a hard time doing that. They are certainly no better than average at tolerating uncertainty or being able to adopt fresh perspectives.
Now that we know to take caution when speaking about the IQ and job performance correlation, what do IQ tests say about success as a doctor?
Doctors and IQ
Since becoming a doctor is so demanding and takes a lot of time and motivation to complete a doctoral degree, most rightly assume that it takes a higher than average intelligence to acquire these accolades and become a medical doctor. However, reality is more nuanced.
McManus et al (2003) put forth three hypotheses: 1) the achievement argument: A-levels ensure maximum competence on sciences which are basic to medicine (biology and chemistry); 2) the ability argument: Academic success depends mainly on cognitive ability; and 3) the motivation argument: Using A-levels is effective because it University education not only reflects intelligence but motivation and good, consistent study skills.
There is evidence that IQ is irrelevant to becoming a doctor and that it did not predict dropping out of the program, career outcome, amount of research publications published, or stress, burnout and satisfaction with taking a career in medicine (McManus et al, 2003). Diplomas, higher academic degrees, and research publications were significantly correlated with personality.
McManus et al (2003) write:
Intelligence did not independently predict dropping off the register, career outcome, or other measures.
Intelligence does not predict careers, thus rejecting the ability argument. A levels predict because they assess achievement, and the structural model shows how past achievements predict future achievement.
And on the causes for dropping out:
All 511 students registered with the General Medical Council, but only 464 were on the 2001 Medical Register. The 47 doctors who left the register (a mean of 11.1 years after qualifying; SD 5.9; range 2-23) had lower A level grades but not lower AH5 scores (table A, bmj.com); see http://www.bmj.com for ROC analysis. Two doctors subsequently returned to the register. Of the remainder, three had died, contact details were available for 35, and no information was available for seven.
So lower intelligence scores were not the cause for dropping out.
McManus et al (2003), however, could not distinguish between the motivation and achievement argument, but falsified the intelligence argument (Hypothesis 2 was falsified, but not 1 and 3).
This was also replicated by McManus et al (2013), where they should that IQ scores did not predict promotion to senior doctor. A-level scores, yet again, predicted success better when it came to doctoral success.
The relationship between IQ and job performance is not as clear-cut as most would like to believe. One of the most important factors there, in my opinion, is the subjectivity of supervisors on the performance of their workers. Numerous factors could influence a supervisors’ view of an individual, biasing the supervisor to a high rating. Furthermore, the corrected correlations are a problem. More recent analyses show a correlation of .25 (Richardson, 2017: 89).
Perhaps more importantly, two studies show that there is no predictive effect on job performance when it comes to IQ for doctors (McManus et al, 2003; McManus et al, 2013). They show that A-level scores predict success better, with personality variables mediating other relationships—not IQ scores.
The fact of the matter is, job performance and IQ is on shaky ground since IQ tests are not constructed valid, and the job performance ratings are based on supervisor ratings which are highly subjective. Analyses in other locations around the world show that IQ does not predict job performance, however, motivation and effort do. IQ does not predict a doctor’s job performance; job performance tests do not prove the validity of IQ tests.
IQ does not predict a doctor’s job performance; job performance tests do not prove the validity of IQ tests.
[Edit: I have come across more data on doctors IQ. Some studies show that complaints by patients on their doctors are related to infractions. Perry and Crean (2005) show that the average IQ for a doctor is 125. They also state that neurocognitive impairment may be responsible for 63% of all physician related adverse events. This same observation is also noted in other studies (Pitkanen, Hurn, and Kopelman, 2008; Lauri et al, 2009; Kataria et al, 2014). Also of note is that these papers—to the best of my knowledge—do not explore the role of stress in cognitive decline. Though Pitkanen, Hurn, and Kopelman (2008) note that depression, PTSD, amnesia, transient global amnesia, alcoholic brain damage, frontotemporal dimentia, dimentia, alzheimer’s disease, vascular dimentia, and post-traumatic amnesia (PTA) influence cognitive decline in doctors.
Veena et al, (2015) show that 88 percent of medical students had near average intelligence, putting in 6 hours a day of studying, while 10 percent of students had above average IQ, spent less time studying but were sincere in their classes.
Veena et al (2015) conclude:
Students with near average IQ work hard in their studies and their academic performance was similar to students with higher IQ. So IQ can`t be made the basis for medical entrance; instead giving weight-age to secondary school results and limiting the number of attempts may shorten the time duration for entry and completion of MBBS degree.
So students with average intelligence work just as hard (if not harder) than people with above average IQ and have similar educational achievement. This shows that IQ can’t be the basis for medical school entry.
This is a really interesting matter and I will cover it more in the future. I’ve been wondering for years if there is data on physician/doctoral malpractice and race I have yet to come across any papers on the matter. If anyone knows of any, please leave some citations.]
By Scott Jameson
Long and short of this issue is that something has to explain why most of the really, really smart people are men. There are two hypotheses: men have a higher mean, and men have a higher standard deviation. They don’t really have to compete, and so some people believe that both are true. Some believe neither, of course.
Let’s start with three facts:
- Women tend to get slammed by men on Raven’s Progressive Matrices; the second graph in the post linked above details this. It’s a difference of 5 IQ points on average, quite a bit, certainly more than on other IQ tests.
- Women tend to lose even harder in visuospatial measures. John Loehlin pointed out in The Handbook of Intelligence that the gap here was a whopping 13.5 points.
- Raven’s is so g loaded because your score is primarily driven by spatial and verbal-analytic abilities.
The biggest subtest difference is spatial, and I think that likely explains the abnormally large differences in Raven’s scores. Other IQ tests, like the SAT, hardly use visual abilities. Women do about as well as men on the SAT. I’ve also seen the White-Asian gap smaller on the SAT than in other IQ tests, and that gap is also driven in large part by spatial scores. Conversely you might expect the SAT to go better for a hypothetical demographic that scores well in math and verbal abilities, but not especially well in spatial. By hypothetically I mean that these people make up like a fifth of the kids at the Ivy Leagues, even more than you’d expect from an average IQ of, I don’t know, 111ish.
Off topic: these differences are probably going to be slighter still now that they’re fastidiously removing every useful element of the test in an effort to make it less “biased” by race. I wonder if colleges will just throw up their shoulders and start looking for kids who do well on the ACT. Moving on.
There are other sex differences in subtest scores. Pulling from Loehlin again: “females tend to have an advantage on verbal tests involving the fluent production of words belonging to a category, such as synonyms.” Women are known to do better on verbal than on math.
Loehlin also points out that girls do better at math in early childhood, but that boys outstrip them by the time it, uh, matters, when they take standardized tests in adolescence.
I have a wild hypothesis that men and women respectively being more oriented towards mathematical and verbal thought corresponds to observed differences in interests. Women are known to read more often than men on average, whereas male dominated activities like sports and video games often have a distinctly mathematical bent. My spurious hypothesis is that doing these different things differentially develops their abilities, constituting an example of crystallized intelligence rather than fluid intelligence; alternatively, they were differentially selected for ability to perform well on tasks that their respective sex does more of, in which case the abilities are innate.
Even if they aren’t innate, it’d be an instance of secondary heritability because evidence tends to show male-female personality differences as innate; in this scenario they are innately prone to practicing different abilities to different extents.
Loehlin points to Hedges and Nowell’s 1995 meta-analysis, showing a higher male variation in IQ and elucidating a few more small subtest differences. I’ve lifted a meaty bit here:
On average, females exhibited a slight tendency to perform better on tests of reading comprehension, perceptual speed, and associative memory, and males exhibited a
slight tendency to perform better on tests of mathematics and social studies. All of the effect sizes were relatively small except for those associated with vocational aptitude scales (mechanical reasoning, electronics information, and auto and shop information) in which average males performed much better than average females. The effect sizes for science were slightly to moderately positive, and those for perceptual speed were slightly to moderately negative. Thus, with respect to the effect size convention, these data suggest that average sex differences are generally rather small.
- There are sex differences in scores of various IQ subtests, including but not limited to female orientation towards verbal and male orientation towards mathematical ability.
- The largest of these differences is a substantial male advantage in spatial ability.
- On any IQ test that doesn’t weight subtests such that men and women perform equally by default, men tend to score a hair better.
- Men also have a higher standard deviation in IQ.
There are more male geniuses, particularly with respect to mathematical genius. There are also more mentally retarded males. I just explained why men tend to populate CERN, NASA, Silicon Valley, and lists of who’s died in the Running of the Bulls.
The general factor of intelligence (g) is said to be physiological. Jensen (1998: xii) states that “Students in all branches of the behavioral and social sciences, as well as students of human biology and evolution, need to grasp the essential psychometric meaning of g, its basis in genetics and brain physiology, and its broad social significance.” There are, furthermore, “a number of suggestive neurological correlates of g, but as yet these have not been integrated into a coherent neurophysiological theory of g” (Jensen, 1998: 257). I personally don’t care for correlations too much anymore, I’m interested in actual causes. Jensen (1998: 578) also states “Although correlated with g [size of the brain, metabolic rate, nerve conduction velocity, and latency and amplitude of evoked electrical potentials], these physiological variables have not yet provided an integrated explanatory theory.”
This seems suspiciously like Dreary’s (2001: 14) statement that there “is no such thing as a theory of human intelligence differences – not in the way that grown-up sciences like physics or chemistry have theories.” If g is physiological, then where is the explanatory theory? On that same matter, where is the explanatory theory for individual intelligence differences? That’s one thing that needs to be explained, in my opinion. I could muster something up off the top of my head, such as individual differences in glucose metabolism in the brain, comparing both high and low IQ people (Cochran et al, 2006; Jensen, 1998: 137), however, that is still not good enough.
In physiology there is sliding filament theory which explains the mechanism of muscle contraction (Cooke, 2004). Why is there no such theory of why individuals differ in intelligence and why have these “suggestive neurological correlates of g” not been formulated into a coherent neurophysiological theory? There are numerous theories in physiology, but a theory of g or why individuals differ in intelligence is not one of them.
It’s like Darwin only saying “Species change“, and that’s it; no theory of how or why. He’s just stating something obvious. Similarly, saying “Person A is smarter or has a higher IQ than person B” is just an observation; there is no theory of how or why for why individuals differ in intelligence. There are theories for group differences (garbage cold winter theory), but no individual differences in intelligence? Hmmm… Sure it’d be a ‘fact that species change over time’, but without a theory of how or why, how useful is that observation? Similarly, it is true that some people are more intelligent than others (score higher on IQ tests), yet there is no explanatory theory as to why? I believe this ties back to the physiological basis for g: are physiologists studying it, and if not, why?
Reaction time (RT) is one of the most talked about physiological correlates in regards to IQ. However, as a fitness professional, I know that exercise can increase reaction time, especially in those with intellectual disabilities (Yildirim et al, 2001). I am now rethinking the correlate between reaction time and IQ, since it can be trained in children, especially those with intellectual disabilities. Clearly, RT can be trained by exercise, participating in sports, and even by playing video games (Green, 2008). So since RT can be trained, I don’t think it’s a good physiological measure for g.
Individuals do differ in individual physiology, however, I have never heard of a physiologist attempting to rank individuals on different traits, nevermind attempting to say that a higher level of one variable is better than a lower variable, say blood pressure or metabolic rate. In fact, individuals with high blood pressure and metabolic rates would need immediate medical attention.
There are also wide variations in how immune systems act when faced with pathogens, bacteria and viruses. Though, “no one dreams of ranking individual differences on a general scale of immunocompetence” (Richardson, 2017: 166). So if g is physiological then why don’t other physiological traits get placed on a rank order, with physiologists praising certain physiological functions as “better”?
Richardson (2017: 166-167) writes:
In sum, no physiologist would suggest the following:
(a) that within the normal range of physiological differences, a higher level is better than any others (as is supposed in the construction of IQ tests);
(b) that there is a general index or “quotient” (a la IQ) that could meaningfully describe levels of physiological sufficiency or ability and individual differences in it;
(c) that “normal” variation is associated with genetic variation (except in rare deleterious conditions; and
(d) the genetic causation of such variation can be meaningfully separated from the environmental causes of the variation.
A preoccupation with ranking variations, assuming normal distributions, and estimating their heritabilities simply does not figure in the field of physiology in the way that it does in the field of human intelligence. This is in stark contrast with the intensity of the nature-nurture debate in the human cognitive domain. But perhaps ideology has not infiltrated the subject of physiology as much as it has that of human intelligence.
This is all true. I know of no physiologist who would suggest such a thing. So does it make sense to compare g with physiological variables—even when classic physiological variables do not have some kind of rank order? Heritabilities for BMR are between .4 and .8, which is in the same range as the heritability of IQ. Can you imagine any physiologist on earth suggesting a rank order for physiological traits such as BMR or stroke volume? I can’t, and if you knew anything about physiological variables then you wouldn’t either.
In sum, I believe that conflating g with physiology is erroneous; mostly because physiologists don’t rank physiological traits in the same ways that human intelligence researchers do. Our physiology is intelligent in and of itself, and this process begins in the cell—the intelligent cell. Our physiological systems are intelligent—in our bodies are dynamic systems that keenly respond to whatever is going on in the environment (think of how the body always attempts to maintain homeostasis). Physiology deals with the study of living organisms—more to the point, how the systems that run the organisms work.
Looking at physiological variables and attempting to detangle environmental and genetic effects is a daunting task—especially the way our physiological systems run (responding to cues from the environment, attempting to maintain homeostasis). So if general intelligence—g—had a true biological underpinning in the body, and if physiologists did study it, then they would not have a rank ordering for g like psychologists do; it’d just be another human trait to study.
So the answer to the question “Do physiologists study g?” is no, and if they did they would not have the variable on a rank order because physiologists don’t study traits in that manner—if a true biological underpinning for g exists. Physiology is an intelligent and dynamic system in and of itself, and the process begins in the intelligent cell, except it is on a larger scale, with numerous physiological variables working in concert, constantly attempting to stay in homeostasis.
After the publishing of the article debunking r/K selection theory last week, I decided to go to a few places and provide the article to a few sites that talk about r/K selection theory and it’s (supposed) application to humans and psychometric qualities. I posted it on a site called ‘truthjustice.net‘, and the owner of the site responded to me:
Phillippe Rushton is not cited a single time in AC’s book. In no way, shape or form does the Theory depend on his opinions.
AC outlines a very coherent theoretical explanation for the differing psychological behavior patterns existing on a bell curve distribution in our population. Especially when it comes to the functioning of the Amygdala for which we have quite a lot of data by now.
Leftists are indeed in favor of early childhood sexualization to increase the quantity of offspring which will inevitably reduce the quality and competitive edge of children. They rank significantly lower on the moral foundations of “loyalty”, “authority” and “purity” as outlined by Jonathan Haidt’s research into moral psychology. Making them more accepting of all sorts of degeneracy, deviancy, and disloyalty to the ingroup.
They desire a redestribution of resources to the less well performing part of our population to reduce competitive stress and advantage while giving far less to charity and being significantly more narcissistic to increase their own reproductive advantage.
Their general mindset becomes more and more nihilistic, atheistic, anarchistic, anti-authority and overall r-selected the further left you go on the bell curve. A denial of these biological realities in our modern age is ridiculous when we can easily measure their psychology and brain functionality in all sorts of ways by now.
Does that now mean that AC is completely right in his opinions on r/K-Selection Theory? No, much more research is necessary to understand the psychological differences between leftists and rightists in full detail.
But the general framework outlined by r/K-Selection Theory very likely applies to the bell curve distribution in psychological behavior patterns we see in our population.
I did respond, however, he removed my comment and banned me after I published my response. My response is here:
“Phillippe Rushton is not cited a single time in AC’s book. In no way, shape or form does the Theory depend on his opinions.”
Meaningless. He uses the r/K continuum so the link in my previous comment is apt.
“AC outlines a very coherent theoretical explanation for the differing psychological behavior patterns existing on a bell curve distribution in our population. Especially when it comes to the functioning of the Amygdala for which we have quite a lot of data by now.”
No, he doesn’t.
2) even if r/K were a valid paradigm, it would not pertain to within species variation,
3) it’s just a ‘put these traits on one end that I don’t like and these traits at the other end that I like and that’s my team while the other team has all of the bad traits’ thing,
4) his theory literally rests on the r/K continuum proposed by Pianka. Furthermore, no experimental rationale “was ever given for the assignment of these traits [the r/K traits Pianka inserted into his continuum] to either category” (Graves, 2002: 135), and
5) the r/K paradigm was discredited in the late 70s (see Graves 2002 above for a review)
“Leftists are indeed in favor of early childhood sexualization to increase the quantity of offspring which will inevitably reduce the quality and competitive edge of children. They rank significantly lower on the moral foundations of “loyalty”, “authority” and “purity” as outlined by Jonathan Haidt’s research into moral psychology. Making them more accepting of all sorts of degeneracy, deviancy, and disloyalty to the ingroup.”
I love Haidt. I’ve read his book and all of his papers and articles. So you notice a few things. Then see the (discredited) r/K paradigm. Then you say “oh! liberals are bad and are on the r side while conservatives are K!!”
Let me ask you this: where does alpha-selection fall into this?
“They desire a redestribution of resources to the less well performing part of our population to reduce competitive stress and advantage while giving far less to charity and being significantly more narcissistic to increase their own reproductive advantage.”
Oh.. about that… liberals have fewer children than conservatives. Liberals are also more intelligent than conservatives. So going by Rushton’s r/K model, liberals are K while conservatives are r (conservatives are less intelligent and have more children). So the two cornerstones of the (discredited) r/K continuum show conservatives breeding more and also are less intelligent while it’s the reverse for liberals. So who is ‘r’ and ‘K’ again?
“Their general mindset becomes more and more nihilistic, atheistic, anarchistic, anti-authority and overall r-selected the further left you go on the bell curve. A denial of these biological realities in our modern age is ridiculous when we can easily measure their psychology and brain functionality in all sorts of ways by now.”
‘r’ and ‘K’ are not adjectives (Anderson, 1991: 57).
Why does no one understand r/K selection theory? You are aware that r/K selection theory is density-dependent selection, correct?
“Does that now mean that AC is completely right in his opinions on r/K-Selection Theory? No, much more research is necessary to understand the psychological differences between leftists and rightists in full detail.”
No, he’s horribly wrong with his ‘theory’. I don’t deny psych differences between libs and cons, but to put them on some (discredited) continuum makes no sense in reality.
“But the general framework outlined by r/K-Selection Theory very likely applies to the bell curve distribution in psychological behavior patterns we see in our population.”
No, it doesn’t. Psych traits are not normally distributed (see above). Just like Rushton, AC saw that some things ‘fit’ into this (discredited) continuum. What’s that mean? Absolutely nothing. He doesn’t even cite papers for his assertion; he called Pianka a leftist and said that he tried to sabotage the theory because he thought that it described libs (huh? this makes no sense). AC is a clear ideologue and is steeped in his own political biases as well as wanting to sell more copies of his book. So he will not admit that he is wrong.
Let me ask you a question: where did liberals and conservatives evolve? What selective pressures brought about these psych traits in these two ‘populations’? Are liberals and conservatives local populations?
I’ve also summarily discredited AC and I am waiting on a reply from him (I will be surprised if he replies).
However, unfortunately for AC et al, concerns have been raised “about the use of psychometric indicators of lifestyle and personality as proxies for life history strategy when they have not been validated against objective measures derived from contemporary life history theory and when their status as causes, mediators, or correlates has not been investigated” (Copping, Campbell, and Muncer, 2014). This ends it right here. People don’t understand density-dependent/independent selection since Rushton never talked about it. That, as has been brought up, is a huge flaw in Rushton’s application of r/K theory to the races of Man.
Liberals are, on average, more intelligent than conservatives (Kanazawa, 2010; Kanazawa, 2014) Lower cognitive ability has been linked to greater prejudice through right-wing ideology and low intergroup contact (Hodson and Busseri, 2012), with social conservatives (probably) having lower IQs. There are also three ‘psychological continents’—Europe, Australia, and, Canada and are the liberal countries whereas Southeast Asia, South Asia, South America and Africa contain more conservative countries with all other countries including Russia, the US and Asia in the middle and “In addition, gross domestic product (GDP) per capita, cognitive test performance, and governance indicators were found to be low in the most conservative group and high in the most liberal group” (Stankov and Lee, 2016). Further, economic liberals—as a group—tend to be better educated than Republicans—so intelligence is positively correlated with socially and economically liberal views (Carl, 2014).
There is also a ‘conservative baby boom‘ in the US—which, to the Rushtonites, is ‘r-selected behavior’. Furthermore, women who reported that religion was ‘very important to them’ reported having higher fertility than women who said that it was ‘somewhat important’ or ‘not important’ (Hayford and Morgan, 2008). Liberals are more likely to be atheist (Kanazawa, 2010), while, of course, conservatives are more likely to be religious (Morrison, Duncan, and Parton, 2015; McAdams et al, 2015).
All in all, even if we were to allow the use of liberals and conservatives as local populations, like Rushton’s erroneous use of r/K theory for human races, the use of r/K theory to explain the conservative/liberal divide makes no sense. People don’t know anything about ecology, evolution, or neuroscience. People should really educate themselves on the matters they speak about—I mean a full-on reading into whatever it is you believe. Because people like TIJ and AC are clearly idealogues, pushing a discredited ecological theory and applying it to liberals and conservatives, when the theory was never used that way in the first place.
For anyone who would like a look into the psychological differences between liberals and conservatives, Jonathan Haidt has an outstanding book outlining the differences between the two ideologies called The Righteous Mind: Why Good People are Divided by Politics and Religion. I actually just gave it a second read and I highly, highly recommend it. If you want to understand the true differences between the two ideologies then read that book. Try to always remember and look out for your own biases when it comes to your political beliefs and any other matter.
For instance, if you see yourself frantically attempting to gather support for a contention in a debate, then that’s the backfire effect in action (Nyhan and Reifler, 2012), and if you have a knowledge of the cognitive bias, you can better take steps to avoid such a heavy-handed bias. This, obviously, occurred with TIJ. The response above is airtight. If this ‘continuum’ did exist, then it’s completely reversed with liberals having fewer children and generally being more intelligent with the reverse for conservatives. So liberals would be K and conservatives would be r (following Rushton’s interpretation of the theory which is where the use of the continuum comes from).
A commenter by the name of bbloggz alerted me to a new paper by Lee Ellis published this year titled Race/ethnicity and criminal behavior: Neurohormonal influences in which Ellis (2017) proposed his theory of ENA (evolutionary neuroandrogenic theory) and applied it to racial/ethnic differences in crime. On the face, his theory is solid and it has great explanatory power for the differences in crime rates between men and women, however, there are numerous holes in the application of the theory in regards to racial/ethnic differences in crime.
In part I, he talks about racial differences in crime. No one denies that, so on to part II.
In part II he talks about environmental causes for the racial discrepancies, that include economic racial disparities, racism and societal discrimination and subordination, a subculture of violence (I’ve been entertaining the honor culture hypothesis for a few months; Mazur (2016) drives a hard argument showing that similarly aged blacks with some college had lower levels of testosterone than blacks with less than high school education which fits the hypothesis of honor culture. Though Ellis’ ENA theory may account for this, I will address this below). However, if the environment that increases testosterone is ameliorated (i.e., honor culture environments), then there should be a subsequent decrease in testosterone and crime, although I do believe that testosterone has an extremely weak association with crime, nowhere near high enough to account for racial differences in crime, the culture of honor could explain a good amount of the crime gap between blacks and whites.
Ellis also speaks about the general stress/strain explanation, stating that blacks have higher rates of self-esteem and Asians the lowest, with that mirroring their crime rates. This could be seen as yet another case for the culture of honor in that blacks with a high self-esteem would feel the need to protect their ‘name’ or whatever the case may be and feel the need for physical altercation based on their culture.
In part III, Ellis then describes his ENA theory, which I don’t disagree with on its face as it’s a great theory with good explanatory power but there are some pretty large holes that he rightly addresses. He states that, as I have argued in the past, females selected men for higher rates of testosterone and that high rates of testosterone masculinize the brain, changing it from its ‘default feminine state’ and that the more androgens the brain is exposed to, the more likely it is for that individual to commit crime.
Ellis cites a study by Goodpaster et al (2006) in which he measured the races on the isokinetic dynamometry, pretty much a leg extension. However, one huge confound is that participants who did not return for follow-up were more likely to be black, obese and had more chronic disease (something that I have noted before in an article on racial grip strength). I really hate these study designs, but alas, it’s the best we have to go off of and there are a lot of holes in them that must be addressed. Though I applaud the researchers’ use of the DXA scan (regular readers may recall my criticisms on using calipers to assess body fat in the bench press study, which was highly flawed itself; Boyce et al, 2014) to assess body fat as it is the gold standard in the field.
Ellis (2017: 40) writes: “as brain exposure to testosterone surges at puberty, the prenatally-programmed motivation to strive for resources, status, and mating opportunities will begin to fully activate.” This is true on the face, however as I have noted the correlation between physical aggression and testosterone although positive is low at .14 (Archer, 1991; Book et al, 2001). Testosterone, as I have extensively documented, does cause social dominance and confidence which do not lead to aggression. However, when other factors are coupled with high testosterone (as noted by Mazur, 2016), high rates of crime may occur and this may explain why blacks commit crime; a mix of low IQ, high testosterone and low educational achievement making a life of crime ‘the smart way’ to live seeing as, as Ellis points out, and that intelligent individuals find legal ways to get resources while less intelligent individuals use illegal ways.
ENA theory may explain racial differences in crime
In part IV he attempts to show how his ENA theory may explain racial differences in crime—with testosterone sitting at the top of his pyramid. However, there are numerous erroneous assumptions and he does rightly point out that more research needs to be done on most of these variables and does not draw any conclusions that are not warranted based on the data he does cite. He cites one study in which testosterone levels were measured in the amniotic fluid of the fetus. The sample was 59 percent white and due to this, the researchers lumped blacks, ‘Hispanics’ and Native Americans together which showed no significant difference in prenatal testosterone levels (Martel and Roberts, 2014).
Umbilical cord and testosterone exposure
Ellis then talks about testosterone in the umbilical cord, and if the babe is exposed to higher levels of testosterone in vitro, then this should account for racial/ethnic differences in crime. However, the study he cited (Argus-Collins et al, 2012) showed no difference in testosterone in the umbilical cord while Rohrmann et al (2009) found no difference in testosterone between blacks and whites but found higher rates of SHBG (sex hormone-binding globulin) which binds to testosterone and makes it unable to leave the blood which largely makes testosterone unable to affect organ development. Thusly, if the finding of higher levels of SHBG in black babes is true, then they would be exposed to less androgenic hormones such as testosterone which, again, goes against the ENA theory.
He also cites two more studies showing that Asian babes have higher levels of umbilical cord testosterone than whites (Chinese babes were tested) (Lagiou et al, 2011; Troisi et al, 2008). This, again, goes against his theory as he rightly noted.
Next he talks about circulating differences in testosterone between blacks and whites. He rightly notes that testosterone must be assayed in the morning within an hour after waking as that’s when levels will be highest, yet cites Ross et al (1986) where assay times were all over the place and thusly testosterone cannot be said to be higher in blacks and whites based on that study and should be discarded when talking about racial differences in testosterone due to assay time being between 10 am and 3 pm. He also cites his study on testosterone differences (Eliss and Nyborg, 1993), but, however, just as Ross et al (1986) did not have a control for WC (waist circumference) Ellis and Nyborg (1993) did not either, so just like the other study that gets cited to show that there is a racial difference in testosterone, they are pretty hugely flawed and should not be used in discussion when discussing racial differences in testosterone. Why do I not see these types of critiques for Ross et al (1986) in major papers? It troubles me…
He also seems to complain that Lopez et al (2013) controlled for physical activity (which increases testosterone) and percent body fat (which, at high levels, decreases testosterone). These variables, as I have noted, need to be controlled for. Testosterone varies and fluctuated by age; WC and BMI vary and fluctuate by age. So how does it make sense to control for one variable that has hormone levels fluctuate by age and not another? Ellis also cites studies showing that older East Asian men had higher levels of testosterone (Wu et al, 1995). Nevertheless, there is no consensus; some studies show Chinese babes have higher levels of testosterone than whites and some studies show that whites babes have higher levels of testosterone than Chinese babes. Indeed, this meta-analysis by Ethnicmuse shows that Asians have the highest levels, followed by Africans then Europeans, so this needs to be explained to save the theory that testosterone is the cause of black overrepresentation of violence (as well as what I showed that testosterone is important for vital functioning and is not the boogeyman the media makes it out to be).
Bone density and crime
Nevertheless, the next variable Ellis talks about is bone density and its relationship to crime. Some studies find that blacks are taller than whites while other show no difference. Whites are also substantially taller than Asian males. Blacks have greater bone density than the other three races, but according to Ellis, this measure has not been shown to have a relationship to crime as of yet.
Penis size, race and crime
Now on to penis size. In two articles, I have shown that there is no evidence for the assertion that blacks have larger penises than whites. However, states that penis length was associated with higher levels of testosterone in Egyptian babes. He states that self-reported penis size correlates with self-reports of violent delinquency (Ellis and Das, 2012). Ellis’ main citations for the claim that blacks have larger penises than other races comes from Nobile (1982), the Kinsey report, and Rushton and Boagert (1987) (see here for a critique of Rushton and Boagert, 1987), though he does cite a study stating that blacks had a longer penis than whites (blacks averaging 5.77 inches while whites averaged 5.53 inches). An HBDer may go “Ahah! Evidence for Rushton’s theory!”, yet they should note that the difference is not statistically significant; just because there is a small difference in one study also doesn’t mean anything for the totality of evidence on penis size and race—that there is no statistical difference!
He then cites Lynn’s (2013) paper which was based on an Internet survey and thus, self-reports are over-measured. He also cites Templer’s (2002) book Is Size Important?, which, of course, is on my list of books to read. Nevertheless, the ‘evidence’ that blacks average larger penises than whites is extremely dubious, it’s pretty conclusive that the races don’t differ in penis size. For further reading, read The Pseudoscience of Race Differences in Penis Size, and read all of Ethnicmuses’ posts on penis size here. It’s conclusive that there is no statistical difference—if that—and any studies showing a difference are horribly flawed.
2d/4d ratio and race
Then he talks about 2d/4d ratio, which supposedly signifies higher levels of androgen exposure in vitro (Manning et al, 2008) however these results have been challenged and have not been replicated (Koehler, Simmons, and Rhodes, 2004; Yan et al, 2008, Medland et al, 2010). Even then, Ellis states that in a large analysis of 250,000 respondents, Asians had the lowest 2d/4d ratio, which if the hypothesis of in vitro hormones affecting digit length is to be believed, they have higher levels of testosterone than whites (the other samples had small ns, around 100).
Prostate-specific antigens, race, and prostate cancer
He then talks about PSA (prostate-specific antigen) rates between the races. Blacks are two times more likely to get prostate cancer, which has been blamed on testosterone. However, I’ve compiled good evidence that the difference comes down to the environment, i.e., diet. Even then, there is no evidence that testosterone causes prostate cancer as seen in two large meta-analyses (Stattin et al, 2003; Michaud, Billups, and Partin, 2015). Even then, rates of PCa (prostate cancer) are on the rise in East Asia (Kimura, 2012; Chen et al, 2015; Zhu et al, 2015) which is due to the introduction of our Western diet. I will cover the increases in PCa rates in East Asia in a future article.
He then reviews the evidence of CAG repeats. There is, however, no evidence that the number of CAG repeats influences sensitivity to testosterone. However, intra-racially, lower amounts of CAG repeats are associated with higher spermatozoa counts—but blacks don’t have higher levels of spermatozoa (Mendiola et al, 2011; Redmon et al, 2013). Blacks do have shorter CAG repeats, and this is consistent with the racial crime gap of blacks > whites > Asians. However, looking at the whole of the evidence, there is no good reason to assume that this has an effect on racial crime rates.
Intelligence and education
Next he talks about racial differences in intelligence and education, which have been well-established. Blacks did have higher rates of learning disabilities than whites who had higher levels of learning disabilities then Asians in a few studies, but other studies show whites and South Asians having different rates, for instance. He then talks about brain size and criminality, stating that the head size of males convicted for violent crimes did not differ from males who committed non-violent crimes (Ikaheimo et al, 2007). I won’t bore anyone with talking about what we know already: that the races differ in average brain size. However, a link between brain size and criminality—to the best of my knowledge—has yet to been discovered. IQ is implicated in crime, so I do assume that brain size is as well (no matter if the correlation is .24 or not; Pietschnig et al, 2015).
Prenatal androgen exposure
Now to wrap things up, the races don’t differ in prenatal androgen exposure, which is critical to the ENA theory; there is a small difference in the umbilical cord favoring blacks, and apparently, that predicts a high rate of crime. However, as noted, blacks have higher levels of SHBG at birth which inhibits the production of testosterone on the organs. Differences in post-pubertal testosterone are small/nonexistent and one should not talk about them when talking about differences in crime or disease acquisition such as PCa. DHT only shows a weak positive correlation with aggression—the same as testosterone (Christiansen and Winkler, 1992; however other studies show that DHT is negatively correlated with measures of physical aggression; Christiansen and Krussmann, 1987; further, DHT is not so evil after all).
Summing it all up
Blacks are not stronger than whites, indeed evidence from the races’ differing somatype, grip strength and leverages all have to do with muscular strength. Furthermore, the study that Ellis cites as ‘proof’ that blacks are stronger than whites is on one measure; an isokinetic dynamometry machine which is pretty much a leg extension. In true tests of strength, whites blow blacks away, which is seen in all major professional competitions all around the world. Blacks do have denser bones which is due to androgen production in vitro, but as of yet, there has been no research done into bone density and criminality.
The races don’t differ on penis size—and if they do it’s by tenths of an inch which is not statisitcally significant and I won’t waste my time addressing it. It seems that most HBDers will see a racial difference of .01 and say “SEE! Rushton’s Rule!” even when it’s just that, a small non-significant difference in said variable. That’s something I’ve encountered a lot in the past and it’s, frankly, a waste of time to converse about things that are not statistically significant. I’ve also rebutted the theory on 2d/4d ration as well. Finally, Asians had a similar level of androgen levels compared to blacks, with whites having the least amount. Along with a hole in the theory for racial differences in androgen causing crime, it’s yet another hole in the theory for racial differences in androgens causing racial differences in penis size and prostate cancer.
On intelligence scores, no one denies that blacks have scored about 1 SD lower than whites for 100 years, no one denies that blacks have a lower educational attainment. In regards to learning disabilities, blacks seem to have the highest rates, followed by Native Americans, than non-Hispanic whites, East Asians and the lowest rates found in South Asians. He states only one study links brain size to criminal behavior and it showed a significant inverse relationship with crime but not other types of offenses.
This is a really good article and I like the theory, but it’s full of huge holes. Most of the variables described by Ellis have been shown to not vary at all or much between the races (re: penis size, testosterone, strength [whites are stronger] prostate cancer caused mainly by diet, 2d/4d ratio [no evidence of it showing a digit ratio difference], and bone density not being studied). Nevertheless, a few of his statements do await testing so I await future studies on the matter. He says that androgen exposure ‘differs by race and ethnicity’, yet the totality of evidence shows ‘not really’ so that cannot be the cause of higher amounts of crime. Ellis talks about a lot of correlates with testosterone, but they do not pass the smell test. Most of it has been rebutted. In fact, one of the central tenets of the ENA theory is that the races should differ in 2d/4d ratio due to exposure of differing levels of the hormone in vitro. Alas, the evidence to date has not shown this—it has in fact shown the opposite.
ENA theory is good in thought, but it really leaves a lot to be desired in regards to explaining racial differences in crime. More research needs to be looked into in regards to intelligence and education and its effect on crime. We can say that low IQ people are more likely to drop out of school and that is why education is related to crime. However, in Mazur (2016) shows that blacks matched for age had lower levels of testosterone if they had some college under their belt. This seems to point in the direction of the ENA theory, however then all of the above problems with the theory still need to be explained away—and they can’t! Furthermore, one of the nails in the coffin should be this: East Asian males are found to have higher levels of testosterone than white males, often enough, and East Asian males actually have the lowest rate of crime in the worle!
This seems to point in the direction of the ENA theory, however then all of the above problems with the theory still need to be explained away—and they can’t! Furthermore, one of the nails in the coffin should be this: East Asian males are found to have higher levels of testosterone than white males, often enough, and East Asian males actually have some of the lowest rate of crime in the world (Rushton, 1995)! So this is something that needs to be explained if it is to be shown that testosterone facilitates aggression and therefore, crime.
I’ve shown—extensively—that there is a low positive correlation between testosterone and physical aggression, why testosterone does not cause crime, and have definitively shown that, by showing how flawed the other studies are that purport to show blacks have higher testosterone levels than whites, along with citing large-scale meta-analyses, that whites and blacks either do not differ or the differences is small to explain any so-called differences in disease acquisition or crime. One final statement on the CAG repeats, they are effect by obesity, men who had shorter CAG repeats were more likely to be overweight, which would skew readings (Gustafsen, Wen, and Koppanati, 2003). So depending on the study—and in most of the studies I cite whites have a higher BMI than blacks—BMI and WC should be controlled for due to the depression of testosterone.
It’s pretty conclusive that testosterone itself does not cause crime. Most of the examples cited by Ellis have been definitively refuted, and his other claims lack evidence at the moment. Even then, his theory rests on the 2d/4d ratio and how blacks may have a lower 2d/4d ratio than whites. However, I’ve shown that there is no significant relationship between 2d/4d ratio and traits mediated by testosterone (Kohler, Simmons, and Rhodes, 2004) so that should be enough to put the theory to bed for good.
Genes account for about 80 percent of the variation in height and IQ, with both height and IQ correlating at .2. Therefore, genes must contribute largely to population variances in height. However, finding certain genes that contribute largely to these two traits is a problem, largely because both traits are polygenic in nature. Recent research has shown that most—or all–genes are height genes. If this is the case, are most—or all—genes IQ genes?
Height is around 80-90 percent heritable (Peeters et al, 2009). What this means is that the difference between the tallest and shortest 5 percent of the population is 11 inches, with 10 inches being accounted for by genes and 1 inch being accounted for by environment (Heine, 2017: 30). The gene that contributes the most to human height has been found to give 1/6th of an inch (Weedon et al, 2007). However, a recent meta-analysis shows that certain rare alleles give as much as 8/10ths of an inch (Hirschhorn, Deloukas, and Lettre, 2017). Furthermore, thousands of gene variants combined explain about 50 percent of human height (Yang et al, 2010). Yang et al (2010) also found 294,831 SNPs related to people’s height, which is—more or less—12 times the number of genes in our genome (Heine, 2017: 30; the number of genes in our genome is in the range of 19,000-20,000; Ezkurdia et al, 2014). Another meta-analysis found that 697 genetic variants explain about 20 percent of the genetic variation (Wood et al, 2014). Furthermore, according to geneticist David Goldstein, “most genes are height genes” (Goldstein, 2009).
Author of the book DNA is not Destiny and cultural and social psychologist Steven J. Heine writes:
“This means if you wanted to genetically engineer a designer baby who you would like to grow up to be tall, you would have to make almost 300,000 genetic alterations to the genome and you still would only be half way there. When the genetic evidence suggests that almost all genes are related to height, then in a way, we learn close to nothing about the genetic basis of height.” (Heine, 2017: 30)
Hirschhorn, Deloukas, and Lettre, (2017) found 83 rare and low-frequency genes that explain 1.7 percent of the adult heritability of height, along with newly identified and novel variants that explained 2.4 percent, “and all independent variants, known and novel together explained 27.4% of heritability. By comparison, the 697 known height SNPs explain 23.3% of height heritability in the same dataset (vs. 4.1% by the new height variants identified in this ExomeChip study)” (pg 7). So 27.4 percent of the variance is explained by known common variants and these new variants discovered.
Americans who drink more milk are, on average, half an inch taller than Americans who don’t recall drinking as much milk, even after controlling for race, income, and education (Wiley, 2005). This shows the importance milk has on skeletal muscle growth. This increase has even been noticed in Japan, where they increased their milk intake using school lunch programs (Takahasi, 1984), which increased their height by 4 inches (Funatogawa et al, 2009).
We also grow more in the spring and summer than in the fall and winter. This is due to ultraviolet radiation from the sun’s rays that synthesize some of the vitamin D we drink that is in the cow’s milk. Clearly, environmental factors (UV rays, milk consumption, overall nutrition, etc) all have a part to play in human height variation (Heine, 2017: 30). However, if all genes may be height genes, may all genes be IQ genes?
In regards to IQ, 3 genetic variants explain .3 IQ points (Rietvald et al, 2014):
After adjusting the estimated effect sizes of the SNPs (each R2 ∼ 0.0006) for the winner’s curse, we estimate each as R2 ∼ 0.0002 (SI Appendix), or in terms of coefficient magnitude, each additional reference allele for each SNP is associated with an ∼0.02 SD increase in cognitive performance [or 0.3 points on the typical intelligence quotient (IQ) scale].
This is the gene with the highest known effect that we currently know of. No “but undiscovered X means Y!!”, because science isn’t based on ‘what ifs’.
To predict one’s intelligence, you would need all genes on an SNP chip—which contains about 500,000 SNPs—to be able to predict half of the individual variation in IQ (Davies et al, 2011; Chabris et al, 2012; Heine, 2017: 175). Just as is the case with height, it seems that it’s possible that most—if not all—genes are IQ genes.
So, clearly, intelligence is highly polygenic, and, contrary to what Plomin says, it’s doubtful that we’ll be able to genotype one to guesstimate their intelligence level.
This is because you need more than 500,000 SNPs on a gene chip and even still, that would only explain half of the variance. So it’s reasonable to assume—as is the case with height—that all genes are IQ genes.
Chabris et al (2012) write:
One SNP, rs2760118 in SSADH (also known as ALDH5A1), exhibited a nominally significant association with g (t = 2.01, p = .04), but this association did not survive a Bonferroni correction. The mean g values (transformed to the IQ scale) by genotype for this SNP were 98.3, 99.7, and 100.6 for genotypes TT, TC, and CC respectively.
So it seems that all genes are height genes and all genes could possibly be IQ genes (that is, having a small effect). If most genes are height genes, and height is linked to IQ, then most genes should be IQ genes as well. Therefore, it is plausible that all genes are IQ genes.
Finally, I need to talk about the study that everyone is talking about, the study that found 52 new genes for intelligence (Sniekers et al, 2017). However, Razib Khan cautions: “My plain words are this: do not trust, and always verify“. A Google search for “gene found for” brings up 26,300,000 hits. As can be seen with the study that was published the other day on the supposed ‘new hominin’ found in Europe, science journalists use fancy and catchy headlines. “Genes for ___ and ___” is a bad way to put it—few traits are caused by a single gene, and most traits are highly polygenic, height and IQ included.
Do I think we’ll disentangle the intricacies involved with height and IQ? One day. But since at the moment, 500,000 SNPs need to be loaded on a gene chip to explain half of the variation in individual IQ.
Since most—or all—genes are related to height and the same may be so for IQ, we don’t really learn anything knowing the genes that control for these two traits. In regards to Heine’s (2017) example of genetic engineering 300,000 SNPs for height and you’d only be halfway there, I’d assume the same would be true for IQ. Both traits are highly polygenic, with thousands of genes controlling these traits. Genetic engineering a human for high intelligence or height looks to be a long shot—at least until far into the future.
What is the relationship between traumatic brain injury (TBI) and IQ? Does IQ decrease? Stay the same? Increase? A few studies have looked at the relationship between TBI and IQ, and the results may be quite surprising to some. Tonight I will look through a few studies and see what the relationship is between TBI and IQ—does IQ decrease substantially or is there only a small decrease? Does it decrease for all subtests or only some?
TBI and IQ
In a sample of 72 people with TBI who had significant brain injuries had an average IQ of 90 (study 1; Bigler, 1995). Bigler also says that whatever correlation exists between brain size and IQ “does not persist post injury” (pg 387). This finding has large implications: can there be a minimal hit to IQ depending on age/severity of injury/brain size/education level?
As will be seen when I review another study on IQ and brain injury, every individual in the cohort in Bigler (1995) was tested after 42 days of brain injury. This does matter, as I will get into below.
Table 1 in study 1 shows that whatever positive relationship between IQ and brain size that is there before injury does not persist after injury (Bigler, 1995: 387). Study 1 showed that, even with mild-to-severe brain damage, there was little change in measured IQ—largely because the correlation between brain size and IQ is .51 at the high end (which I will use—the true correlation is between .24 [Pietschnig et al, 2015] to .4 [Rushton and Ankney, 2009]), this means that if the correlation were to be that high, brain size would only explain 25 percent of the variation in IQ (Skoyles, 1999). That leaves a lot of room for other reasons for differences in brain size and IQ in individuals and groups.
In study 2 (Bigler, 1995: 389-391), he looked into whether or not there were differences in IQ between high and low brain volume people (95 men). Results summed in table 3 (pg 390). Those with low brain volume (1185), aged 28, had an IQ of 82.61 while those with high brain volume (1584), aged 34 had an IQ of 92 (both cohorts had similar education). Bigler showed in study 1 IQ was maintained post injury, so we can say that this was their IQ preinjury.
In table 2, Bigler (1995) compares IQs and brain volumes of mild-to-moderate and moderate-to-severe individuals with TBI. Brain volume in the moderate-to-severe group was 1289.2 whereas for the mild-to-moderate TBI-suffering individuals had a mean brain volume of 1332.9. Amazingly, both groups had IQ scores in the normal range (90.0 for moderate-to-severe TBI and 90.7 for individuals suffering from mild-to-moderate TBI. In study 3, Bigler (1995) shows that trauma-induced atrophic changes in the brain aren’t related to IQ postinjury, nor to the amount of focal lesion volume.
Nevertheless, Bigler (1995) shows that those with bigger brains had less of a cognitive hit after TBI than those with smaller brains. PumpkinPerson pointed me to a study that shows that TBI stretches far back into our evolutionary history, with TBI seen in australopithecine fossils along with erectus fossils found throughout the world. This implies that TBI was a driver for brain size (Shivley et al, 2012); if the brain is bigger, then if/when TBI is acquired, the cognitive hit will be lessened (Stern, 2002). This is a great theory for explaining why we have large brains despite the negatives that come with them—if we were to acquire TBI in our evolutionary past, then the hit to our cognition would not be too great, and so we could still pass our genes to the next generation.
The fact that changes in IQ are minimal when brain damage is acquired shows that brain size isn’t as important as some brain-size-fetishists would like you to believe. Though, preinjury (PI) IQ was not tested, I have one study where it was.
Wood and Rutterford (2006) showed results similar to Bigler (1995)—minimal change to IQ occurs after TBI. The whole cohort pre-injury (PI) had a 99.79 IQ. T1 (early measure) IQ for the cohort was 90.96 while T2 (late measure) IQ for the cohort was 92.37. For people with greater than 11th-grade education (n=30), IQ decreased from 106.57 PI to 95.19 in T1 to 100.17 in T2. For people with less than an 11th-grade education (n=44), IQ PI was 95.16 and decreased to 86.99 in T1 and increased to 87.96 in T2. Male (n=51) and female (n=23) were similar, with male PI IQ being 99.04 to women’s 101.44 with a 90.13 IQ in T1 for men with a 90.72 IQ in T1 for women. In T2 for men it was 92.94 and for women, it was 92.83. So this cohort shows the same trends as Bigler (1995).
The most marked difference in subtests post-injury was in vocabulary (see table 3) with similarities staying the same, and digit symbol, and block design increasing between T1 and T2. Neither group differed between T1 and T2. The only significant association in performance change over time was years of education. Less educated people were at greater risk for cognitive decline (see table 2).
The difference for PI IQ after T2 for less educated people was 7.2 whereas for more educated people it was 6.4. Though more educated people gained back more IQ points between T1 and T2 (4.98 points) compared to less educated people (.97 IQ points). And: “The participants in our study represent a subgroup of patients with severe head injury reported in a larger study assessing long‐term psychosocial outcome.”
Bigler (1995) didn’t have PI IQ, but Wood and Rutterford (2006) did, and from T1 to T2 (Bigler 1995 tested what would be equivalent to T1 in the Wood and Rutterford 2006 study), IQ hardly increased for those with lower education (.97 points) but substantially increased for those with higher education (4.98 points) with there being a similar difference between PI IQ and T2 IQ for both groups.
Brain-derived neurotrophic protective factor (BDNF) also promotes survival and synaptic plasticity in the human brain (Barbey et al, 2014). They genotyped 156 Vietnam War soldiers with frontal lobe lesion and “focal penetrating head injuries” for the BDNF polymorphism. Though they did find differences in the groups with and without the BDNF polymorphism, writing that there were “substantial average differences between these groups in general intelligence (≈ half a standard deviation or 8 IQ points), verbal comprehension (6 IQ points), perceptual organization (6 IQ points), working memory (8 IQ points), and processing speed (8 IQ points) after TBI” (Barbey et al, 2014). This supports the hypothesis that BDNF is protective against TBI; and since BDNF was important in our evolutionary history which is secreted by the brain while endurance running (Raichlen and Polk, 2012), this could have also been another protective factor against hits to cognition that were acquired, say, during hunts or fights.
Nevertheless, one study found in a sample of 181 children Crowe et al (2012) found that children with mild-to-moderate TBI had IQ scores in the average range, whereas children with severe TBI had IQ scores in the low average range (80 to 90; table 3).
Infants with mild TBI had IQ scores of 99.9 (n=20) whereas infants with moderate TBI has IQs of 98.0 (n=23) and infants with severe TBI had IQs of 90.7 (n=7); preschoolers with mild TBI had IQ scores of 103.8 (n=11), whereas preschoolers with moderate TBI had IQ scores of 100.1 (n=19) and preschoolers with severe TBI had IQ scores of 85.8 (n=13); middle schoolers with mild TBI had IQ scores of 93.9 (n=10), whereas middle schoolers with moderate TBI had IQ scores of 93.5 (n=21), and middle schoolers with severe TBI had IQ scores of 86.1 (n=14); finally, children with mild TBI in late childhood had a mean FSIQ of 107.3 (n=17), while children with moderate TBI had IQs of 99.5 in late childhood (n=15), and children with severe TBI in late childhood had FSIQs of 94.7 (Crowe et al, 2012; table 3). This shows that age of acquisition and severity influence IQ scores (along with their subtests), and that brain maturity matters for maintaining average intelligence post-TBI. Königs et al (2016) also show the same trend; the outlook is better for children with mild TBI, while children faired far worse with severe TBI compared to mild when compared to adults (also seen in Crowe et al, 2012).
People who got into motor vehicle accidents suffered a loss of 14 IQ points (n=33) after being tested 20 months postinjury (Parker and Rosenblum, 1996). The WAIS-IV Technical and Interpretive Manual also shows a similar loss of 16 points (pg 111-112), however, the 22 subjects were tested within 6 to 18 months within acquiring their TBI, with no indication of whether or not a follow-up was done. IQ will recover postinjury, but education, brain size, age, and severity all are factors that contribute to how many IQ points will be gained. However, adults who suffer mild, moderate, and severe TBIs have IQs in the normal range. TBI severity also had a stronger effect on children aged 2 to 7 years of age at injury, with white matter volume and results on the Glasgow Coma Scale (which is used to assess consciousness after a TBI) were related to the severity of the injury (Levin, 2012).
TBI can occur with a minimal hit to IQ (Bigler, 1995; Wood and Rutterford, 2006; Crowe et al, 2012). IQs can still be in the average range at a wide range of ages/severities, however the older one is when they suffer a TBI, the more likely it is that they will incur little to no loss in IQ (depending on the severity, and even then they are still in the average range). It is interesting to note that TBI may have been a selective factor in our brain evolution over the past 3 million years from australopithecines to erectus to Neanderthals to us. However, the fact that people with severe TBI can have IQ scores in the normal range shows that the brain size/IQ correlation isn’t all it’s cracked up to be.
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by Scott Jameson
RaceRealist and I have been ruminating on a lot of stuff lately. Here’s a fun one: what economic system works best relative to what we know about human health? In my mind there are two approaches: the libertarian approach, and quasi-fascism.
In the libertarian approach, there’s no regulation of sugar placed in our food. That’s already the case. But here’s an improvement: you don’t have to pay for anyone’s gastric bypass after they overeat that sugar.
In the fascist approach, there is regulation of sugar, because a fascist state does not allow people to poison each other for profit. You still have to pay for others’ medical expenses, but those expenses will be lower.
Here’s an advantage to the libertarian approach. In that society, the people who stuff their faces and refuse to get off the couch- who are dumber and lazier on average, probably- will have a higher mortality rate on average. Eugenics need not cost a dime.
But you run into a snag, sand in the gears of your hands-off system, when Big Food kicks out a whole bunch of crappy dietary advice, at which point a minority of reasonably intelligent people will be led astray, perhaps to the grave. How could a libertarian society stop that from taking place? Would it even bother? Could the system broadly work in spite of this snag?
A libertarian society doesn’t pay for idiots to have children. That’s good, but half of your population (women) are unlikely to ever support it. Women don’t do libertarianism; observe Rand Paul’s demographic Achilles Heel on page 25. When women asked men what to do about so-and-so’s eighth unpaid for child, we’d have to look them in the eyes and give a deadpan “let’s hope private charity can handle it.” There was a time, before FDR, when women would’ve accepted that answer. They were still in the kitchen back then, and I don’t know how to put them back there.
A fascist society has more hands-on eugenics, possibly genome editing or embryo selection. Also good. Expensive, but obviously worth it.
We welcome your input on these issues.
As an aside, White men are well-known as the most conservative, small government, nationalist group out there in our current political atmosphere. I always hear people spewing the schmaltziest nonsense about the values of the Founding Fathers. They were, relative to our political compass, nationalist libertarians. Accordingly, modern nationalists and libertarians do best with the exact same demographics that used to vote on candidates back then: property-owning White men. The sole reason that Ron and Rand Paul couldn’t get elected is that they are too similar to the Founding Fathers. Any other candidate who blathers on about the Founding values is simply a liar, and their obvious lies show a disrespect of your intelligence.
If you’re a libertarian, but not an ethno-nationalistic and patriarchal thinker, then you simply haven’t gotten the memo: women and minorities do not want to create the same world that you do, nor will they ever. Evolution gave us women who want social safety nets and other races which are better off if they parasitize off of your tax dollars. All of the most libertarian societies that ever existed (early US, ancient Athens, Roman Republic) were entirely run by White men, and adding women to the electorate gave us the welfare state. Aristophanes was right.
We’re also ruminating on the difference between IQ and expertise. I know of no mentally complicated task of which one can be a master without being intelligent. Take the IQs of chess grandmasters and you will find no morons.
Contrast that with purely physical activities. I bet you there are some really stupid people out there who are great at dancing for example. A prodigiously capable cerebellum may not predict an equally capable frontal lobe.
Discounting tasks which exclusively require things like simple physical coordination, muscle memory, etc, I ought to think that IQ is the biggest component of expertise.