Professor Black Truth is a youtube personality who reminds me of a black version of “Philosopher” and other alt-right extremists. Just as Philosopher thinks Ashkenazi elites conspire to undermine white interests (by propping up blacks), professor Black Truth thinks the elite serves white interests and conspires to oppress blacks (by propping up tools for white supremacy).
I estimate Professor Black Truth to be more verbally intelligent than 99.75% of Americans (verbal IQ 142(U.S. norms)) but he draws bad conclusions on social issues. He thinks Michael Jackson was innocent and has yet to progress beyond Chomsky talking points when it comes to U.S. foreign policy.
Estimated social IQ? 104 (slightly above the U.S. average)
Estimated overall IQ? About 128 (higher than 97% of America).
Of course that’s very rough because as a listener I can only observe his verbal skills and social understanding, not the many other abilities that are also part of intelligence.
In my opinion he is bitter that his high IQ didn’t take him as far in life as he thought it would and is resentful of the patronizing praise he probably got from much less intelligent white frat boys in college. Unlike Obama (who he views as a closet homosexual), his high IQ probably made him more of a freak than a star, and so he rationalizes his modest success by viewing Obama and other black elites as tools for white supremacy,
Despite his flawed analysis, he’s an extremely talented broadcaster with a darkly entertaining exaggerated delivery, much like a comic book villain. In this episode he accuses Ocasio-Cortez of being an anti-black bigot:
Imagine a pushy mother really wants her average 9.5-year-old son to get into a gifted class. Because IQ tests are normed for age and her son looks young for his age, she decides to tell the psychologist he’s only 6.5, so that he will get a much higher score.
Now assuming the boy is average for a 9.5-year-old in all cognitive domains he should score an age-ratio IQ of 146 in all domains, if the psychologist believes he’s 6.5 (because 9.5 is 146% of 6.5), however modern IQ tests use the deviation scale, where scores are assigned not by how many years advanced a kid is, but by one’s rank, relative to other American kids in one’s own age group. Being in the top 99% gives one an IQ of 65, being in the top 90% gives one an IQ of 80, being in the top 50% gives one an IQ of 100, being in the top 10% gives one an IQ of 120, and being in the top 1% gives one an IQ of 135, and being in the top 0.1% gives one an IQ of 146 etc.
Now obviously the average 9.5-year-old pretending to be a 6.5-year-old will get a high IQ in every domain, but his margin of superiority varies dramatically depending on the test. If he taken the WISC-R for example (before the norms expired), here’s how he’d have scored (for the readers convenience, I converted subtest scores, normally scaled from 1 to 19 into IQ equivalents):
Test:
IQ based on age ratio method: MA 9.5/CA 6.5(100)
Deviation IQ
Deviation IQ corrected for reliability
Information (general knowledge test)
146
130
137
Similarities (verbal abstract reasoning)
146
120
122
Arithmetic (mental math)
146
140
145
Vocabulary
146
138
144
Comprehension (Common sense & social judgement)
146
130
136
Digit Span (attention & rote memory)
146
120
123
Picture Completion (visual alertness)
146
120
122
Picture Arrangement (social interpretation)
146
120
123
Block Design (spatial organization)
146
120
122
Object Assembly (spatial integration)
146
115
117
Mazes (visual planning)
146
120
122
Verbal IQ
146
141
143
Performance IQ
146
128
129
Full-scale IQ
146
139
140
Because more reliable tests show larger correlations with age, the fourth column corrects deviation IQs for reliability. This was done by dividing the deviation from the mean by the square root of the reliability at the age the boy is claiming to be. Note that Digit Span and Mazes are optional tests not used to calculate the composite IQs (in bold) unless they are substituted for a core test. Digit Span was not used to calculate any composite score but Mazes was substituted for one of the core tests (Coding) because Coding is not the same test at all ages.
Now after adjusting for test reliability, our gifted 6.5-year-old (who is secretly an average 9.5-year-old) had deviation IQs ranging from 117 (object assembly) to 145 (Arithmetic).
Why such a huge discrepancy? The most obvious answer is that 9.5-year-olds have two cognitive advantages over 6.5-year-olds. Not only are their brains bigger and more developed, but they’ve also have three extra years of life experience. On tests that require novel problem solving, their advantage will be modest because it only reflects their neurological superiority. It can not reflect their life experience advantage because by definition, novel problems are things we’ve had little experience with.
By contrast, on tests that required acquired knowledge they have two advantages. Not only does the 9.5-year-old have more neurological ability to reason arithmetically, learn and remember facts, and infer the meaning of words, but he’s also had three extra years to acquire number concepts, general knowledge, and vocabulary. Because “crystallized” tests require both neurolgical ability and experience, they show a much steeper age progression than fluid tests (and a much steeper decline in old age, though this is confounded with the Flynn effect), which require only neurological ability (beyond some basic experience threshold that virtually all Americans reach by age 5 or so). Indeed looking at the age progression is a good way to quantify crystalized vs fluid.
Notice also that the tests that show the biggest age effects also tend to be the ones that show the biggest family effects per James Flynn’s method. Because both reflect experience.
Now let’s imagine when the pseudo-gifted boy turned 16.83 and he wanted to get into Mensa, and they were unwilling to accept scores from the past. He is still exactly average in all domains for his true age, but still looking young he tells the psychologist he is only 10.5. She doesn’t buy it, but she’s not about to turn down $800 for a few hours work so she plays along.
Here are his results:
Test:
IQ based on age ratio method: MA 16.83/CA 10.5(100)
Deviation IQ
Deviation IQ corrected for unreliability
Information (general knowledge test)
160
133
136
Similarites (verbal abstract reasoning)
160
130
134
Arithmetic (mental math)
160
120
123
Vocabulary
160
138
141
Comprehension (Common sense & social judgement)
160
130
136
Digit Span (attention & rote memory)
160
110
112
Picture Completion (visual alertness)
160
120
124
Picture Arrangement (social interpretation)
160
115
118
Block Design (spatial organization)
160
125
127
Object Assembly (spatial integration)
160
125
131
Mazes (visual planning)
160
118
122
Verbal IQ
160
139
141
Performance IQ
160
130
132
Full-scale IQ
160
139
140
Because more reliable tests show larger correlations with age, the fourth column corrects deviation IQs for reliability. This was done by dividing the deviation from the mean by the square root of the reliability at age 10.5
Once again, his years of extra life experience (relative to the age group he’s pretending to be) gave him a huge advantage on knowledge based tests like Vocabulary and Information. And once again, novel tasks like Block Design, Picture Arrangement, Mazes and Digit Span showed less advantage.
But what happened to Arithmetic? His advanced age gave him a huge advantage when he was a 9.5-year-old pretending to be 6.5, but now that he is 16.83 pretending to be 10.5, this subtest is even less age dependent than Block Design. The likely explanation is that once kids acquire basic number concepts in school (addition, subtraction, multiplication, and division) arithmetic depends less on experience and more on neurology.
This is why one can never say, categorically, that a given test measures fluid or crystallized ability. It depends on the population taking it. For example the Raven progressive matrices might be a fluid test within generations but a crystallized test between generations. Even something as seemingly crystallized as the math SAT might measure fluid ability in 17-year-olds with at least four years of advanced math. Among all 17-year-olds, it will still correlate with (but not so much be directly caused by) fluid ability because those with more fluid ability are likely to take advanced math in the first place.
Another interesting case is Similarities, which requires one to infer the link between common things (how are chess and scrabble alike?). This showed small age effects in early childhood, but large age effects in later childhood. At the lower end this test is just about the ability to see associations, but at the higher end, it becomes increasingly dependent on diction and sometimes esoteric concepts, making it more experience dependent.
One question is why, if crystallized tests are more culture dependent, do they often load more on psychometic g (the general factor of IQ tests believed to be a property of the physical brain). Perhaps as some have suggested, once you control for age, in countries where everyone has opportunity for schooling, knowledge tests measure both the ability to learn over an entire lifetime and the ability to store and retrieve a lifetime of learning. By contrast fluid tests only measure the ability to learn in the testing room, and not the ability to store and retrieve it years later. One theory is the more parts of the brain a test samples, the more g loaded it is, which would make sense if g is just overall cognition.
Using twin studies, scientists divide phenotypic variation into three categories: DNA variation, shared environmental variation, and unshared environmental variation. Shared environment are all the experiences MZ twins reared together have in common (same upbringing, same schools, same womb) while unshared environment are all the experiences they don’t share (position within the womb, getting hit on the head, having an inspiring teacher).
The best estimate using massive datasets suggest that within Western democracies, DNA explains 41% of IQ variation at age 9, 55% at age 12, 66% at age 17, and 74% in adulthood. By contrast shared environment explains 33% at age 9, 18% at age 12, 16% at age 17, and 10% in adulthood (Bouchard 2013, figure 2). That leaves unshared environment explaining 26% of the variation at age 9, 27% at age 12, 19% at 17, and 16% in adulthood.
You don’t have to believe these associations are causal, but they are real. They’ve been more or less replicated using studies comparing (1) MZ twins with DZ twins, (2) MZ twins raised apart, (3) unrelated people reared in the same home. Although all of these methods depend of different assumptions, they all converge on the same conclusion: the predictive power of DNA skyrockets from childhood to adulthood while the predictive power of shared environment plummets. The same pattern (known as the Wilson effect) has also been observed for other phenotypes and in other species.
But why? Shouldn’t environment get more important as we age since experience has increasing time to accumulate? One theory is that more and more genes become active as we age. A more popular theory is that we select environments that maximize our genotype, so environment becomes just a magnifier of genes, not a causal force in its own right. So genetically smart people will stay in school and genetically strong people will lift weights and take steroids etc. People invest in where they’re more likely to be rewarded.
But here’s where things get really interesting. The Wilson effect behaves differently on different types of IQ tests. In his book Does your Family make you smarter? James Flynn notes that cognitive inequality increases from childhood to later adulthood (because good genes cause good environments and bad genes cause bad environments, the smart get smarter and the dumb get dumber, relative to the average person their age) but this pattern is much more pronounced on some tests than others.
Flynn describes three types of tests:
Type 1: Tests that show large family effects (shared environment) that decay slowly. This include tests involving vocabulary (define “rudimentary”), general knowledge (How old is the Earth?) verbal abstraction (how are a brain and a computer the same?) and social comprehension (why do you need a passport to travel?)
Type 2: Tests that show small family effects that decay fast. These include spatial manipulation (use these two triangles to make a square) and noticing incongruities (what’s missing or absurd in a picture of a common object or scene).
Type 3: Tests that show that large family effects that decay fast. These tests include clerical speed and arithmetic.
Flynn argues that type 1 tests involve skills that children learn from observing their parents talk, hence the large family effect. By contrast he says of type 2 tests:
Aside from the occasional jigsaw puzzle, they have no part in everyday life. Children never see their parents performing these cognitive tasks as part of normal behavior. Family effects are weak, even among preschoolers. Since these subtests match environment with genetic potential so young, they would be an ideal measure (for, say, 5-year-olds) of genes for intelligence.
From pages 53-54 of Does Your Family Make You Smarter? by James Flynn
In other words, Type 2 tests measure “novel problem solving”, while type 1 tests measure acquired abilities. A more provocative interpretation is type 2 tests measure real intelligence, while type 1 just measure knowledge and experience. This is the age-old distinction between aptitude tests vs achievement tests, culture fair vs culture loaded, fluid vs crystallized.
And yet Flynn largely rejects Cattell-Horn-Carroll’s theory that fluid ability (novel problem solving) is invested to acquire crystallized ability (accumulated knowledge) writing:
…fluid skill is just as heavily influenced by family environment as the most malleable crystallized skill (vocabulary) and therefore, neither skill deserves to be called an investment and the other a dividend.
From page 132 of Does Your Family Make You Smarter? by James Flynn
Flynn of course is referring to the greatest irony in the history of psychometrics and the biggest mistake of Arthur Jensen’s career: the Raven Progressive Matrices (long worshiped by Jensen and Jensenistas as the most culture fair measure of pure intelligence ever invented) is a type 1 test!
Which of the 8 choices completes the above pattern? Image from from Carpenter, P., Just, M., & Shell, P. (1990, July)
But let’s not throw the baby out with the bath water. There’s no need to abandon CHC investment theory just because a major test got mischaracterized. But at the same time, it doesn’t feel right to reclassify the Raven as a crystallized test, Research is needed to understand why the Raven is so culturally sensitive when it superficially looks like a measure of novel problem solving. Is it measuring some kind of implicit crystallized knowledge we’re not conscious of like being familiar with patterns, columns and rows and reasoning through the process of elimination, or are the family effects on the non-cognitive part of the test (having the motivation to persist and concentrate on such an abstract task). Flynn argues that the brain is like a muscle, but if so, the Raven is an exercise most have never done before, so why isn’t it a type 2 test?
Flynn might argue that if your family helped you with abstract problems in algebra or had philosophical discussions about hypothetical concepts, you’ve been exercising for the Raven all your life, but this seems like a bit of a stretch. All the research shows that cognitive training has narrow transfer (i.e. practicing chess will only make you slightly better at checkers, and not at all better at scrabble) though perhaps the Raven’s uniquely abstract (general) nature allows it to slightly buck this trend.
Modern IQ tests force test scores to fit a normal distribution, but I’ve long suspected the distribution of intelligence is anything but normal. After all, if you look at the distribution of wealth and income (a crude measure of intelligence), you find the richest people are worth millions of times more than the poorest. And that’s just because they’re not paying their fair share, as Elizabeth Warren would have us believe. We see the same skewed distribution in academic output, with the most productive scientists publishing orders of magnitude more than the least productive.
A member of Prometheus society once hypothesized that the human mind works in parallel, so that complex problem solving speed doubles every 10 IQ points (he later suggested 5)?
And yet at the same time, Arthur Jensen seemed to believe intelligence was normally distributed and in support of this cited cognitive measures that form a natural scale with equal intervals and a true zero point such as the total number of words a person knows or the number of digits they can repeat after one hearing, both of which were normally distributed. What was missing however from Jensen’s examples was complex on-the-spot problem solving.
Thanks to my research on how people today would score on the oldest Wechsler tests (the ancient WBI) I have some novel data on how long it takes people to solve visuo-spatial items. The WBI includes a subtest called Object Assembly where you have to fit a bunch of odd shaped cardboard cutouts together to make a familiar object, and over the past decade or so, this was administered to a relatively random sample of White young adults (n = 17). One seven piece item was easy enough that all 17 were able to complete it within the 3 minute time limit, yet hard enough that no one could solve it immediately.
Normally I wouldn’t show items from an actual IQ test, but the WBI is over 80 years old and Object Assembly is no longer part of current Wechsler subtests.
[update april 1, 2020, I decided to remove the photos to be safe, but it’s too bad because those were gorgeous photos I took]
When one test participant saw these cardboard cutouts being placed on the table, he apologized for being unable to contain his laughter. A painful reminder that my life’s work is considered a joke by much of the population. And yet for all their apparent absurdity, these silly little tests remain the crowning achievement of social science with one’s score being largely destiny .
Even on this one item, there seemed to be a correlation between IQ and occupation/education. The time taken to complete the puzzle ranged from 14 seconds (a professional with a Masters degree in Engineering from a top Canadian university) to a 137 seconds (a roofer with only a high school diploma). Below are the times of all 17 participants ranked from fastest to slowest.
In a normal distribution, 68% fall within one standard deviation of the mean. In this distribution, 71% fell within one standard deviation of the mean (22 to 82 seconds) which is pretty damn close. Also in a normal distribution, 95% fall within two standard deviations (-8 to 112 seconds) and in my sample, 94% did (even closer!).
So simply by picking at least a moderately g loaded novel problem that is both easy enough that no one gives up, yet hard enough that everyone is forced to think, and measuring performance on a natural scale (time taken in seconds) a normal curve emerges, though a somewhat truncated one (the slowest time is much further from the mean than the fastest, perhaps because human hands can only assemble puzzles so fast, regardless of how quick the mind is).
To convert from time in seconds to IQ, all one needs to do is make the natural mean of 52 seconds equal to the IQ mean of 100, and make each natural standard deviation (30 seconds) faster or slower than 52 seconds, equal to 15 IQ points (the IQ standard deviation) above or below 100, respectively.
Thus the elite Masters degree in Engineering professional gets an IQ of 119 (14 seconds) and the high school only roofer gets an IQ of 58 (137 seconds). But note that even though IQ appears to be a true interval scale (meaning an X point gap between any two points on the IQ scale are equivalent), it is not a ratio scale, meaning IQs can not be meaningfully multiplied. So even though IQ 119 is about twice as high as IQ 58, the difference in actual problem solving speed is about an order of magnitude. This is because unlike height, weight and time in seconds to solve puzzles (which can be meaningfully multiplied) the IQ scale has no true zero point.
Of course the normal curve only applies to the biologically normal population, so it’s interesting to note that it’s now standard procedure to exclude pathological cases from IQ test norming samples. Indeed one man was excluded from my sample after he told me that years ago he had suffered a stroke (quite unusual for a man in his thirties). This man struggled greatly with the above puzzle, only joining 25% of the cuts within the 3 minute time limit. The only way to estimate what his time would have been had he not given up is divide 3 minutes by 25% which gives 12 minutes (720 seconds). This is more than 22 standard deviations slower than the mean of the normal sample, and equivalent to an IQ of -234! Such extreme deviations remind us how sensitive the normal curve is to the normality of the sample.
*one person solved the puzzle in 67 seconds, but the ear pieces were reversed, so only 75% of the cuts were correctly joined. I thus considered this equivalent to a perfect performance at 75% of the speed (67 seconds/0.75 = 89 seconds).
Have you ever wondered why we have to go all the way to Africa to see a safari? For Africa is the land of 13000 lb elephants and 18 foot tall giraffes.
But what many do not realize is that 40,000 years ago, the whole World looked like an African safari. North America and Eurasia were home to Pachystruthio dmanisensis, a flightless bird that stood 11.5 feet tall and weighed nearly a 1000 lbs.
North America was also home to the short-faced bear which stood up to 14 feet tall, weighed about 1700 lbs, and could run up to 40 miles per hour. And of course who could forget the 13000 lb mammoth, which lived on every continent except Australia.
Scientists call these giant animals “megafauna” (mega = big, and fauna = animals). We still have megafauna in the world, but there used to be a whole lot more of it. In fact, it appears that having a large number of large-bodied animals in an ecosystem is actually the normal state for our planet, at least for the geologic era we are living in today, the Cenozoic (or “Age of Mammals”) . But sometime in the past 50,000 years (very recent geologically), everywhere except for Africa, most of those large animals became extinct. And we still aren’t sure why!
Some scientists think megafauna survived in Africa because humans evolved there so large animals had more time to adapt to us. However members of the genus Homo have been living outside Africa for 2 million years, so Eurasian megafauna had time to adapt to us too. Another theory is that megafauna were killed off by the extreme climate changes that megafauna endured outside Africa.
But in asking why megafauna went extinct everywhere except Africa, politically correct scientists are forced to ignore the elephant in the room (pun intended): HBD. If Arthur Jensen was correct about the black-white IQ gap being genetic, perhaps Africans simply hadn’t evolved the intelligence to hunt large game.
But that can’t be the whole story. If racial differences in IQ evolved because we needed more intelligence to survive the non-tropics, how were Australian aboriginals (who retain a tropical phenotype) able to kill off 100% of their giant mammals? Migrating from Africa to Australia means their ancestors must have spent some time in the non-tropical ice age Middle East. Was this enough time for them to evolve the intelligence to hunt big game or was the big game in Australia simply easier to hunt because it had not had the time to evolve ways to avoid humans?
If cold climate selected humans were especially evolved for hunting big game, and if the big game on continents where humans had never been were especially bad at evading human predators, then these two factors predict the biggest megafauna massacre of all should have occurred in the Americas where both conditions were met: cold adapted hunters (humans entered the Americas from Siberia) entering a continent where humans had never been.
And indeed that seems to be the case. Paleo-biologist Rebecca Terry at Oregon State University says “pretty advanced weaponry was definitely present, and the extinctions in the New World in North America and South America were really extreme as a result.”
11,000 years ago (shortly after modern humans entered the New World), the average weight of a non-human mammal in North America was about 200 pounds compared to only 15 pounds today.
The following article is an updated revision of an article I published in August 2019 about how 21st century Northern Americans score on an IQ test normed before the second World War. The reason for the update is that in December 2019, the sample size of my study increased by 13% (from n = 15 to n = 17). I had originally hoped to collect more data before publishing an update but with the uncertainty surrounding the coronavirus crisis, it’s unclear when that will be.
The Flynn effect, popularized by James Flynn, refers to the fact that IQ tests supposedly get easier with time. Although by definition the average IQ of American or British (white) people is always 100, the older the IQ test, the easier it is to score 100. Thus to keep the average at 100, tests like the Wechsler must be renormed every 10 years or so, otherwise the average IQ would increase by about 3 points per decade.
Although scholars continue to debate whether the Flynn effect reflects a genuine increase in intelligence (perhaps caused by prenatal nutrition or mental stimulation) or just greater test sophistication caused by modernity, there’s been remarkably little skepticism about the existence of the Flynn effect itself.
If an American born in the nineteen-thirties has an I.Q. of 100, the Flynn effect says that his children will have I.Q.s of 108, and his grandchildren I.Q.s of close to 120—more than a standard deviation higher. If we work in the opposite direction, the typical teen-ager of today, with an I.Q. of 100, would have had grandparents with average I.Q.s of 82—seemingly below the threshold necessary to graduate from high school. And, if we go back even farther, the Flynn effect puts the average I.Q.s of the schoolchildren of 1900 at around 70, which is to suggest, bizarrely, that a century ago the United States was populated largely by people who today would be considered mentally retarded.
While few people believe our grandparents were genuinely mentally retarded, it’s taken for granted that they would have scored in the mentally retarded range by today’s standards.
But is this true? I began having doubts over a decade ago when I examined the items on the first Wechsler intelligence scale ever made: the ancient WBI (Wechsler Bellevue intelligence scale). Meticulously normed on New Yorkers in the 1930s, this test remains far and away the most comprehensive look we have at early 20th century white Northern American intelligence, and while some of the subtests looked easy by today’s standards, others, especially vocabulary, looked harder.
The Kaufman effect
What also struck me was how little instruction, probing or coaching people got when taking the ancient WBI, compared to its modern descendant the WAIS-IV. This matters a lot because the way the Flynn effect is calculated on the Wechsler is by giving a new sample of people both the newest Wechsler and its immediate predecessor, in random order to cancel out practice effects, and then seeing which version they score higher on. If they average 3 points lower on the WAIS-IV normed in 2006 than on the WAIS-III normed in 1995, it’s assumed IQ increased by 3 points in 11 years.
The problem with this method (as Alan Kaufman may have discovered before me) is that the subset of the sample that took the newer version first has a huge advantage on the older version compared to the norming sample of the older test (over and above the practice effect which is controlled for), because the norming sample of the older test was never given coaching and probing.
Statistical artifact
A Promethean once said maybe the Flynn effect is just a statistical artifact of some kind. He never told me what he meant, but it got me thinking:
One problem with how the Flynn Effect is calculated on the Wechsler is that it’s assumed that gains over time can be added. For example it’s assumed that you can add the supposed 7.8 IQ gain from WAIS normings 1953.5 -1978 to the 4.2 IQ gain from normings 1978 – 1995 to the 3.7 IQ gain from normings 1995-2006, for a grand total of 15.7 IQ points from normings 1953.5 – 2006.
This would make sense if he were talking about an absolute scale like height, but is problematic when talking about a sliding scale like IQ. For example, suppose the raw number of questions correctly answered in 1953.5 was 20 with an SD of 2. By 1953.5 standards, 20 = IQ 100 and every 2 points = 15 IQ points above or below 100. Now suppose in 1978, people averaged 22 with an SD of 1. That’s a gain of 15 IQ points by 1953.5 standards. Now suppose in 1995 people average 23 with an SD of 2. That’s a gain of 15 IQ points by 1978 standards. Adding the two gains together implies a 30 point gain from 1953.5 to 1995, but by both 1953 and 1993 standards, the difference is only 23 points.
Changing content
Another problem with studying the Flynn effect is the content of tests like the Wechsler is constantly changing. This is especially problematic when studying long-term trends in general knowledge and vocabulary. If words that are obscure in the 1950s become popular in the 1970s, then people in the 1970s will score high on the 1950s vocabulary test. Meanwhile the 1970s vocabulary test may contain words that don’t become popular until the 1990s, Thus adding the vocabulary gains from the 1950s to the 1970s to the gains from the 1970s to the 1990s, might give the false impression that people in the 1990s will do especially well on a 1950s vocabulary test, when in reality, many words from the 1950s may have peaked in the 1970s and are even more obscure in the 1990s than they were in the 1950s.
An ambitious study
Given the Kaufman effect, the statistical artifact, and changing content, I realized the only way to truly understand the Flynn effect is to take the oldest quality IQ test I could find and replicate its original norming on a modern sample.
In 2008 I made it my mission to replicate Wechsler’s 1935-1938 norming of the very first Wechsler scale. Ideally I should have flown to New York where Wechsler had normed his original scale, but if Wechsler could use white New Yorkers as representative of all of white America (WWI IQ tests showed white New Yorkers matched the national white average), I could use white Ontarians as representative of all of white Northern America (indeed white Americans and white Canadians have virtually the same IQs). The target age group was 20-34 because this was the reference age group Wechsler had used to norm his subtests.
It took over a decade but I was gradually able to arrange for 17 randomly selected white young adults to take the one hour test. They were non-staff recruited from about half a dozen fast food/ coffeehouse locations in lower to upper middle class urban and suburban Ontario. The final sample ranged in education from 9.5 years (early high school dropout) to 18 years (Masters Degree in Engineering from one of Canada’s top universities). The mean self-reported education level was 12.9 years (SD = 2.12) suggesting that despite the lack of female participants, the sample was fairly representative (the average Canadian over 25 has about 13 years of schooling); in cases where those below the age of 25 were in the process of finishing a degree, they were credited as having it.
Testing conditions were not optimum (environments were sometimes noisy, at least one person had a few beers before testing; another was literally falling asleep during the test) and 17 people is way to small a sample to draw statistically significant conclusions about 11 different subtests. One man with a conspicuously low score was removed from the sample after he stated that he had years ago suffered a stroke.
Nonetheless, the below table shows how whites tested in 2008 to 2019 compared to Wechsler’s 1935-1938 sample, with the last column showing the expected scores of the 21st century sample, extrapolating gains James Flynn calculated from 1953.5 to 2006 (see page 240 of his book Are We Getting SMARTER?) to the current study: circa 1937 to circa 2013.5.
Note: the 11 subtests were scaled to have a mean of 10 and an SD of 3 in the original young adult norming sample, while the verbal, performance and full-scale IQs were scaled to have a mean of 100 and an SD of 15. Note also that vocabulary is alternate test, not used to calculate either verbal or full-scale IQ on the WBI. One third of my sample did not take Digit Symbol so for these, Performance and full-scale IQs were calculated via prorating.
Test:
Nationally representative sample of young white adults (NY, 1935 to 1938)
Randomish sample of young white adults (2008 to 2019, ON, Canada)
Expected WBI scores in 2008-2019 based on Flynn’s calculated rate of increase
Information (general knowledge test)
10 (SD 3)
8.41 ( SD 2.55)
12.3
Similarites (verbal abstract reasoning)
10 (SD 3)
13.35 (SD 2.91)
15.54
Arithmetic (mental math)
10 (SD 3)
9.18 (SD 4.34)(this subtest contained a unit conversion item that seemed biased against Canadians so for those who advanced far enough to fail this item, scores were prorated; had they not been the mean would have been 7.53 (SD 3.54))
11.02
Vocabulary
10 (SD 3)
9 (SD 2.5)
14.95
Comprehension (Common sense & social judgement)
10 (SD 3)
9.47(SD 2.93)
13.93
Digit Span (attention & rote memory)
10 (SD 3)
9.71 (SD 2.63)
11.46
Picture Completion (visual alertness)
10 (SD 3)
10.71 (SD 3.1)
14.52
Picture Arrangement (social interpretation)
10 (SD 3)
10.24 (SD 2.73)
13.35
Block Design (spatial organization)
10 (SD 3)
13.12 (SD 3.31)
12.91
Object Assembly (spatial integration)
10 (SD 3)
11.82 (SD 1.89)
14.06
Digit Symbol (Rapid eye-hand coordination)
10 (SD 3)
11.12 (SD 2.82)(note: only 12 of the 17 subjects took this subtest)
14.66
Verbal IQ
100 (SD 15)
103.8 (SD 14.73)
Performance IQ
100 (SD 15)
109.3 (SD 12.11)
Full-scale IQ
100 (SD 15)
106.9 (SD 13.63)
122
Conclusion
The Flynn effect is dramatically smaller than we’ve been led to believe at least on tests of specific information that may become obscure over generations. By contrast Similarities (abstract reasoning) and Block Design (spatial analysis) have indeed increased by amounts comparable with Flynn’s research. These two abilities may conspire to explain why some of the largest Flynn effects have been claimed on the Raven Progress Matrices, an abstract reasoning test using a spatial medium.
It’s unclear if these are nutritional gains caused by increasing brain size, neuroplastic gains caused by cultural stimulation, or mere teaching to the test caused by schooling, computers and brain games.
Lynn (1990) argued the Flynn effect was caused by nutrition, citing a twin study proving nutrition gains are more pronounced on Performance IQ (consistent with the Flynn effect). Research on identical twins (where one twin gets better prenatal nutrition than the other) has shown that by age 13, the well nourished twin exceeds his less nourished counterpart by about 0.5 SD on both head circumferenceand Performance IQ, but not at all on verbal IQ. Thus it’s interesting that 21st century young Northern American men today exceed their WWII counterparts by about 0.5 SD on both head circumference (22.61″ vs 22.3″) and Performance IQ (109 vs about 100).
One possibility is that Performance IQ gains are entirely caused by improvements in the biological environment (prenatal health and nutrition), while verbal IQ gains are entirely caused by cultural advances (i.e. education); though somewhat negated by knowledge obsolescence.
Loved ones keep begging to go out for a walk and get some fresh air. They say it’s not good for me to be hiding in the house all day and that I need exposure to the sun. I went for a half hour walk yesterday but spent most of my time on the road because I didn’t want to pass other humans on the sidewalk. But even when no one else is around, I’m still not sure if it’s safe to walk. What if this virus stays in the air just waiting for someone to walk through it?
This whole thing could have been minimized if we had just stopped international travel a few weeks earlier. Virtually every case in my city was brought here by someone traveling abroad.
I wonder if our failure to contain this thing is evidence of dysgenics?
It’s during times like this we see who are the truly intelligent. It’s the people who can adapt the situation to our advantage by making masks, test kits, ventilators, developing vaccines, and pushing common sense public policy.
The great writers, artists and philosophers may think they’re the most intelligent, but they’re not the ones who can save us.
An ad hoc assertion is essentially a hypothesis that has not been independently verified and is invoked to save a theory from falsification.
You’ve been doing that since I first started commenting here. To the point that the theory itself is just losing any real explanatory power it had. An example would be the statement that cold temperatures select for IQ but if the environment is too inhospitable, civilization will likely not emerge.
Actually it’s been independently verified over and over again and contradicted never. All six independent civilizations were created South of the Caspian sea by peoples who had spent tens of thousands of years outside the tropics. It’s one of the least ad hoc theories you’ve ever heard of because it has not a single exception.
So even though anatomically modern humans have been around for about 195,000 years, all six civilizations emerged 6000 to 3500 years ago. Coincidence? No. We didn’t evolve the cognitive ability to build civilization until we left the tropics and were exposed to the ice age, and even those who had evolved to the ice age could only build civilization if they ended up in bountiful land (i.e. South of the Caspian sea).
This perfectly explains both why civilization took so long, and why it was only created by certain peoples.
So because the IQ needed to build civilization evolved in high latitude (cold), but the lands amenable to civilization were low latitude (warm) it took our species 189,000 years before we had the right kind of humans on the right type of land during an interglacial period. These were humans who had migrated North but not too far North (i.e. Middle Easterners), or those who migrated as far North as the arctic circle, only to return to the tropics (i.e. the Mayans)
arctic peoples have large brains for a very NOT just so story reason; the exact same adaptation is found in arctic mammals and in ice age humans and it’s very simple, a big round head is more insulating than a small or long head, allen’s rule.
Arthur Jensen agrees writing:
climate also influenced the evolution of brain size apparently indirectly through its direct effect on head size, particularly the shape of the skull. Head size and shape are more related to climate than is the body as a whole. Because the human brain metabolizes 20 percent of the body’s total energy supply, it generates more heat in relation to its size than any other organ. The resting rate of energy output of the average European adult male’s brain is equal to about three-fourths that of a 100-watt light bulb. Because temperature changes in the brain of only four to five degrees Celsius are seriously adverse to the normal functioning of the brain, it must conserve heat (in a cold environment) or dissipate heat (in a hot environment). Simply in terms of solid geometry, a sphere contains a larger volume (or cubic capacity) for its total surface area than does any other shape. Conversely, a given volume can be contained in a sphere that has a smaller surface area than can be contained by a nonspherical shape, and less spherical shapes will lose more heat by radiation. Applying these geometric principles to head size and shape, one would predict that natural selection would favor a small head with a less spherical shape (brachycephalic) shape because of its better heat dissipation in hot climates, and would favor a more spherical (dollichocephalic) shape because of its better heat conservation in cold climates….
From The g Factor page 436
So even if cold climates didn’t require any extra intelligence to survive in, they did require more brain mass just to keep warm, and given the moderate causal correlation between IQ and brain size, they would have selected for intelligence indirectly as a byproduct of thermoregulation.
There is also likely a causal correlation between IQ and brain sphericity (independent of size) because a sphere is the shape that minimizes the distance between neurons and thus presumably maximizes brain efficiency.
So it seems that not only could cold winters have selected for high IQ directly because of the intelligence needed to survive the cold, but they also may have selected indirectly via thermoregulation of brain size and brain shape.
The question for HBDers is how do we test these three potential causes to determine how big a role (if any) each played in population differences in IQ?
Cold Winter Theory (CWT) is the theory that population differences in IQ are largely explained by the ancestral climates that the peoples evolved in, with colder climates selecting for higher IQs because of the difficulty figuring out how to build warm shelters, make warm clothes, create fire, get food etc. Modern CWT can be credited to Richard Lynn, though the idea is so intuitive that it was independently inferred by multiple historical thinkers throughout the centuries.
Now if you don’t believe population IQ differences are genetic in origin, then you don’t need an evolutionary theory like CWT to explain the correlation between a population’s ancestral climate and their mean IQ; in theory it might be explained by non-genetic factors like parasite load.
But if you do believe they’re genetic, CWT is the obvious cause: Hominoids have spent 25 million years adapting to the tropics so these may not require as much novel problem solving as the arctic, which we only encountered in the last 40,000 years. Among extant hunter-gatherers, the higher the latitude, the more diverse and complex their tool kit. There’s a reason people travel South for vacation and take vacation in the summer and why many camp grounds close for the winter. It seems cold weather is generally more challenging than warm weather.
Nonetheless I’ve been alerted to yet another attempt to debunk CWT, this time by W. Buckner on a blog called TRADITIONS OF CONFLICT (hat-tip to MeLo & RR). Buckner makes three main arguments against CWT.
Argument 1: Climate can’t explain the low IQ of Bushmen because the Kalahari is sometimes cold.
Counterargument
While it’s true that temperatures can sink as low as 0 °C in the Kalahari desert, this is nothing compared to the lows of -30°C in Ukraine, -52°C in Kazakhstan, and -68°C reached in Russia; three countries that makeup the Pontic-Caspian steppe, the homeland of the Indo-Europeans, far and away the most successful language group on the planet, giving rise to nearly half of the World’s population. With their wits perhaps sharpened by millennia of surviving extreme cold, they domesticated the horse and used them to brilliantly exploit the wheel, allowing their chariots to conquer almost everyone from Europe to India in record time.
Argument 2: Cold climates don’t require more intelligence to hunt because tropical people hunt too.
CWT claims that because plant foods are scarce in cold, high latitude places, people needed to be smart enough to cooperatively and strategically hunt large game, while tropical peoples could mindlessly pick berries all day. Buckner debunks this claim by noting that hunter-gatherers of all latitudes depend roughly equally on hunted animals for subsistence.
Counterargument
While Buckner might be correct that today, even tropical hunter-gathers depend as much on hunted animals as their Northern counterparts (at least land-animals, Northern hunter-gatherers do more fishing); it was likely untrue in the Paleolithic when population differences were evolving.
Living in Eurasia 300,000 to 30,000 years ago…in places like the Polar Urals and southern Siberia—not bountiful in the best of times, and certainly not during ice ages. In the heart of a tundra winter, with no fruits and veggies to be found, animal meat—made of fat and protein—was likely the only energy source.
Further evidence that cold climate Paleolithic peoples were more hunting dependent than their tropical counterparts is the fact that the former likely drove the mammoth to extinction, while the tropical dwelling elephant remains extant.
Argument 3: cold climates don’t require more intelligence to make clothes because tropical tribes can make clothes too.
Part of CWT is that the need for warm clothing as humans migrated North selected for high intelligence because those lacking the cognitive ability to make such clothes quickly froze to death (or their babies did) leaving those with high IQ DNA as the survivors.
To counter this point, Buckner mentions the elaborate costumes donned by the Bororo hunter-gatherers of Mato Grasso, Brazil during ceremonies, to prove that tropical people evolved just as much tailoring talent.
Anthropologist Vincent Petrullo is quoted:
The dancer was painted red with urucum and down pasted on his breast. His face was also smeared with urucum. Around his arms were fastened armlets made from strips of burity palm leaf, and his face was covered with a mask made of woman’s hair. The foreskin of the penis was tied with a narrow strip of burity palm leaf, for these men under their tattered European clothing still carry this string. A skirt of palm leaf strips was worn, and a jaguar robe was thrown over his shoulders. The skins of practically every speeies of snake to be found in the pantanal hung from his head down his back over the jaguar robe, which was worn with the fur on the ontside. The inner surface of the hide was painted with geometrie patterns, in red and black, but no one could explain the symbolism. A magnificent headdress consisting of many pieces, and containing feathers of many birds of the pantanal completed the costume with the addition of deerhoof rattles worn on the right ankle.
Firstly, although the Bororo currently live in the tropics, they are descended from cold adapted people who crossed the Beringia land bridge from Siberia to present-day Alaska during the Ice Age, and then spread southward throughout the Americas over the following generations. Their tailoring skills may have evolved during those ancestral cold journeys.
Secondly, just because some members of the Bororo have elaborate tailoring skills does not mean these people on average have the tailoring skills of high lattitidue hunter-gatherers. The existence of a few talented tropical tailors no more debunks the tailoring supremacy of high latitude people than the existence of a few really tall women debunks the male height advantage.
Lastly, although the Bororo costume is elaborate, it mostly just consists of wearing many skins on top of one another and attaching lots of things to one’s body. While this is impressive, it is nowhere near the proficiency of making body hugging clothes that cling to one snugly during the fierce winter. It reminds me a bit of cold nghts where I throw more and more blankets on myself to feel warm. This never works as well as putting on a pair of tightly knit jogging pants and a figure hugging sweater.
For all their pomp and circumstance, one dressed only in ceremonial Bororo costume could expect frostbite in less than 5 minutes during ice age winter Russia. Clearly this is nowhere near solving the problem of warm clothing, and that’s because it makes no use of one of the most revolutionary inventions of all time.
According to journalist Jacob Pagano “…researchers found that humans developed eyed sewing needles in what is now Siberia and China as early as 45,000 years ago.”
So crucial were eyed sewing needles that they are credited with allowing our species to out-survive the Neanderthals. A 2010 article in the guardian describes archaeologist Brian Fagan’s view:
While Neanderthals shivered in rags in winter, humans used vegetable fibres and needles – created by using stone awls – to make close-fitting, layered clothing and parkas: the survival of the snuggest, in short..
Of course no one is suggesting that cold climate was the only cause of population IQ gaps (it certainly doesn’t explain the high IQs of Ashkenazi Jews who largely descend from the warm Middle East). But it may help explain the more ancient differences between macro-level populations like North East Asians, West Eurasians and those from the tropics. I find it interesting to note that IQ tests involving spatial ability show larger gaps between humans from warm and cold regions than tests involving verbal skill. This is the opposite of what a culture bias explanation would predict, but is consistent with CWT since natural selection may have favored spatial ability in the cold for sewing, building shelters, making fires and hunting etc.
