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The cold winter theory (CWT) is a theory that purports to explain why those whose ancestors evolved in colder climes are more “intelligent” than those whose ancestors evolved in warmer climes. Popularized by Rushton (1997), Lynn (2006), and Kanazawa (2012), the theory—supposedly—accounts for the “haves” and the “have not” in regard to intelligence. However, the theory is a just-so story, that is, it explains what it purports to explain without generating previously unknown facts not used in the construction of the theory. PumpkinPerson is irritated by people who do not believe the just-so story of the CWT writing (citing the same old “challenges” as Lynn which were dispatched by McGreal):
The cold winter theory is extremely important to HBD. In fact I don’t even understand how one can believe in racially genetic differences in IQ without also believing that cold winters select for higher intelligence because of the survival challenges of keeping warm, building shelter, and hunting large game.
The CWT is “extremely important to HBD“, as PP claims, since there needs to be an evolutionary basis for population differences in “intelligence” (IQ). Without the just-so story, the claim that racial differences in “intelligence” are “genetically” based crumbles.
Well, here is the biggest “challenge” (all other refutations of it aside) to the CWT. Notions of which population are or are not “intelligent” change with the times. The best example is what the Greeks—specifically Aristotle—wrote about the intelligence of those who lived in the north. Maurizio Meloni, in his 2019 book Impressionable Biologies: From the Archaeology of Plasticity to the Sociology of Epigenetics captures this point (pg 41-42; emphasis his):
Aristotle’s Politics is a compendium of all these ideas [Orientals being seen as “softer, more delicate and unwarlike” along with the structure of militaries], with people living in temperate (mediocriter) places presented as the most capable of producing the best political systems:
“The nations inhabiting the cold places and those of Europe are full of spirit but somewhat deficient in intelligence and skill, so that they continue comparatively free, but lacking in political organization and the capacity to rule their neighbors. The peoples of Asia on the other hand are intelligent and skillful in temperament, but lack spirit, so that they are in continuous subjection and slavery. But the Greek race participates in both characters, just as it occupies the middle position geographically, for it is both spirited and intelligent; hence it continues to be free and to have very good political institutions, and to be capable of ruling all mankind if it attains constitutional unity.” (Pol. 1327b23-33, my italics)
Views of direct environmental influence and the porosity of bodies to these effects also entered the military machines of ancient empires, like that of the Romans. Offices such as Vegetius (De re militari, I/2) suggested avoiding recruiting troops from cold climates as they had too much blood and, hence, inadequate intelligence. Instead, he argued, troops from temperate climates be recruited, as they possess the right amount of blood, ensuring their fitness for camp discipline (Irby, 2016). Delicate and effemenizing land was also to be abandoned as soon as possible, according Manilius and Caesar (ibid). Probably the most famous geopolitical dictum of antiquity reflects exactly this plastic power of places: “soft lands breed soft men”, according to the claim that Herodotus attributed to Cyrus.
Isn’t that weird, how things change? Quite obviously, which population is or is not “intelligent” is based on the time and place of the observation. Those in northern Europe, who are purported to be more intelligent than those who live in temperate, hotter climes—back in antiquity—were seen to be less intelligent in comparison to those who lived in more temperate, hotter climes. Imagine stating what Aristotle said thousands of years ago in the present day—those who push the CWT just-so story would look at you like you’re crazy because, supposedly, those who live in and evolved in colder climes had to plan ahead and faced a tougher environment in comparison to those who lived closer to the equator.
Imagine we could transport Aristotle to the present day. What would he say about our perspectives on which population is or is not intelligent? Surely he would think it ridiculous that the Greeks today are less “intelligent” than those from northern Europe. But that only speaks to how things change and how people’s perspectives on things change with the times and who is or is not a dominant group. Now imagine that we can transport someone (preferably an “IQ” researcher) to antiquity when the Greeks were at the height of their power. They would then create a just-so story to justify their observations about the intelligence of populations based on their evolutionary history.
Anatoly Karlin cites Galton, who claims that ancient Greek IQ was 125, while Karlin himself claims IQ 90. I cite Karlin’s article not to contest his “IQ estimates”—nor Galton’s—I cite it to show the disparate “estimates” of the intelligence of the ancient Greeks. Because, according to the Greeks, they occupied the middle position geographically, and so they were both spirited and intelligent compared to Asians and northern Europeans.
This is similar to Wicherts, Boorsboom, and Dolan (2010) who responded to Rushton, Lynn, and Templer. They state that the socio-cultural achievements of Mesopotamia and Egypt stand in “stark contrast to the current low level of national IQ of peoples of Iraq and Egypt and that these ancient achievements appear to contradict evolutionary accounts of differences in national IQ.“ One can make a similar observation about the Maya. Their cultural achievements stand in stark contrast to their “evolutionary history” in warm climes. The Maya were geographically isolated from other populations and they still created a writing system (independently) along with other cultural achievements that show that “national IQs” are irrelevant to what the population achieved. I’m sure an IQ-ist can create a just-so story to explain this away, but that’s not the point.
Going back to what Karlin and Galton stated about Greek IQ, their IQ is irrelevant to their achievements. Whether or not their IQ was 120-125 or 90 is irrelevant to what they achieved. To the Mesopotamians and Egyptians, they were more intelligent than those from northern climes. They would, obviously, think that based on their achievements and the lack of achievements in the north. The achievements of peoples in antiquity would paint a whole different picture in regard to an evolutionary theory of human intelligence—and its distribution in human populations.
So which just-so story (ad hoc hypothesis) should we accept? Or should we just accept that which population is or is not “intelligent” and capable of constructing militaries is contingent based on the time and the place of the observation? Looking at “national IQs” of peoples in antiquity would show a huge difference in comparison to what we observe today about the “national IQs” (supposedly ‘intelligence’) of populations around the world. In antiquity, those who lived in temperate and even hotter climes had greater achievements than others. Greeks and Romans argued that peoples from northern climes should not be enlisted in the military due to where they were from.
These observations from the Greeks and Romans about who and who not to enlist in the military, along with their thoughts on Northern Europeans prove that perspectives on which population is or is not “intelligent” is contingent based on the time and place. This is why “national IQs” should not be accepted, not even accounting for the problems with the data (Richardson, 2004; also see Morse, 2008; also see The Ethics of Development: An Introduction by Ingram and Derdak, 2018). Seeing the development of countries/populations in antiquity would lead to a whole different evolutionary theory of the intelligence of populations, proving the contingency of the observations.
JP Rushton’s career was pretty much nothing but peddling bullshit. In the beginning of his career, he was a social learning theorist. He published a book Altruism, Socialization, and Society (Rushton, 1980). I bought the book a few years back when I was still a hardcore Rushton defender to see what he wrote about before he started pushing IQ and evolutionary theories about human races and I thought it was pretty good. In any case, Rushton got a lot wrong. So much so, that his career was, in my opinion, wasted peddling bullshit. Rushton was shown to be wrong time and time again on r/K theory and cold winter theory; Rushton was shown to be wrong time and time again on his crime theorizing; and Rushton’s and Jensen’s papers on the causes of the black-white IQ gap rest on a misunderstanding of heritability. In this piece, I will cover those three subjects.
Recently, two new papers have appeared that have a bone to pick with Rushton: One by Flynn (2019) and the other by Cernovsky and Litman (2019). Flynn discusses Rushton’s claims on the method of correlated vectors, his cold winter theory (that Asians and Europeans were subjected to harsher climates which led to higher levels of intelligence and therefore IQ) and his misuse of regression to the mean. He also discussed how the black-white IQ gap is environmental in nature (which is the logical position to hold, since IQ tests are tests of middle-class knowledge and skills (Richardson, 2002) and they are not construct valid).
Cold Winters Theory
Rushton theorized that, due to exposure to harsher environments, that Europeans and East Asians evolved to be more intelligent than Africans who stayed in the, what I assume to be, less harsh environments of Africa (Rushton, 1985). This is Rushton’s “Differential K theory.” Flynn (2019) writes that he “can supply an evolutionary scenario for almost any pattern of current IQ scores.” And of course, one can do that with any evolutionary adaptive hypothesis.
Even Frost (2019) admits that “there is no unified evolutionary theory of human intelligence, other than the general theory of evolution by natural selection.” But since “natural selection” is not a mechanism (Fodor, 2008; Fodor and Piattelli-Palmarini, 2010), then it cannot explain the evolution of intelligence differences, nevermind the fact that, mostly, these claims are pushed by differences in non-construct valid IQ test scores.
In any case, Rushton’s theory is a just-so story.
Judith Anderson (1991) refuted Rushton’s hypothesis on ecological grounds. Rushton asserted that Africans were r-selected whereas Asians and Europeans were more K-selected. Rushton, however, did not even use alpha-selection, which is selection for competitive ability. So r- and K selection is based on density-independence and density-dependence. K-selection is expected to favor genotypes that persist at high densities—increasing K—whereas r-selection is expected to favor genotypes that increase more quickly at low densities—increasing r. Alpha-selection can also occur at high or low population densities but is more likely in high densities. Though alpha-selection “favours genotypes that, owing to their negative effects on others, often reduce the growth rate and the maximum population size” (Anderson, 1991: 52). I further discussed the huge flaws with Rushton’s r/K model here. So Rushton’s theory fails on those grounds, along with many others.
When it came to race, Rushton was a lumper, not a splitter. What I mean by these terms is simple: lumpers lump together Native Americans with East Asians and Pacific Islanders with Africans while splitters split them into further divisions. Why was Rushton a lumper? Because it fit more with his theory, of course. I remember back when I was a Rushton-ist, and I was, too, a lumper, that to explain away the low IQs of Native Americans—and in turn their achievements—was that they still had their intelligence from the cold winters and that’s when they did their achievements. Then, as they spent more time in hotter climates, they became dumber. In any case, there is no justification for lumping Native Americans with East Asians. Looking through Rushton’s book, he gives no justification for his lumping, so I can only assume that it is bias on his part. Now I will justify the claim that splitting is better than lumping. (Rushton also gave no definition of race, and according to Cernovsky and Litman (2019: 54), Rushton “failed to provide any scientific definition of race …”
Race is both a social and biological construct. I can justify the claim that Natives and East Asians are distinct races in one way here: ask both groups if the other is the same race. What do you think the answer will be? Now, onto genetics.
Spencer (2014) discusses the results from Tishkoff et al (2009), saying that when they added 134 ethnic groups to the ones found in the HDGP sample of 52, the K=5 partition clustered Caucasians, Mongoloids, and three distinct sets of Africans. Mongoloids, in this case, being East Asians, Native Americans, and Oceanians. But Tishkoff et al oversampled African ethnic groups. This, though, does not undercut my argument: of course when you oversample ethnic groups you will get the result of Tishkoff et al (2009) and since Africans were oversampled, the populations more genetically similar were grouped into the same cluster, which, of course, does not mean they are the same race.
Census racial discourse is just national racial discourse. The census uses defers to the OMB to define race. How does the OMB define race? The OMB defines “race” as “sets of” populations. Race in US racial discourse designates a set of population groups, thus, race is a particular, not a kind.
I can then invoke Hardimon’s (2017) argument for the existence of minimalist races:
1 There are differences in patterns of visible physical features which correspond to geographic ancestry.
2 These patterns are exhibited between real groups.
3 These groups that exhibit these physical patterns by geographic ancestry satisfy conditions of minimalist race.
C Race exists.
Now we can say:
1 If Native Americans and East Asians are phenotypically distinct, then they are different races.
2 Native Americans and East Asians are phenotypically distinct.
C Therefore Native Americans and East Asians are different races.
Rushton arbitrarily excluded data that did not fit his theory. How dishonest. Cernovsky and Litman (2019) write:
When Rushton presented crime statistics derived from 2 Interpol Yearbooks as allegedly supporting his thesis that Negroids are more crime inclined than Caucasoids, he arbitrarily excluded disconfirmatory data sets. When all data from the same two Interpol Yearbooks are re-calculated, most of the statistically significant trends in the data are in the direction opposite to Rushton’s beliefs: Negroids had lower crime rates than Caucasoids with respect to sexual offenses, rapes, theft, and violent theft or robbery, with most correlation coefficients exceeding .60. While we do not place much credence in such Interpol statistics as they only reproduce information provided by government officials of different countries, our re-analysis indicated that Rushton excluded data that would discredit his theory.
Further throwing a wrench into Rushton’s assertions is his claim that Mongoloids constitutes both East Asians and Native Americans. Well, Central America has some of the highest crime rates in the world—even higher than in some African countries. What is the ad-hoc justification for explaining away this anomaly if they truly are the same race? If they are the same race, why is the crime rate so much higher in Central America? Surely, Rushton’s defenders would claim something along the lines of recent evolution towards X, Y, and Z. But then I would say, then on what basis are they the same race? No matter what Rushton’s defenders say, they are boxed into a corner.
Lastly, Rushton and Jensen (2005) argued, on the basis of heritability estimates, and twin studies, that the black-white IQ gap is largely genetic in nature. But there are a few problems. They rely largely on a slew of trans-racial adoption studies, all of which have been called into question (Thomas, 2017). IQ tests, furthermore, are not construct valid (Richardson and Norgate, 2015; Richardson, 2017). Heritability estimates also fail. This is because, in non-controlled environments these stats do not tell us much, if anything (Moore and Shenk, 2016). Likewise, Guo (2000: 299) concurs, writing “it can be argued that the term ‘heritability’, which carries a strong conviction or connotation of something ‘heritable’ in everyday sense, is no longer suitable for use in human genetics and its use should be discontinued.” (For more arguments against heritability, read Behavior Genetics and the Fallacy of Nature vs Nurture.)
Rushton and Jensen (2005) also relied on the use of twin studies, however, all of the assumptions that researchers use to attempt to immunize their arguments from refutation are circular and ad hoc; they also agree that MZs experience more similar environments than DZs, too (Joseph et al, 2014; Joseph, 2016, see a summary here; Richardson, 2017). In any case, the fact that G and E interact means that heritability estimates are, in effect, useless in humans. Farm animals are bred in highly controlled environments; humans are not. Thus, we cannot—and should not—accept the results of twin studies; they cannot tell us whether or not genes are responsible for any behavioral trait.
There was a lot that Rushton got wrong. His cold winters theory is a just-so story; East Asians and Native Americans are not the same race; heritability estimates are not a measure of how much genes have to do with phenotypic variation within or between groups; IQ tests are not construct valid; r/K selection theory was slayed as early as 1991 and then again in 2002 (Graves, 2002); twin studies are not informative when it comes to how much genes influence traits, they only measure environmental similarity; and finally, Rushton omitted data that did not fit his hypothesis on racial differences in crime.
It’s sad to think that one can spend a career—about 25 years—spewing nothing but pseudoscience. One of the only things I agree with him on is that races are real—but when it comes to the nuances, I disagree with him, because there are five races, not three. Rushton got a lot wrong, and I do not know why anyone would defend him, even when these glaring errors are pointed out. (For a good look into Rushton’s lies, see Dutton’s (2018) book J. Philippe Rushton: A Life History Perspective and my mini-review on the book.)
HBDers purport that as one moves further north from Africa that IQ raises as a function of how the population in question needed to survive. The explanation is that as our species migrated out of Africa, more “intelligence” was needed and this is what explains the current IQ disparities across the world: the ancestors of populations evolving in different areas with different demands then changed their “IQs” and this then is responsible for differential national development between nations. Cold winter theory (CWT) explains these disparities.
On the other hand is the vitamin D hypothesis (VDH). The VDH purports to explain why populations have light skin at northern latitudes. As the migration north out of Africa occurred, peoples needed to get progressively lighter in order to synthesize vitamin D. The observation here is that as light skin is selected for in locations where UVB is absent, seasonal or more variable whereas dark skin is selected for where UVB is stronger. So we have two hypotheses: but there is a problem. Only one of these hypotheses makes novel predictions. Predictions of novel predictions are what science truly is. A predicted fact is a novel fact for a hypothesis if it wasn’t used in the construction of the hypothesis (Musgrave, 1988). In this article, I will cover both the CWT and VDH, predictions of facts that each made (or didn’t make) and which can be called “science”.
Cold winter theory
The cold winter theory, formulated by Lynn and Rushton, purports to give an evolutionary explanation for differences in national IQs: certain populations evolved in areas with deathly cold winters in the north, while those who lived in tropical climes had, in comparison to those who evolved in the north, an “easier time to live”. Over time as populations adapted to their environments, differences in ‘intelligence’ (whatever that is) evolved due to the different demands of each environment, or so the HBDers say.
Put simply, the CWT states that IQ differences exist due to different evolutionary pressures. Since our species migrated into cold, novel environments, this was the selective pressure needed for higher levels of ‘intelligence’. On the other hand, humans who remained in Africa and other tropical locations did experience these novel, cold environments and so their ‘intelligence’ stayed at around the same level as it was 70,000 years ago. Many authors hold this theory, including Rushton (1997), Lynn (2006), Hart, (2007) Kanazawa (2008), Rushton and Templer (2012; see my thoughts on their hypothesis here) and Wade (2014). Lynn (2013) even spoke of a “widespreadonsensus” on the CWT, writing:
“There is widespread consensus on this thesis, e.g. Kanazawa (2008), Lynn (1991, 2006), and Templer and Arikawa (2006).”
So this “consensus” seems to be a group of his friends and his own publications. We can change this sentence to ““There is widespread consensus on this thesis, including two of my publications, a paper where the author assumes that the earth is flat: “First, Kanazawa’s (2008) computations of geographic distance used Pythagoras’ theorem and so the paper assumed that the earth is flat (Gelade, 2008).” (Wicherts et al, 2012) and another publication where the authors assume hot weather leads to lower intelligence. Oh yea, they’re all PF members. Weird.” That Lynn (2013) calls this “consensus” is a joke.
What caused higher levels of ‘intelligence’ in those that migrated out of Africa? Well, according to those who push the CWT, finding food and shelter. Kanazawa, Lynn, and Rushton all argue that finding food, making shelter and hunting animals were all harder in Eurasia than in Africa.
One explanation for high IQs of people who evolved recently in northern climes is their brain size. Lynn (2006: 139) cites data showing the average brain sizes of populations, along with the temperatures in that location:
Do note the anomaly with the Arctic peoples. To explain this away in an ad-hoc manner, Lynn (2006: 156-7) writes:
These severe winters would be expected to have acted as a strong selection for increased intelligence, but this evidently failed to occur because their IQ is only 91. The explanation for this must lie in the small numbers of the Arctic Peoples whose population at the end of the twentieth century was only approximately 56,000 as compared with approximately 1.4 billion East Asians.
This is completely ad-hoc. There is no independent verifier for the claim. That the Arcitic don’t have the highest IQs but experienced the harshest temperatures and therefore have the biggest brain size is a huge anomaly, which Lynn (2006) attempts to explain away by population size.
He does not explain why natural selection among Arctic peoples would result in larger brain sizes or enhanced visual memory yet the same evolutionary pressures associated with a cold environment would not also produce higher intelligence. Arctic peoples have clear physical adaptations to the cold, such as short, stocky bodies well-suited to conserving heat.
Furthermore, the argument that Lynn attempts is on the mutations/population size is special pleading—he is ignoring anomalies in his theory that don’t fit it. However, “evolution is not necessary for temperature and IQ to co-vary across geographic space” (Pesta and Poznanski, 2014).
If high ‘intelligence’ is supposedly an adaptation to cold temperatures, then what is the observation that disconfirms a byproduct hypothesis? On the other hand, if ‘intelligence’ is a byproduct, which observation would disconfirm an adaptationist hypothesis? No possible observation can confirm or disconfirm either hypothesis, therefore they are just-so stories. Since a byproduct explanation would explain the same phenomena since byproducts are also inherited, then just saying that ‘intelligence’ is a byproduct of, say, needing larger heads to dissipate heat (Lieberman, 2015). One can make any story they want to fit the data, but if there is no prediction of novel facts then how useful is the hypothesis if it explains the data it purports to explain and only the data it purports to explain?
It is indeed possible to argue that hotter climates need higher levels of intelligence than colder climates, which has been argued in the past (see Anderson, 1991; Graves, 2002; Sternberg, Grigorenko, and Kidd, 2005). Indeed, Sternberg, Grigorenko, and Kidd (2005: 50) write: “post hoc evolutionary arguments … can have the character of ad hoc “just so” stories designed to support, in retrospect, whatever point the author wishes to make about present-day people.” One can think up any “just-so” story to explain any data. But if the “just-so” story doesn’t make any risky predictions of novel facts, then it’s not science, but pseudoscience.
Vitamin D hypothesis
The VDH is simple: those populations that evolved in areas with seasonal, absent, or more variable levels of UVB have lighter skin than populations that evolved in areas with strong UVB levels year-round (Chaplan and Jablonksi, 2009: 458). Robins (2009) is a huge critic of the VDH, though her objections to the VDH have been answered (and will be discussed below).
The VDH is similar to the CWT in that it postulates that the adaptations in question only arose due to migrations out of our ancestral lands. We can see a very strong relationship between high UVB rays and dark skin and conversely with low UVB rays and light skin. Like with the CWT, the VDH has an anomaly and, coincidentally, the anomaly has to do with the same population involved in the CWT anomaly.
Arctic people have dark-ish skin for living in the climate that they do. But since they live in very cold climates then we have a strange anomaly here that needs explaining. We only need to look at the environment around them. They are surrounded by ice. Ice reflects UVB rays. UVB rays hit the skin. Arctic people consume a diet high in vitamin D (from fish). Therefore what explains Arctic skin color is UVB rays bouncing off the ice along with their high vitamin D diet. The sun’s rays are, actually, more dangerous in the snow than on the beach, with UVB rays being 2.5 more times dangerous in the snow than beach.
Evolution in different geographic locations over tens of thousands of years caused skin color differences. Thus, we can expect that, if peoples are out of the conditions where their ancestors evolved their skin color, that there would then be expected complications. For example, if human skin pigmentation is an adaptation to UV rays (Jablonski and Chaplan, 2010), we should expect that, when populations are removed from their ancestral lands and are in new locations with differing levels of UV rays, that there would be a subsequent uptick in diseases caused by vitamin D deficiencies.
This is what we find. We find significant differences in circulating serum vitamin D levels, and these circulating serum vitamin D levels then predict health outcomes in certain populations. This would only be true if sunlight influenced vitamin D production and that skin progressively gets lighter as one moves away from Africa and other tropical locations.
Skin pigmentation regulates vitamin D production (Neer, 1975). This is due to the fact that when UVB rays strike the skin, we synthesize vitamin D, and the lighter one’s skin is, the more vitamin D can be synthesized in areas with fewer UVB rays. (Also see Daraghmeh et al, 2016 for more evidence for the vitamin D hypothesis.)
P1) UV rays generate vitamin D in human skin
P2) Human populations that migrate to climates with less sunlight get fewer UV rays
P3) To produce more vitamin D, the skin needs to get progressively lighter
C) Therefore, what explains human skin variation is climate and UV rays linked to vitamin D production in the skin.
Science is the generation of novel facts from risky predictions (Musgrave, 1988; Winther, 2009). And so, hypotheses that predict novel facts from risky predictions are scientific hypotheses, whereas those hypotheses that need to continuously backtrack and think up ad-hoc hypotheses are then pseudoscientific. Pseudoscience is simple enough to define. The Stanford Encyclopedia of Philosophy defines it as:
“A pretended or spurious science; a collection of related beliefs about the world mistakenly regarded as being based on scientific method or as having the status that scientific truths now have.”
All theories have a protective belt of ad hoc hypotheses. Theories become pseudoscientific when they fail to make new predictions and must take on more and more ad-hoc hypotheses that have no predictive value. If the ad-hoc hypotheses that are added to the main hypothesis have no predictive value then the new explanations for whichever hypothesis that is in danger of being falsified are just used to save the hypothesis from being refuted and it thus becomes pseudoscience.
In the case of CWT, it makes no prediction of novel facts; it only explains the data that it purports to explain. What is so great about the CWT if it makes no predictions of novel facts and only explains what it purports to explain? One may attempt to argue that it has made some ‘novel’ predictions but the ‘predictions’ that are proposed are not risky at all.
For example, Hart (2007: 417) makes a few “predictions”, but whether or not they’re “risky” or “novel” I’ll let you decide (I think they’re neither, of course). He writes that very few accomplishments will be made by Africans, or Australian or New Guinean Aborigines; members of those groups will not be highly represented in chess; and that major advances in scientific fields will come from those of European ancestry or the “Monglids”, Koreans, Chinese or Japanese.
On the other hand, Hart (2007: 417) makes two more “predictions”: he says that IQ data for Congoid Pygmies, Andaman Islanders, and Bantu-speaking people are few and far between and he believes that when enough IQ testing is undertaken there he expects IQ values between 60 and 85. Conversely, for the Lapps, Siberians, Eskimoes, Mongols and Tibetans, he predicts that IQ values should be between 85-105. He then states that if these “predictions” turn out to be wrong then he would have to admit that his hypothesis is wrong. But the thing is, he chose “predictions” that he knew would come to pass and therefore these are not novel, risky predictions but are predictions that Hart (2007) knows would come to pass.
What novel predictions has the VDH made? This is very simple. The convergent evolution of light skin was predicted in all hominids that trekked out of Africa and into colder lands. This occurred “because of the importance of maintaining the potential for producing pre-vitamin D3 in the skin under conditions of low annual UVB (Jablonski and Chaplin, 2000; Jablonski, 2004)” while these predictions “have been borne out by recent genetic studies, which have demonstrated that depigmented skin evolved independently by different molecular mechanisms multiple times in the history of the human lineage” (Chaplan and Jablonksi, 2009: 452). This was successfully predicted by Chaplan and Jablonski (2000).
The VDH still holds explanatory scope and predictive success; no other agent other than vitamin D can explain the observation that light skin is selected for in areas where there is low, absent or seasonal UVB. Conversely, in areas where there is a strong, year-round presence of UVB rays, dark skin is selected for.
Scientific hypotheses predict novel facts not known before the formulation of the hypothesis. The VDT has successfully predicted novel facts, whereas I am at a loss thinking of a novel fact that the CWT predicted.
In order to push an adaptationist hypothesis for CWT and ‘intelligence’, one must propose an observation that would confirm the adaptationist hypothesis while at the same time disconfirming the byproduct hypothesis. Since byproducts are inherited to, the byproduct hypothesis would predict the same things that an adaptationist hypothesis would. Thus, the CWT is a just-so story since no observation would confirm or disconfirm either hypothesis. On the other hand, the CWT doesn’t make predictions of novel facts, it makes “predictions” that are already known and would not undermine the hypothesis if disproved (but there would always be a proponent of the CWT waiting in the wings to propose an ad-hoc hypothesis in order to save the CWT, but I have already established that it isn’t science).
On the other hand, the VDT has successfully predicted that hominins that trekked out of Africa would have light skin which was then subsequently confirmed by genomic evidence. The fact that strong UVB rays year-round predict dark skin whereas seasonal, absent, or low levels of UVB predict light skin has been proved to be true. With the advent of genomic testing, it has been shown that hominids that migrated out of Africa did indeed have lighter skin. This is independent verification for the VDH; the VDH has predicted a novel fact whereas the CWT has not.
Tests of delayed gratification, such as the Marshmallow Experiment, show that those who can better delay their gratification have better life outcomes than those who cannot. The children who succumbed to eating the treat while the researcher was out of the room had worse life outcomes than the children who could wait. This was chalked up to cognitive processes by the originator of the test, while individual differences in these cognitive processes also were used as explanations for individual differences between children in the task. However, it doesn’t seem to be that simple. I did write an article back in December of 2015 on the Marshmallow Experiment and how it was a powerful predictor, but after extensive reading into the subject, my mind has changed. New research shows that social trust has a causal effect on whether or not one would wait for the reward—if the individual trusted the researcher he or she was more likely to wait for the other reward than if they did not trust the researcher, in which they were more likely to take what was offered in the first place.
The famous Marshmallow Experiment showed that children who could wait with a marshmallow or other treat in front of them while the researcher was out of the room, they would get an extra treat. The children who could not wait and ate the treat while the researcher was out of the room had worse life outcomes than the children who could wait for the other treat. These lead researchers to the conclusion that the ability to delay gratification depended on ‘hot’ and ‘cold’ cognitive processes. According to Walter Mischel, the originator of the study method, the ‘cool’ system is the thinking one, the cognitive system, which reminds you that you get a reward if you wait, while the ‘hot’ system is the impulsive system, the system that makes you want the treat now and not want to wait for the other treat (Metcalfe and Mischel, 1999).
Some of these participants were followed up on decades later, and those who could better delay their gratification had lower BMIs (Schlam et al, 2014); scored better on the SAT (Shoda, Mischel, and Peake, 1990) and other tests of educational attainment (Ayduk et al, 2000); along with other positive life outcomes. So it seems that placing a single treat—whether it be a marshmallow or another sweet treat—would predict one’s success, BMI, educational attainment and future prospects in life and that there are underlying cognitive processes, between individuals that lead to differences between them. But it’s not that simple.
After Mischel’s studies in the 50s, 60s and 70s on delayed gratification and positive and negative life outcomes (e.g., Mischel, 1958; Mischel, 1961; Mischel, Ebbeson, and Zeiss, 1972) it was pretty much an accepted fact that delaying gratification somehow was related to these positive life outcomes, while the negative life outcomes were partly a result of the lack of ability to delay gratification. Though in 2014, a study was conducted showing that ability to delay gratification depends on social trust (Michaelson et al, 2013).
Using Amazon’s Mechanical Turk, (n = 78, 34 male, 39 female and 5 who preferred not to state their gender) completed online surveys and read three vignettes in order—trusty, untrustworthy and neutral—while using a scale of 1-7 to note how likeable, trustworthy, and how sharing their likelihood of sharing. Michaelson et al (2013) write:
Next, participants completed intertemporal choice questions (as in Kirby and Maraković, 1996), which varied in immediate reward values ($15–83), delayed reward values ($30–85), and length of delays (10–75 days). Each question was modified to mention an individual from one of the vignettes [e.g., “If (trustworthy individual) offered you $40 now or $65 in 70 days, which would you choose?”]. Participants completed 63 questions in total, with 21 different questions that occurred once with each vignette, interleaved in a single fixed but random order for all participants. The 21 choices were classified into 7 ranks (using the classification system from Kirby and Maraković, 1996), where higher ranks should yield higher likelihood of delaying, allowing a rough estimation of a subject’s willingness to delay using a small number of trials. Rewards were hypothetical, given that hypothetical and real rewards elicit equivalent behaviors (Madden et al., 2003) and brain activity (Bickel et al., 2009), and were preceded by instructions asking participants to consider each choice as if they would actually receive the option selected. Participants took as much time as they needed to complete the procedures.
When one’s trust was manipulated in the absence of a reward, within the group of subjects influenced their ability to delay gratification, along with how trustworthy one was perceived to be, influenced their ability to delay gratification. So this suggests that, in the absence of rewards, when social trust is reduced, ability to delay gratification would be lessened. Due to the issues of social trust manipulation due to the order of how the vignettes were read, they did a second experiment using the same model using 172 participants (65 males, 63 females, and 13 who chose not to state their gender). Though in this experiment, a computer-generated trustworthy, untrustworthy and neutral face was presented to the participants. They were only paid $.25 cents, though it has been shown that the compensation only affects turnout, not data quality (Burhmester, Kwang, and Gosling, 2011).
In this experiment, each participant read a vignette and there was a particular face attached to it (trustworthy, untrustworthy and neutral), which were used in previous studies on this matter. They found that when trust was manipulated in the absence of a reward between the subjects, this influenced the participants’ willingness and to delay gratification along with the perceived trustworthiness influencing it as well.
Michaelson et al (2013) conclude that the ability to delay gratification is predicated on social trust, and present an alternative hypothesis for all of these positive and negative life outcomes:
Social factors suggest intriguing alternative interpretations of prior findings on delay of gratification, and suggest new directions for intervention. For example, the struggles of certain populations, such as addicts, criminals, and youth, might reflect their reduced ability to trust that rewards will be delivered as promised. Such variations in trust might reflect experience (e.g., children have little control over whether parents will provide a promised toy) and predisposition (e.g., with genetic variations predicting trust; Krueger et al., 2012). Children show little change in their ability to delay gratification across the 2–5 years age range (Beck et al., 2011), despite dramatic improvements in self-control, indicating that other factors must be at work. The fact that delay of gratification at 4-years predicts successful outcomes years or decades later (Casey et al., 2011; Shoda et al., 1990) might reflect the importance of delaying gratification in other processes, or the importance of individual differences in trust from an early age (e.g., Kidd et al., 2012).
Another paper (small n, n = 28) showed that the children’s perception of the researchers’ reliability predicted delay of gratification (Kidd, Palmeri, and Aslin, 2012). They suggest that “children’s wait-times reflected reasoned beliefs about whether waiting would ultimately pay off.” So these tasks “may not only reflect differences in self-control abilities, but also beliefs about the stability of the world.” Children who had reliable interactions with the researcher waited about 4 times as long—12 minutes compared to 3 minutes—if they thought the researcher was trustworthy. Sean Last over at the Alternative Hypothesis uses these types of tasks (and other correlates) to show that blacks have lower self-control than whites, citing studies showing correlations with IQ and delay of gratification. Though, as can be seen, alternative explanations for these phenomena make just as much sense, and with the new experimental evidence on social trust and delaying gratification, this adds a new wrinkle to this debate. (He also shortly discusses ‘reasons’ why blacks have lower self-control, implicating the MAOA alleles. However, I have already discussed this and blaming ‘genes for’ violence/self-control doesn’t make sense.)
Michaelson and Munakata (2016) show more evidence for the relationship between social trust and delaying gratification. When children (age 4 years, 5 months, n = 34) observed an adult as trustworthy, they were able to wait for the reward, compared to when they observed the adult as untrustworthy they ate the treat thinking that, since they observed the adult as untrustworthy, they were not likely to get the second marshmallow than if they waited for the adult to return if they believed him to be untrustworthy. Ma et al (2018) also replicated these findings in a sample of 150 Chinese children aged 3 to 5 years old. They conclude that “there is more to delay of gratification than cognitive capacity, and they suggest that there are individual differences in whether children consider sacrificing for a future outcome to be worth the risk.” Those who had higher levels of generalized trust waited longer, even when age and level of executive functioning were controlled for.
Romer et al (2010) show that people who are more willing to take risks may be more likely to engage in risky behavior that provides insights to that specific individual on why delaying gratification and having patience leads to longer-term rewards. This is a case of social learning. However, people who are more willing to take risks have higher IQs than people who do not. Though SES was not controlled for, it is possible that the ability to delay gratification in this study came down to SES, with lower class people taking the money, while higher class people deferred. Raine et al (2002) showed a relationship between sensation seeking in 3-year-old children from Mauritius, which then was related to their ‘cognitive scores’ at age 11. As usual, parental occupation was used as a measure of ‘social class’, and since SES does not capture all aspects of social class then controlling for the variable does not seem to be too useful. Because a confound here could be that children from higher classes have more of a chance to sensation seek which may cause higher IQ scores due to cognitive enrichment. Either way, you can’t say that IQ ’causes’ delayed gratification since there are more robust predictors such as social trust.
Though the relationship is there, what to make of it? Since exploring more leads to, theoretically, more chances to get things wrong and take risks by being impulsive, those who are more open to experience will have had more chances to learn from their impulsivity, and so learn to delay gratification through social learning and being more open. ‘IQ’ correlating with it, in my opinion, doesn’t matter too much; it just shows that there is a social learning component to delaying gratification.
In conclusion, there are alternative ways to look at the results from Marshmallow Experiments, such as social trust and social learning (being impulsive and seeing what occurs when an impulsive act is carried out may have one learn, in the future, to wait for something). Though these experiments are new and the research is young, it’s very promising that there are other explanations for delayed gratification that don’t have to do with differences in ‘cognitive ability’, but depend on social trust—trust between the child and the researcher. If the child sees the researcher is trustworthy, then the child will wait for the reward, whereas if they see the researcher is not trustworthy, they ill take the marshmallow or whatnot, since they believe the researcher is not trustworthy and therefore won’t stick to their word. (I am also currently reading Mischel’s 2014 book Marshmallow Test: Mastering Self-Control and will have more thoughts on this in the future.)
We’re only one month into the new year and I may have come across the most ridiculous paper I think I’ll read all year. The paper is titled Knowledge of resting heart rate mediates the relationship between intelligence and the heartbeat counting task. They state that ‘intelligence’ is related to heartbeat counting task (HCT), and that HBC is employed as a measure of interoception—which is a ‘sense’ that helps one understand what is going on in their body, sensing the body’s internal state and physiological changes (Craig, 2003; Garfinkel et al, 2015).
Though, the use of HCT as a measure of interoception is controversial (Phillips et al, 1999; Brener and Ring, 2016) mostly because it is influenced by prior knowledge of one’s resting heart rate. The concept of interoception has been around since 1906, with the term first appearing in scientific journals in the 1942 (Ceunen, Vlaeyen, and Dirst, 2016). It’s also interesting to note that interoceptive accuracy is altered in schizophrenics (who had an average IQ of 101.83; Ardizzi et al, 2016).
Murphy et al (2018) undertook two studies: study one demonstrated an association with ‘intelligence’ and HCT performance whereas study 2 demonstrated that this relationship is mediated by one’s knowledge of resting heart rate. I will briefly describe the two studies then I will discuss the flaws (and how stupid the idea is that ‘intelligence’ partly is responsible for this relationship).
In both studies, they measured IQ using the Wechsler intelligence scales, specifically the matrix and vocabulary subtests. In study 1, they had 94 participants (60 female, 33 female, and one ‘non-binary’; gotta always be that guy eh?). In this study, there was a small but positive correlation between HCT and IQ (r = .261).
In study 2, they sought to again replicate the relationship between HCT and IQ, determine how specific the relationship is, and determine whether higher IQ results in more accurate knowledge of one’s heart rate which would then improve their scores. They had 134 participants for this task and to minimize false readings they were asked to forgo caffeine consumption about six hours prior to the test.
As a control task, participants were asked to complete a timing accuracy test (TAT) in which they were asked to count seconds instead of heartbeats. The correlation with HCT performance and IQ was, again, small but positive (r = -.211) with IQ also being negatively correlated with the inaccuracy of resting heart rate estimations (r = .363), while timing accuracy was not associated with the inaccuracy of heart rate estimates, IQ or HCT. In the end, knowledge of average resting heart rate completely mediated the relationship between IQ and HCT.
This study replicated another study by Mash et al (2017) who show that their “results suggest that cognitive ability moderates the effect of age on IA differently in autism and typical development.” This new paper then extends this analysis showing that it is fully mediated by prior knowledge of average resting heart rate, and this is key to know.
This is simple: if one has prior knowledge of their average resting heart rate and their fitness did not change from the time they were aware of their average resting heart rate then when they engage in the HCT they will then have a better chance of counting the number of beats in that time frame. This is very simple! There are also other, easier, ways to estimate your heart rate without doing all of that counting.
Heart rate (HR) is a strong predictor of cardiorespiratory fitness. So it would follow that those who have prior knowledge of their HRs would more fitness savvy (the authors don’t really say too much about the subjects if there is more data when the paper is published in a journal I will revisit this). So Murphy et al (2018) showed that 1) prior knowledge of resting heart rate (RHR) was correlated—however low—with IQ while IQ was negatively correlated with the inaccuracy of RHR estimates. So the second study replicated the first and showed that the relationship was specific (HCT correlated with IQ, not any other measure).
The main thing to keep in mind here is that those who had prior knowledge of their RHR scored better on the task; I’d bet that even those with low IQs would score higher on this test if they, too, had prior knowledge of their HRs. That’s, really, what this comes down to: if you have prior knowledge of your RHR and your physiological state stays largely similar (body fat, muscle mass, fitness, etc) then when asked to estimate your heart rate by, say, using the radial pulse method (placing two fingers along the right side of the arm in line just above the thumb), they, since they have prior knowledge, will more accurately guess their RHR, if they had low or high IQs, regardless.
I also question the use of the HCT as a method of interoception, in line with Brener and Ring (2016: 2) who write “participants with knowledge about heart rate may generate accurate counting scores without detecting any heartbeat sensations.” So let’s say that HCT is a good measure of interoception, then it still remains to be seen whether or not manipulating subjects’ HRs would change the accuracy of the analyses. Other studies have shown that testing HR after one exercises, people underestimate their HR (Brener and Ring, 2016: 2). This, too, is simple. To get your max HR after exercise, subtract your age from 220. So if you’re 20 years old, your max HR would be 200, and after exercise, if you know you’re body and how much energy you have expended, then you will be able to estimate better with this knowledge.
Though, you would need to have prior knowledge, of course, of these effects and knowledge of these simple formulas to know about this. So, in my opinion, this study only shows that people who have a higher ‘IQ’ (more access to cultural tools to score higher on IQ tests; Richardson, 2002) are also more likely to, of course, go to the doctor for checkups, more likely to exercise and, thusly, be more likely to have prior knowledge of their HR and score better than those with lower IQs and less access to these types of facilities where they would have access to prior knowledge and get health assesments to have prior knowledge like those with higher IQs (which are more likely to be middle class and have more access to these types of facilities).
I personally don’t think that HCT is a good measure of interoception due to the criticisms brought up above. If I have prior knowledge of my HR (average HR for a healthy person is between 50-75 BPM depending on age, sex, and activity (along with other physiological components) (Davidovic et al, 2013). So, for example,if my average HR is 74 (I just checked mine last week and I checked it in the morning, and averaged 3 morning tests one morning was 73, the other morning was 75 and the third was 74 for an average of 74 BPM), and I had this prior knowledge before undergoing this so-called HCT interoception task, I would be better equipped to score better than one who does not have the same prior knowledge of his own heart rate as I do.
In conclusion, in line with Brener and Ring (2016), I don’t think that HCT is a good measure for interoception, and even if it were, the fact that prior knowledge fully mediates this relationship means that, in my opinion, other methods of interoception need to be found and studied. The fact that if someone has prior knowledge of their HR can and would skew things—no matter their ‘IQ’—since they know that, say, their HR is in the average range (50-75 BPM). I find this study kind of ridiculous and it’s in the running for most ridiculous things I have read all year. Prior knowledge (both with RHR and PEHR; post-exercise heart rate) of these variables will have you score better and, since IQ is a measure of social class then with the small correlation between HCT and IQ found by Murphy et al (2018), some (but most is not) is mediated by IQ, which is just largely tests for skills found in a narrow social class, so it’s no wonder that they corrrlate—however low—and the reason why the relationship was found is obvious, especially if you have some prior knowledge of this field.
I was on Warski Live the other night and had an extremely short back-and-forth with Jared Taylor. I’m happy I got the chance to shortly discuss with him but I got kicked out about 20 minutes after being there. Taylor made all of the same old claims, and since everyone continued to speak I couldn’t really get a word in.
I first stated that Jared got me into race realism and that I respected him. He said that once you see the reality of race then history etc becomes clearer.
To cut through everything, I first stated that I don’t believe there is any utility to IQ tests, that a lot of people believe that people have surfeits of ‘good genes’ ‘bad genes’ that give ‘positive’ and ‘negative’ charges. IQ tests are useless and that people ‘fetishize them’. He then responded that IQ is one of, if not the, most studied trait in psychology to which JF then asked me if I contended that statement and I responded ‘no’ (behavioral geneticists need to work to ya know!). He then talked about how IQ ‘predicts’ success in life, e.g., success in college,
Then, a bit after I stated that, it seems that they painted me as a leftist because of my views on IQ. Well, I’m far right (not that my politics matters to my views on scientific matters) and they made it seem like I meant that Jared fetishized IQ, when I said ‘most people’.
Then Jared gives a quick rundown of the same old and tired talking points how IQ is related to crime, success, etc. I then asked him if there was a definition of intelligence and whether or not there was consensus in the psychological community on the matter.
I quoted this excerpt from Ken Richardson’s 2002 paper What IQ Tests Test where he writes:
Of the 25 attributes of intelligence mentioned, only 3 were mentioned by 25 per cent or more of respondents (half of the respondents mentioned `higher level components’; 25 per cent mentioned ‘executive processes’; and 29 per cent mentioned`that which is valued by culture’). Over a third of the attributes were mentioned by less than 10 per cent of respondents (only 8 per cent of the 1986 respondents mentioned `ability to learn’).
Jared then stated:
“Well, there certainly are differing ideas as to what are the differing components of intelligence. The word “intelligence” on the other hand exists in every known language. It describes something that human beings intuitively understand. I think if you were to try to describe sex appeal—what is it that makes a woman appealing sexually—not everyone would agree. But most men would agree that there is such a thing as sex appeal. And likewise in the case of intelligence, to me intelligence is an ability to look at the facts in a situation and draw the right conclusions. That to me is one of the key concepts of intelligence. It’s not necessarily “the capacity to learn”—people can memorize without being particularly intelligent. It’s not necessarily creativity. There could be creative people who are not necessarily high in IQ.
I would certainly agree that there is no universally accepted definition for intelligence, and yet, we all instinctively understand that some people are better able to see to the essence of a problem, to find correct solutions to problems. We all understand this and we all experience this in our daily lives. When we were in class in school, there were children who were smarter than other children. None of this is particularly difficult to understand at an intuitive level, and I believe that by somehow saying because it’s impossible to come up with a definition that everyone will accept, there is no such thing as intelligence, that’s like saying “Because there may be no agreement on the number of races, that there is no such thing as race.” This is an attempt to completely sidetrack a question—that I believe—comes from dishonest motives.”
(“… comes from dishonest motives”, appeal to motive. One can make the claim about anyone, for any reason. No matter the reason, it’s fallacious. On ‘ability to learn’ see below.)
Now here is the fun part: I asked him “How do IQ tests test intelligence?” He then began talking about the Raven (as expected):
“There are now culture-free tests, the best-known of which is Raven’s Progressive Matrices, and this involves recognizing patterns and trying to figure out what is the next step in a pattern. This is a test that doesn’t require any language at all. You can show an initial simple example, the first square you have one dot, the next square you have two dots, what would be in the third square? You’d have a choice between 3 dots, 5 dots, 20 dots, well the next step is going to be 3 dots. You can explain what the initial patterns are to someone who doesn’t even speak English, and then ask them to go ahead and go and complete the suceeding problems that are more difficult. No language, involved at all, and this is something that correlates very, very tightly with more traditonal, verbally based, IQ tests. Again, this is an attempt to measure capacity that we all inherently recognize as existing, even though we may not be able to define it to everyone’s mutual satisfaction, but one that is definitely there.
Ultimately, we will be able to measure intelligence through direct assessment of the brain, that it will be possible to do through genetic analysis. We are beginning to discover the gene patterns associated with high intelligence. Already there have been patent applications for IQ tests based on genetic analysis. We really aren’t at the point where spitting in a cup and analyzing the DNA you can tell that this guy has a 140 IQ, this guy’s 105 IQ. But we will eventually get there. At the same time there are aspects of the brain that can be analyzed, repeatedly, with which the signals are transmitted from one part of the brain to the other, the density of grey matter, the efficiency with which white matter communicates between the different grey matter areas of the brain.
I’m quite confident that there will come a time where you can just strap on a set of electrodes and have someone think about something—or even not think about anything at all—and we will be able to assess the power of the brain directly through physical assessment. People are welcome to imagine that this is impossible, or be skeptical about that, but I think we’re defintely moving in that direction. And when the day comes—when we really have discovered a large number of the genetic patterns that are associated with high intelligence, and there will be many of them because the brain is the most complicated organ in the human body, and a very substantial part of the human genome goes into constructing the brain. When we have gotten to the bottom of this mystery, I would bet the next dozen mortgage payments that those patterns—alleles as they’re called, genetic patterns—that are associated with high intelligence will not be found to be equally distributed between people of all races.”
Then immediately after that, the conversation changed. I will respond in points:
1) First off, as I’m sure most long-time readers know, I’m not a leftist and the fact that (in my opinion) I was implied to be a leftist since I contest the utility of IQ is kind of insulting. I’m not a leftist, nor have I ever been a leftist.
2) On his points on definitions of ‘intelligence’: The point is to come to a complete scientific consensus on how to define the word, the right way to study it and then think of the implications of the trait in question after you empirically verify its reality. That’s one reason to bring up how there is no consensus in the psychological community—ask 50 psychologists what intelligence is, get numerous different answers.
3) IQ and success/college: Funny that gets brought up. IQ tests are constructed to ‘predict’ success since they’re similar already to achievement tests in school (read arguments here, here, and here). Even then, you would expect college grades to be highly correlated with job performance 6 years after graduation from college right? Wrong. Armstrong (2011: 4) writes: “Grades at universities have a low relationship to long-term job performance (r = .05 for 6 or more years after graduation) despite the fact that cognitive skills are highly related to job performance (Roth, et al. 1996). In addition, they found that this relationship between grades and job performance has been lower for the more recent studies.” Though the claim that “cognitive skills are highly related to job performance” lie on shaky ground (Richardson and Norgate, 2015).
4) My criticisms on IQ do not mean that I deny that ‘intelligence exists’ (which is a common strawman), my criticisms are on construction and validity, not the whole “intelligence doesn’t exist” canard. I, of course, don’t discard the hypothesis that individuals and populations can differ in ‘intelligence/intelligence ‘genes’, the critiques provided are against the “IQ-tests-predict-X-in-life” claims and ‘IQ-tests-test-‘intelligence” claims. IQ tests test cultural distance from the middle class. Most IQ tests have general knowledge questions on them which then contribute a considerable amount to the final score. Therefore, since IQ tests test learned knowledge present in some cultures and not in others (which is even true for ‘culture-fair’ tests, see point 5), then learning is intimately linked with Jared’s definition of ‘intelligence’. So I would necessariliy state that they do test learned knowledge and test learned knowledge that’s present in some classes compared to others. Thusly, IQ tests test learned knowledge more present in some certain classes than others, therefore, making IQ tests proxies for social class, not ‘intelligence’ (Richardson, 2002; 2017b).
5) Now for my favorite part: the Raven. The test that everyone (or most people) believe is culture-free, culture-fair since there is nothing verbal thusly bypassing any implicit suggestion that there is cultural bias in the test due to differences in general knowledge. However, this assumption is extremely simplistic and hugely flawed.
For one, the Raven is perhaps one of the most tests, even more so than verbal tests, reflecting knowledge structures present in some cultures more than others (Richardson, 2002). One may look at the items on the Raven and then proclaim ‘Wow, anyone who gets these right must be ‘intelligent”, but the most ‘complicated’ Raven’s items are not more complicated than everyday life (Carpenter, Just, and Shell, 1990; Richardson, 2002; Richardson and Norgate, 2014). Furthermore, there is no cognitive theory in which items are selected for analysis and subsequent entry onto a particular Raven’s test. Concerning John Raven’s personal notes, Carpenter, Just, and Shell (1990: 408) show that John Raven—the creator of the Raven’s Progressive Matrices test—used his “intuition and clinical experience” to rank order items “without regard to any underlying processing theory.”
Now to address the claim that the Raven is ‘culture-free’: take one genetically similar population, one group of them are foraging hunter-gatherers while the other population lives in villages with schools. The foraging people are tested at age 11. They score 31 percent, while the ones living in more modern areas with amenities get 72 percent right (‘average’ individuals get 78 percent right while ‘intellectually defective’ individuals get 47 percent right; Heine, 2017: 188). The people I am talking about are the Tsimane, a foraging, hunter-gatherer population in Bolivia. Davis (2014) studied the Tsimane people and administered the Raven test to two groups of Tsimane, as described above. Now, if the test truly were ‘culture-free’ as is claimed, then they should score similarly, right?
Wrong. She found that reading was the best predictor of performance on the Raven. Children who attend school (presumably) learn how to read (with obviously a better chance to learn how to read if you don’t live in a hunter-gatherer environment). So the Tsimane who lived a more modern lifestyle scored more than twice as high on the Raven when compared to those who lived a hunter-gatherer lifestyle. So we have two genetically similar populations, one is exposed to more schooling while the other is not and schooling is the most related to performance on the Raven. Therefore, this study is definitive proof that the Raven is not culture-fair since “by its very nature, IQ testing is culture bound” (Cole, 1999: 646, quoted by Richardson, 2002: 293).
6) I doubt that we will be able to genotype people and get their ‘IQ’ results. Heine (2017) states that you would need all of the SNPs on a gene chip, numbering more than 500,000, to predict half of the variation between individuals in IQ (Davies et al, 2011; Chabris et al, 2012). Furthermore, since most genes may be height genes (Goldstein, 2009). This leads Heine (2017: 175) to conclude that “… it seems highly doubtful, contra Robert Plomin, that we’ll ever be able to estimate someone’s intelligence with much precision merely by looking at his or her genome.”
I’ve also critiqued GWAS/IQ studies by making an analogous argument on testosterone, the GWAS studies for testosterone, and how testosterone is produced in the body (its indirectly controlled by DNA, while what powers the cell is ATP, adenosine triphosphate (Kakh and Burnstock, 2009).
7) Regarding claims on grey and white matter: he’s citing Haier et al’s work, and their work on neural efficiency, white and grey matter correlates regarding IQ, to how different networks of the brain “talk” to each other, as in the P-FIT hypothesis of Jung and Haier (2007; numerous critiques/praises). Though I won’t go in depth on this point here, I will only say that correlations from images, correlations from correlations etc aren’t good enough (the neural network they discuss also may be related to other, noncognitive, factors). Lastly, MRI readings are known to be confounded by noise, visual artifacts and inadequate sampling, even getting emotional in the machine may cause noise in the readings (Okon-Singer et al, 2015) and since movements like speech and even eye movements affect readings, when describing normal variation, one must use caution (Richardson, 2017a).
8) There are no genes for intelligence (I’d also say “what is a gene?“) in the fluid genome (Ho, 2013), so due to this, I think that ‘identifying’ ‘genes for’ IQ will be a bit hard… Also touching on this point, Jared is correct that many genes—most, as a matter of fact—are expressed in the brain. Eighty-four percent, to be exact (Negi and Guda, 2017), so I think there will be a bit of a problem there… Further complicating these types of matters is the matter of social class. Genetic population structures have also emerged due to social class formation/migration. This would, predictably, cause genetic differences between classes, but these genetic differences are irrelevant to education and cognitive ability (Richardson, 2017b). This, then, would account for the extremely small GWAS correlations observed.
9) For the last point, I want to touch briefly on the concept of heritability (because I have a larger theme planned for the concept). Heritability ‘estimates’ have both group and individual flaws; environmental flaws; genetic flaws (Moore and Shenk, 2017), which arise due to the use of the highly flawed CTM (classical twin method) (Joseph, 2002; Richardson and Norgate, 2005; Charney, 2013; Fosse, Joseph, and Richardson, 2015). The flawed CTM inflates heritabilities since environments are not equalized, as they are in animal breeding research for instance, which is why those estimates (which as you can see are lower than the sky-high heritabilities that we get for IQ and other traits) are substantially lower than the heritabilities we observe for traits observed from controlled breeding experiments; which “surpasses almost anything found in the animal kingdom” (Schonemann, 1997: 104).
Lastly, there are numerous hereditarian scientific fallacies which include: 1) trait heritability does not predict what would occur when environments/genes change; 2) they’re inaccurate since they don’t account for gene-environment covariation or interaction while also ignoring nonadditive effects on behavior and cognitive ability; 3) molecular genetics does not show evidence that we can partition environment from genetic factors; 4) it wouldn’t tell us which traits are ‘genetic’ or not; and 5) proposed evolutionary models of human divergence are not supported by these studies (since heritability in the present doesn’t speak to what traits were like thousands of years ago) (Bailey, 1997). We, then, have a problem. Heritability estimates are useful for botanists and farmers because they can control the environment (Schonemann, 1997; Moore and Shenk, 2017). Regarding twin studies, the environment cannot be fully controlled and so they should be taken with a grain of salt. It is for these reasons that some researchers call to end the use of the term ‘heritability’ in science (Guo, 2000). For all of these reasons (and more), heritability estimates are useless for humans (Bailey, 1997; Moore and Shenk, 2017).
Still, other authors state that the use of heritability estimates “attempts to impose a simplistic and reified dichotomy (nature/nurture) on non-dichotomous processes.” (Rose, 2006) while Lewontin (2006) argues that heritability is a “useless quantity” and that to better understand biology, evolution, and development that we should analyze causes, not variances. (I too believe that heritability estimates are useless—especially due to the huge problems with twin studies and the fact that the correct protocols cannot be carried out due to ethical concerns.) Either way, heritability tells us nothing about which genes cause the trait in question, nor which pathways cause trait variation (Richardson, 2012).
In sum, I was glad to appear and discuss (however shortly) with Jared. I listened to it a few times and I realize (and have known before) that I’m a pretty bad public speaker. Either way, I’m glad to get a bit of points and some smaller parts of the overarching arguments out there and I hope I have a chance in the future to return on that show (preferably to debate JF on IQ). I will, of course, be better prepared for that. (When I saw that Jared would appear I decided to go on to discuss.) Jared is clearly wrong that the Raven is ‘culture-free’ and most of his retorts were pretty basic.
(Note: I will expand on all 9 of these points in separate articles.)
My articles get posted on the Reddit board /r/hbd and, of course, people don’t like what I write about IQ. I get accused of reading ‘Richardson n=34 studies’ even though that was literally one citation in a 32 page paper that does not affect his overall argument. (I will be responding to Kirkegaard and UnsilencedSci in separate articles.) I’ll use this time to respond to criticisms from the Reddit board.
He’s peddling BS, say this:
“But as Burt and his associates have clearly demonstrated, teachers’ subjective assessments afford even more reliable predictors.”
Well, no, teachers are in fact remarkably poor at predicting student’s success in life. Simple formulas based on school grades predict LIFE success better than teachers, notwithstanding the IQ tests.
You’re incorrect. As I stated in my response to The Alternative Hypothesis, the correlation between teacher’s judgement and student achievement is .66. “The median correlation, 0.66, suggests a moderate to strong correspondence between teacher judgements and student achievement” (Hoge and Coladarci, 1989: 303). This is a higher correlation than what was found in the ‘validation studies’ from. Hunter and Schmidt.
He cherry-picks a few bad studies and ignores entire bodies of evidence with sweeping statements like this:
“This, of course, goes back to our good friend test construction. ”
Test construction is WHOLLY IRRELEVANT. It’s like saying: “well, you know, the ether might be real because Michelson-Morley experiment has been constructed this way”. Well no, it does not matter how MM experiment has been constructed as long as it tests for correct principles. Both IQ and MM have predictive power and it has nothing to do with “marvelling”, it has to do whether the test, regardless of its construction, can effectively predict outcomes or not.
This is a horrible example. You’re comparing the presuppositions of the test constructors who have in their mind who is or is not intelligent and then construct the test to confirm those preconceived notions to an experiment that was used to find the presence and properties of aether? Surely you can think of a better analogy because this is not it.
More BS: “Though a lot of IQ test questions are general knowledge questions, so how is that testing anything innate if you’ve first got to learn the material, and if you have not you’ll score lower?”
Of course the IQ tests do NOT test much of general knowledge. Out of 12 tests in WAIS only 2 deal with general knowledge.
The above screenshot is from Nisbett (2012: 14) (though it’s the WISC, not WAIS they’re similar, all IQ tests go through item analysis, tossing items that don’t conform to the test constructors’ presuppositions).
Either way, our friend test construction makes an appearance here, too. This is how these tests are made and they are made to conform to the constructor’s presuppositions. The WISC and WAIS have similar subtests, either way. Test anxiety, furthermore, leads to a lessened performance on the block design and picture arrangement subtests (Hopko et al, 2005) and moderate to severe stress, furthermore, is related to social class and IQ test performance. Stress affects the growth of the hippocampus and PFC (prefrontal cortex) (Davidson and McEwing, 2012) so does it seem like an ‘intellectual’ thing here? Furthermore, all tests and batteries are tried out on a sample of children, with items not contributing to normality being tossed out, therefore ‘item analysis’ forces what we ‘see’ regarding IQ tests.
Even the great Jensen said in his 1980 book Bias in Mental Testing (pg 71):
It is claimed that the psychometrist can make up a test that will yield any type of score distribution he pleases. This is roughly true, but some types of distributions are easier to obtain than others.
This holds for tbe WAIS, WISC, the Raven, any type of IQ test. This shows how arbitrary the ‘item selection’ is. No matter what type of ‘IQ test’ you attempt to use to say ‘It does test “intelligence” (whatever that is)!!’ the reality of test construction and constructing tests to fit presuppositions and distributions cannot be ran away from.
The other popular test, Raven’s Progressive Matrices does not test for general knowledge at all.
This is a huge misconception. People think that just because there are no ‘general knowledge questions’ or anything verbal regarding the Matrices then it must test an innate power, thus mysterious ‘g’. However, this is wrong and he clearly doesn’t keep up with recent data:
Reading was the greatest predictor of performance Raven’s, despite controlling for age and sex. Attendance was so strongly related with Raven’s performance [school attendance was used as a proxy for motivation]. These findings suggest that reading, or pattern recognition, could be fundamentally affecting the way an individual problem solves or learns to learn, and is somehow tapping into ‘g’. Presumably the only way to learn to read is through schooling. It is, therefore, essential that children are exposed to formal education, have the mother to go/stay in school, and are exposed to consistent, quality training in order to develop the skills associated with your performance. (pg 83) Variable Education Exposure and Cognitive Task Performance Among the Tsimane, Forager- Horticulturalists.
Furthermore, according to Richardson (2002): “Performance on the Raven’s test, in other words, is a question not of inducing ‘rules’ from meaningless symbols, in a totally abstract fashion, but of recruiting ones that are already rooted in the activities of some cultures rather than others.”
The assumption that the Raven is ‘culture free’ because it’s ‘just shapes and rote memory’ is clearly incorrect. James Thompson even said to me that Linda Gottfredson said that people only think the Raven is a ‘test of pure g’ because Jensen said it, which is not true.
This is completely wrong in so many ways. No understanding of normalization. Suggestion that missing heritability is discovering environmentally. I think a distorted view of the Flynn Effect. I’ll just stick to some main points.
I didn’t imply a thing about missing heritability. I only cited the article by Evan Charney to show how populations become stratified.
RR: There is no construct validity to IQ tests
First, let’s go through the basics. All IQ tests measure general intelligence (g), the positive manifold underlying every single measure of cognitive ability. This was first observed over a century ago and has been replicated across hundreds of studies since. Non-g intelligences do not exist, so for all intents and purposes it is what we define as intelligence. It is not ‘mysterious’
Thanks for the history lesson. 1) we don’t know what ‘g’ is. (I’ve argued that it’s not physiological.) So ‘intelligence’ is defined as ‘g’ yet which we don’t know what ‘g’ is. His statement here is pretty much literally ‘intelligence is what IQ tests test’.
It would be correct to say that the exact biological mechanisms aren’t known. But as with Gould’s “reification” argument, this does not actually invalidate the phenomenon. As Jensen put it, “what Gould has mistaken for “reification” is neither more nor less than the common practice in every science of hypothesizing explanatory models or theories to account for the observed relationships within a given domain.” Poor analogies to white blood cells and breathalyzer won’t change this.
It’s not a ‘poor analogy’ at all. I’ve since expanded on the construct validity argument with more examples of other construct valid tests like showing how the breathalyzer is construct valid and how white blood cell count is a proxy for disease. They have construct validity, IQ tests do not.
RR: I said that I recall Linda Gottfredson saying that people say that Ravens is culture-fair only because Jensen said it
This has always been said in the context of native, English speaking Americans. For example it was statement #5 within Mainstream Science on Intelligence. Jensen’s research has demonstrated this. The usage of Kuwait and hunter gatherers is subsequently irrelevant.
Point 5 on the Mainstream Science on Intelligence memo is “Intelligence tests are not culturally biased against American blacks or other native-born, English-speaking peoples in the U.S. Rather, IQ scores predict equally accurately for all such Americans, regardless of race and social class. Individuals who do not understand English well can be given either a nonverbal test or one in their native language.”
This is very vague. Richardson (2002) has noted how different social classes are differentially prepared for IQ test items:
I shall argue that the basic source of variation in IQ test scores is not entire (or even mainly) cognitive, and what is cognitive is not general or unitary. It arises from a nexus or sociocognitive-affective factors determining individuals: relative preparedness for the demands of the IQ test.
The fact of the matter is, all social classes aren’t prepared in the same way to take the IQ test and if you read the paper you’d see that.
RR: IQ test validity
I’ll keep this short. There exist no predictors stronger than g across any meaningful measures of success. Not education, grades, upbringing, you name it.
Yes there are. Teacher assessment which has a higher correlation than the correlation between ‘IQ’ and job performance.
RR: Another problem with IQ test construction is the assumption that it increases with age and levels off after puberty.
The very first and most heavily researched behavioral trait’s heritability has been intelligence. Only through sheer ignorance could the term “assumption” describe findings from over a century of inquiry.
Yes the term ‘assumption’ was correct. You do realize that, of course, the increase in IQ heritability is, again, due to test construction? You can also build that into the test as well, by putting more advanced questions, say high school questions for a 12 year old, and heritability would seem to increase due to just how the test was constructed.
Finally, IanTichszy says:
That article is thoroughly silly.
First, the IQ tests predict real world-performance just fine: http://thealternativehypothesis.org/index.php/2016/04/15/the-validity-of-iq/
I just responded to this article this week. They only ‘predicts real-world performance just fine’ because they’re constructed to and even then, high-achieving children in achievement rarely become high achieving adults whereas low-achieving adults tend to become successful adults. There are numerous problems with TAH’s article which I’ve already covered.
That is the important thing, not just correlation with blood pressure or something biological. Had g not predicted real-world performance from educational achievement to job performance with very high reliability, it would be useless, but it does predict those.
Test construction. You can’t get past that by saying ‘it does predict’ because it only predicts because it’s constructed to (I’d call it ‘post-dict’).
Second, on Raven’s Progressive Matrices test: the argument “well Jensen just said so” is plain silly. If RPM is culturally loaded, a question: just what culture is represented on those charts? You can’t reasonably say that. Orangutans are able to solve simplified versions of RPM, apparently they do not have a problem with cultural loading. Just look at the tests yourself.
Of course it’s silly to accept that the Raven is culture free and tests ‘g’ the best just ‘because Jensen said so’. The culture loading of the Raven is known, there is a ‘hidden structure’ in them. Even the constructors of the Raven have noted this where they state that they transposed the items to read from left to right, not right to left which is a tacit admission of cultural loading. “The reason that some people fail such problems is exactly the same reason some people fail IQ test items like the Raven Matrices tests… It simply is not the way the human cognitive system is used to being engaged” (Richardson, 2017: 280).
Furthermore, when items are familiar to all groups, even young children are capable of complex analogical reasoning. IQ tests “test for the learned factual knowledge and cognitive habits more prominent in some social classes than in others. That is, IQ scores are measures of specific learning, as well as self-confidence and so on, not general intelligence“ (Richardson, 2017: 192).
Another piece of misinformation: claiming that IQs are not normally distributed. Well, we do not really know the underlying distribution, that’s the problem, only the rank order of questions by difficulty, because we do not have absolute measure of intelligence. Still, the claim that SOME human mental traits, other than IQ, do not have normal distribution, in no way impacts the validity of IQ distribution as tests found it and projected onto mean 100 and standard dev 15 since it reflects real world performance well.
Physiological traits important for survival are not normally distributed (of course it is assumed that IQ both tests innate physiological differences and is important for survival so if it were physiological it wouldn’t be normally distributed either since traits important for survival have low heritabilities). It predicts real world performance well because, see above and my other articles on thus matter.
If you know even the basic facts about IQ, it’s clear that this article has been written in bad faith, just for sake of being contrarian regardless of the truth content or for self-promotion.
No, people don’t know the basic facts of IQ (or its construction). My article isn’t written in bad faith nor is it being contrarian regardless of the truth content or for self-promotion. I can, clearly, address criticisms to my writing.
In the future, if anyone has any problems with what I write then please leave a comment here on the blog at the relevant article. Commenting on Reddit on the article that gets posted there is no good because I probably won’t see it.
Ryan Faulk, like most IQ-ists, believes that the correlation with job performance and IQ somehow is evidence for its validity. He further believes that because self- and peer-ratings correlate with one’s IQ scores that that is further evidence for IQ’s validity.
Well too bad for Faulk, correlations with other tests and other IQ tests lead to circular assumptions. The first problem, as I’ve covered before, is that there is no agreed-upon model or description of IQ/intelligence/’g’ and so therefore we cannot reliably and truthfully state that differences in ‘g’ this supposed ‘mental power’ this ‘strength’ is what causes differences in test scores. Unfortunately for Ryan Faulk and other IQ-ists, again, coming back to our good old friend test construction, it’s no wonder that IQ tests correlate around .5—or so is claimed—with job performance, however IQ test scores correlate at around .5 with school achievement, which is caused by some items containing knowledge that has been learned in school, such as “In what continent is Egypt?” and Who wrote Hamlet?” and “What is the boiling point of water?” As Ken Richardson writes in his 2017 book Genes, Brains, and Human Potential: The Science and Ideology of Intelligence (pg 85):
So it should come as no surprise that performance on them [IQ tests] is associated with school performance. As Robert L. Thorndike and Elizabeth P. Hagen explained in their leading textbook, Educational and Psychological Measurement, “From the very way in which the tests were assembled [such correlation] could hardly be otherwise.”
So, obviously, neither of the two tests determine independently that they measure intelligence, this so-called innate power, and because they’re different versions of the same test there is a moderate correlation between them. This goes back to item analysis and test construction. Is it any wonder, then, why correlations with IQ and achievement increase with age? It’s built into the test! And while Faulk does cite high correlations from one of Schmidt and Hunter’s meta-analyses on the subject, what he doesn’t tell you is that one review found a correlation of .66 between teacher’s assessment and future achievement of their students later in life (higher than the correlation with job performance and IQ) (Hoge and Coladarci, 1989.) They write (pg 303): “The median correlation, 0.66, suggests a moderate to strong correspondence between teacher judgments and student achievement.” This is just like what I quoted the other day in my response to Grey Enlightenment where I quoted Layzer (1972) who wrote:
Admirers of IQ tests usually lay great stress on their predictive power. They marvel that a one-hour test administered to a child at the age of eight can predict with considerable interest whether he will finish college. But as Burt and colleagues have clearly demonstrated, teachers subjective assessments afford even more reliable predictors. This is almost a truism.
So the correlation of .5 between occupation level and IQ is self-fulfilling, which are not independent measures. In regard to the IQ and job performance correlation, which I’ve discussed in the past, studies in the 70s showed much lower correlations, between .2 and .3, which Jensen points out in The g Factor.
The problem with the so-called validity studies carried out by Schmidt and Hunter, as cited by Ryan Faulk, is that they included numerous other tests that were not IQ tests in their analysis like memory tests, reading tests, the SAT, university admission tests, employment selection tests, and a variety of armed forces tests. “Just calling these “general ability tests,” as Schmidt and Hunter do, is like reducing a diversity of serum counts to a “general. blood test” (Richardson, 2017: 87). Of course the problem with using vastly different tests is that they tap into different abilities and sources of individual differences. The correlation between SAT scores and high school grades is .28 whereas the correlation between both the SAT and high school grades and IQ is about .2. So it’s clearly not testing the same “general ability” that’s being tested.
Furthermore, regarding job performance, it’s based on one measure: supervisor ratings. These ratings are highly subjective and extremely biased with age and halo effects seen with height and facial attractiveness being seen to sway judgments on how well one works. Measures of job performance are unreliable—especially from supervisors—due to the assumptions and biases that go into the measure.
Do IQ tests test neural processes? Not really. One of the most-studied variables is reaction time. The quicker they react to a stimulus, supposedly, the higher their IQ is in average as they are quicker to process information, the story goes. Detterman (1987) notes that other factors other than ‘processing speed’ can explain differences in reaction time, including but not limited to, stress, understanding instructions, motivation to do said task, attention, arousal, sensory acuity, confidence, etc. Khodadadi et al (2014) even write “The relationship between reaction time and IQ is too complicated and reveal a significant correlation depends on various variables (e.g. methodology, data analysis, instrument etc.).” Complex cognition in real life is also completely different than the simple questions asked in the Raven (Richardson and Norgate, 2014).
It is easy to look at the puzzles that make up IQ tests and be convinced that they really do test brain power. But then we ignore the brain power thst nearly everyone displays in their everyday lives. Some psychologists have noticed thst people who stumble over formal tests of cognitive can bangle highly complex problems in their real lives all the time. As Michael Eysenck put it in his well-known book Psychology, “There is an apparent contradiction between our ability to deal effectively with out everyday environment and our failure to perform well on many laboratory reasoning tasks.” We can say the same about IQ tests.
Real-life problems combine many more variables that change over time and interact. It seems that the ability to do pretentious problems in a pencil-and-paper (or computer) format, like IQ test items, is itself a learned, if not-so-complex skill. (Richardson, 2017: 95-96)
Finally, Faulk cites studies showing that how intelligent people and their peers rates themselves and others predicted how well they did on IQ tests. This isn’t surprising. Since they correlate with academic achievement at .5 then if one is good academically then they’d have a high test score more often than not. That friends rate friends high and they end up matching scores is no surprise either as people generally group together with other people like themselves and so therefore will have similar achievements. That is not evidence for test validity though!! See Richardson and Norgate (2015) “In scientific method, generally, we accept external, observable differences as a valid measure of an unseen function when we can mechanistically relate differences in one to diffences in the other …” So even Faulk’s attempt to ‘validate’ IQ tests using peer- and self-ratings of ‘intelligence’ (whatever that is) falls on its face since its not a true measure of validity. It’s not construct validity. (EDIT: Psychological constructs are validated ‘by testing whether they relate to measures of other constructs as specified by theory‘ (Strauss and Smith, 2009). This doesn’t exist for IQ therefore IQ isn’t construct valid.)
In sum, Faulk’s article leaves a ton to be desired and doesn’t outright prove that there is validity to IQ tests because, as I’ve shown in the past, validity for IQ is nonexistent, though some have tried (using correlations with job performance as evidence) but Richardson and Norgate (2015) take down those claims and show that the correlation is between .2 and .3, not the .5+ cited by Hunter and Schmidt in their ‘validation studies’. The criteria laid out by Faulk does not prove that there is true construct validity to IQ tests and due to test construction, we see these correlations with educational achievement.
I’ve had a few discussions with Grey Enlightenment on this blog, regarding construct validity. He has now published a response piece on his blog to the arguments put forth in my article, though unfortunately it’s kind of sophomoric.
He calls himself a ‘race realist’yet echoes the same arguments used by those who oppose such realism.
1) One doesn’t have to believe in racial differences in mental traits to be a race realist as I have argued twice before in my articles You Don’t Need Genes to Delineate Race and Differing Race Concepts and the Existence of Race: Biologically Scientific Definitions of Race. It’s perfectly possible to be a race realist—believe in the reality of race—without believing there are differences in mental traits—‘intelligence’, for instance (whatever that is).
2) That I strongly question the usefulness and utility of IQ due to its construction doesn’t mean that I’m not a race realist.
3) I’ve even put forth an analogous argument on an ‘athletic abilities test’ where I gave a hypothetical argument where a test was constructed that wasn’t a true test of athletic ability and that it was constructed on the basis of who is or is not athletic, per the constructors’ presuppositions. In this hypothetical scenario, am I really denying that athletic differences exist between races and individuals? No. I’d just be pointing out flaws in a shitty test.
Just because I question the usefulness and (nonexistent) validity of IQ doesn’t mean that I’m not a race realist, nor that I believe groups or individuals are ‘the same’ in ‘intelligence’ (whatever that may be; which seems to be a common strawman for those who don’t bow to the alter of IQ).
Blood alcohol concentration is very specific and simple; human intelligence by comparison is not . Intelligence is polygenic (as opposed to just a single compound) and is not as easy to delineate, as, say, the concentration of ethanol in the blood.
It’s irrelevant how ‘simple’ blood alcohol concentration is. The point of bringing it up is that it’s a construct valid measure which is then calibrated against an accepted and theoretical biological model. The additive gene assumption is false, that is, genes being independent of the environment giving ‘positive charges’ as Robert Plomin believes.
He says IQ tests are biased because they require some implicit understanding if social constructs, like what 1+1 equals or how to read a word problem, but how is a test that is as simple as digit recall or pattern recognition possibly a social construct.
What is it that allows individuals to be better than others on digit recall or pattern recognition (what kind of pattern recognition?)? The point of my 1+1 statement is that it is construct valid regarding one’s knowledge of that math problem whereas for the word problem, it was a quoted example showing how if the answer isn’t worded correctly it could be indirectly testing something else.
He’s invoking a postmodernist argument that IQ tests do not measure an innate, intrinsic intelligence, but rather a subjective one that is construct of the test creators and society.
I could do without the buzzword (postmodernist) though he is correct. IQ tests test what their constructors assume is ‘intelligence’ and through item analysis they get the results they want, as I’ve shown previously.
If IQ tests are biased, how is then [sic] that Asians and Jews are able to score better than Whiles [sic] on such tests; surely, they should be at a disadvantage due to implicit biases of a test that is created by Whites.
If I had a dollar for every time I’ve heard this ‘argument’… We can just go back to the test construction argument and we can construct a test that, say, blacks and women score higher than whites and men respectively. How well would that ‘predict’ anything then, if the test constructors had a different set of assumptions?
IQ tests aren’t ‘biased’, as much as lower class people aren’t as prepared to take these tests as people in higher classes (which East Asians and Jews are in). IQ tests score enculturation to the middle class, even the Flynn effect can be explained by the rise in the middle class, lending credence to the aforementioned hypothesis (Richardson, 2002).
Regarding the common objection by the left that IQ tests don’t measures [sic] anything useful or that IQ isn’t correlated with success at life, on a practical level, how else can one explain obvious differences in learning speed, income or educational attainment among otherwise homogeneous groups? Why is it in class some kids learn so much faster than others, and many of these fast-learners go to university and get good-paying jobs, while those who learn slowly tend to not go to college, or if they do, drop out and are either permanently unemployed or stuck in low-paying, low-status jobs? In a family with many siblings, is it not evident that some children are smarter than others (and because it’s a shared environment, environmental differences cannot be blamed).
1) I’m not a leftist.
2) I never stated that IQ tests don’t correlate with success in life. They correlate with success in life since achievement tests and IQ tests are different versions of the same test. This, of course, goes back to our good friend test construction. IQ is correlated with income at .4, meaning 16 percent of the variance is explained by IQ and since you shouldn’t attribute causation to correlations (lest you commit the cum hoc, ergo propter hoc fallacy), we cannot even truthfully say that 16 percent of the variation between individuals is due to IQ.
3) Pupils who do well in school tend to not be high-achieving adults whereas children who were not good pupils ended up having good success in life (see the paper Natural Learning in Higher Education by Armstrong, 2011). Furthermore, the role of test motivation could account for low-paying, low-status jobs (Duckworth et al, 2011; though I disagree with their consulting that IQ tests test ‘intelligence’ [whatever that is] they show good evidence that in low scorers, incentives can raise scores, implying that they weren’t as motivated as the high scorers). Lastly, do individuals within the same family experience the same environment the same or differently?
As teachers can attest, some students are just ‘slow’ and cannot grasp the material despite many repetitions; others learn much more quickly.
This is evidence of the uselessness of IQ tests, for if teachers can accurately predict student success then why should we waste time and money to give a kid some test that supposedly ‘predicts’ his success in life (which as I’ve argued is self-fulfilling)? Richardson (1998: 117) quotes Layzer (1973: 238) who writes:
Admirers of IQ tests usually lay great stress on their predictive power. They marvel that a one-hour test administered to a child at the age of eight can predict with considerable accuracy whether he will finish college. But as Burt and his associates have clearly demonstrated, teachers’ subjective assessments afford even more reliable predictors. This is almost a truism.
Because IQ tests test for the skills that are required for learning, such as short term memory, someone who has a low IQ would find learning difficult and be unable to make correct inferences from existing knowledge.
Right, IQ tests test for skills that are required for learning. Though a lot of IQ test questions are general knowledge questions, so how is that testing anything innate if you’ve first got to learn the material, and if you have not you’ll score lower? Richardson (2002) discusses how people in lower classes are differentially prepared for IQ tests which then affects scores, along with psycho-social factors that do so as well. It’s more complicated than ‘low IQ > X’.
All of these sub-tests are positively correlated due to an underlying factor –called g–that accounts for 40-50% of the variation between IQ scores. This suggests that IQ tests measure a certain factor that every individual is endowed with, rather than just being a haphazard collection of questions that have nothing to do with each other. Race realists’ objection is that g is meaningless, but the literature disagrees “… The practical validity of g as a predictor of educational, economic, and social outcomes is more far-ranging and universal than that of any other known psychological variable. The validity of g is greater the complexity of the task.”
I’ve covered this before. It correlates with the aforementioned variables due to test construction. It’s really that easy. If the test constructors have a different set of presuppositions before the test is constructed then completely different outcomes can be had just by constricting a different test.
Then what about ‘g’? What would one say then? Nevertheless, I’ve heavily criticized ‘g’ and its supposed physiology, and if physiologists did study this ‘variable’ and if it truly did exist, 1) it would not be rank ordered because physiologists don’t rank order traits, 2) they don’t assume normal variations, they don’t estimate heritability and attempt to untangle genes from environment, 3) they don’t assume that normal variation is related to genetic variation (except in rare cases, like down syndrome, for instance), and 4) nor do they assume within the normal range of physiological differences that a higher level is ‘better’ than a lower. My go-to example here is BMR (basal metabolic rate). It has a similar heritability range as IQ (.4 to .8; which is most likely overestimated due to the use of the flawed twin method, just like the heritability of IQ), so is one with a higher BMR somehow ‘better’ than one with a lower BMR? This is what logically follows from assuming that ‘g’ is physiological and all of the assumptions that come along with it. It doesn’t make logical, physiological sense! (Jensen, 1998: 92 further notes that “g tells us little if anything about its contents“.)
All in all, I thank Grey Enlightenment for his response to my article, though it leaves a lot to be desired and if he responds to this article then I hope that it’s much more nuanced. IQ has no construct validity, and as I’ve shown, the attempts at giving it validity are circular, and done by correlating it with other IQ tests and achievement tests. That’s not construct validity.
The word ‘construct’ is defined as “an idea or theory containing various conceptual elements, typically one considered to be subjective and not based on empirical evidence.” Whereas the word ‘validity’ is defined as “the quality of being logically or factually sound; soundness or cogency.” Is there construct validity for IQ tests? Are IQ tests tested against an idea or theory containing various conceptual elements? No, they are not.
Cronbach and Meehl (1955) define construct validity, which they state is “involved whenever a test is to be interpreted as a measure of some attribute or quality which is not “operationally defined.”” Though, the construct validity for IQ tests has been fleeting to investigators. Why? Because there is no theory of individual IQ differences to test IQ tests on. It is even stated that “there is no accepted unit of measurement for constructs and even fairly well-known ones, such as IQ, are open to debate.” The ‘fairly well-known ones’ like IQ are ‘open to debate’ because no such validity exists. The only ‘validity’ that exists for IQ tests is correlations with other tests and attempted correlations with job performance, but I will show that that is not construct validity as is classicly defined.
Construct validity can be easily defined as the ability of a test to measure the concept or construct that it is intended to measure. We know two things about IQ tests: 1) they do not test ‘intelligence’ (but they supposedly do a ‘good enough job’ so that it does not matter) and 2) it does not even test the ‘construct’ that it is intended to measure. For example, the math problem ‘1+1’ is construct valid regarding one’s knowledge and application of that math problem. Construct validity can pretty much be summed up as the proof that it is measuring what the test intends…but where is this proof? It is non-existent.
Richardson (1998: 116) writes:
Psychometrists, in the absence of such theoretical description, simply reduce score differences, blindly to the hypothetical construct of ‘natural ability’. The absence of descriptive precision about those constructs has always made validity estimation difficult. Consequently the crucial construct validity is rarely mentioned in test manuals. Instead, test designers have sought other kinds of evidence about the valdity of their tests.
The validity of new tests is sometimes claimed when performances on them correlate with performances on other, previously accepted, and currently used, tests. This is usually called the criterion validity of tests. The Stanford-Binet and the WISC are often used as the ‘standards’ in this respect. Whereas it may be reassuring to know that the new test appears to be measuring the same thing as an old favourite, the assumption here is that (construct) validity has already been demonstrated in the criterion test.
Some may attempt to say that, for instance, biological construct validity for IQ tests may be ‘brain size’, since brain size is correlated with IQ at .4 (meaning 16 percent of the variance in IQ is explained by brain size). However, for this to be true, someone with a larger brain would always have to be ‘more intelligent’ (whatever that means; score higher on an IQ test) than someone with a smaller brain. This is not true, so therefore brain size is not and should not be used as a measure of construct validity. Nisbett et al (2012: 144) address this:
Overall brain size does not plausibly account for differences in aspects of intelligence because all areas of the brain are not equally important for cognitive functioning.
For example, breathalyzer tests are construct valid. There is a .93 correlation (test-retest) between 1 ml/kg bodyweight of ethanol in 20, healthy male subjects. Furthermore, obtaining BAC through gas chromatography of venous blood, the two readings were highly correlated at .94 and .95 (Landauer, 1972). Landauer (1972: 253) writes “the very high accuracy and validity of breath analysis as a correct estimate of the BAL is clearly shown.” Construct validity exists for ad-libitum taste tests of alcohol in the laboratory (Jones et al, 2016).
There is a casual connection between what one breathes into the breathalyzer and his BAC that comes out of the breathalyzer and how much he had to drink. For example, for a male at a bodyweight of 160 pounds, 4 drinks would have him at a BAC of .09, which would make him unfit to drive. (‘One drink’ being 12 oz of beer, 5 oz of wine, or 1.25 oz of 80 proof liquor.) He drinks more, his BAC reading goes up. Someone is more ‘intelligent’ (scores higher on an IQ test), then what? The correlations obtained from so-called ‘more intelligent people’, like glucose consumption, brain evoked potentials, reaction time, nerve conduction velocity, etc have never been shown to determine higher ‘ability’ to score higher on IQ tests. That, too, would not even be construct validation for IQ tests, since there needs to be a measure showing why person A scored higher than person B, which needs to hold one hundred percent of the time.
Another good example of the construct validity of an unseen construct is white blood cell count. White blood cell count was “associated with current smoking status and COPD severity, and a risk factor for poor lung function, and quality of life, especially in non-currently smoking COPD patients. The WBC count can be used, as an easily measurable COPD biomarker” (Koo et al, 2017). In fact, the PRISA II test has white blood cell count in it, which is a construct valid test. Even elevated white blood cell count strongly predicts all-cause and cardiovascular mortality (Johnson et al, 2005). It is also an independent risk factor for coronary artery disease (Twig et al, 2012).
A good example of tests supposedly testing one thing but testing another is found here:
As an example, think about a general knowledge test of basic algebra. If a test is designed to assess knowledge of facts concerning rate, time, distance, and their interrelationship with one another, but test questions are phrased in long and complex reading passages, then perhaps reading skills are inadvertently being measured instead of factual knowledge of basic algebra.
Numerous constructs have validity—but not IQ tests. It is assumed that they test ‘intelligence’ even though an operational definition of intelligence is hard to come by. This is important, as if there cannot be an agreement on what is being tested, how will there be construct validity for said construct in question?
Richardson (2002) writes that Detterman and Sternberg sent out a questionnaire to a group of theorists which was similar to another questionnaire sent out decades earlier to see if there was an agreement on what ‘intelligence’ is. Twenty-five attributes of intelligence were mentioned. Only 3 were mentioned by more than 25 percent of the respondents, with about half mentioning ‘higher level components’, one quarter mentioned ‘executive processes’ while 29 percent mentioned ‘that which is valued by culture’. About one-third of the attributes were mentioned by less than 10 percent of the respondents with 8 percent of them answering that intelligence is ‘the ability to learn’. So if there is hardly any consensus on what IQ tests measure or what ‘intelligence’ is, then construct validity for IQ seems to be very far in the distance, almost unseeable, because we cannot even define the word, nor actually test it with a test that’s not constructed to fit the constructors’ presupposed notions.
Now, explaining the non-existent validity of IQ tests is very simple: IQ tests are purported to measure ‘g’ (whatever that is) and individual differences in test scores supposedly reflect individual differences in ‘g’. However, we cannot say that it is differences in ‘g’ that cause differences in individual test scores since there is no agreed-upon model or description of ‘g’ (Richardson, 2017: 84). Richardson (2017: 84) writes:
In consequence, all claims about the validity of IQ tests have been based on the assumption that other criteria, such as social rank or educational or occupational acheivement, are also, in effect, measures of intelligence. So tests have been constructed to replicate such ranks, as we have seen. Unfortunately, the logic is then reversed to declare that IQ tests must be measures of intelligence, because they predict school acheivement or future occupational level. This is not proper scientific validation so much as a self-fulfilling ordinance.
Construct validity for IQ does not exist (Richardson and Norgate, 2015), unlike construct validity for breathalyzers (Landauer, 1972) or white blood cell count as a disease proxy (Wu et al, 2013; Shah et al, 2017). So, if construct validity is non-existent, then that means that there is no measure for how well IQ tests measure what it’s ‘purported to measure’, i.e., how ‘intelligent’ one is over another because 1) the definition of ‘intelligence’ is ill-defined and 2) IQ tests are not validated against agreed-upon biological models, though some attempts have been made, though the evidence is inconsistent (Richardson and Norgate, 2015). For there to be true validity, evidence cannot be inconsistent; it needs to measure what it purports to measure 100 percent of the time. IQ tests are not calibrated against biological models, but against correlations with other tests that ‘purport’ to measure ‘intelligence’.
(Note: No, I am not saying that everyone is equal in ‘intelligence’ (whatever that is), nor am I stating that everyone has the same exact capacity. As I pointed out last week, just because I point out flaws in tests, it does not mean that I think that people have ‘equal ability’, and my example of an ‘athletic abilities’ test last week is apt to show that pointing out flawed tests does not mean that I deny individual differences in a ‘thing’ (though athletic abilities tests are much better with no assumptions like IQ tests have.))