a predicted fact is a novel fact for a theory if it was not used to construct that theory — where a fact is used to construct a theory if it figures in the premises from which that theory was deduced. (Musgrave, 1988; cf Mayo, 1991: 524)
Introduction
Previously I demonstrated that the HBD movement is a racist movement. I showed this by arguing that it perfectly tracks with John Lovchik’s definition of racism, which is where “racism is a system of ranking human beings for the purpose of gaining and justifying an unequal distribution of political and economic power.” There is, however, a different issue—an issue that comes from the philosophy of science. So a theory is scientific if and only if it is based on empirical evidence, subject to falsifiability and testability, open to modification or rejection based on further experimentation or observation and—perhaps most importantly—is capable of generating novel predictions, where a novel prediction goes beyond existing knowledge and expectation and can be verified through empirical testing.
Here I will show that HBD doesn’t make any novel predictions, and I will also discuss one old attempt at showing that it does and that it is an example of a degenerative research programme. Effectively, I will argue that contrary to what is claimed, HBD is a degenerating research programme.
On so-called novel predictions
HBD and evolutionary psychology falls prey to the same issues that invalidate both of them. They both rely on ad hoc and post hoc storytelling. In a previous article on novel predictions, I stated:
A risky, novel prediction refers to a prediction made by a scientific theory or hypothesis that goes beyond what is expected or already known within an existing framework (novelness). It involves making a specific claim about a future observation or empirical result that, if confirmed, would provide considerable evidence in support of the scientific theory or hypothesis.
So EP and HBD are cut from the same cloth. John Beerbower (2016) puts the issue succinctly:
At this point, it seems appropriate to address explicitly one debate in the philosophy of science—that is, whether science can, or should try to, do more than predict consequences. One view that held considerable influence during the first half of the twentieth century is called the predictivist thesis: that the purpose of science is to enable accurate predictions and that, in fact, science cannot actually achieve more than that. The test of an explanatory theory, therefore, is its success at prediction, at forecasting. This view need not be limited to actual predictions of future, yet to happen events; it can accommodate theories that are able to generate results that have already been observed or, if not observed, have already occurred. Of course, in such cases, care must be taken that the theory has not simply been retrofitted to the observations that have already been made—it must have some reach beyond the data used to construct the theory.
HBDers promote the tenets that intelligence (IQ), along with behavior and socioeconomic outcomes are strongly associated with genetic differences among individuals and groups. They also use the cold winter theory (CWT) to try to intersect these tenets and show how they evolved over time. According to the CWT, the challenges of surviving in colder climates such as the need to hunt, plan ahead, and cooperate exerted selective pressures which favored genes which fostered higher intelligence in populations that inhabited these regions. I have previously shown years back that the CWT lacks novel predictive power, and that there are devastating response to the CWT which show the invalidity of the theory. Rushton used it in his long-refuted r/K selection theory for human races. Further, for example Jablonski and Chaplin (2000) successfully predicted that “multiple convergences of light skin evolved in different modern human populations and separately in Neanderthals” (Chaplin and Jablonski, 2009: 457). This was a successfully predicted novel fact, something that HBD doesn’t do.
Urbach (1974) (see Deakin, 1976 for response) in criticizing “environmentalism” and contrasting it with “hereditarianism”, claimed that hereditarianism made novel predictions. He also claimed that the “hard core” of the hereditarian research programme was that (1) cognitive ability of all people is due to general intelligence and individual and (2) group differences are due to heredity. We know that (1) is false, since general intelligence is a myth and we know that (2) is false since group differences are due to environmental factors since Jensen’s default hypothesis is false (along with the fact that Asians are a selected population). Further Urbach (1974: 134-135) writes that 4 novel facts of hereditarianism are “(i) of the degree of family resemblances in IQ, (ii) of IQ-related social mobility, (iii) of the distribution of IQ’s, and (iv) of the differences in sibling regression for American Negroes and whites.”
But the above aren’t novel predictions.
(i) Hereditarianism predicts that intelligence has a significant hereditary component, leading to similarities in IQ scores among family members. (Nevermind the fact that environments are inherited by these family members as well.) The prediction appears specific, but it’s not novel in the framework of hereditarianism. The idea that IQ is heritable and that family members share similarities in IQ has been a main tenet of hereditarianism for decades, even in 1974 at the time of publication of Urbach’s paper,rather than offering a new or unexpected insight.
(ii) Hereditarianism also suggests that differences in IQ also have implications for social mobility, with people with higher IQs having a greater change for more upward social mobility. This, too, isn’t novel within the hereditarian framework since even in 1974 and the decades before then this was known.
(iii) Hereditarianism also predicts that IQ scores follow a normal distribution, with a majority of people clustering around the middle. This, too, isn’t a novel prediction, since even Binet unconsciously built his test to have a normal distribution (Nash, 1987: 71). (Also note that Binet knew that his scales weren’t measures but thought that for practical measures they were; Michell, 2012.) Terman constructed his test to also have it. Urbach (1974: 131) states that “even if researchers had set out to obtain a particular distribution of IQ’s, there was no divine guarantee that their efforts would have been successful.” But we know that the process of building a normal distribution is done by choosing only items that conform to the normal distribution are selected, since items most are likely to get right are kept while on both ends items are also kept. In their psychometrics textbook, Rusk and Golombok (2009: 85) state that “it is common practice to carry out item analysis in such a way that only items that contribute to normality are selected.” Jensen (1980: 71) even stated “It is claimed that the psychometrist can make up a test that will yield any kind of score distribution he pleases. This is roughly true, but some types of distributions are much easier to obtain than others.”
(iv) Lastly, hereditarianism predicts that differences in sibling regression or the extent to which sibling IQ scores deviate from the population mean could vary between racial and ethnic groups. The prediction seems specific, but it reflects assumptions of genetic influences on psychological trait—which already were assumptions of hereditarian thought at that time and even today. Thus, it’s not a new or unexpected insight.
Therefore, the so-called novel predictions referenced by Urbach are anything but and reflect existing assumptions and concepts in the field at the time of publication, or he’s outright wrong (as is the case with the normal distribution).
Modern day hereditarians may claim that the correlation between genetics and IQ/educational attainment validates their theories and therefore counts as novel. However, the claim that genes would correlate with IQ has been a central tenet in this field for literally 100 years. Thus, a prediction that there would be a relationship between genes and IQ isn’t new. Nevermind the fact that correlations are spurious and meaningless (Richardson, 2017; Richardson and Jones, 2019) along with the missing heritability problem. Also note that as sample size increase, so to does the chance for spurious correlations, (Calude and Longo, 2016). The hereditarian may also claim that predicting group differences in IQ based on genetic and environmental factors is a novel prediction. Yet again, the idea that these contribute to IQ has been known for decades. The general prediction isn’t novel at all.
So quite obviously, using the above definition of “novel fact” from Musgrave, HBD doesn’t make any novel predictions of previously unknown facts not used in the construction of the theory. The same, then, would hold true for an HBDer who may say something along the lines of “I predict that a West African descendant will win the 100m dash at the next Olympics.” This doesn’t qualify as a novel prediction of a novel fact, either. This is because it relies on existing knowledge related to athletics and racial/ethnic demographics. It’s based in historical data and trends of West African descendants having been successful at previous 100m dash events at the Olympics. Therefore, since it’s not a novel insight that goes beyond the bounds of the theory, it doesn’t qualify as “novel” for the theory.
Why novel predictions matter
Science thrives on progress, so without theories/hypotheses that make novel predictions, a scientific program would stagnate. The inability of hereditarianism to generate risky, novel predictions severely limits it’s ability in explaining human behavior. Novel predictions also provide opportunities for empirical testing, so without novel predictions, hereditarianism lacks the opportunity for rigorous empirical testing. But a proponent could say that whether or not the predictions are novel, there are still predictions that come to pass based on hereditarian ideas.
Without novel prediction, hereditarianism is confined to testing hypotheses that are well-known or widely accepted in the framework or the field itself. This then results in a narrow focus, where researchers merely confirm their pre-existing beliefs instead of challenging them. Further, constantly testing beliefs that aren’t novel leads to confirmation bias where researchers selectively seek out what agrees with them while ignoring what doesn’t (Rushton was guilty of this with his r/K selection theory). Without the generation of novel predictions, hereditarianism lacks innovation. Lastly, the non-existence of novel predictions raises questions about the progressiveness of the framework. True scientific progress is predicated on the formulation of testing novel hypotheses which challenge existing paradigms. Merely claiming that a field generates testable and successful novel predictions and therefore that field is a progressive one is unfounded.
Thus, all hereditarianism does is accommodate, there is no true novel predictive power from it. So instead of generating risky, novel predictions that could potentially falsity the framework, hereditarians merely resort to post-hoc explanations, better known as just-so stories to fit their preconceived notions about human behavior and diversity. HBD claims are also vague and lack the detail needed for rigorous testing—the neck isn’t stuck out far enough for where if the prediction fails that the framework would be refuted. That’s because the predictions are based on assumptions they already know. Thus, HBD is merely narrative construction, and we can construct narratives about any kind of trait we observe today have the story conform with the fact that the trait still exists today. Therefore hereditarianism is in the same bad way as evolutionary psychology.
In this discussion I have tried to show that hereditarian/HBD theories make no novel predictions. They are merely narrative construction. The proposed evolutionary explanation for racial differences in IQ relying on the CWT is ad hoc, meaning it’s a just-so story. Lynn even had to add in something about population size and mutation rates since Arctic people, who have the biggest brain size, don’t have the highest IQ which is nothing more than special pleading.
Urbach’s (1974) four so-called novel predictions of hereditarianism are anything but, since they are based on assumptions already held by hereditarianism. They represent extensions or reformulation of existing assumptions, while also relying on retrospective storytelling.
Therefore, the failure of hereditarianism to produce successful, risky novel predictions should rightly raise concerns about its scientific validity and the scientific credibility of the program. So the only rational view is to reject hereditarianism as a scientific enterprise, since it doesn’t make novel predictions and it’s merely, quite obviously, a way to make prejudices scientific. Clearly, based on what a novel prediction of a novel fact entails, HBD/hereditarian theory doesn’t make any such predictions of novel facts.
What I term “the Berka-Nash measurement objection” is—I think—one of the most powerful arguments against not only the concept of IQ “measurement” but against psychological “measurement” as a whole—this also compliments my irreducibility of the mental arguments. (Although there are of course contemporary authors who argue that IQ—and other psychological traits—are immeasurable, the Berka-Nash measurement objection I think touches the heart of the matter extremely well). The argument that Karel Berka (1983) mounted in Measurement: Its Concepts, Theories, and Problems is a masterclass in defining what “measurement” means and the rules needed for what designates X is a true measure and Y as a true measurement device. Then Roy Nash (1990) in Intelligence and Realism: A Materialist Critique of IQ brought Berka’s critique of extraphysical (mental) measurement to a broader audience, simplifying some of the concepts that Berka discussed and likened it to the IQ debate, arguing that there is no true property that IQ tests measure, therefore IQ tests aren’t a measurement device and IQ isn’t a measure.
I have found only one response to this critique of mental measurement by hereditarians—that of Brand et al (2003). Brand et al think they have shown that Berka’s and Nash’s critique of mental measurement is consistent with IQ, and that IQ can be seen as a form of “quasi-quantification.” But their response misses the mark. In this article I will argue how it misses the mark and it’s for these reasons: (1) they didn’t articulate the specified measured object, object of measurement and measurement unit for IQ and they overlooked the challenges that Berka discussed about mental measurement; (2) they ignored the lack of objectively reproducible measurement units; (3) they misinterpreted what Berka meant by “quasi-quantification” and then likening it to IQ; and (4) they failed to engage with Berka’s call for precision and reliability.
IQ, therefore, isn’t a measurable construct since there is no property being measured by IQ tests.
Brand et al’s arguments against Berka
The response from Brand et al to Berka’s critiques of mental measurement in the context of IQ raises critical concerns of Berka’s overarching analysis on measurement. So examining their arguments against Berka reveals a few shortcomings which undermine the central tenets of Berka’s thesis of measurement. From failing to articulate the fundamental components of IQ measurement, to overlooking the broader philosophical issues that Berka addressed, Brand et al’s response falls short in providing a comprehensive rebuttal to Berka’s thesis, and in actuality—despite the claims from Brand et al—Berka’s argument against mental measurement doesn’t lend credence to IQ measurement—it effectively destroys it, upon a close, careful reading of Berka (and then Nash).
(1) The lack of articulation of a specified measured object, object of measurement and measurement unit for IQ
“If the necessary preconditions under which the object of measurement can be analyzed on a higher level of qualitative aspects are not satisfied, empirical variables must be related to more concrete equivalence classes of the measured objects. As a rule, we encounter this situation at the very onset of measurement, when it is not yet fully apparent to what sort of objects the property we are searching for refers, when its scope is not precisely delineated, or if we measure it under new conditions which are not entirely clarified operationally and theoretically. This situation is therefore mainly characteristic of the various cases of extra-physical measurement, when it is often not apparent what magnitude is, in fact, measured, or whether that which is measured really corresponds to our projected goals.” (Berka, 1983: 51)
“Both specific postulates of the theory of extraphysical measurement, scaling and testing – the postulates of validity and reliability – are then linked to the thematic area of the meaningfulness of measurement and, to a considerable extent, to the problem area of precision and repeatability. Both these postulates are set forth particularly because the methodologists of extra-physical measurement are very well aware that, unlike in physical measurement, it is here often not at all clear which properties are the actual object of measurement, more precisely, the object of scaling or counting, and what conclusions can be meaningfully derived from the numerical data concerning the assumed subject matter of investigation. Since the formulation, interpretation, and application of these requirements is a subject of very vivid discussion, which so far has not reached any satisfactory and more or less congruent conclusions, in our exposition we shall limit ourselves merely to the most fundamental characteristics of these postulates.” (Berka, 1983: 202-203)
“At any rate, the fact that, in the case of extraphysical measurement, we do not have at our disposal an objectively reproducible and significantly interpretable measurement unit, is the most convincing argument against the conventionalist view of a measurement, as well as against the anti-ontological position of operationalism, instrumentalism, and neopositivism.” (Berka, 1983: 211)
One glaring flaw—and I think it is the biggest—in Brand et al’s response is their failure to articulate the specified measured object, object of measurement and measurement unit for IQ. Berka’s insistence on precision in measurement requires a detailed conception of what IQ tests aim to measure—we know this is “IQ” or “intelligence” or “g“, but they then of course would have run into how to articulate and define it in a physical way. Berka emphasized that the concept of measurement demands precision in defining what is being measured (the specified measured object), the entity being measured (the object of measurement), and the unit applied for measurement (the measurement unit). Thus, for IQ to be a valid measure and for IQ tests to be a valid measurement device, it is crucial to elucidate exactly what the tests measure the nature of the mental attribute which is supposedly under scrutiny, and the standardized unit of measurement.
Berka’s insistence on precision aligns with a fundamental aspect of scientific measurement—the need for a well defined and standardized procedure to quantify a particular property. This is evidence for physical measurement, like the length of an object being measured using meters. But when transitioning to the mental, the challenge lies in actually measuring something that lacks a unit of measurement. (And as Richard Haier (2014) even admits, there is no measurement unit for IQ like inches, liters or grams.) So without a clear and standardized unit for mental properties, claims of measurement are therefore suspect—and impossible. Moreover, by sidestepping this crucial aspect of what Berka was getting at, their argument remains vulnerable to Berka’s foundational challenge regarding the essence of what is being measured along with how it is quantified.
Furthermore, Brand et al failed to grapple with what Berka wrote on mental measurement. Brand et al’s response would have been more robust if it had engaged with Berka’s exploration of the inherent intracacies and nuances involved in establishing a clear object of measurement for IQ, and any mental attributes.
Measurement units have to be a standardized and universally applicable quantity or physical property while allowing for standardized comparisons across different measures. And none exists for IQ, nor any other psychological trait. So we can safely argue that psychometrics isn’t measurement, even without touching contemporary arguments against mental measurement.
(2) Ignoring the lack of objectively reproducible measurement units
A crucial aspect of Berka’s critique involves the absence of objectively reproducible measurement units in the realm of measurement. Berka therefore contended that in the absence of such a standardized unit of measurement, the foundations for a robust enterprise of measurement are compromised. This is yet another thing that Brand et al overlooked in their response.
Brand et al’s response lacks a comprehensive examination of how the absence of objectively reproducible measurement units in mental measurement undermines the claim that IQ is a measure. They do not engage with Berka’s concern that the lack of such units in mental measurement actually hinders the claim that IQ is a measure. So the lack of attention to the absence of objectively reproducible measurement units in mental measurement actually weakens, and I think destroys, Brand et al’s response. They should have explored the ramifications of a so-called measure without a measurement unit. So this then brings me to their claims that IQ is a form of “quasi-quantification.”
(3) Misinterpretation of “quasi-quantification” and its application to IQ
Brand et al hinge their defense of IQ on Berka’s concept of “quasi-quantification”, which they misinterpret. Berka uses “quasi-quantification” to describe situations where the properties being measured lack the clear objectivity and standardization found in actual physical measurements. But Brand et al seem to interpret “quasi-quantification” as a justification for considering IQ as a valid form of measurement.
Brand et al’s misunderstanding of Berka’s conception of “quasi-quantification” is evidence in their attempt to equate it with a validation of IQ as a form of measurement. Berka was not endorsing it as a fully-fledged form of measurement, but he highlighted the limitations and distinctiveness compared to traditional quantification and measurement. Berka distinguishes between quantification, pseudo-quantification, and quasi-quantification. Berka explicitly states that numbering and scaling—in contrast to counting and measurement—cannot be regarded as kinds of quantification. (Note that “counting” in this framework isn’t a variety of measurement, since measurement is much more than enumeration, and counted elements in a set aren’t magnitudes.) Brand et al fail to grasp this nuanced difference, while mischaracterizing quasi-quantification as a blanket acceptance of IQ as a form of measurement.
Berka’s reservations of quasi-quantification are rooted in the challenges and complexities associated with mental properties, acknowledging that they fall short of the clear objectivity found in actual physical measurements. So Brand et al’s interpretation overlooks this critical aspect, which leads them to erroneously argue that accepting IQ as quasi-quantification is sufficient to justify its status as measurement.
Brand et al’s arguments against Nash
Nash’s book, on the other hand, is a much more accessible and pointed attack on the concept of IQ and it’s so-called “measurement.” He spends the book talking about the beginnings of IQ testing to the Flynn Effect, Berka’s argument and then ends with talking about test bias. IQ doesn’t have a true “0” point (like temperature, which IQ-ists have tried to liken to IQ, and the thermometer to IQ tests—there is no lawful property like the relation between mercury and temperature in a thermometer and IQ and intelligence, so again the hereditarian claim fails). But most importantly, Nash made the claim that there is actually no property to be measured by IQ tests—what did he mean by this?
Nash of course doesn’t deny that IQ tests rank individuals on their performance. So the claim that IQ is a metric property is already assumed in IQ theory. But the very fact that people are ranked doesn’t justify the claim that people are then ranked according to a property revealed by their performance (Nash, 1990: 134). Moreover, if intelligence/”IQ” were truly quantifiable, then the difference between 80 and 90 IQ and 110 and 120 IQ would represent the same cognitive difference between both groups of scores. But this isn’t the case.
Nash is a skeptic of the claim that IQ tests measure some property. (As I am.) So he challenges the idea that there is a distinct and quantifiable property that can be objectively measured by IQ tests (the construct “intelligence”). Nash also questions whether intelligence possesses the characteristics necessary for measurement—like a well-defined object of measurement and measurement unit. Nash successfully argued that intelligence cannot be legitimately expressed in a metric concept, since there is no true measurement property. But Brand et al do nothing to attack the arguments of Berka and Nash and they do not at all articulate the specified measured object, object of measurement and measurement unit for IQ, which was the heart of the critique. Furthermore, a precise articulation of the specified measured object when it comes to the metrication of X (any psychological trait) is necessary for the claim that X is a measure (along with articulating the object of measurement and measurement unit). But Brand et al did not address this in their response to Nash, which I think is very telling.
Brand et al do rightly note Nash’s key points, but they fall far, far from the mark in effectively mounting a sound argument against his view. Nash argues that IQ test results can only, at best, be used for ordinal comparisons of “less than, equal to, greater than” (which is also what Michell, 2022 argues, and the concludes the same as Nash). This is of course true, since people take a test and their performance is based on the type of culture they are exposed to (their cultural and psychological tools). Brand et al failed to acknowledge this and grapple with its full implications. But the issue is, Brand et al did not grapple at all with this:
The psychometric literature is full of plaintive appeals that despite all the theoretical difficulties IQ tests must measure something, but we have seen that this is an error. No precise specification of the measured object, no object of measurement, and no measurement unit, means that the necessary conditions for metrication do not exist. (Nash, 1990: 145)
All in all, a fair reading of both Berka and Nash will show that Brand et al slithered away from doing any actual philosophizing on the phenomena that Berka and Nash discussed. And, therefore, that their “response” is anything but.
Conclusion
Berka’s and Nash’s arguments against mental measurement/IQ show the insurmountable challenges that the peddlers of mental measurement have to contend with. Berka emphasized the necessity of clearly defining the measured object, object of measurement and measurement unit for a genuine quantitative measurement—these are the necessary conditions for metrication, and they are nonexistent for IQ. Nash then extended this critique to IQ testing, then concluding that the lack of a measurable property undermines the claim that IQ is a true measurement.
Brand et al’s response, on the other hand, was pitiful. They attempted to reconcile Berka’s concept of “quasi-quantification” with IQ measurement. Despite seemingly having some familiarity with both Berka’s and Nash’s arguments, they did not articulate the specified measured object, object of measurement and measurement unit for IQ. If Berka really did agree that IQ is “quasi-quantification”, then why did Brand et al not articulate what needs to be articulated?
When discussing Nash, Brand et al failed to address Nash’s claim that IQ can only IQ can only allow for ordinal comparisons. Nash emphasized numerous times in his book that an absence of a true measurement property challenges the claim that IQ can be measured. Thus, again, Brand et al’s response did not successfully and effectively engage with Nash’s key points and his overall argument against the possibility of intelligence/IQ (and mental measurement as a whole).
Berka’s and Nash’s critiques highlight the difficulties of treating intelligence (and psychological traits as a whole) as quantifiable properties. Brand et al did not adequately address and consider the issues I brought up above, and they outright tried to weasle their way into having Berka “agree” with them (on quasi-quantification). So they didn’t provide any effective counterargument against them, nor did they do the simplest thing they could have done—which was articulate the specified measured object, object of measurement and measurement unit for IQ. The very fact that there is no true “0” point is devestating for claims that IQ is a measure. I’ve been told on more than one occasion that “IQ is a unit-less measure”—but they doesn’t make sense. That’s just trying to cover for the fact that there is no measurement unit at all, and consequently, no specified measured object and object of measurement.
For these reasons, the Berka-Nash measurement objection remains untouched and the questions raised by them remain unanswered. (It’s simple: IQ-ists just need to admit that they can’t answer the challenge and that psychological traits aren’t measurable like physical traits. But then their whole worldview would crumble.) Maybe we’ll wait another 40 and 30 years for a response to the Berka-Nash measurement objection, and hopefully it will at least try harder than Brand et al did in their failure to address these conceptual issues raised by Berka and Nash.
Jensen’s default hypothesis proposes that individual and group differences in IQ are primarily explained genetic factors. But Fagan and Holland (2002) question this hypothesis. For if differences in experience lead to differences in knowledge, and differences in knowledge lead to differences in IQ scores, then Jensen’s assumption that blacks and whites have the same opportunity to learn the content is questionable, and I’d think it false. It is obvious that there are differences in opportunity to acquire knowledge which would then lead to differences in IQ scores. I will argue that Jensen’s default hypothesis is false due to this very fact.
In fact, there is no good reason to accept Jensen’s default hypothesis and the assumptions that come with it. Of course different cultural groups are exposed to different kinds of knowledge, so this—and not genes—would explain why different groups score differently on IQ tests (tests of knowledge, even so-called culture-fair tests are biased; Richardson, 2002). I will argue that we need to reject Jensen’s default hypothesis on these grounds, because it is clear that groups aren’t exposed to the same kinds of knowledge, and so, Jensen’s assumption is false.
Jensen’s default hypothesis is false due to the nature of knowledge acquisition
Jensen (1998: 444) (cf Rushton and Jensen, 2005: 335) claimed that what he called the “default hypothesis” should be the null that needs to be disproved. He also claimed that individual and group differences are “composed of the same stuff“, in that they are “controlled by differences in allele frequencies” and that these differences in allele frequencies also exist for all “heritable” characters, and that we would find such differences within populations too. So if the default hypothesis is true, then it would suggest that differences in IQ between blacks and whites are primarily attributed to the same genetic and environmental influences that account for individual differences within each group. So this implies that genetic and environmental variances that contribute to IQ are therefore the same for blacks and whites, which supposedly supports the idea that group differences are a reflection of individual differences within each group.
But if the default hypothesis were false, then it would challenge the assumption that genetic and environmental influences in IQ between blacks and whites are proportionally the same as seen in each group. Thus, this allows us to talk about other causes of variance in IQ between blacks and whites—factors other than what is accounted for by the default hypothesis—like socioeconomic, cultural, and historical influences that play a more substantial role in explaining IQ differences between blacks and whites.
In the present study, we ensured that Blacks and Whites were given equal opportunity to learn the meanings of relatively novel words and we conducted tests to determine how much knowledge had been acquired. If, as Jensen suggests, the differences in IQ between Blacks and Whites are due to differences in intellectual ability per se, then knowledge for word meanings learned under exactly the same conditions should differ between Blacks and Whites. In contrast to Jensen, we assume that an IQ score depends on information provided to the learner as well as on intellectual ability. Thus, if differences in IQ between Blacks and Whites are due to unequal opportunity for exposure to information, rather than to differences in intellectual ability, no differences in knowledge should obtain between Blacks and Whites given equal opportunity to learn new information. Moreover, if equal training produces equal knowledge across racial groups, than the search for racial differences in IQ should not be aimed at the genetic bases of IQ but at differences in the information to which people from different racial groups have been exposed.
There are reasons to think that Jensen’s default hypothesis is false. For instance, since IQ tests are culture-bound—that is, culturally biased—then they are biased against a group so they therefore are biased for a group. Thus, this introduces a confounding factor which challenges the assumption of equal genetic and environmental influences between blacks and whites. And since we know that cultural differences in the acquisition of information and knowledge vary by race, then what explains the black-white IQ gap is exposure to information (Fagan and Holland, 2002, 2007).
The Default Hypothesis of Jensen (1998) assumes that differences in IQ between races are the result of the same environmental and genetic factors, in the same ratio, that underlie individual differences in intelligence test performance among the members of each racial group. If Jensen is correct, higher and lower IQ individuals within each racial group in the present series of experiments should differ in the same manner as had the African-Americans and the Whites. That is, in our initial experiment, individuals within a racial group who differed in word knowledge should not differ in recognition memory. In the second, third, and fourth experiments individuals within a racial group who differed in knowledge based on specific information should not differ in knowledge based on general information. The present results are not consistent with the default hypothesis.(Fagan and Holland, 2007: 326)
Historical and systematic inequalities could also lead to differences in knowledge acquisition. The existence of cultural biases in educational systems and materials can create disparities in knowledge acquisition. Thus, if IQ tests—which reflect this bias—are culture-bound, it also questions the assumption that the same genetic and environmental factors account for IQ differences between blacks and whites. The default hypothesis assumes that genetic and environmental influences are essentially the same for all groups. But SES/class differences significantly affect knowledge acquisition, so if challenges the default hypothesis.
For years I have been saying, what if all humans have the same potential but it just crystallizes differently due to differences in knowledge acquisition/exposure and motivation? There is a new study that shows that although some children appeared to learn faster than others, they merely had a head start in learning. So it seems that students have the same ability to learn and that so-called “high achievers” had a head start in learning (Koedinger et al, 2023). They found that students vary significantly in their initial knowledge. So although the students had different starting points (which showed the illusion of “natural” talents), they had more of a knowledge base but all of the students had a similar rate of learning. They also state that “Recent research providing human tutoring to increase student motivation to engage in difficult deliberate practice opportunities suggests promise in reducing achievement gaps by reducing opportunity gaps (63, 64).”
So we know that different experiences lead to differences in knowledge (it’s type and content), and we also know that racial groups for example have different experiences, of course, in virtue of their being different social groups. So these different experiences lead to differences in knowledge which are then reflected in the group IQ score. This, then, leads to one raising questions about the truth of Jensen’s default hypothesis described above. Thus, if individuals from different racial groups have unequal opportunities to be exposed to information, then Jensen’s default hypothesis is questionable (and I’d say it’s false).
Intelligence/knowledge crystalization is a dynamic process shaped by extensive practice and consistent learning opportunities. So the journey towards expertise involves iterative refinement with each practice opportunity contribute to the crystallization of knowledge. So if intelligence/knowledge crystallizes through extensive practice, and if students don’t show substantial differences in their rates of learning, then it follows that the crystalization of intelligence/knowledge is more reliant on the frequency and quality of learning opportunities than on inherent differences in individual learning rates. It’s clear that my position enjoys some substantial support. “It’s completely possible that we all have the same potential but it crystallizes differently based on motivation and experience.” The Fagan and Holland papers show exactly that in the context of the black-white IQ gap, showing that Jensen’s default hypothesis is false.
I recently proposed a non-IQ-ist definition of intelligence where I said:
So a comprehensive definition of intelligence in my view—informed by Richardson and Vygotsky—is that of a socially embedded cognitive capacity—characterized by intentionality—that encompasses diverse abilities and is continually shaped by an individual’s cultural and social interactions.
So I think that IQ is the same way. It is obvious that IQ tests are culture-bound and tests of a certain kind of knowledge (middle-class knowledge). So we need to understand how social and cultural factors shape opportunities for exposure to information. And per my definition, the idea that intelligence is socially embedded aligns with the notion that varying sociocultural contexts do influence the development of knowledge and cognitive abilities. We also know that summer vacation increases educational inequality, and that IQ decreases during the summer months. This is due to the nature of IQ and achievement tests—they’re different versions of the same test. So higher class children will return to school with an advantage over lower class children. This is yet more evidence in how knowledge exposure and acquisition can affect test scores and motivation, and how such differences crystallize, even though we all have the same potential (for learning ability).
Conclusion
So intelligence is a dynamic cognitive capacity characterized by intentionality, cultural context and social interactions. It isn’t a fixed trait as IQ-ists would like you to believe but it evolves over time due to the types of knowledge one is exposed to. Knowledge acquisition occurs through repeated exposure to information and intentional learning. This, then, challenges Jensen’s default hypothesis which attributes the black-white IQ gap primarily to genetics.Since diverse experiences lead to varied knowledge, and there is a certain type of knowledge in IQ tests, individuals with a broad range of life experiences varying performance on these tests which then reflect the types of knowledge one is exposed to during the course of their lives. So knowing what we know about blacks and whites being different cultural groups, and what we know about different cultures having different knowledge bases, then we can rightly state that disparities in IQ scores between blacks and whites are suggested to be due to environmental factors.
Unequal exposure to information creates divergent knowledge bases which then influence the score on the test of knowledge (IQ test). And since we now know that despite initial differences in initial performance that students have a surprising regularity in learning rates, this suggests that once exposed to information, the rate of knowledge acquisition remains consistent across individuals which then challenges the assumption of innate disparities in learning abilities. So the sociocultural context becomes pivotal in shaping the kinds of knowledge that people are exposed to. Cultural tools environmental factors and social interactions contribute to diverse cognitive abilities and knowledge domains which then emphasize the contextual nature of not only intelligence but performance in IQ tests. So what this shows is that test scores are reflective of the kinds of experience the testee was exposed to. So disparities in test scores therefore indicate differences in learning opportunities and cultural contexts
So a conclusive rejection of Jensen’s default hypothesis asserts that the black-white IQ gap is due to exposure to different types of knowledge. Thus, what explains disparities in not only blacks and whites but between groups is unequal opportunities to exposure of information—most importantly the type of information found on IQ tests. My sociocultural theory of knowledge acquisition and crystalization offers a compelling counter to hereditarian perspectives, and asserts that diverse experiences and intentionality learning efforts contribute to cognitive development. The claim that all groups or individuals are exposed to similar types of knowledge as Jensen assumes is false. By virtue of being different groups, they are exposed to different knowledge bases. Since this is true, and IQ tests are culture-bound and tests of a certain kind of knowledge, then it follows that what explains group differences in IQ and knowledge would therefore be differences in exposure to information.
In the disciplines of psychology and psychometrics, intelligence has long been the subject of study, attempting to reduce intelligence to a number based on what a class-biased test spits out when an individual takes an IQ test. But what if intelligence resisted quantification, and we can’t state that IQ tests can put a number to one’s intelligence? The view I will present here will conceptualize intelligence as a psychological trait, and since it’s a psychological trait, it’s then resistant to being reduced to anything physical and it’s also resistant to quantification. I will draw on Vygotsky’s socio-cultural theory of learning and development and his emphasis on the role of culture, social interactions and cultural tools in shaping intelligence and then I will explain that Vygotsky’s theory supports the notion that intelligence is socially and contextually situated. I will then draw on Ken Richardson’s view that intelligence is a socially dynamic trait that’s irreducible, created by sociocultural tools.
All in all, the definition that I will propose here will be irrelevant to IQ. Although I do conceptualize psychological traits as irreducible, it is obvious that IQ tests are class-specific knowledge tests—that is they are biased against certain classes and so it follows that they are biased for certain classes. But the view that I will articulate here will suggest that intelligence is a complex and multifaceted construct that is deeply influenced by cultural and social factors and that it resists quantification because intentionality is inherent in it. And I don’t need to posit a specified measured object, object of measurement and measurement unit for my conception because I’m not claiming measurability.
Vygotsky’s view
Vygotsky is most well-known for his concepts of private speech, more knowledgeable others, and the zone of proximal development (ZPD). Intelligence involves the internalization of private speech, where individuals engage in a self-directed dialogue to solve problems and guide their actions. This internalized private speech then represents an essential aspect of one’s cognitive development, and reflects an individual’s ability to think and reason independently.
Intelligence is then nurtured through interactions with more knowledgeable others (MKOs) in a few ways. MKOs are individuals who possess a deeper understanding or expertise in specific domains. MKOs provide guidance, support, and scaffolding, helping individuals to reach higher levels of cognitive functioning and problem solving.
Along with MKOs, the ZPD is a crucial aspect in understanding intelligence. It represents a range of tasks that individuals can’t perform independently, but can achieve with guidance and support—it is the “zone” where learning and cognitive development take place. e. So intelligence isn’t only about what one can do alone, but also what one can achieve with the assistance of a MKO. Thus, in this context, intelligence is seen as a dynamic process of development where individuals continuously expand their ZPD through sociocultural interactions. So MKOs play a pivotal role in facilitating learning and cognitive development by providing the necessary help to individuals within their ZPD. The ZPD concept underscores the fact and idea that learning is most effective when it is in this zone, where the learner is neither too challenged or too comfortable, but is then guided by a MKO to reach higher levels of competence in what they’re learning.
So the takeaway from this discussion is this: Intelligence isn’t merely a product of individual cognitive abilities, but it is deeply influenced by cultural and social interactions. It encompasses the capacity for private speech which demonstrates an individual’s capacity to think and reason independently. It also involves learning and development ad facilitated by MKOs who contribute to an individual cognitive growth. And the ZPD underscores the importance of sociocultural guidance in shaping and expanding an individual’s intelligence, while reflecting the dynamic and collaborative nature of cognitive development within the sociocultural context. So intelligence, as understood here, is inseparable from Vygotsky’s concepts of private speech, more knowledgeable others and the ZPD and it highlights the dynamic interplay between individual cognitive processes and sociocultural interactions in the development of intelligence.
Davidson (1982) stated that “Neither an infant one week old nor a snail is a rational creature. If the infant survives long enough, he will probably become rational, while this is not true of the snail.” And on Vygotsky’s theory, the infant becomes rational—that is, intelligent—by interacting with MKOs, and internalizing private speech when they learn to talk and think in cultural contexts in their ZPD. Infants quite clearly have the capacity to become rational, and they begin to become rational through interactions with MKOs and caregivers who guide their cognitive growth within their ZPD. This perspective, then, highlights the role of social and cultural influences in the development of infant’s intelligence and their becoming rational creatures. Children are born into both cultural and linguistically-mediated environments, which is put well by Vasileva and Balyasnikova (2019):
Based on the conceptualization of cultural tools by Vygotsky (contrary to more traditional socio-cultural schools), it follows that a child can be enculturated from birth. Children are not only born in a human-created environment, but in a linguistically mediated environment that becomes internalized through development.
Richardson’s view
Ken Richardson has been a critic of IQ testing since the 1970s being one editor of the edited volume Race and Intelligence: The Fallacies Behind the Race-IQ Controversy. He has published numerous books critiquing the concept of IQ, most recently Understanding Intelligence (Richardson, 2022). (In fact, Richardson’s book was what cured me of my IQ-ist delusions and set me on the path to DST.) Nonetheless,
Again, these dynamics would not be possible without the co- evolution of interdependencies across levels: between social, cognitive, and aff active interactions on the one hand and physiological and epigenetic processes on the other. As already mentioned, the burgeoning research areas of social neuroscience and social epigenetics are revealing ways in which social/cultural experiences ripple through, and recruit, those processes.
For example, different cognitive states can have different physiological, epigenetic, and immune-system consequences, depending on social context. Importantly, a distinction has been made between a eudaimonic sense of well-being, based on social meaning and involvement, and hedonic well-being, based on individual plea sure or pain. These different states are associated with different epigenetic processes, as seen in the recruitment of different transcription factors (and therefore genes) and even immune system responses.18 All this is part of the human intelligence system.
In that way human evolution became human history. Collaboration among brains and the emergent social cognition provided the conceptual breakout from individual limits. It resulted in the rapid progress seen in human history from original hunter-gatherers to the modern, global, technologiocal society—all on the basis of the same biological system with the same genes.
So intelligence emerges from the specific activities, experiences, and resources that individuals encounter throughout their development. Richardson’s view, too, is a Vygotskian one. And like Vygotsky, he emphasizes the significant cultural and social aspects in shaping human intelligence. He rejects the claim that human intelligence is reducible to a number (on IQ tests), genes, brain physiology etc.
Human intelligence cannot be divorced from the sociocultural context in which it is embedded and operates in. So in this view, intelligence is not “fixed” as the genetic reductionist IQ-ists would like you to believe, but instead it can evolve and adapt over time in response to learning, the environment, and experiences. Indeed, this is the basis for his argument on the intelligent developmental system. Indeed, Richardson (2012) even argues that “IQ scores might be more an index of individuals’ distance from the cultural tools making up the test than performance on a singular strength variable.” And due to what we know about the inherent bias in the items on IQ tests (how they’re basically middle-class cultural knowledge tests), it seems that Richardson is right here. Richardson (1991; cf 2001) even showed that when Raven’s progressive matrices items were couched in familiar contexts, the children were able to complete them, even when the same exact rules were there between Richardson’s re-built items and the abstract Raven’s items. This shows that couching items in cultural context even with the same rules as the Raven shows that cultural context matters for these kinds of items.
Returning the concept of cultural tools that Richardson brought up in the previous quote (which is derived from Vygotsky’s theory), cultural tools encompass language, knowledge, and problem solving abilities which are culturally-specific and influenced by that culture. These tools are embedded in IQ tests, influencing the problems presented and the types of questions. Thus, it follows that if one is exposed to different psychological and cultural tools (basically, if one is exposed to different knowledge bases of the test), then they will score lower on a test compared to another person whom is exposed to the item content and structure of the test. So individuals who are more familiar with the cultural references, language patterns, and knowledge will score better than those that don’t. Of course, there is still room here for differences in individual experiences, and these differences influence how individuals approach problem solving on the tests. Thus, Richardson’s view highlights that IQ scores can be influenced by how closely aligned an individual’s experiences are with the cultural tools that are embedded on the test. He has also argued that non-cognitive, cultural, and affective factors explain why individuals score differently on IQ tests, with IQ not measuring the ability for complex cognition (Richardson, 2002; Richardson and Norgate, 2014, 2015).
So contrary to how IQ-ists want to conceptualize intelligence (as something static, fixed, and genetic), Richardson’s view is more dynamic, and looks to the cultural and social context of the individual.
Culture, class, and intelligence
Since I have conceptualized intelligence as a socially embedded and culturally-influenced and dynamic trait, class and culture are deeply intertwined in my conception of intelligence. My definition recognizes that intelligence is culturally-influenced by cultural contexts. Culture provides different tools (cultural and psychological) which then develop and individual’s cognitive abilities. Language is a critical cultural (also psychological) tool which shapes how individuals think and communicate. So intelligence, in my conception and definition, encompasses the ability to effectively use these cultural tools. Furthermore, individuals from different cultures may developm unique problem solving strategies which are embedded in their cultural experiences.
Social class influences access to educational and cultural resources. Higher social classes often have greater access to quality education, books, and cultural experiences and this can then influence and impact an individual’s cognitive development and intelligence. My definition also highlights the limitations of reductionist approaches like IQ tests. It has been well-documented that IQ tests have class-specific knowledge and skills on them, and they also include knowledge and scenarios which are more familiar to individuals from certain social and cultural backgrounds. This bias, then, leads to disparities in IQ scores due to the nature of IQ tests and how the tests are constructed.
A definition of intelligence
Intelligence: Noun
Intelligence, as a noun, refers to the dynamic cognitive capacity—characterized by intentionality—possessed by individuals. It is characterized by a connection to one’s social and cultural context. This capacity includes a wide range of cognitive abilities and skills, reflecting the multifaceted nature of human cognition. This, then, shows that only humans are intelligent since intentionality is a human-specific ability which is due to the fact that we humans are minded beings and minds give rise and allow intentional action.
A fundamental aspect of intelligence is intentionality, which signifies that cognitive processes are directed towards single goals, problem solving, or understanding within the individual’s social and cultural context. So intelligence is deeply rooted in one’s cultural and social context, making it socially embedded. It’s influenced by cultural practices, social interactions, and the utilization of cultural tools for learning and problem solving. So this dynamic trait evolves over time as individuals engage with their environment and integrate new cultural and social experiences into their cognitive processes.
Intelligence is the dynamic capacity of individuals to engage effectively with their sociocultural environment, utilizing a diverse range of cognitive abilities (psychological tools), cultural tools, and social interactions. Richardson’s perspective emphasizes that intelligence is multifaceted and not reducible to a single numerical score, acknowledging the limits of IQ testing. Vygotsky’s socio-cultural theory underscores that intelligence is deeply shaped by cultural context, social interactions, and the use of cultural tools for problem solving and learning. So a comprehensive definition of intelligence in my view—informed by Richardson and Vygotsky—is that of a socially embedded cognitive capacity—characterized by intentionality—that encompasses diverse abilities and is continually shaped by an individual’s cultural and socialinteractions.
In essence, within this philosophical framework, intelligence is an intentional multifaceted cognitive capacity that is intricately connected to one’s cultural and social life and surroundings. It reflects the dynamic interplay of intentionality, cognition and socio-cultural influences. Thus is closely related to the concept of cognition in philosophy, which is concerned with how individuals process information, make sense of the world, acquire knowledge and engage in thought processes.
What IQ-ist conceptions of intelligence miss
The two concepts I’ll discuss are the two most oft-cited concepts that hereditarian IQ-ists talk about—that of Gottfredson’s “definition” of intelligence and Jensen’s attempt at relating g (the so-called general factor of intelligence) to PC1.
Gottfredson’s “definition” is the most-commonly cited one in the psychometric IQ-ist literature:
Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings-“catching on,” “ making sense” of things, or “figuring out” what to do.
I have pointed out the nonsense that is her “definition” since she says it’s “not merely book learning, a narrow academic skill or test-taking smarts“, yet supposedly, IQ tests “measure” this, and it’s based on… Book learning, is an academic skill and knowledge of the items on the test. That this “definition” is cited as something that is related to IQ tests is laughable. A research paper from OpenAI even cited this “definition” in their paper Sparks of Artifical Intelligence: Early Experiments with GPT4” (Bubeck et al, 2023), but the reference was seemingly removed. Strange…
Spearman “discovered” gin 1903, but his g theory was refuted mere years later. (Nevermind the fact that Spearman saw what he wanted to see in his data; Schlinger, 2003.) In fact, Spearman’s g falsified in 1947 by Thurstone and then again in 1992 by Guttman (Heene, 2008). Then Jensen came along trying to revive the concept, and he likened it to PC1. Here are the steps that show the circularity in Jensen’s conception:
(1) If there is a general intelligence factor “g,” then it explains why people perform well on various cognitive tests.
(2) If “g” exists and explains test performance, the absence of “g” would mean that people do not perform well on these tests.
(3) We observe that people do perform well on various cognitive tests (i.e., test performance is generally positive).
(4) Therefore, since “g” would explain this positive test performance, we conclude that “g” exists.
Nonetheless, Jensen’s g is an unfalsifiable tautology—it’s circular. These are the “best” conceptions of intelligence the IQ-ists have and they’re either self-contradictory nonsense (Gottfredson’s), already falsified (Spearman’s) or unfalsifiable circular tautology (Jensen’s). What makes Spearman’s g even more nonsensical was that he posited g as a mental energy (Jensen, 1999), and more recently it has been proposed that this mental energy can be found in mitochondrial cells (Geary, 2018, 2019, 2020, 2021). Though I have also shown how this is nonsense.
Conclusion
In this article, I have conceptualized intelligence as a socially embedded and culturally-influenced cognitive capacity characterized by intentionality. It is a dynamic trait which encompasses diverse abilities and is continually shaped by an individual’s cultural and social context and social interactions. I explained Vygotsky’s theory and also explained how his three main concepts relate to the definition I have provided. I then discussed Richardson’s view of intelligence (which is also Vygotskian), and showed how IQ tests are merely an index of one’s distance from the cultural tools that are embedded on the IQ test.
In discussing my conception of intelligence, I then contrasted it with the two “best” most oft-cited conceptions of “intelligence” in the psychological/psychometric literature (Gottfredson’s and Spearman’s/Jensen’s). I then showed how they fail. My conception of intelligence isn’t reductionist like the IQ-ists (they try to reduce intelligence/IQ to genes or physiology or brain structure), but it is inherently holistic in recognizing how intelligence develops over the course of the lifespan, from birth to death. My definition recognizes intelligence as a dynamic, changing trait that’s not fixed like the hereditarians claim it is, and in my conception there is no use for IQ tests. At best, IQ tests merely show what kind of knowledge and experiences one was exposed to in their lives due to the cultural tools inherent on the test. So my inherently Vygotskian view shows how intelligence can be conceptualized and then developed during the course of the human lifespan.
Intelligence, as I have conceived of it, is a dynamic and constantly-developing trait, which evolved through our experiences, cultural backgrounds, and how we interact with the world. It is a multifaceted, context-sensitive capacity. Note that I am not claiming that this is measurable, it cannot be reduced to a single quantifiable measure. And since intentionality is inherent in the definition, this further underscores how it resists quantification and measurability.
In sum, the discussions here show that the IQ-ist concept is lacking—it’s empty. And how we should understand intelligence is that of an irreducible, socially and culturally-influenced, dynamic and constantly-developing trait, which is completely at-ends with the hereditarian conception. Thus, I have argued for intelligence without IQ, since IQ “theory” is empty and it doesn’t do what they claim it does (Nash, 1990). I have been arguing for the massive limitations in IQ for years, and my definition here presents a multidimensional view, highlights the cultural and contextual influence, and emphasizes it’s dynamic nature. The same cannot be said for reductionist hereditarian conceptions.
In the realm of educational assessment and psychometrics, a distinction between IQ and achievement tests needs to be upheld. It is claimed that IQ is a measure of one’s potential learning ability, while achievement tests show what one has actually learned. However, this distinction is not strongly supported in my reading of this literature. IQ and achievement tests are merely different versions of the same evaluative tool. This is what I will argue in this article: That IQ and achievement tests are different versions of the same test, and so any attempt to “validate” IQ tests based not only on other IQ tests, achievement tests and job performance is circular, I will argue that, of course, the goal of psychometrics in measuring the mind is impossible. The hereditarian argument, when it comes to defending their concept and the claim that they are measuring some unitary and hypothetical variable, then, fails. At best, these tests show one’s distance from the middle class, since that’s the where most of the items on the test derive from. Thus, IQ and achievement tests are different versions of the same test and so, they merely show one’s “distance” from a certain kind of class-specific knowledge (Richardson, 2012), due to the cultural and psychologicaltools one must possess to score well on these tests (Richardson, 2002).
Circular IQ-ist arguments
IQ-ists have been using IQ tests since they were brought to America by Henry Goddard in 1913. But one major issue (one they still haven’t solved—and quite honestly never will) was that they didn’t have any way to ensure that the test was construct valid. So this is why, in 1923, Boring stated that “intelligence is what intelligence tests test“, while Jensen (1972: 76) said “intelligence, by definition, is what intelligence tests measure.” However, such statements are circular and they are circular because they don’t provide real evidence or explanation.
Boring’s claim that “intelligence is what intelligence tests test” is circular since it defines intelligence based on the outcome of “intelligence tests.” So if you ask “What is intelligence“, and I say “It’s what intelligence tests measure“, I haven’t actually provided a meaningful definition of intelligence. The claim merely rests on the assumption that “intelligence tests” measure intelligence, not telling us what it actually is.
Jensen’s (1976) claim that “intelligence, by definition, is what intelligence tests measure” is circular for similar reasons to Boring’s since it also defines intelligence by referring to “intelligence tests” and at the same time assumes that intelligence tests are accurately measuring intelligence. Neither claim actually provides an independent understanding of what intelligence is, it merely ties the concept of “intelligence” back to its “measurement” (by IQ tests). Jensen’s Spearman’s hypothesis on the nature of black-white differences has also been criticized as circular (Wilson, 1985). Not only was Jensen (and by extension Spearman) guilty of circular reasoning, so too was Sternberg (Schlinger, 2003). Such a circular claim was also made by Van der Mass, Kan, and Borsboom (2014).
But Jensen seemed to have changed his view, since in his 1998 book The g Factor, he argues that we should dispense with the term “intelligence”, but curiously that we should still study the g factor and assume identity between IQ and g… (Jensen made many more logical errors in his defense of “general intelligence”, like saying not to reify intelligence on one page and then a few pages later reifying it.) Circular arguments have been identified in not only Jensen’s writings Spearman’s hypothesis, but also in using construct validity to validate a measure (Gordon, Schonemann; Guttman, 1992: 192).
The same circularity can be seen when discussions of the correlation between IQ and achievement tests is brought up. “These two tests correlate so they’re measuring the same thing”, is an example one may come across. But the error here is assuming that mental measurement is possible and that IQ and achievement tests are independent of each other. However, IQ and achievement tests are different versions of the same test. This is an example of circular validation, which occurs when a test’s “validity” is established by the test itself, leading to a self-reinforcing loop.
IQ tests are often validated with other older editions of the test. For example, the newer version of the S-B would be “validated” against the older version of the test that the newer version was created to replace (Howe, 1997: 18; Richardson, 2002: 301), which not only leads to circular “validation”, but would also lead to the same assumptions from the older test constructors (like Terman) which would still then be alive in the test itself (since Terman assumed men and women should be equal in IQ and so this assumption is still there today). IQ tests are also often “validated” by comparing IQ test results to outcomes like job performance and academic performance. Richardson and Norgate (2015) have a critical review of the correlation between IQ and job performance, arguing that it’s inflated by “corrections”, while Sackett et al, 2023 show “a mean observed validity of .16, and a mean corrected for unreliability in the criterion and for range restriction of .23. Using this value drops cognitive ability’s rank among the set of predictors examined from 5th to 12th” for the correlation between “general cognitive ability” and job performance.
But this could lead to circular validation, in that if a high IQ is used as a predictor of success in school or work, then success in school or work would be used as evidence in validating the IQ test, which would then lead to a circular argument. The test’s validity is being supported by the outcome that it’s supposed to predict.
Achievement tests are destined to see what one had learned or achieved regarding a certain kind of subject matter. Achievement tests are often validated by correlating test scores with grades or other kinds of academic achievement (which would also be circular). But if high achievement test scores are used to validate the test and those scores are also used as evidence of academic achievement, then that would be circular. Achievement tests are “validated” on their relationship between IQ tests and grades. Heckman and Kautz (2013) note that “achievement tests are often validated using other standardized achievement tests or other measures of cognitive ability—surely a circular practice” and “Validating one measure of cognitive ability using other measures of cognitive ability is circular.” But it should also be noted that the correlation between college grades and job performance 6 or more years after college is only .05 (Armstrong, 2011).
Now what about the claim that IQ tests and achievement tests correlate so they measure the same thing? Richardson (2017) addressed this issue:
For example, IQ tests are so constructed as to predict school performance by testing for specific knowledge or text‐like rules—like those learned in school. But then, a circularity of logic makes the case that a correlation between IQ and school performance proves test validity. From the very way in which the tests are assembled, however, this is inevitable. Such circularity is also reflected in correlations between IQ and adult occupational levels, income, wealth, and so on. As education largely determines the entry level to the job market, correlations between IQ and occupation are, again, at least partly, self‐fulfilling
The circularity inherent in likening IQ and achievement tests has also been noted by Nash (1990). There is no distinction between IQ and achievement tests since there is no theory or definition of intelligence and how, then, this theory and definition would be likened to answering questions correctly on an IQ test.
But how, to put first things first, is the term ‘cognitive ability’ defined? If it is a hypothetical ability required to do well at school then an ability so theorised could be measured by an ordinary scholastic attainment test. IQ measures are the best measures of IQ we have because IQ is defined as ‘general cognitive ability’. Actually, as we have seen, IQ theory is compelled to maintain that IQ tests measure ‘cognitive ability’ by fiat, and it therefore follows that it is tautologous to claim that IQ tests are the best measures of IQ that we have. Unless IQ theory can protect the distinction it makes between IQ/ability tests and attainment/ achievement tests its argument is revealed as circular. IQ measures are the best measures of IQ we have because IQ is defined as ‘general cognitive ability’: IQ tests are the only measures of IQ.
The fact of the matter is, IQ “predicts” (is correlated with) school achievement since they are different versions of the same test (Schwartz, 1975; Beaujean et al, 2018). Since the main purpose of IQ tests in the modern day is to “predict” achievement (Kaufman et al, 2012), then if we correctly identify IQ and achievement tests as different versions of the same test, then we can rightly state that the “prediction” is itself a form of circular reasoning. What is the distinction between “intelligence” tests and achievement tests? They both have similar items on them, which is why they correlate so highly with each other. This, therefore, makes the comparison of the two in an attempt to “validate” one or the other circular.
I can now argue that the distinction between IQ and achievement tests is nonexistent. If IQ and achievement tests are different versions of the same test, then they share the same domain of assessing knowledge and skills. IQ and achievement tests contain similar informational content on them, and so they can both be considered knowledge tests—class-specific knowledge. IQ and achievement tests share the same domain of assessing knowledge and skills. Therefore, IQ and achievement tests are different versions of the same test. Put simply, if IQ and achievement tests are different versions of the same test, then they will have similar item content, and they do so we can correctly argue that they are different versions of the same test.
Moreover, even constructing tests has been criticized as circular:
Given the consistent use of teachers’ opinions as a primary criterion for validity of the Binet and Wechsler tests, it seems odd to claim then that such tests provide “objective alternatives to the subjective judgments of teachers and employers.” If the tests’ primary claim to predictive validity is that their results have strong correlations with academic success, one wonders how an objective test can predict performance in an acknowledged subjective environment? No one seems willing to acknowledge the circular and tortuous reasoning behind the development of tests that rely on the subjective judgments of secondary teachers in order to develop an assessment device that claims independence of those judgments so as to then be able to claim that it can objectively assess a student’s ability to gain the approval of subjective judgments of college professors. (And remember, these tests were used to validate the next generation of tests and those tests validated the following generation and so forth on down to the tests that are being given today.) Anastasi (1985) comes close to admitting that bias is inherent in the tests when he confesses the tests only measure what many anthropologists would called a culturally bound definition of intelligence. (Thorndike and Lohman, 1990)
Conclusion
It seems clear to me that almost the whole field of psychometrics is plagued with the problem of inferring causes from correlation and using circular arguments in an attempt to justify and validate the claim that IQ tests measure intelligence by using flawed arguments that relate IQ to job and academic performance. However this idea is very confused. Moreover, circular arguments aren’t only restricted to IQ and achievement tests, but also in twin studies (Joseph, 2014; Joseph et al, 2015). IQ and achievement tests merely show what one knows, not their learning potential, since they are general knowledge tests—tests of class-specific knowledge. So even Gottfredson’s “definition” of intelligence fails, since Gottfredson presumes IQ to be a measure of learning ability (nevermind the fact that the “definition” is so narrow and I struggle to think of a valid way to operationalize it to culture-bound tests).
The fact that newer versions of tests already in circulation are “validated” against other older versions of the same test means that the tests are circularly validated. The original test (say the S-B) was never itself validated, and so, they’re just “validating” the newer test on the assumption that the older one was valid. The newer test, in being compared to its predecessor, means that the “validation” is occuring on the other older test which has similar principles, assumptions, and content to the newer test. The issue of content overlap, too, is a problem, since some questions or tasks on the newer test could be identical to questions or tasks on the older test. The point is, both IQ and achievement tests are merely knowledge tests, not tests of a mythical general cognitive ability.
Lead industries have a history of downplaying or shifting the blame to avoid accountability for the deleterious effects of lead on public health, especially in vulnerable populations like children. As of the year 2002, about 35 percent of all low-income housing had lead hazards (Jacobs et al, 2002). Though another more recent analysis stated that 38 millions homes in the US (about 40 percent of homes) contained at least trace levels of lead, which was added to the paint before the use of lead in residential paint was banned in 1978. The American Healthy Homes Survey showed that 37.5 millions homes had at least some levels of lead in the paint (Dewalt et al, 2015). Since lead paint is more likely to be found in low-income households, public housing (Rabito, Shorter, and White, 2003) and minorities are more likely to be low-income, then it follows that minorities are more likely to be exposed to lead paint in the home—this is what we find (Carson, 2018; Eisenberg et al, 2020; Baek et al, 2021; McFarland, Hauer, and Reuben, 2022). The fact of the matter is, there is a whole host of negative effects of lead on the developing child, and there is no “safe level” of lead exposure, a point I made back in 2018:
This story reminds me of a similar story, which I will discuss at the end, one of Waneta Hoyt and SIDS. I will compare these two and argue that the underlying issues are the same, privileging genetic factors over other, more obvious environmental factors. After discussing how Big Lead attempted to downplay and shift the blame of what lead was doing to these children, I will liken it to the Waneta Hoyt case.
Big Lead’s downplaying of the deleterious effects of lead on developing children
We have known that lead pipes were a cause of lead poisoning since the late 1800s, and lead companies attempted to reverse this by publishing studies and reports that showed that lead was better than other kinds of materials that could be used for the same purpose (Rabin, 2008). The Lead Industries Association (LIA) even blocked bans against lead paint and pipes, even after being aware of the issues they caused. So why, even after knowing that lead pipes were a primary cause of lead poisoning, were they used to distribute water and paint homes? The answer is simple: Corporate lobbying and outright lying and downplaying of the deleterious effects of lead. Due to our knowledge of the effects of lead in pipes and consequently drinking water, they began to be phased out around the 1920s. One way they attempted to downplay the obviously causal association between lead pipes and deleterious effects was to question it and say it still needed to be tested, one Lead Industries of America (LIA) member noted (quoted in Rabin, 2008):
Of late the lead industries have been receiving much undesirable publicity regarding lead poisoning. I feel the association would be wise to devote time and money on an impartial investigation which would show once and for all whether or not lead is detrimental to health under certain conditions of use.
Lead industries even promoted the use of lead in paint even after it was known that it leads to negative effects if paint chips are ingested by children (Rabin, 1989; Markowitz and Rosner, 2000). So we now have two examples on how Big Lead arranged to downplay the obviously causal, deleterious effects of lead on the developing child. But there are some more sinister events hiding in these shadows, and that is actually putting low-income (mostly black) families into homes with lead paint in order to study their outcomes and blood, as Harriet Washington (2019: 56-57) wrote in her A Terrible Thing to Waste: Environmental Racism and its Assault on the American Mind:
But Baltimore slumlords find removing this lead too expensive and some simply abandon the toxic houses. Cost concerns drove the agenda of the KKI researchers, who did not help parents completely remove children from sources of lead exposure. Instead, they allowed unwitting children to be exposed to lead in tainted homes, thus using the bodies of the children to evaluate cheaper, partial lead-abatement techniques of unknown efficacy in the old houses with peeling paint. Although they knew that only full abatement would protect these children, scientists decided to explore cheaper ways of reducing the lead threat.
So the KKI encouraged landlords of about 125 lead-tainted housing units to rent to families with young children. It offered to facilitate the landlords’ financing for partial lead abatement—only if the landlords rented to families with young children. Available records show that the exposed children were all black.
KKI researchers monitored changes in the children’s health and blood-lead levels, noting the brain and developmental damage that resulted from different kinds of lead-abatement programs.
These changes in the children’ bodies told the researchers how efficiently the different, economically stratified abatement levels worked. The results were compared to houses that either had been completely lead-abated or that were new and presumed not to harbor lead.
Scientists offered parents of children in these lead-laden homes incentives such as fifteen-dollar payments to cooperate with the study, but did not warn parents that the research potentially placed their children at risk of lead exposure.
Instead, literature given to the parents promised that researchers would inform them of any hazards. But they did not. And parents were not warned that their children were in danger, even after testing showed rising lead content in their blood.
Quite obviously, the KKI (Kennedy Krieger Institute) and the landlords were a part of an unethical study with no informed consent. The study was undertaken to test the effectiveness of three measures which cost a differing amount of money (Rosner and Markowitz, 2012) but this study was clearly unethical (Sprigg, 2004).
The Maryland Court of Appeals (2001) called this “innately inappropriate.” This is also obviously a case in which lower-income (majority black) people were already exposed to the higher levels of lead, and they then put them into homes that were “partially abated” of lead and comparing them to homes that had no lead. They knew that only full lead abatement would have been protective but still chose to place them into homes with “partial abatement” but they knowingly chose the cheaper option at the cost of the health of children. They also didn’t expose the parents to the full context of what they were trying to accomplish, thereby putting unwitting people into their clearly unethical study.
In 2002, Tamiko Jones and others brought on a suit against the owner of the apartment building and National Lead Industries, claiming that lead paint in the home was the cause of their children’s maladies and negative outcomes (Tamiko Jones, et al., v. NL Industries, et al. (Civil Action No. 4:03CV229)). Unfortunately, after a 3 week trial, the defendants lost the case and subsequent appeals were denied. But some of the things that the witnesses the defense brought up to the court caught my attention, since it’s similar to the story of Waneta Hoyt.
NL Industries attempted what I am calling “the gene defense.” The gene defense they used was that the children’s problems weren’t caused by lead in the paint, but it was caused by genetic and familial factors which then led to environmental deprivation. One of the mothers in the case, Sherry Wragg, was quoted as saying “My children didn’t have problems until we moved in here.” So the children that moved into this apartment building with their parents began to have behavioral and cognitive problems after they moved in, and they stated that it was due to the paint that had lead in it.
So the plaintiffs were arguing that the behavioral and cognitive deficits the children had were due to the leaded paint. Although the defense did acknowledge that the plaintiffs suffered from “economic deprivation”, which was a contributor to their maladies, they tried to argue that a familial history of retardation and environmental and economic deprivation explained the cognitive and behavioral deficits. But the defense argued that these deficits were explained by familial factors and genes which then explained the environmental deprivation. (Though the Court did recognize that the defense witnessed did not have expertise in toxicology.)
Plaintiffs first seek to strike two experts who provide arguably duplicative expert testimony that plaintiffs’ neurological deficits were most likely caused by genetic, familial and environmental factors, rather than lead exposure. For example, Dr. Barbara Quinten, director of the medical genetics department at Howard University, testified to her view that various plaintiffs had familial histories of low intelligence and/or mental retardation which explained their symptoms. Dr. Colleen Parker, professor of pediatrics and neurology at the University of Mississippi Medical Center, similarly testified that such factors as “familial history of retardation, poor environmental stimulation, and economic deprivation,” rather than elevated blood lead levels, explained the plaintiffs’ deficits.
So it seems that the defense was using the “genes argument” for behavior and cognition to try to make it ambiguous as to what was the cause of the issues the children were having. This is, yet again, another way in which IQ tests have been weaponized. “IQ has a hereditary, genetic component, and this family has familial history of these issues, so it can’t be shown that our lead paint was the cause of the issues.” The use of the genetic component of IQ has clearly screwed people groom ring awarded what they should have rightfully gotten. This is, of course, an example of environmental racism,
Parallels with the Waneta Hoyt case
The story of Big Lead and their denial of the deleterious effects of lead paint reminds me of another similar issue: That of the case of Waneta Hoyt and SIDS. This parallels this case like this: Waneta Hoyt was killing her children by suffocating them, and a SIDS researcher—Alfred Steinschneider—claimed that the cause was genetic, ignoring all signs that Waneta was the cause of her children’s death. This is represented by genes (Steinschneider) and environment (Waneta). In the case of the current discussion, this is represented by genes (Big Lead and their attempts to pinpoint genetic causes for what lead did) and environment (actual environmental effects of lead on the developing child).
There is a pattern in these two cases: Looking to genetic causes and bypassing the actual environmental cause. Genetic factors are represented by Steinschneider and Big Lead while they ignore or downplay the actual environmental causes (represented by Waneta Hoyt and the actual effects of lead on developing children). Selective focus like this, quite clearly, did lead to ignoring or overlooking crucial information. In the Hoyt case, it lead to the death of a few infants which could have been prevented (if Steinschneider didn’t have such tunnel vision for his genetic causation for SIDS). In the Big Lead case, NL Industries and it’s witnesses pointed to genetic factors or individual behaviors as the culprit for the causes of the negative behaviors and cognitive deficits for the children. In both cases, confirmation bias was thusly a main culprit.
Conclusion
The search for genetic causes and understanding certain things to be genetically caused has caused great harm. Big Lead and its attempted downplaying of the deleterious effects of lead paint while shifting blame to genetic factors reminds us that genetic reductionism and determinism is still here, and that corporate entities will attempt to use genetic arguments in order to ensure their interests are secured. Just as in the Waneta Hoyt case, where a misdirection towards genetic factors cloaked the true cause of the harm (which was environmental), the focus on genetics by Big Lead shifted shifted attention away from the true cause and put it on causes coming from inside the body.
The lobbying efforts of Big Lead damage for countless numbers of children and their families. And by hiding with genetic arguments, trying to deflect the harmful effects of what their leaded paint did to children, they chose to go to the genes argument, pushed by hereditarians as an explanation of the IQ gap. This, as well, is yet more evidence that IQ tests (along with association studied to identify causal genes for IQ) should be banned since they have clearly caused harms to people, in this case, not getting what they should have gotten by winning a court case that they should have won. Big Lead successfully evaded accountability here, and they did so with genetic reductionism.
Quite obviously, the KKI knowingly placed black families into homes that they knew had lead paint for experimentation purposes and this was highly unethical. This shows the environmental injustice and environmental racism, where vulnerable populations are used for nothing more than an experiment without their consent and knowledge. The parallels here are obvious in how Big Lead attempted to divert blame from the environmental effects of lead and implicate genetic factors and familial histories of retardation. This strategy is mirrored in the Waneta Hoyt case. Although Steinschneider didn’t have any kind of biases like Big Lead have, he did have a bias in attempting to pinpoint a genetic cause for SIDS which left him unable to see that the cause of the deaths was the mother of the children, Waneta Hoyt.
Last week I articulated a framework I call Dualistic Experiential Constructivism (DEC). DEC is a theoretical framework which draws on mind-body dualism, experiential learning, and constructivism to explain human development, knowledge acquisition, and the formation of psychological traits and mind. In the DEC framework, knowledge construction and acquisition are seen as due to a dynamic interplay between individual experiences and the socio-cultural contexts that they occur in. It has a strong emphasis on the significance of personal experiences, interacting with others, shaping cognitive processes, social understanding and the social construction of knowledge by drawing on Vygotsky’s socio-historical theory of learning and development, which emphasizes the importance of cultural tools and the social nature of learning. It recognizes that genes are not sufficient for psychological traits, but necessary for them. It emphasizes that the manifestation of psychological traits and mind are shaped by experiences, interactions between the socio-cultural-environmental context.
DEC shares a key point with constructivism—that of rejecting passive learning and highlight the importance of the learner’s active engagement in the construction of knowledge. Kolb’s experiential learning theory proposes that people learn best through direct experiences and reflecting on those experiences, while DEC emphasizes the fact significance of experiential learning in shaping one’s cognitive processes and understanding of knowledge. DEC also relies heavily on Vygotsky’s socio-historical theory of learning and development, where both the DEC and Vygotsky’s theory emphasize the role of socio-cultural factors in shaping human development along with the construction of knowledge. Vygotsky’s theory also highlights the importance of social interaction, cultural and psychological tools and historical contexts, which DEC draws from. Cognitive development and knowledge arise from dynamic interactions between individuals and their environment while also acknowledging the reciprocal influences between the individual and their social context. (This is how DEC can also be said to be a social constructivist position.) DEC is also similar to Uri Bronfenbrenner’ecological systems theory, which emphasizes the influence of multiple environmental systems on human development. With DEC’s focus on how individuals interact with their cultural contexts, it is therefore similar to ecological systems theory. Finally, DST shares similarities with Learner’s relational developmental systems theory focusing on interactions, genes as necessary but not sufficient causes for the developing system, rejecting reductionism and acknowledging environmental and cultural contexts in shaping human development. They are different in the treatment of mind-body dualism and the emphasis on cultural tools in shaping cognitive development and knowledge acquisition.
Ultimately, DEC posits that individuals actively construct knowledge through their engagement with the world, while drawing upon their prior experiences, interactions with others and cultural resources. So the socio-cultural context in which the individual finds themselves in plays a vital role in shaping the nature of learning experiences along with the construction of meaning and knowledge. Knowing this, how would race, gender, and class be integrated into the DEC and how would this then explain test score disparities between different classes, men and women, and races?
Social identities and test score differences: The impact of DEC on gender, race and class discrepancies
Race, class, and gender can be said to be social identities. Since they are social identities, they aren’t inherent or fixed characteristics in individuals, they are social categories which influence an individual’s experiences, opportunities, and interaction within society. These social identities are shaped by cultural, historical, and societal factors which intersect in complex ways, leading to different experiences.
When it comes to gender, it has been known that boys and girls have different interests and so they have different knowledge basis. This has been evidenced since Terman specifically constructed his test to eliminate differences between men and women in his Stanford-Binet, and also evidenced by the ETS changing the SAT to reflect these differences between men and women (Rosser, 1989; Mensh and Mensh, 1991). So when it comes to the construction of knowledge and the engagement with the world, an individual’s gender influences the way they perceive the world, and interpret social dynamics and act in social situations. There is also gendered test content, as Rosser (1989) shows for the SAT. Thus, the concept of gender in society influences test scores since men and women are exposed to different kinds of knowledge; the fact that there are “gendered test items” (items that reflect or perpetuate gender biases, stereotypes or assumptions in its presentation).
But men and women have negligible differences in full-scale IQ, so how can DEC work here? It’s simple: men are better spatially and women are better verbally. Thus, by choosing which items they want on the test, test constructors can build the conclusions they want into the test. DEC emphasizes socio-cultural influences on knowledge exposure, stating that unique socio-cultural and historical experiences and contexts influences one’s knowledge acquisition. Cultural/social norms and gendered socialization can also shape one’s interests and experiences, which would then influence knowledge production. Further, test content could have gender bias (as Rosser, 1989 pointed out), and subjects that either sex are more likely to have interest in could have skewed answer outcomes (as Rosser showed). Stereotype threat is also another thing that could influence this, with one study conceptualizing stereotype threat gender as being responsible for gender differences in advanced math (Spencer, Steele, and Quinn, 1999). Although stereotype threat affects different groups in different ways, one analysis showed empirical support “for mediators such as anxiety, negative thinking, and mind-wandering, which are suggested to co-opt working memory resources under stereotype threat” (Pennington et al, 2016). Lastly, intersectionality is inherent in DEC. Of course the experiences of a woman from a marginalized group would be different from the experiences of a woman from a privileged group. So these differences could influence how gender intersects with other identities when it comes to knowledge production.
When it comes to racial differences in test scores, DEC would emphasis the significance of understanding test score variations as reflecting multifaceted variables resulting from the interaction of cultural influences, experiential learning, societal contexts and historical influences. DEC rejects the biological essentialism and reductionism of hereditarianism and their claims of innate, genetic differences in IQ—it contextualizes test score differences. It views test scores as dynamic outcomes, which are influenced by social contexts, cultural influences and experiential learning. It also highlights cultural tools as mediators of knowledge production which would then influence test scores. Language, communication styles, educational values and other cultural resources influence how people engage with test content and respond to test items. Of course, social interactions play a large part in the acquisition of knowledge in different racial groups. Cultural tools are shared and transmitted through social interactions within racial communities. Historical legacies and social structures could impact access to cultural tools along with educational opportunities that would be useful to score well on the test, which then would affect test performance. Blacks and whites are different cultural groups, so they’re exposed to different kinds of knowledge which then influences their test scores.
Lastly, we come to social class. People from families in higher social strata benefit from greater access to educational resources—along with enriching experiences—like attending quality pre-schools and having access to educational materials, materials that are likely to be in the test items on the test. The early learning experiences then set the foundation for performing well on standardized tests. Lower class people could have limited access to these kinds of opportunities, which would impact their readiness and therefore performance on standardized tests. Cultural tools and language also play a pivotal role in shaping class differences in test scores. Parents of higher social class could is language and communication patterns that could potentially contribute to higher test scores. Conversely, lower social classes could have lack of exposure to the specific language conventions used in test items which would then influence their performance. Social interactions also influence knowledge production. Higher social classes foster discussions and educational discourses which support academic achievement, and also the peer groups in would also provide additional academic support and encouragement which would lend itself to higher test scores. On the other hand, lower class groups have limited academic support along with fewer opportunities for social interactions which are conducive to learning the types of items and structure of the test. It has also been shown that there are SES disparities in language acquisition due to the home learning environment, and this contributes to the achievement gap and also school readiness (Brito, 2017). Thus, class dictates if one is or is not ready for school due to their exposure to language in their home learning environment. Therefore, in effect, IQ tests are middle-class knowledge tests (Richardson, 2001, 2022). So one who is not exposed to the specific, cultural knowledge on the test wouldn’t score as well as someone who is. Richardson (1999; cf, Richardson, 2002) puts this well:
So relative acquisition of relevant background knowledge (which will be closely associated with social class) is one source of the elusive common factor in psychometric tests. But there are other, non-cognitive, sources. Jensen seems to have little appreciation of the stressful effects of negative social evaluation and systematic prejudice which many children experience every day (in which even superficial factors like language dialect, facial appearance, and self-presentation all play a major part). These have powerful effects on self concepts and self-evaluations. Bandura et al (1996) have shown how poor cognitive self-efficacy beliefs acquired by parents become (socially) inherited by their children, resulting in significant depressions of self-expectations in most intellectual tasks. Here, g is not a general ability variable, but one of ‘self-belief’.
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Reduced exposure to middle-class cultural tools and poor cognitive self-efficacy beliefs will inevitably result in reduced self-confidence and anxiety in testing situations.
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In sum, the ‘common factor’ which emerges in test performances stems from a combination of (a) the (hidden) cultural content of tests; (b) cognitive self-efficacy beliefs; and (c) the self-confidence/freedom-from-anxiety associated with such beliefs. In other words, g is just an mystificational numerical surrogate for social class membership. This is what is being distilled when g is statistically ‘extracted’ from performances. Perhaps the best evidence for this is the ‘Flynn effect,’ (Fkynn 1999) which simply corresponds with the swelling of the middle classes and greater exposure to middle-class cultural tools. It is also supported by the fact that the Flynn effect is more prominent with non-verbal than with verbal test items – i.e. with the (covertly) more enculturated forms.
(1) If children of different class levels have experiences of different kinds with different material, and (2) if IQ tests draw a disproportionate amount of test items from the higher classes, then (3) higher class children should have higher scores than lower-class children.
The point that ties together this analysis is that different groups are exposed to different knowledge bases, which are shaped by their unique cultural tools, experiential learning activities, and social interactions. Ultimately, these divergent knowledge bases are influenced by social class, race, and gender, and they play a significant role in how people approach educational tests which therefore impacts their test scores and academic performance.
Conclusion
DEC offers a framework in which we can delve into to explain how and why groups score differently on academic tests. It recognizes the intricate interplay between experiential learning, societal contexts, socio-historical contexts and cultural tools in shaping human cognition and knowledge production. The part that the irreducibility of the mental plays is pivotal in refuting hereditarian dogma. Since the mental is irreducible, then genes nor brain structure/physiology can explain test scores and differences in mental abilities. In my framework, the irreducibility of the mental is used to emphazies the importance of considering subjective experiences, emotions, conscious awareness and the unique perspectives of individuals in understanding human learning.
Using DEC, we can better understand how and why races, social classes and men and women score differently from each other. It allows us to understand experiential learning and how groups have access to different cultural and psychological tools in shaping cognitive development which would then provide a more nuanced perspective on test score differences between different social groups. DEC moves beyond the rigid gene-environment false dichotomy and allows us to understand how groups score differently, while rejecting hereditarianism and explaining how and why groups score differently using a constructivist lens, since all human cognizing takes place in cultural contexts, it follows that groups not exposed to certain cultural contexts that are emphasized in standardized testing may perform differently due to variations in experiential learning and cultural tools.
In rejecting the claim that genes cause or influence mental abilities/psychological traits and differences in them, I am free to reason that social groups score differently not due to inherent genetic differences, but as a result of varying exposure to knowledge and cultural tools. With my DEC framework, I can explore how diverse cultural contexts and learning experiences shape psychological tools. This allows a deeper understanding of the dynamic interactions between the individual and their environment, emphasizing the role of experiential learning and socio-cultural factors in knowledge production. Gene-environment interactions and the irreducibility of the mental allow me to steer clear of genetic determinist explanations of test score differences and correctly identity such differences as due to what one is exposed to in their lives. In recognizing G-E interactions, DEC acknowledges that genetic factors are necessary pre-conditions for the mind, but genes alone are not able to explain how mind arises due to the irreducibility principle. So by considering the interplay between genes and experiential learning in different social contexts, DEC offers a more comprehensive understanding of how individuals construct knowledge and how psychological traits and mind emerge, steering away from genetically reductionistic approaches to human behavior, action, and psychological traits.
I also have argued how mind-body dualism and developmental systems theory refute hereditarianism, thus framework I’ve created is a further exposition which challenges traditional assumptions in psychology, providing a more holistic and nuanced understanding of human cognition and development. By incorporating mind-body dualism, it rejects the hereditarian perspective of reducing psychology and mind to genes and biology. Thus, hereditarianism is discredited since it has a narrow focus on genetic determinism/reductionism. It also integrates developmental systems theory, where development is a dynamic process influenced by multiple irreducible interactions between the parts that make up the system along with how the human interacts with their environment to acquire knowledge. Thus, by addressing the limitations (and impossibility) of hereditarian genetic reductionism, my DEC framework provides a richer framework for explaining how mind arises and how people acquire different psychological and cultural tools which then influence their outcomes and performance on standardized tests.
One of the first critics of IQ tests after they were brought to America and used by the US army was journalist Walter Lippmann. Lippmann was very prescient with some of his argumentation against IQ, making similar anti-measurement arguments to anti-IQ-ists today. Although he was arguing against the “army intelligence tests” (the alpha and beta along with the Stanford-Binet), his criticisms hold even today and for any so-called IQ test since they are “validated” on their agreement with other tests (that weren’t themselves validated). He rightly noted that the test items are chosen arbitrarily (and that the questions chosen reflected the test constructor’s biases), and that the test isn’t a measure at all but a sorter of sorts, which in effect classifies people. This is similar to what Garrison (2009) argued in his book A Measure of Failure. He also argued that “IQ” isn’t like length or weight, which is what Midgley(2018) argued and also what Haier (2014, 2018) stated about IQ test scores—they are not like inches, liters, or grams.
Lippmann’s critique of IQ
Lippmann got it right in one major way—he stated that IQ tests results give the illusion of measurement because the results “are expressed in numbers.” However, measurement is much more complex than that—there needs to be a specified measured object, object of measurement and measurement unit for X to be a measure, and if there isn’t then X isn’t a measure. Lippmann stated that Terman couldn’t demonstrate that he was “measuring intelligence” (McNutt, 2013: 10).
Because the results are expressed in numbers, it is easy to make the mistake of thinking that the intelligence test is a measure like a foot rule or a pair of scales. It is, of course, a quite different sort of measure. For length and weight are qualities which men have learned how to isolate no matter whether they are found in an army of soldiers, a heap of bricks, or a collection of chlorine molecules. Provided the footrule and the scales agree with the arbitrarily accepted standard foot and standard pound in the Bureau of Standards at Washington they can be used with confidence. But “intelligence” is not an abstraction like length and weight; it is an exceedingly complicated notion which nobody has as yet succeeded in defining.
He then invents puzzles which can be employed quickly and with little apparatus, that will according to his best guess test memory, ingenuity, definition and the rest. He gives these puzzles to a mixed group of children and sees how children of different ages answer them. Whenever he finds a puzzles that, say, sixty percent of the twelve year old children can do, and twenty percent of the eleven year olds, he adopts that test for the twelve year olds. By a great deal of fitting he gradually works out a series of problems for each age group which sixty percent of his children can pass, twenty percent cannot pass and, say, twenty percent of the children one year younger can also pass. By this method he has arrived under the Stanford-Binet system at a conclusion of this sort: Sixty percent of children twelve years old should be able to define three out of the five words: pity, revenge, charity, envy, justice. According to Professor Terman’s instructions, a child passes this test if he says that “pity” is “to be sorry for some one”; the child fails if he says “to help” or “mercy.” A correct definition of “justice” is as follows: “It’s what you get when you go to court”; an incorrect definition is “to be honest.”
A mental test, then is established in this way: The tester himself guesses at a large number of tests which he hopes and believes are tests of intelligence. Among these tests those finally are adopted by him which sixty percent of the children under his observation can pass. The children whom the tester is studying select his tests.
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What then do the tests accomplish? I think we can answer this question best by starting with an illustration. Suppose you wished to judge all the pebbles in a large pile of gravel for the purpose of separating them into three piles, the first to contain the extraordinary pebbles, the second normal pebbles, and the third the insignificant pebbles. You have no scales. You first separate from the pile a much smaller pile and pick out one pebble which you guess is the average. You hold it in your left hand and pick up another pebble in your right hand. The right pebble feels heavier. You pick up another pebble. It feels lighter. You pick up a third. It feels still lighter. A fourth feels heavier than the first. By this method you can arrange all the pebbles from the smaller pile in a series running from the lightest to the heaviest. You thereupon call the middle pebble the standard pebble, and with it as a measure you determine whether any pebble in the larger pile is sub-normal, a normal or a supernormal pebble.
This is just about what the intelligence test does. It does not weigh or measure intelligence by any objective standard. It simply arranges a group of people in a series from best to worst by balancing their capacity to do certain arbitrarily selected puzzles, against the capacity of all the others. The intelligence test, in other words, is fundamentally an instrument for classifying a group of people. It may also be an instrument for measuring their intelligence, but of that we cannot be at all sure unless we believe that M. Binet and Mr. Terman and a few other psychologists have guessed correctly but, as we shall see later, the proof is not yet at hand.
The intelligence test, then, is an instrument for classifying a group of people, rather than “a measure of intelligence.” People are classified within a group according to their success in solving problems which may or may not be tests of intelligence.
Even though Lippmann was writing over 100 years ago in 1922, his critiques have stood the test of time. Being one of the first critics of hereditarian dogman, he took on Terman in the pages of The New Republic, and I don’t think Lippmann’s main arguments were touched—and 100 years later, it looks to be more of the same. Still, as shown above, even some psychologists admit that certain things that are true of physical measures aren’t true of IQ.
Jansen (2010: 134) noted that “Lippmann vehemently opposed introducing I.Q. tests into the schools on democratic grounds, contending that it would lead to an intellectual caste system.” Lippmann and other environmentalists in the 1920s sought to understand variation in IQ as due to environment, and that all individuals had the same “biological capacity” for “intelligence” (Mancuso and Dreisinger, 1969). (They also state that a physiological intelligence was the “logical outcome” of the scientific materialism of the 19th century, a point I have made myself.) Not only is the concept of “IQ/intelligence” arbitrary, it is indeed used as an ideological tool (Gonzelez, 1979; Richardson, 2017).
The fact of the matter is, IQ-ists back then—and I would say now as well—were guilty of the naming fallacy (Conley, 1986):
Walter Lippmann had exposed most of its critical weaknesses in a series of articles in the New Republic in 1922. He emphasized the fundamental point that “intelligence is not an abstraction like length and weight; it is an exceedingly complicated notion which nobody has yet succeeded in defining.”33 Then, in 1930, C.C. Brigham, one of the most influential scientific proponents of eugenics, recanted. Accepting Lippmann’s point, he accused himself and his colleagues of a “naming fallacy” which allowed them “to slide mysteriously from the score in the test to the hypothetical faculty suggested by the name given to the test.”’34 He repudiated the whole concept of national and racial comparisons based on intelligence test scores, and then, in what is surely the most remarkable statement I have ever read in a scientific publication, concluded, “One of the most pretentious of these comparative racial studies—the writer’s own—was without foundation.”35
Finally, returning to the test items being arbitrary and reflecting the test constructor’s biases, this was outright admitted by Terman. He stated that he created a “norm intelligent” group which led to the “developing [of] an exclusion-inclusion criteria that favored the [US born white men of north European descent], test developers created a norm “intelligent” (Gersh, 1987, p.166) population “to differentiate subjects of known superiority from subjects of known inferiority” (Terman, 1922, p. 656) (Bazemore-James, Shinaprayoon, and Martin, 2017). This, of course, proves Lippmann’s point about these tests—the test’s constructors assumed ahead of time who is or is not “intelligent” and then devise tests with specific item content to get their desired distribution—Terman outright admitted this. Lippmann was 100 percent right about that issue.
Conclusion
Lippmann’s critiques of IQ, although they were written 100 years ago, can still be made today. Lippmann got much right in his critiques of Terman and other hereditariansm psychologists, and his claim that if you can’t define something then you can’t measure it is valid. Although Lippmann’s warnings on the abuse of IQ testing were not heeded, the fact of the matter is, Lippmann was in the right in this debate 100 years ago. One of Lippmann’s main points—that test items are chosen arbitrarily and chosen to agree with a priori biases—still holds true today, since the Stanford-Binet is now on its 5th edition and since it is “validated” on its “agreement” with older versions of the Stanford-Binet, this assumption is carried into the modern day. (Do note that Terman assumed that men and women should have similar IQ scores and so devised his test to reflect this, and this shows, again, that the previous biased that the test constructors held were then built into the test. So this shows that what chat be built in can be built out.)
Lippmann’s observations on the limitations of IQ tests and their use in reinforcing stereotypes have stood the test of time. So by analyzing his arguments we can draw parallels to critiques of IQ today. So although Lippmann’s critiques of IQ are 100 years old there is still much value in them, since the arguments he made back then still can be made today, since there has been basically little to no progress in that time period of 100 years since he made his critiques.
It’s interesting that a journalist of all people would be able to mount the kind of critique against IQ that he did, while arguing with one of the men who first used IQ tests on a large level in America (Terman). This just goes to show that the IQ debate in the past 100 years has hardly made any progress on the hereditarian side, since they still rely on twin studies and other similar studies to argue for a genetic or hereditary hypothesis of mental abilities. But mental abilities and psychological traits are molded by what one experiences in their lives—indeed, one’s IQ is an outcome of one’s life experiences due to the kind of cultural and psychological tools that people acquire as they age.
Lippmann correctly argued that nothing measurable underlies the concept of “intelligence”—and that argument he made is one that is very familiar to readers of this blog. This argument is one that is quite powerful, and it is obviously quite old as evidenced by Lippmann’s prescient critique of “IQ.” Lippmann had some great knowledge on how tests were constructed and on the methodological and theoretical pitfalls that still plague psychometrics today. While psychometricians have yet to address the serious pitfalls that invalidate their field, the use of their methods continues to perpetuate biased outcomes and reinforce social inequalities, since it is claimed that where a group falls on their “intelligence” score is where they fall on the social hierarchy. This then goes back to Terman’s assumptions when he created the Stanford-Binet. Have any ideas about society and how it’s structured? Have any ideas about the so-called distribution of “intelligence”? Just build it into the test and then claim that your test is showing biologically natural intelligence levels between people. This is what Lippmann argued against, and he was right to argue against it; he knew that the test constructors used items that showed what they wanted.
So, anyone who wants to argue against the concept of IQ would do well to read Lippmann’s 6 articles arguing against the concept of IQ/intelligence.
For decades, hereditarians have attempted to partition traits into relative genetic and environmental causes. The assumption here is of course that G and E are separable, independent components and another assumption is that we can discover the relative contribution of G and E by performing certain tests and statistical procedures. However, since Oyama’s publication of The Ontogeny of Information in 1985, this view has been called into question. The view that Oyama articulated is a philosophical theory based on the irreducible interactions between all developmental resources called developmental systems theory (DST)
However, we can go further. We can use the concept of dualism and argue that psychology is irreducible to the physical and so it’s irreducible to genes. We can then use the concepts laid forth in DST like that of gene-environment and the principle of biological relativity and argue that the development of organisms is irreducible to any one resource. Then, for the formation of mind and psychological traits in humans, we can say that they arise due to human-specific ecological contexts. I will call this view Dualistic Experiential Constructivism (DEC), and I will argue that it invalidates any and all attempts at partitioning G and E into quantifiable components. Thus, the hereditarian research program is bound to fail since it rests on a conceptual blunder.
The view that refutes the claim that genes and environment, nature and nurture, can’t be separated is this:
(1) Suppose that there can be no environmental effect without a biological organism to act on. (2) Suppose there can be no organism outside of its context (like the organism-environment system). (3) Suppose the organism cannot exist without the environment. (4) Suppose the environment has certain descriptive properties if and only if it is connected to the organism. Now here is the argument.
P1: If there can be no environmental effect without a biological organism to act on, and if the organism cannot exist without the environment, then the organism and environment are interdependent. P2: If the organism and environment are interdependent, and if the environment has certain descriptive properties if and only if it is connected to the organism, then nature and nurture are inseparable. C: Thus, nature and nurture are inseparable.
Rushton and Jensen’s false dichotomy
Rushton and Jensen (2005) uphold a 50/50 split between genes and environment and call this the “hereditarian” view. On the other side is the “culture-only” model which is 0 percent genes and 100 percent environment regarding black-white IQ differences. Of course note the false dichotomy here: What is missing? Well, an interactive GxE view. Rushton and Jensen merely put that view into their 2-way box and called it a day. They wrote:
It is essential to keep in mind precisely what the two rival positions do and do not say—about a 50% genetic–50% environmental etiology for the hereditarian view versus an effectively 0% genetic–100% environmental etiology for the culture-only theory. The defining difference is whether any significant part of the mean Black–White IQ difference is genetic rather than purely cultural or environmental in origin. Hereditarians use the methods of quantitative genetics, and they can and do seek to identify the environmental components of observed group differences. Culture-only theorists are skeptical that genetic factors play any independently effective role in explaining group differences.
Most of those who have taken a strong position in the scientific debate about race and IQ have done so as either hereditarians or culture-only theorists. Intermediate positions (e.g., gene–environment interaction) can be operationally assigned to one or the other of the two positions depending on whether they predict any significant heritable component to the average group difference in IQ. For example, if gene–environment interactions make it impossible to disentangle causality and apportion variance, for pragmatic purposes that view is indistinguishable from the 100% culture-only program because it denies any potency to the genetic component proposed by hereditarians.
Rushton and Jensen did give an argument here, here it is formalized:
P1: Gene-environment interactions make it impossible to disentangle causality and apportion variance correctly. P2: If it is impossible to disentangle and apportion variance, then the view denying any potency to the genetic component proposed by hereditarians becomes indistinguishable from a 100% culture-only perspective. C: Thus, for pragmatic purposes, the view denying any potency to the genetic component is indistinguishable from a 100% culture-only program.
This argument is easy enough to counter. Rushton and Jensen are explicitly putting the view that refutes their whole research program into their 2 boxes—their 50/50 split between genes and environment, and the 0 percent genes and 100 percent environment. The view that Rushton and Jensen articulated is basically a developmental systems theory (DST) view. DST highlights the interactive and dynamic nature of development. Rushton and Jensen’s view is clearly gene-centric, where gene-centric means centered on genes. I would impute to them—based on their writings—that genes are a sufficient, privileged cause for IQ, and traits as a whole. But that claim is false (Noble, 2012).
Although I understand where they’re coming from here, they’re outright wrong.
Put simply, they need to put everything into this box in order to legitimatize their “research.” Although I would be a “culture-only theorist” to them regarding my views on the cause of IQ gaps (since there is no other way to be), my views on genetic causation are starkly different than theirs are.
Most may know that I deny the claim that genes can cause or influence differences in psychological traits between people. (And that genes are outright causes on their own, independent of environment.) I hold this view due to conceptual arguments. The interactive view (of which is more complex than Rushton and Jensen are describing), is how development is carried out, with no one resource having primacy over another—a view called the causal parity thesis. This is the principle of biological relativity (Noble, 2012). This theory asserts that there is no privileged level of causation, and so if there is no privileged level of causation, then that holds for all of the developmental resources that interact to make up the phenotype. Thus, hereditarianism is false since hereditarianism privileges genes over other developmental resources when no developmental resource is privileged in biological systems.
Rushton and Jensen almost had it—if GxE makes it hard or impossible to disentangle causality and apportion variance, then the hereditarian program cannot and will not work since, basically, they apportion variance into G and E causes and claim that independent genetic effects are possible. However, many authors have a conceptual argument on heritability, for if G and E and anything else interact, then they are not separable, and if they are not separable, they are not quantifiable. For example, Burt and Simon (2015: 107) argue that the “conceptual model is unsound and the goal of heritability studies is biologically nonsensical given what we now know about the way genes work.”
When it comes to “denying potency” to the “genetic component”, Rushton and Jensen seem to be quite specific in what they mean by this. Of course, a developmentalist (a GxE supporter) would not deny that genes are NECESSARY for the construction of the phenotype, though they would deny the PRIMACY that hereditarians place on genes. Genes are nothing special, they are not special resources when compared to other resources.
Of course, hereditarianism is a reductionist discipline. And by reductionist, I mean it attempts to break down the whole to the sum of its parts to ascertain the ontogeny of the desired object. Reductionism is false, and so that would apply to genetic and neuroreduction. Basically, reducing X to genes or the brain/brain physiology is the wrong way to go about this. Rushton (2003) even explicitly stated his adherence to the reductionist paradigm in a small commentary of Rose’s (1998)Lifelines. He repeats his “research” into brain size differences between races and argues that, due to the .4 correlation between MRI and IQ, due to differences in brain size between races (see here for critique) and since races have different cognitive abilities, then this is a “+” for reductionist science.
Since the behavioral genetic research program is reductive, it is necessarily committed to genetic determinism, even though most don’t explicitly state this. The way that Rushton and Jensen articulated the GxE (DST) view fit into their false dichotomy to try to reject it outright without grappling with its implications for organismal development. Unfortunately for the view put forth by Rushton and Jensen, organisms and environment are constantly interacting with each other. If they constantly interact, then they are not separable. If they are not separable, then the distinction made by Rushton and Jensen fails. If the distinction made by Rushton and Jensen fails, then ultimately, the quest of behavioral genetics—to apportion variance into genetic and environmental causes—fails.
Another hereditarian who tries to argue against interactionism is Gottfredson (2009) with her “interactionism fallacy.” Heritability estimates, it is claimed, can partition causes of variance between G and E components. Gottfredson—like all other hereditarians, I claim—completely misrepresent the view and (wilfully?) misunderstand what developmental systems theorists are saying. People like Rushton, Jensen, and Gottfredson quite obviously claim that science can solve the nature-nurture debate. The fact of the matter that destroys hereditarian assumptions and claims about the separability of nature and nurture is this: The genome is reactive (Fox-Keller, 2014) that is, it reacts to what is occurring in the environment, whether that be the environment outside or inside of the body.
At the molecular level, the nurture/nature debate currently revolves around reactive genomes and the environments, internal and external to the body, to which they ceaselessly respond. Body boundaries are permeable, and our genome and microbiome are constantly made and remade over our lifetimes. Certain of these changes can be transmitted from one generation to the next and may, at times, persist into succeeding generations. But these findings will not terminate the nurture/nature debate – ongoing research keeps arguments fueled and forces shifts in orientations to shift. Without doubt, molecular pathways will come to light that better account for the circumstances under which specific genes are expressed or inhibited, and data based on correlations will be replaced gradually by causal findings. Slowly, “links” between nurture and nature will collapse, leaving an indivisible entity. But such research, almost exclusively, will miniaturize the environment for the sake of accuracy – an unavoidable process if findings are to be scientifically replicable and reliable. Even so, increasing recognition of the frequency of stochastic, unpredictable events ensures that we can never achieve certainty. (Locke and Pallson, 2016)
The implication here is that science cannot resolve this debate, since “nature and nurture are not readily demarcated objects of scientific inquiry” (Locke and Pallson, 2016: 18). So if heritability estimates are useful for understanding phenotypic variation, then the organism and environment must not interact. If these interactions are constant and pervasive, then it becomes challenging—and I claim impossible—to accurately quantify the relative contribution of genes and environment. But the organism and environment constantly interact. Thus, heritability estimates aren’t useful for understanding phenotypic variation. This undermines the interpretability of heritability and invalidates any and all claims as to the relative contribution of G and E made by any behavioral geneticist.
The interactive view of G and E state that genes are necessary for traits but not sufficient for them. While genetic factors do of course lay the foundation for trait development, so do the other resources that interact with the genes (the suite of them) that are necessary for trait development. I can put the argument like this:
P1: An interactive view acknowledges that genes contribute to the development of traits. P2: Genes are necessary pre-conditions for the expression of traits. P3: Genes alone are not sufficient to fully explain the complexity of traits. C: Thus, an interactive view states that genes are necessary pre-conditions for traits but not sufficient on their own.
Why my view is not blank slatism: On Dualistic Experiential Constructivism
Now I need to defend my view that the mind and body are distinct substances, so the mental is irreducible to the physical, so genes can’t cause psychology. One may say “Well that makes you a blank slatist since you deny that the mind has any innate properties.” Fortunately, my view is more complex than that.
I have been espousing certain points of view for years on this blog: The irreducibility of the mental, genes can’t cause mental/psychological traits, mind is constructed through interacting with other humans in species-relevant contexts to eventually form mind, and so-called innate traits are learned and experience-dependent. How can I reconcile these views? Doesn’t the fact that I deny any and all genetic influence on psychology due to my dualistic commitments mean I am a dreaded “blank slatist”? No it does not and I will explain why.
I call my view “Dualistic Experiential Constructivism” (DEC). It’s dualistic since it recognizes that the mind and body are separate, distinct substances. It’s experiential since it highlights the role of experiential factors in the forming of mind and the construction of knowledge and development of psychological traits. It is constructivist since individuals actively construct their knowledge and understanding of the world by interacting with other humans. Also in this framework is the concept of gene-environment interaction, where G and E interact to be inseparable and non-independent interactants.
Within the DEC framework, gene-environment interactions are influential in the development of cognition, psychology and behavior. This is because due to genes being necessary for the construction of humans, they need to be there to ensure they begin growing once conceived. Then, the system begins interacting irreducibly with other developmental interactants, which then begin to form the phenotype and eventually a human forms. So genes provide a necessary pre-condition for traits, but in this framework they are not sufficient conditions.
In Vygotsky’s socio-historical theory of learning and development, Vygotsky argued that individuals acquire psychological traits through interacting with other humans in certain social and environmental contexts through the use of cultural and psychological tools. Language, social interactions and culture mediate the cognitive development which then fosters higher-order thinking. Thus, Vygotsky’s theory highlights the dynamic and interactive nature of human development which emphasizes the social contexts of the actors in how mind is shaped and developed. So Vygotsky’s theory supports the idea I hold that mind is shaped through interactions and experiences within certain socio-historical contexts. So it would seem that adherence to this theory would mean that there are critical points in child development, where if the child does not get the rich exposure they need in order to develop their abilities, they then may never acquire the ability, indicating a critical window in which these abilities can be acquired (Vyshedakiy, Mahapatra, and Dunn, 2017). Cases of feral children allow us to see how one would develop without the role of social interaction and cultural tools in cognitive development. That these children are so stunted in their psychology and language shows the critical window in which children can learn and understand a language. The absence of social experiences in feral children thusly supports Vygotsky’s theory regarding the significance of cultural and social factors in shaping the mind. And cognitive development. Vygotsky’s theory is very relevant here, since it shows the necessary socio-historical and cultural experiences need to occur for higher order thinking, psychology, and mind to develop in humans. And since newborns, infants and young children are surrounded by what Vygotsky called More Knowledgeable Others, they learn from and copy what they see from people who already know how to act in certain social and cultural situations, which then develops an individual’s psychology and mind.
There is also another issue here: The fact that species-typical behaviors develop in reliable ecological contexts. If we assume this holds for humans—and I see no reason not to—then there need to be certain things in the environment that then jettison the beginnings of the construction of mind in humans, and this is in relevant social-historical-ecological contexts, basically, environments are inherited too.
In an article eschewing the concept of “innateness”, Blumberg (2018) has a great discussion on how species-typical traits arise. Quite simply, it’s due to the construction of species-specific niches which then allow the traits to reliably appear over time:
Species-typical behaviors can begin as subtle predispositions in cognitive processing or behavior. They also develop under the guidance of species-typical experiences occurring within reliable ecological contexts. Those experiences and ecological contexts, together comprising what has been called an ontogenetic niche, are inherited along with parental genes16. Stated more succinctly, environments are inherited—a notion that shakes the nature-nurture dichotomy to its core. That core is shaken still further by studies demonstrating how even our most ancient and basic appetites, such as that for water, are learned17. Our natures are acquired.
Contrasting the DEC with hereditarianism shows exactly how different they are and how DEC answers hereditarianism with a different framework. DEC offers an alternative perspective on the construction of psychological traits and mind in humans, and strongly emphasizes the role of individual experiences and environmental factors (like the social) in allowing the mind to form and shape psychological traits, but it does in fact highlight the need for genetic factors—though in a necessary, not sufficient, way. DEC suggests that genes alone aren’t enough to account for psychology. It argues that the mind is irreducible to the physical (genes, brain/brain structure) and that the development of psychological traits (and along with it the mind) requires the interactive influences of the individual, experiences, and environmental context.
There is one more line of evidence I need to discuss before I conclude—that of clonal populations living in the same controlled environment and what it does and does not show, along with the implications of behavioral genetic hereditarian explanations of behavior. Kate Laskowski’s (2022) team observed how genetically identical fish behaved in controlled environments. Substantial individuality still arises in clonal fishes with the same genes while being in a controlled environment. These studies from Laskowski’s team suggests that behavioral individuality “might be an inevitable and potentially unpredictable outcome of development” (Bierbach, Laskowski, and Wolf, 2017). So the argument below captures this fact, and is based on the assumption that if genes did cause psychological traits and behavior, then individuals with an identical genome would have identical psychology and behavior. But these studies show that they do not, so the conclusion follows that mind and psychological traits aren’t determined by psychology.
(P1) If the mind is determined by genetic factors, then all individuals with the same genetic makeup would exhibit identical psychological traits. (P2) Not all individuals with the same genetic makeup exhibit identical psychological traits. (C) Thus, mind isn’t determined by genetic factors.
I think it is a truism that an entailment of the hereditarian view would be identical genes would mean identical psychology and behavior. Quite obviously, experimental results have shown that this quite simply is not the case. If the view espoused by Rushton and Jensen and other hereditarians were true, then organisms with identical genomes would have the same behavior and psychology. But we don’t find this. Thus, we should reject hereditarianism since their claim has been tested in clonal populations and gas been found wanting.
Now how is my view not blank slatism? I deny the claim that psychology reduces to anything physical, and I deny that innate traits are a thing, so can there be nuances, or am I doomed to be labeled a blank slatist? Genetic factors are necessary pre-conditions for the mind but there are no predetermined, hardwired traits in them. While genetic factors lay the groundwork for this, the importance of learning, experience, and relevant ecological contexts must not be discounted here. While I recognize the interplay between genes and environment and other resources, I do not hold to the claim that any of them are sufficient to explain mind and psychology. I would say that Vygotsky’s theory shows how and why people and groups score differently on so-called psychological tests. There is the interplay between the child, the socio-cultural environment, and the individuals in that environment. Thus, by being in these kinds of environments, this allows the formation of mind and psychology (which is shown in cases of feral children), meaning that hereditarianism is ill-suited to explaining this with their fixation on genes, even when genes can’t explain psychology. If the mental is irreducible to the physical and genes are physical, then genes can’t explain the mental. This destroys the hereditarian argument.
Conclusion
Vygotsky’s theory provides a socio-cultural framework which acknowledges the role of subjective experiences within social contexts. Individuals engage in social interactions, and collaborative activities as conscious beings, and in doing so, they share their subjective experiences to the collective construction of knowledge and understanding. The brand of dualism I push entails that psychology doesn’t reduce to anything physical, which includes genes and the brain. But I do of course recognize the interactions between all developmental resources, I don’t think that any of them along are explanatory regarding psychology and behavior like the hereditarian, that’s one of the biggest differences between hereditarianism and the DEC. My view is similar to that of relational developmental systems theory (Lerner, 2021a, b). Further, this view is similar to Oyama’s (2002) view where she conceptualizes “nature” as a natural outcome of the organism-environment system (inline with Blumberg, 2018), and nurture as the ongoing developmental process. Thus, Oyama has reconceptualized the nature nurture debate.
Of course, my claim that psychology isn’t reducible to genes would put me in the “100% percent culture-only” camp that Rushton and Jensen articulated. However, there is no other way to be about this debate, since races are different cultural groups and different cultural groups are exposed to different cultural and psychological tools which lead to differences in knowledge and therefore lead to score differences. So I reject their dichotomy they mounted and I also reject the claim that the interactive view is effectively a “culture-only” view. But, ultimately, the argument that psychology doesn’t reduce to genes is sound, so hereditarianism is false. Furthermore, the hereditarian claim that genes cause differences in psychology and behavior is called into question due to the research on clonal populations. This shows that individuality arises randomly, and is not caused by genetic differences since there were no genetic differences.
The discussion surrounding the specific IQ debate concerning the hereditarian explanation necessitates a thorough examination of the intricate interplay between genetics and environment. A mere environmental explanation seems to be the only plausible rationale for the observed black-white IQ gap, considering that psychological states cannot be solely ascribed/reduced to genetic factors. In light of this, any attempts to dichotomize nature versus nurture, as was exemplified by Rushton and Jensen, fail to capture the essence of the matter at hand. Their reductionist approach, encapsulating a “100% culture-only program” within their 2 boxes that shows their adherence to the false dichotomy, followed by the triumphal proclamation of their seemingly preferred “50/50 split between genes and environment” explanation (although they later advocate an 80/20 perspective), can be regarded as nothing more than a fallacious oversimplification.
I have presented a comprehensive framework which challenges hereditarianism and provides an alternative perspective on the nature of human psychology and development. I integrated the principles of mind-body dualism, Vygotsky’s socio-historical theory of learning and development, and gene-environment interactions calling it Dualistic Experiential Constructivism, which acknowledges the interplay between genes, environment, and other developmental resources. Ultimately, DEC promoted a more holistic and interactive view in understanding the origin of mind through social processes and species-typical contextual-dependent events, while acknowledging genes as a necessary template for these things, since the organism is what is navigating the environment.
So this is the answer to hereditarianism—a view in which all developmental resources interact and are irreducible, in which first-personal subjective experiences with others of the species taking place in reliable ecological contexts jettison the formation of mind and psychological traits. This is called Dualistic Experiential Constructivism, and it entails a few different other frameworks that then coalesce into the view against hereditarianism that I hold.
“the study of the brains of human races would lose most of its interest and utility” if variation in size counted for nothing ([Broca] 1861 , p. 141). Quoted in Gould, 1996: 115)
The law is: small brain, little achievement; great brain, great achievement (Ridpath, 1891: 571)
I can’t hope to give as good a review as Gould’s review in Mismeasure of Man on the history of skull measuring, but I will try to show that hereditarians are mistaken in their brain size-IQ correlations and racial differences in brain size as a whole.
The claim that brain size is causal for differences in intelligence is not new. Although over the last few hundred years there has been back and forth arguments on this issue, it is generally believed that there are racial differences in brain size and that this racial difference in brain size accounted for civilizational accomplishments, among other things. Notions from Samuel Morton which seem to have been revived by Rushton in the 80s while formulating his r/K selection theory show that the racism that was incipient in the time period never left us, even after 1964. Rushton and others merely revived the racist thought of those from the 1800s.
Using MRI scans (Rushton and Ankney, 2009) and measuring the physical skull, Rushton asserted that the differences in brain size and quality between races accounted for differences in IQ. Although Rushton was not alone in this belief, this belief on the relationship between brain weight/structure and intelligence goes back centuries. In this article, I will review studies on racial differences in brain size and see if Rushton et al’s conclusions hold on not only brain size being causally efficacious for IQ but there being racial and differences in brain size and the brain size and IQ correlation.
The Morton debate
Morton’s skull collection has received much attention over the years. Gould (1978) first questioned Morton’s results on the ranking of skulls. He argued that when the data was properly reinterpreted, “all races have approximately equal capacities.” The skulls in Morton’s collection were collected from all over. Morton’s men even robbed graves to procure skulls for Morton, even going as far to take “bodies in front of grieving relatives and boiled flesh off fresh corpses” (Fabian, 2010: 178). One man even told Morton that grave robbing gave him a “rascally pleasure” (Fabian, 2010: 15). Indeed, grave robbing seems to have been a way to procure many skulls which were used in these kinds of analyses (Monarrez et al, 2022). Nevertheless, since skulls house brains, the thought is that by measuring skulls then we can ascertain the brain of the individual that the skull belonged to. A larger skull would imply a larger brain. And larger brains, it was said, belong to more “intelligent” people. This assumption was one that was held by the neurologist Broca, and this then justified using brain weight as a measure of intelligence. Though in 1836, an anti-racist Tiedemann (1836) argued that there were no differences in brain size between whites and blacks. (Also see Gould, 1999 for a reanalysis of Tiedemann where he shows C > M > N in brain size, but concludes that the “differencesare tiny and probably of no significancein the judgment of intelligence” (p 10).) It is interesting to note that Tiedemann and Morton worked with pretty much the same data, but they came to different conclusions (Gould, 1999; Mitchell, 2018).
In 1981 Gould published his landmark book The Mismeasure of Man (Gould, 1981/1996). In the book, he argued that bias—sometimes unconscious—pervaded science and that Morton’s work on his skull collection was a great example of this type of bias. Gould (1996: 140) listed many reasons why group (race) differences in brain size have never been demonstrated, citing Tobias (1970):
After all, what can be simpler than weighing a brain?—take it out, and put it on the scale. One set of difficulties refers to problems of measurement itself: at what level is the brain severed from the spinal cord; are the meninges removed or not (meninges are the brain’s covering membranes, and the dura mater, or thick outer covering, weighs 50 to 60 grams); how much time elapsed after death; was the brain preserved in any fluid before weighing and, if so, for how long; at what temperature was the brain preserved after death. Most literature does not specify these factors adequately, and studies made by different scientists usually cannot be compared. Even when we can be sure that the same object has been measured in the same way under the same conditions, a second set of biases intervenes—influences upon brain size with no direct tie to the desired properties of intelligence or racial affiliation: sex, body size, age, nutrition, nonnutritional environment, occupation, and cause of death.
Nevertheless, in Mismeasure, Gould argued that Morton had unconscious bias where he packed the skulls of smaller African skulls more loosely while he would pack the skulls of a smaller Caucasian skull tighter (Gould made this inference due to the disconnect between Morton’s lead shot and seed measurements).
Plausible scenarios are easy to construct. Morton, measuring by seed, picks up a threateningly large black skull, fills it lightly and gives it a few desultory shakes. Next, he takes a distressingly small Caucasian skull, shakes hard, and pushes mightily at the foramen magnum with his thumb. It is easily done, without conscious motivation; expectation is a powerful guide to action. (1996: 97)
…
Yet through all this juggling, I detect no sign of fraud or conscious manipulation. Morton mad e no attempt to cove r his tracks and I must presume that he was unaware he had left them. He explained all his procedure s and published all his raw data. All I can discern is an a priori conviction about racial ranking so powerful that it directed his tabulations along preestablished lines. Yet Morton was widely hailed as the objectivist of his age, the man who would rescue American science from the mire of unsupported speculation. (1996: 101)
But in 2011, a team of researchers tried to argue that Morton did not manipulate data to fit his a priori biases (Lewis et al, 2011). They claimed that “most of Gould’s criticisms are poorly supported or falsified.” They argued that Morton’s measurements were reliable and that Morton really was the scientific objectivist many claimed him to be. Of course, since Gould died in 2002 shortly after publishing his magnum opus The Stuecure of Evolutionary Theory, Gould could not defend his arguments against Morton.
Weisberg (2014) was the first to argue against Lewis et al’s conclusions on Gould. Weisberg argued that while Gould sometimes overstated his case, most of his arguments were sound. Weisberg argued that, contra what Lewis et al claimed, they did not falsify Gould’s claim, which was that the difference between shot and seed measurements showed Morton’s unconscious racial bias. While Weisberg rightly states that Lewis et al uncovered some errors that Gould made, they did not successfully refute two of Gould’s main claims: “that there is evidence that Morton’s seed‐based measurements exhibit racial bias and that there are no significant differences in mean cranial capacities across races in Morton’s collection.”
There is prima facie evidence of racial bias in Morton’s (or his assistant’s) seed‐basedmeasurements. This argument is based on Gould’s accurate analysis of the difference between the seed‐ and shot‐based measurements of the same crania.
Gould is also correct about two other major issues. First, sexual dimorphism is a very suspicious source of bias in Morton’s reported averages. Since Morton identified most of his sample by sex, this is something that he could have investigated and corrected for. Second, when one takes appropriately weighted grand means of Morton’s data, and excludes obvious sources of bias including sexual dimorphism, then the average cranial capacity of the five racial groups in Morton’s collection is very similar. This was probably the point that Gould cared most about. It has been reinforced by my analysis.
[This is Weisberg’s reanalysis]
So Weisberg successfully defended Gould’s claim that there are no general differences in the races as ascribed by Morton and his contemporaries.
In 2015, another defense of Gould was mounted (Kaplan, Pigliucci and Banta, 2015). Like Weisberg before them, they also state that Gould got some things right and some things wrong, but his main arguments weren’t touched by Lewis et al. Kaplan et al stated that while Gould was right to reject Morton’s data, he was wrong to believe that “a more appropriate analysis was available.” They also argue due to the “poor datasetno legitimateinferences to“natural” populations can be drawn.” (See Luchetti, 2022 for a great discussion of Kaplan, Pigliucci and Banta.)
In 2016, Weisberg and Paul (2016) argued that Gould assumed that Morton’s lead shot method was an objective way to ascertain the cranial capacities of skulls. Gould’s argument rested on the differences between lead shot and seed. Then in 2018, Mitchell (2018) published a paper where he discovered lost notes of Morton’s and he argued that Gould was wrong. He, however, admitted that Gould’s strongest argument was untouched—the “measurement issue” (Weisberg and Paul, 2016) was Gould’s strongest argument, deemed “perceptive” by Mitchell. In any case, Mitchell showed that the case of Morton isn’t one of an objective scientist looking to explain the world sans subjective bias—Morton’s a priori biases were strong and strongly influenced his thinking.
Lastly, ironically Rushton used Morton’s data from Gould’s (1978) critique, but didn’t seem to understand why Gould wrote the paper, nor why Morton’s methodology was highly suspect. Rushton basically took the unweighted average for “Ancient Caucasian” skulls, and the sex/age of the skulls weren’t known. He also—coincidentally I’m sure—increased the “Mongoloid skull” size from 85 to 85.5cc (Gould’s table had it as 85cc). Amazingly—and totally coincidentally, I’m sure—Rushton miscited Gould’s table and basically combined Morton’s and Gould’s data, increased the skull size slightly of “Mongoloids” and used the unweighted average of “Ancient Caucasian” skulls (Cain and Vanderwolf, 1990). How honest of Rushton. It’s ironic how people say that Gould lied about Morton’s data and that Gould was a fraud, when in all actuality, Rushton was the real fraud, never recanting on his r/K theory, and now we can see that Rushton actually miscited and combined Gould’s and Morton’s results and made assumptions without valid justification.
The discussion of bias in science is an interesting one. Since science is a social endeavor, there necessarily will be bias inherent in it, especially when studying humans and discussing the causes of certain peculiarities. I would say that Gould was right about Morton and while Gould did make a few mistakes, his main argument against Morton was untouched.
Skull measuring after Morton
The inferiority of blacks and other non-white races has been asserted ever since the European age of discovery. While there were of course 2 camps at the time—one which argued that blacks were not inferior in intelligence and another that argued they were—the claim that blacks are inferior in intelligence was, and still is, ubiquitous. They argued that smaller heads meant that one was less intelligent, and if groups had smaller heads then they too were less intelligent than groups that had smaller heads. This then was used to argue that blacks hadn’t achieved any kind of civilizational accomplishments since they were intellectually inferior due to their smaller brains (Davis, 1869; Campbell, 1891; Hoffman, 1896; Ridpath, 1897; Christison, 1899).
Robert Bean (1906) stated, using cadavers, that his white cadavers had larger frontal lobes than his black cadavers. He concluded that blacks were more objective than whites who were more subjective, and that white cadavers has larger frontal and anterior lobes than black cadavers. However, it seems that Bean did not state one conclusion—that the brain’s of his cadavers seemed to show no difference. Gould (1996: 112) discusses this issue (see Mall, 1909: 8-10, 13; Reuter, 1927). Mall (1909: 32) concluded, “In this study of several anatomical characters said to vary according to race and sex, the evidence advanced has been tested and found wanting.”
Franz Boas also didn’t agree with Bean’s analysis:
Furthermore, in “The Anthropological Position of the Negro,” which appeared in Van Norden)- Magazine a few months later, Boas attempted to refute Bean by arguing that “the anatomical differences” between blacks and whites “are minute,” and “no scientific proof that will stand honest proof … would prove the inferiority of the negro race.”39 (Williams, 1996: 20)
In 1912, Boas argued that the skull was plastic, so plastic that changes in skull shape between immigrants and their progeny were seen. His results were disputed (Sparks and Jantz, 2002), though Gravlee, Bernard, and Leonard (2002) argued that Boas was right—the shape of the skull indeed was influenced by environmental factors.
When it comes to sex, brain size, and intelligence, this was discredited by Alice Lee in her thesis in 1900. Lee created a way to measure the brain of living subjects and she used her method on the Anthropological Society and showed a wife variation, with of course overlapping sizes between men and women.
Lee, though, was a staunch eugenicist and did not apply the same thinking to race:
After dismantling the connection between gender and intellect, a logical route would have been to apply the same analysis to race. And race was indeed the next realm that Lee turned to—but her conclusions were not the same. Instead, she affirmed that through systematic measurement of skull size, scientists could indeed define distinct and separate racial groups, as craniometry contended. (The Statistician Who Debunked Sexist Myths About Skull Size and Intelligence)
Contemporary research on race, brain size, and intelligence
Starting from the mid-1980s when Rushton first tried to apply r/K to human races, there was a lively debate in the literature, with people responding to Rushton and Rushton responding back (Cain and Vanderwolf, 1990; Lynn, 1990; Rushton, 1990; Mouat, 1992). Why did Rushton seemingly revive this area of “research” into racial differences in brain size between human races?
Centring Rushton’s views on racial differences needs to start in his teenage years. Rushton stated that being surrounded by anti-white and anti-western views led to him seeking out right-wing ideas:
JPR recalls how the works of Hans Eysenck were significantly influential to the teenage Rushton, particularly his personality questionnaires mapping political affiliation to personality. During those turbulent years JPR describes bundled as growing his hair long becoming outgoing but utterly selfish. Finding himself surrounded by what he described as anti-white and anti-western views, JPR became interested in right-wing groups. He went about sourcing old, forbidden copies of eugenics articles that argued that evolutionary differences existed between blacks and whites. (Forsythe, 2019) (See alsoDutton, 2018.)
Knowing this, it makes sense how Rushton was so well-versed in old 18 and 1900s literature on racial differences.
For decades, J. P. Rushton argued that skulls and brains of blacks were smaller than whites. Since intelligence was related to brain size in Rushtonian r/K selection theory, this meant that what would account for some of the intelligence differences based on IQ scores between blacks and whites could be accounted for by differences in brain size between them. Since the brain size differences between races accounted for millions of brain cells, this could then explain race differences in IQ (Rushton and Rushton, 2003). Rushton (2010) went as far to argue that brain size was an explanation for national IQ differences and longevity.
Rushton’s thing in the 90s was to use MRI to measure endocranial volumes (eg Rushton and Ankney, 1996). Of course they attempt to show how smaller brain sizes are found in lower classes, women, and non-white races. Quite obviously, this is scientific racism, sexism, and classism (which Murray 2020 also wrote a book on). In any case, Rushton and Ankney (2009) tried arguing for “general mental ability” and whole brain size, trying to argue that the older studies “got it right” in regard to not only intelligence and brain size but also race and brain size. (Rushton and Ankney, just like Rushton and Jensen 2005, cited Mall, 1909 in the same sentence as Bean, 1906 trying to argue that the differences in brain size between whites and blacks were noted then, when Mall was a response specifically to Bean! See Gould 1996 for a solid review of Bean and Mall.) Kamin and Omari (1998) show that whites had greater head height than blacks while blacks had greater head length and circumference. They described many errors that Lynn, Rushton and Jensen made in their analyses of race and head size. Not only did Rushton ignore Tobias’ conclusions when it comes to measuring brains, he also ignored the fact that American Blacks, in comparison to American, French and English whites, had larger brains in Tobias’ (1970) study (Weizmann et al, 1990).
Rushton and Ankney (2009) review much of the same material they did in their 1996 review. They state:
The sex differences in brain size present a paradox. Women have proportionately smaller average brains than men but apparently have the same intelligence test scores.
This isn’t a paradox at all, it’s very simple to explain. Terman assumed that men and women should be equal in IQ and so constructed his test to fit that assumption. Since Terman’s Stanford-Binet test is still in use today, and since newer versions are “validated” on older versions that held the same assumption, then it follows that the assumption is still alive today. This isn’t some “paradox” that needs to be explained away by brain size, we just need to look back into history and see why this is a thing. The SAT has been changed many times to strengthen or weaken sex differences (Rosser, 1989). It’s funny how this completely astounds hereditarians. “There are large differences in brain size between men and women but hardly if any differences in IQ, but a 1 SD difference in IQ between whites and blacks which is accounted for in part by brain size.” I wonder why that never struck them as absurd? If Rushton accepted brain weight as an indicator that IQ test scores reflected differences in brain size between the races, then he would also need to accept that this should be true for men and women (Cernovsky, 1990), but Rushton never proposed anything like that. Indeed he couldn’t, since sex differences in IQ are small or nonexistent.
In their review papers, Rushton and Ankney, as did Rushton and Jensen (I should assume that this was Rushton’s contribution to the paper since he also has the same citations and arguments in his book and other papers) consistently return to a few references: Mall, Bean, Vint and Gordon, Ho et al and Beals et al. Cernovsky (1995) has a masterful response to Rushton where he dismantles his inferences and conclusions based on other studies. Cernovsky showed that Rushton’s claim that his claim that there are consistent differences between races in brain size is false; Rushton misrepresented other studies which showed blacks having heavier brains and larger cranial capacities than whites. He misrepresented Beals et al by claiming that the differences in the skulls they studied are due to race when race was spurious, climate explained the differences regardless of race. And Rushton even misrepresented Herskovits’ data which showed no difference in regarding statute or crania. So Rushton even misrepresented the brain-body size literature.
Now I need to discuss one citation line that Rushton went back to again and again throughout his career writing about racial differences. In articles like Rushton (2002)Rushton and Jensen (2005), Rushton and Ankney (2007, 2009) Rushton went back to a similar citation line: Citing 1900s studies which show racial differences. Knowing what we know about Rushton looking for old eugenics articles that showed that evolutionary differences existed between blacks and whites, this can now be placed into context.
Weighing brains at autopsy, Broca (1873) found that Whites averaged heavier brains than Blacks and had more complex convolutions and larger frontal lobes. Subsequent studies have found an average Black–White difference of about 100 g (Bean, 1906; Mall, 1909; Pearl, 1934; Vint, 1934). Some studies have found that the more White admixture (judged independently from skin color), the greater the average brain weight in Blacks (Bean, 1906; Pearl, 1934). In a study of 1,261 American adults, Ho et al. (1980) found that 811 White Americans averaged 1,323 g and 450 Black Americans averaged 1,223 g (Figure 1).
There are however, some problems with this citation line. For instance, Mall (1909) was actually a response to Bean (1906). Mall was race-blind to where the brains came from after reanalysis and found no differences in the brain between blacks and whites. Regarding the Ho et al citation, Rushton completely misrepresented their conclusions. Further, brains that are autopsied aren’t representative of the population at large (Cain and Vanderwolf, 1990; see also Lynn, 1989; Fairchild, 1991). Rushton also misrepresented the conclusions in Beals et al (1984) over the years (eg, Rushton and Ankney, 2009). Rushton reported that they found his same racial hierarchy in brain size. Cernovsky and Littman (2019) stated that Beals et al’s conclusion was that cranial size varied with climatic zone and not race, and that the correlation between race and brain size was spurious, with smaller heads found in warmer climates, regardless of race. This is yet more evidence that Rushton ignored data that fid not fit his a priori conclusions (see Cernovsky, 1997; Lerner, 2019: 694-700). Nevertheless, it seems that Rushton’s categorization of races by brain size cannot be valid (Peters, 1995).
It would seem to me that Rushton was well-aware of these older papers due to what he read in his teenage years. Although at the beginning of his career, Rushton was a social learning theorist (Rushton, 1980), quite obviously Rushton shifted to differential psychology and became a follower—and collaborator—of Jensenism.
But what is interesting here in the renewed ideas of race and brain size are the different conclusions that different investigators came to after they measured skulls. Lieberman (2001) produced a table which shies different rankings of different races over the past few hundred years.
Table 1 from Lieberman, 2001 showing different racial hierarchies in the 19th and 20th century
As can be seen, there is a stark contrast in who was on top of the hierarchy based on the time period the measurements were taken. Why may this be? Obviously, this is due to what the investigator wanted to find—if you’re looking for something, you’re going to find it.
Rushton (2004) sought to revive the scala naturae, proposing that g—the general factor of intelligence—sits a top a matrix of correlated traits and he tried to argue that the concept of progress should return to evolutionary biology. Rushton’s r/K theory has been addressed in depth, and his claim that evolution is progressive is false. Nevertheless, even Rushton’s claim that brain size was selected for over evolutionary history also seems to be incorrect—it was body size that was, and since larger bodies have larger brains this explains the relationship. (See Deacon, 1990a, 1990b.)
Salami et al (2017) used brains from fresh cadavers, severing them from the spinal cord at the forum magnum and they completely removed the dura mater. This then allowed them to measure the whole brain without any confounds due to parts of the spinal cord which aren’t actually parts of the brain. They found that the mean brain weight for blacks was 1280g with a ranging between 1015g to 1590g while the mean weight of male brains was 1334g. Govender et al (2018) showed a 1404g mean brain weight for the brains of black males.
Rushton aggregated data from myriad different sources and time periods, claiming that by aggregating even data which may have been questionable in quality, the true differences in brain size would appear when averaged out. Rushton, Brainerd, and Pressley, 1983 defended the use of aggregation stating “By combining numerous exemplars, such errors of measurement are averaged out, leaving a clearer view of underlying relationships.” However, this method that Rushton used throughout his career has been widely criticized (eg, Cernovsky, 1993; Lieberman, 2001).
Rushton was quoted as saying “Even if you take something like athletic ability or sexuality—not to reinforce stereotypes or some such thing—but, you know, it’s a trade-off: more brain or more penis. You can’t have both.” How strange—because for 30 years Rushton pushed stereotypes as truth and built a whole (invalid) research program around them. The fact of the matter is, for Rushton’s hierarchy when it comes to Asians, they are a selected population in America. Thus, even there, Rushton’s claim rests on values taken from a selected population into the country.
While Asians had larger brains and higher IQ scores, they had lower sexual drive and smaller genitals; blacks had smaller brains and lower IQ scores with higher sexual drive and larger genitals; whites were just right, having brains slightly smaller than Asians with slightly lower IQs and lower sexual drive than blacks but higher than Asians along with smaller genitals than blacks but larger than Asians. This is Rushton’s legacy—keeping up racial stereotypes (even then, his claims on racial differences in penis size do not hold.)
The misleading arguments on brain size lend further evidence against Rushton’s overarching program. Thus, this discussion is yet more evidence that Rushton was anything but a “serious scholar” who trolled shopping malls asking people their sexual exploits. He was clearly an ideologue with a point to prove about race differences which probably manifested in his younger, teenage years. Rushton got a ton wrong, and we can now add brain size to that list, too, due to his fudging of data, misrepresenting data, and not including data that didn’t fit his a priori biases.
Quite clearly, whites and Asians have all the “good” while blacks and other non-white races have all the “bad.” And thus, what explains social positions not only in America but throughout the world (based on Lynn’s fraudulent national IQs; Sear, 2020) is IQ which is mediated by brain size. Brain size was but a part of Rushton’s racial rank ordering, known as r-K selection theory or differential K theory. However, his theory didn’t replicate and it was found that any differences noticed by Rushton could be environmentally-driven (Gorey and Cryns, 1995; Peregrine, Ember and Ember, 2003).
The fact of the matter is, Rushton has been summarily refuted on many of his incendiary claims about racial differences, so much so that a couple of years ago quite a few of his papers were retracted (three in one swipe). While a theoretical article arguing about the possibility that melanocortin and skin color may mediate aggression and sexuality in humans (Rushton and Templer, 2012). (This appears to be the last paper that Rushton published before his death in October, 2012. How poetic that it was retracted.) This was due mainly to the outstanding and in depth look into the arguments and citations made by Rushton and Templer. (See my critique here.)
Conclusion
Quite clearly, Gould got it right about Morton—Gould’s reanalysis showed the unconscious bias that was inherent in Morton’s thoughts on his skull collection. Gould’s—and Weisberg’s—reanalysis show that there are small differences in skulls of Morton’s collection. Even then, Gould’s landmark book showed that the study of racial differences—in this case, in brain and skull size—came from a place of racist thought. Writings from Rushton and others carry on this flame, although Rushton’s work was shown to have considerable flaws, along with the fact that he outright ignored data that didn’t fit his a priori convictions.
Although comparative studies of brain size have been widely criticized (Healy and Rowe, 2007), they quite obviously survive today due to the assumptions that hereditarians have between “IQ” and brain size along with the assumption that there are racial differences in brain size and that these differences are causal for socially-important things. However, as can be seen, the comparative study of racial brain sizes and the assumption that IQ is causally mediated by it are hugely mistaken. Morton’s studies were clouded by his racial bias, as Gould and Weisberg and Kaplan et al showed. When Rushton, Jensen, and Lynn arose, they they tried to carry on that flame, correlating head size and IQ while claiming that smaller head sizes and—by identity—smaller brains are related to a suite of negative traits.
The brain is of course an experience-dependent organ and people are exposed to different types of knowledge based on their race and social class. This difference in knowledge exposure based on group membership, then, explains IQ scores. Not any so-called differences in brain size, brain physiology or genes. And while Cairo (2011) concludes that “Everything indicates that experience makes the great difference, and therefore, we contend that the gene-environment interplay is what defines the IQ of an individual“, genes are merely necessary for that, not sufficient. Of course, since IQ is an outcome of experience, this is what explains IQ differences between groups.
Table 1 from Lieberman (2001) is very telling about Gould’s overarching claim about bias in science. As the table shows, the hierarchy in brain size was constantly shifting throughout the years based on a priori biases. Even different authors coming to different conclusions in the same time period on whether or not there are differences in brain size between races pop up. Quite obviously, the race scientists would show that race is the significant variable in whatever they were studying and so the average differences in brain size then reflect differences in genes and then intelligence which would then be reflected in civilizational accomplishments. That’s the line of reasoning that hereditarians like Rushton use when operating under these assumptions.
Science itself isn’t racist, but racist individuals can attempt to use science to import their biases and thoughts on certain groups to the masses and use a scientific veneer to achieve that aim. Rushton, Jensen and others have particular reasons to believe what they do about the structure of society and how and why certain racial groups are in the societal spot they are in. However, these a priori conceptions they had then guided their research programs for the rest of their lives. Thus, Gould’s main claim in Mismeasure about the bias that was inherent in science is well-represented: one only needs to look at contemporary hereditarian writings to see how their biases shape their research and interpretations of data.
In the end, we don’t need just-so stories to explain how and why races differ in IQ scores. We most definitely don’t need any kinds of false claims about how brain size is causal for intelligence. Nor do we need to revive racist thought on the causes and consequences of racial differences in brain size. Quite obviously, Rushton was a dodgy character in his attempt to prove his tri-archic racial theory using r/K selection theory. But it seems that when one surveys the history of accounts of racial differences in brain size and how these values were ascertained, upon critical examination, such differences claimed by the hereditarian all but dissappear.
NotPoliticallyCorrect 