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The Conceptual Impossibility of Hereditarian Intelligence
3650 words
Introduction
For more than 100 years—from Galton and Spearman to Burt, Jensen, Rushton, Lynn and today’s polygenic score enthusiasts—hereditarian thinkers have argued that general intelligence is a unitary, highly heritable biological trait and that observed individual and group level differences in IQ and it’s underlying “g” factor primarily reflect genetic causation. The Bell Curve brought such thinking into the mainstream from obscure psychology journals, and today hereditarian behavioral geneticists claim that 10 to 20 percent of the variance in education and cognitive performance has been explained by GWA studies (see Richardson, 2017). The consensus is that intelligence within and between populations is largely genetic in nature.
While hereditarianism is empirically contested and morally wrong, the biggest kill-shot is that it is conceptually impossible, and one can use many a priori arguments from philosophy of mind to show this. Donald Davidson’s argument against the possibility of psychophysical laws, Kripke’s reading of Wittgenstein, and Nagel’s argument from indexicality can be used to show that hereditarianism is a category error. Ken Richardson’s systems theory can then be used to show that g is an artifact of dynamic systems (along with test construction), and Vygotsky’s cultural-historical psychology shows that higher mental functions (which hereditarians try to explain biologically) originate as socially scaffolded, inter-mental processes mediated by cultural tools and interactions with more knowledgeable others, not individual genetic endowment.
Thus, these metaphysical, normative, systemic, developmental and phenomenological refutations show that hereditarianism is based on a category mistake. Ultimately, what hereditarianism lacks is a coherent object to measure—since psychological traits aren’t measurable at all. I will show here how hereditarianism can be refuted with nothing but a priori logic, and then show what really causes differences in test scores within and between groups. Kripke’s Wittgenstein and the argument against the possibility of psychophysical laws, along with a Kim-Kripke normativity argument against hereditarianism show that hereditarianism just isn’t a logically tenable position. So if it’s not logically tenable, then the only way to explain gaps in IQ is an environmental one.
I will begin with showing that no strict psychophysical laws can link genes/brain states to mental kinds, then demonstrating that even the weaker functional-reduction route collapses at the very first step because no causal-role definition of intentionality (intelligence) is possible. After that I will add the general rule following considerations from Kripke’s Wittgenstein and then add it to my definition of intelligence, showing that rule-following is irreducibly normative and cannot be fixed by any internal state and that no causal-functional definition is possible. Then I will show that the empirical target of hereditarianism—the g factor—is nothing more than a statistical artifact of historically contingent, culturally-situated rule systems and not a biological substrate. These rule systems do not originate internally, but they develop as inter-mental relations mediated by cultural tools. Each of these arguments dispenses with attempted hereditarian escapes—the very notion of a genetically constituted, rank-orderable general intelligence is logically impossible.
We don’t need “better data”—I will demonstrate that the target of hereditarian research does not and cannot exist as a natural, measurable, genetically-distributed trait. IQ scores are not measurements of a psychological magnitude (Berka, 1983; Nash, 1990); no psychophysical laws exist that can bridge genes to normative mental kinds (Davidson, 1979), and the so-called positive manifold is nothing more than a cultural artifact due to test construction (Richardson, 2017). Thus, what explains IQ variance is exposure to the culture in which the right rules are used regarding the IQ test.
Psychophysical laws don’t exist
Hereditarianism implicitly assumes a psychophysical law like “G -> P.” Psychophysical laws are universal, necessary mappings between physical states and mental states. To reduce the mental to the physical, you need lawlike correlations—whenever physical state P occurs, mental state M occurs. These laws must be necessary, not contingent. They must bridge the explanatory gap from the third-personal to the first-personal. We have correlations, but correlations don’t entail identity. If correlations don’t entail identity, then the correlations aren’t evidence of any kind is psychophysical law. So if there are no psychophysical laws, there is no reduction and there is no explanation of the mental.
Hereditarianism assumes type-type psychophysical reduction. Type-type identity posits that all instances of a mental type correspond to all instances of a physical type. But hereditarians need bridge laws—they imply universal mappings allowing reduction of the mental to the measurable physical. But since mental kinds are anomalous, type-type reduction is impossible.
Hereditarians claim that genes cause g which then cause intelligence. This requires type-type reduction. Intelligence kind = g kind = physical kind. But g isn’t physical—it’s a mathematical construct, the first PC. Only physical kinds can be influenced by genes;nonphysical kinds cannot. Even if g correlates with brain states, correlation isn’t identity. Basically, no psychophysical laws means no reduction and therefore no mental explanation.
If hereditarianism is true, then intelligence is type-reducible to g/genes. If type-reduction holds, then strict psychophysical laws exist. So if hereditarianism is true, then strict psychophysical laws exist. But no psychophysical laws exist, due to multiple realizablilty and Davidson’s considerations. So hereditarianism is false.
We know that the same mental kind can be realized in different physical kinds, meaning that no physical kind correlates one-to-one necessarily with a mental kind. Even if we generously weaken the demand from strict identity to functional laws, hereditarian reduction still fails (see below).
The Kim-Kripke normativity argument
Even the only plausible route to mind-body reduction that most physicalists still defend collapses a priori for intentional/cognitive states because no causal-functional definition can ever capture the normativity of meaning and rule following (Heikenhimo, 2008). Identity claims like water = h2O only work because the functional profile is already reducible. Since the functional profile of intentional intelligence is not reducible, there is no explanatory bridge from neural states to the normativity of thought. So identity claims fail—this just strengthens Davidson’s conclusions. Therefore, every reductionist strategy that could possibly license the move from “genetic variance -> variation in intelligence” is blocked a priori.
(1) If hereditarianism is true, then general intelligence as a real cognitive capacity must be reducible to the physical domain (genes, neural states, etc).
(2) The only remaining respectable route to mind-body reduction of cognitive/intentional processes is Kim’s three-step functional-reduction model.
(C1) So if hereditarianism is true, then general intelligence must he reducible to Kim’s three-step functional-reduction model.
(3) Kim-style reduction requires—as its indispensable first step—an adequate causal-functional definition of the target property (intelligence, rule-following, grasping meaning, etc) that preserves the established normative meaning of the concept without circularly using mental/intentional vocabulary in the definiens.
(4) Any causal-functional definition of intentional/cognitive states necessarily obliterates the normative distinction between correct and incorrect application (Kripke’s normativity argument applied to mental content).
(C2) Therefore, no adequate causal-functional definition of general intelligence is possible, even in principle.
(5) If no adequate causal-functional definition is possible, then Kim-style functional reduction of general intelligence is impossible.
(C3) So Kim-style functional reduction of general intelligence is impossible.
(C4) So hereditarianism is false.
A hereditarian can resist Kim-Kripke in 4 ways but each fails. (1) They can claim intelligence need not be reducible, but then genes cannot causally affect it, dissolving hereditarianism into mere correlation. (2) They can reject Kim-style reduction in factor of non-reductive or mechanistic physicalism, but these views still require functional roles and collapse under Kim’s causal exclusion argument. (3) They can insist that intelligence has a purely causal-functional definition (processing efficiency or pattern recognition), but such definitions omit the normativity of reasoning and therefore do no capture intelligence at all. (4) They can deny that normativity matters, but removing correctness conditions eliminates psychological content and makes “intelligence” unintelligible, destroying the very trait hereditarianism requires. Thus, all possible routes collapse into contradiction or eliminativism.
The rule-following argument against hereditarianism
Imagine a child who is just learning to add. She adds 68+57=125. We then say that she is correct. Why is 125 correct and 15 incorrect? It isn’t correct because she feels sure, because someone who writes 15 could be just as sure. It isn’t correct because her brain lit up in a certain way, because the neural pattern could also belong to someone following a different rule. It isn’t correct because all of her past answers, because all past uses were finite and are compatible with infinitely many bizzare rules that only diverge now. It isn’t correct because of her genes or any internal biological state, because DNA is just another finite physical fact inside of her body.
There is nothing inside of her head, body or genome that reaches out and touches the difference between correct and incorrect. But the difference is real. So where does it lie? It lives outside of her in the shared community practices. Correctness is a public status, not a private possession. Every single thing that IQ tests reward—series completion, analogies, classification, vocabulary, matrix reasoning—is exactly this kind of going on correctly. So every single point on an IQ test is an act whose rightness is fixed in the space of communal practice. What we call “intelligence” exists only between us—between the community, society and culture in which an individual is raised.
Intelligence is a normative ability. To be intelligent is to go on in the same way, to apply concepts correctly, to get it right when solving new problems, reasoning, understanding analogies, etc. So intelligence = rule-following (grasping and correctly applying abstract patterns).
Rule following is essentially normative—there is a difference between seeming right and being right. Any finite set of past performances is compatible with an infinite set of many rules. No fact about an individual—neither physical nor mental content—uniquely determines the rule they are following. So no internal state fixes the norm. Thus, rule following cannot be constituted by internal/genetic states. No psychophysical law can connect G to correct rule following (intelligence).
Therefore rule-following is set by participation in a social practice. Therefore, normative abilities (intelligence, reasoning, understanding) are socially, not genetically, constituted. So hereditarianism is logically impossible.
At its core, intelligence is the ability to get it right. Getting it right is a social status conferred by participation in communal practices. No amount of genetic or neural causation can confer that status—because no internal state can fix the normative fact. So the very concept of “genetically constituted general intelligence” is incoherent. Therefore, hereditarianism is logically impossible.
(1) H -> G -> P
Hereditarianism -> genes/g -> normative intelligence
(2) P -> R
Normative intelligence -> correct rule-following.
(3) R -> ~G
Rule following cannot be fixed by internal physical/mental states.
So ~(G -> P)
So ~H.
The Berka-Nash measurement objection
This is a little-known critique of psychology and IQ. First put forth in Karel Berka’s 1983 book Measurement: It’s Concepts, Theories, and Problems, and then elaborated on in Roy Nash’s (1990) Intelligence and Realism: A Materialist Critique of IQ.
If hereditarianism is true, then intelligence must be a measurable trait (with additive structure, object, and units) that genes can causally influence via g. If intelligence is measurable, then psychophysical laws must exist to map physical causes to mental kinds. But no such measurability or laws exist. Thus, hereditarianism is false.
None of the main, big-name hereditarians have ever addressed this type of argument. (Although Brand et al, 2003 did attempt to, their critique didn’t work and they didn’t even touch the heart of the matter.) Clearly, the argument shows that hereditarian psychology is weak to such critique. The above argument shows that IQ is quasi-quantification, without an empirical object, no structure, or lawful properties
The argument for g is circular
“Subtests within a battery of intelligence tests are included n the basis of them showing a substantial correlation with the test as a whole, and tests which do not show such correlations are excluded.” (Tyson, Jones, and Elcock, 2011: 67)
g is defined as the common variance of pre-selected subtests that must correlate. Subtests are included only if they correlate. A pattern guaranteed by construction cannot be evidence of a pre-existing biological unity. So g is a tautological artifact, not a natural kind that genes can cause.
Hereditarians need g to be a natural kind trait that genes can act upon. But g is an epiphenomenal artifact due to test construction produced by current covariation of culturally specific cognitive tasks in modern school societies. Artifacts of historically contingent cultural ecologies are not natural kind traits. So g is not a natural kind. So hereditarianism is false.
The category error argument
Intelligence is a first-person indexical act. g is a third-person statistical abstraction. There can be no identity between a phenomenonal act and a statistical abstraction. So g cannot be intelligence—no reduction is possible.
There is no such thing as genetically constituted general intelligence since intelligence is a rational normative competence, the g factor is an epiphenomenal artifact of a historically contingent self-organizing cultural-cognitive ecology, and higher psychological functions originate as social relations mediated by cultural tools which only later appear individual. Hereditarianism tries to explain a normative status with causal mechanisms, a dynamic cultural artifact with a fixed trait, and an inter-mental function with intra-cranial genetics.
g is a third-person statistical construct. Intelligence, as a psychological trait, consists of first-person indexical cognitive acts. Category A – third-person, impersonal (g, PGS, allele frequencies, brain scans). Category B – first-person, subjective, experiential).
Genetic claims assert that differences in g (category A) are caused by differences in genes and that this then explains differences in intelligence (category B). For such claims to be valid, g (category A) must be identical to intelligence (category B). But g has no first-person phenomenology meaning no one experiences using g, while intelligence does. So g (category A) cannot be identical to intelligence (category B).
Thus, claiming genes cause differences in g which then explain group differences in intelligence commits a category error, since a statistical artifact is equated with a lived, psychological reality.
A natural-kind trait must be individuated independent of the measurement procedure. g is individuated only by the procedure (PC1 extracted from tests chosen for their intercorrelations). Therefore, g is not a natural-kind trait. Only natural kinds can plausibly be treated as biological traits. Thus, g is not a biological trait.
Combining this argument with the Kim-Kripke normativity argument shows that hereditarians don’t just reify a statistical abstraction, they try to reduce a normative category into a descriptive one.
Vygotsky’s social genesis of higher functions
Higher psychological functions originate as social relations mediated by cultural tools which only later appear individual. If hereditarianism is true, then higher psychological functions originate as intra-individual genetic endowments. A function cannot originate both as inter-mental social relations and as intra-individual genetic endowments. So hereditarianism is false.
Intelligence is not something a sole individual possesses—it is something a person achieves within a cultural-historical scaffold. Intelligence is not an individual possession that cab be ranked by genes, it is a first-person indexical act that is performed within, and made possible by, that social scaffold.
Ultimately, Vygotsky’s claim is ontological, not merely developmental. Higher mental functions are constituted by social interaction and cultural tools. Thus, their ontological origin cannot be genetic because the property isn’t intrinsic, it’s relational. No amount of intra-individual genetic variation can produce a relational property.
Possible counters
“We don’t need reduction, we only need prediction/causal inference. We’re only showing genes -> brains -> test scores.” If genes or polygenic scores causally explain the intentional-level fact that someone got question 27 right, there must be a strict law covering the relation. There is none. All they have is physical-physical causation—DNA -> neural firing -> finger movement. The normative fact that the movement was the correct one is never touched by any physical law.
“Intelligence is just “whatever enables success on complex cognitive tasks—we can functionalize it that way and avoid normativity.” This is the move that Heikenhimo (2008) takes out. Any causal-role description of “getting it right on complex tasks” obliterates the distinction between getting it right and merely producing behavior that happens to match. The normativity argument shows you can’t define “correct application” in purely causal terms without eliminativism or circularity.
“g is biologically real because it correlates with brain volume, reaction time, PGSs, etc.” Even if every physical variable perfectly correlated with getting every Raven item right, it still wouldn’t explain why one pattern is normatively correct and another isn’t. The normative status is anomalous and socially constituted. Correlation isn’t identity and identity is impossible.
“Heritability is just a population statistic.” Heritability presupposes that the trait is well-defined and additive in the relevant population. The Berka-Nash measurement objection shows that IQ (and any psychological trait) is not quantitatively-structured trait with a conjoint measurement structure. Without that, h2 is either undefined or meaningless.
Even then, the hereditarian can agree with the overall argument I’ve mounted here and say something like: “Psychometrics and behavioral genetics have replaced the folk notion of intelligence with a precise, operational successor concept: general cognitive ability as indexed by the first principle component of cognitive test variance. This successor concept is quantitative, additive, biologically real and has non-zero heritability. We aren’t measuring the irreducibly normative thing you’re talking about; we’re measuring something else that is useful and genetically influenced.” Unfortunately, this concept fails once you ask what justifies treating the first PC as a causal trait. As soon as you claim it causes anything at the intentional-level (higher g causes better reasoning, generic variance causes higher g which causes higher life success), they are back to needing psychophysical laws or a functional definition that bridges the normative gap. If they then retreat to pure physical prediction, they have then abandoned the claim that genes cause intelligence differences. Therefore, this concept is either covertly normative (and therefore irreducible), or purely descriptive/physical (therefore being irrelevant to intelligence.)
A successor concept can replace a folk concept if and only if it preserves the explanatorily relevant structure. But replacing “intelligence” with “PC1 of test performance” destroys the essential normative structure of the concept. Therefore, g cannot serve as a scientific successor to the concept of intelligence.
“We don’t need laws, identity, or functional definitions. Intelligence is a real pattern in the data. PGSs, brain volume, reaction time, educational attainment and job performance all compress onto a single and robust predictive dimension. That dimension is ontologically real in exactly the same way as temperature is real in statistical mechanics even before we had microphysical reduction. The heritability of the pattern is high. Therefore genes causally contribute to the pattern. g, the single latent variable, compresses performance across dozens of cognitive tests, predicts school grades, job performance, reaction time, brain size, PGSs with great accuracy. This compression is identical across countries, decades, and test batteries. So g is as real as temperature.” This “robust, predictive pattern” is real only as conformity to culturally dominant rule systems inside modern test-taking societies. The circularity of g still rears its head.
Conclusion
Hereditarianism rests on the unspoken assumption that general intelligence is a natural-kind, individual-level, biologically-caused property that can be lawfully tied to, or functionality defined in terms of, genes and brain states. Davidson shows there are no psychophysical laws; Kim-Kripke show even functional definitions are impossible; Kripke-Wittgenstein show that intelligence is irreducibly normative and holistic; Richardson/Vygotsky show that g is a cultural artifact and that higher mental faculties are born inter-mental;
Because IQ doesn’t measure any quantitatively-structured psychological trait (Berka-Nash), and no psychophysical laws exist (Davidson), the very notion of additive genetic variance contributing to variance in IQ is logically incoherent – h2 is therefore 0.
Hereditarianism requires general intelligence to be (1) a natural-kind trait located inside the skull (eg Jensen’s g), (2) quantitatively-structured so that genetic variance components are meaningful, (3) reducible—whether by strict laws or functional definition—to physical states that genes can modulate, and (4) the causal origin of correct rule-following on IQ tests. Every one of these requirements is logically impossible: no psychophysical laws exist (Davidson), no functional definitions of intentional states is possible (Heikenhimo), rule-following is irreducibly normative and socially constituted (Kripke-Wittgenstein), IQ lacks additive quantitative structure (Berka, Nash, Michell, Richardson) higher mental functions originate as social relations (Vygotsky).
Now I can say that: 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, and realized through rule-governed pra gives that determine the correctness of reasoning, problem solving and concept application.
Differences in IQ, therefore, aren’t due to differences in genes/biology (no matter what the latest PGS/neuroimaging study tells you). They show an individual’s proximity to the culturally and socially defined practices on the test. So from a rule-following perspective, each test item has a normatively correct solution, determined by communal standards. So IQ scores show the extent to which someone has internalized the relevant, culturally-mediated rules, not a fixed, heritable mental trait.
So the object that hereditarians have been trying to measure and rank by race doesn’t and cannot exist. There is no remaining, respectable position for the hereditarian to turn to. They would all collapse into the same category error: trying to explain a normative, inter-mental historically contingent status with intra-cranial causation.
No future discovery—no better PGSs, no perfect brain scan, no new and improved test battery—can ever rescue the core hereditarian claim. Because the arguments here are conceptual. Hereditarianism is clearly a physicalist theory, but because physicalism cannot accommodate the normativity and rule following that constitute intelligence, the hereditarian position inherits physicalism’failure, making it untenable. Hereditarianism needs physicalism to be true. But since physicalism is false, so is hereditarianism.
(1) If hereditarianism is true then general intelligence must be a quantitatively-structured, individual-level, natural-kind trait that is either (a) linked by strict psychophysical laws or (b) functionally reducible to physical states genes can modulate.
(2) No such trait is possible since no psychophysical laws exist (Davidson), no functional reduction of intentional/normative states is possible (Kim-Kripke normativity argument), and rule-following correctness is irreducibly social and non-quantitative (Wittgenstein/Kripke, Berka, Nash, Michell, Richardson, Vygotsky).
(C) Therefore, hereditarianism is false.
Gould’s Argument Against the “General Factor of Intelligence”
2050 words
Introduction
In his 1981 book The Mismeasure of Man, Stephen Jay Gould mounted a long, historical argument, against scientific racism and eugenics. A key point to the book was arguing against the so-called “general factor of intelligence” (GFI). Gould argued that the GFI was a mere reification—an abstraction treated as a concrete entity. In this article, I will formalize Gould’s argument from the book (that g is a mere statistical abstraction), and that we, therefore, should reject the GFI. Gould’s argument is one of ontology—basically what g is or isn’t. I have already touched on Gould’s argument before, but this will be a more systematic approach in actually formalizing the argument and defending the premises.
Spearman’s g was falsified soon after he proposed it. Jensen’s g is an unfalsifiable tautology, a circular construct where test performance defines intelligence and intelligence explains performance. Geary’s g rests on an identity claim: that g is identical to mitochondrial functioning and can be localized to ATP, but it lacks causal clarity and direct measurability to elevate it beyond a mere correlation to a real, biologically-grounded entity.
Gould’s argument against the GFI
In Mismeasure, Gould attacked historical hereditarian figures as reifying intelligence as a unitary, measurable entity. Mainly attacking Spearman’s Burt, Gould argued that since Spearman saw positive correlations between tests that, therefore, there must be a GFI to explain test intercorrelations. Spearman’s GFI is the first principle component (PC1), which Jensen redefined to be g. (We also know that Spearman saw what he wanted to see in his data; Schlinger, 2003.) Here is Gould’s (1981: 252) argument against the GFI:
Causal reasons lie behind the positive correlations of most mental tests. But what reasons? We cannot infer the reasons from a strong first principal component any more than we can induce the cause of a single correlation coefficient from its magnitude. We cannot reify g as a “thing” unless we have convincing, independent information beyond the fact of correlation itself.
Using modus tollens, the argument is:
(P1) If g is a real, biologically-grounded entity, then it should be directly observable or measurable independently of statistical correlations in test performance.
(P2) But g is not directly observable or measurable as a distinct entity in the brain or elsewhere; it is only inferred from factor analysis of test scores.
(C) So g is not a real biologically-grounded entity—it is a reification, an abstraction mistaken for a concrete reality.
(P1) A real entity needs a clear, standalone existence—not just a shadow in data.
(P2) g lacks this standalone evidence, it’s tied to correlations.
(C) So g isn’t real; it’s reified.
Hereditarians treat g as quantifiable brainstuff. That is, they assume that it can already be measured. For g to be more than a statistical artifact, it would need to have an independent, standalone existence—like an actual physical trait—and not merely just be a statistical pattern in data. But Gould shows that no one has located where in the brain this occurs—despite even Jensen’s (1999) insistence about g being quantifiable brainstuff:
g…[is] a biological [property], a property of the brain
The ultimate arbiter among various “theories of intelligence” must be the physical properties of the brain itself. The current frontier of g research is the investigation of the anatomical and physiological features of the brain that cause g.
…psychometric g has many physical correlates…[and it] is a biological phenomenon.
Just like in Jensen’s infamous 1969 paper, he wrote that “We should not reify g as an entity…since it is only a hypothetical construct“, but then he contradicted himself 10 pages later writing that g (“intelligence”) “is a biological reality and not just a figment of social conventions.” However, here are the steps that Jensen uses to infer that g exists:
(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.
Put another way, the argument is: If g exists then it explains test performance; we see test performance; therefore g exists. Quite obviously, it seems like logic wasn’t Jensen’s strong point.
But if g is reified as a unitary, measurable entity, then it must be a simple, indivisible capacity which uniformly underlies all cognitive abilities. So if g is a simple, indivisible capacity that uniform underlies all cognitive abilities, then it must be able to be expressed as a single, consistent property unaffected by the diversity of cognitive tasks. So if g is reified as a unitary, real entity, then it must be expressed as a single cognitive property unaffected by the diversity of cognitive tasks. But g cannot be expressed as a single, consistent property unaffected by the diversity of cognitive tasks, so g cannot be reified as a unitary, real entity. We know, a priori, that a real entity must have a nature that can be defined. Thus, if g is real then it needs to be everything (all abilities) and one thing (a conceptual impossibility). (Note that step 4 in my steps is the rectification that Gould warned about.) The fact of the matter is, the existence of g is circularly tied to the test—which is where P1 comes into play.
“Subtests within a battery of intelligence tests are included n the basis of them showing a substantial correlation with the test as a whole, and tests which do not show such correlations are excluded.” (Tyson, Jones, and Elcock, 2011: 67)
This quote shows the inherent circularity in defining intelligence from a hereditarian viewpoint. Since only subtests that correlate are chosen, there is a self-reinforcing loop, meaning that the intercorrelations merely reflect test design. Thus, the statistical analysis merely “sees” what is already built into the test which then creates a false impression of a unified general factor. So using factor analysis to show that a general factor arises is irrelevant—since it’s obviously engineered into the test. The claim that “intelligence is what IQ tests measure” (eg Van der Maas, Kan, and Borsboom, 2014) but the tests are constructed to CONFIRM a GFI. Thus, g isn’t a discovered truth, it’s a mere construct that was created due to how tests themselves are created. g emerges from IQ tests designed to produce correlated subtest scores, since we know that subtests are included on the basis of correlation. The engineering of this positive manifold creates g, not as a natural phenomenon, but as a human creation. Unlike real entities which exist independently of how we measure them, g’s existence hinges on test construction which then stripes it of its ontological autonomy.
One, certainly novel, view on the biology supposedly underlying g is Geary’s (2018, 2019, 2020, 2021) argument that mitochondrial functioning—specifically the role of mitochondrial functioning in producing ATP through oxidative phosphorylation—is the biological basis for g. Thus, since mitochondria fuel cellular processes including neuronal activity, Geary links that efficiency to cognitive performance across diverse tasks which then explains the positive manifold. But Geary relies on correlations between mitochondrial health and cognitive outcomes without causal evidence tying it to g. Furthermore, environmental factors like pollutants affect mitochondrial functioning which means that external influences—and not an intrinsic g—could drive the observed patterns. Moreover, Schubert and Hagemann (2020) showed that Geary’s hypothesis doesn’t hold under scrutiny. Again, g is inferred from correlational outcomes, and not observed independently. Since Geary identifies g with mitochondrial functioning, he assumes that the positive manifold reflects a single entity, namely ATP efficiency. Thus, without proving the identity, Geary reifies a correlation into a thing, which is what Gould warned about not doing. Geary also assumes that the positive manifold demands a biological cause, making it circular (much like Jensen’s g). My rejection of Geary’s hypothesis hinges on causality and identity—mitochondrial functioning just isn’t identical with the mythical g.
The ultimate claim I’m making here is that if psychometricians are actually measuring something, then it must be physical (going back to what Jensen argued about g having a biological basis and being a brain property). So if g is what psychometricians are measuring, then g must be a physical entity. But if g lacks a physical basis or the mental defies physical reduction, then psychometrics isn’t measuring anything real. This is indeed why psychometrics isn’t measurement and, therefore, why a science of the mind is impossible.
For something to exist as a real, biological entity, it must exhibit real verifiable properties, like hemoglobin and dopamine, and it must exhibit specific, verifiable properties: a well-defined structure or mechanism; a clear function; and causal powers that can be directly observed and measured independently of the tools used to detect it. Clearly, these hallmarks distinguish real entities from mere abstractions/statistical artifacts. As we have seen, g doesn’t meet the above criteria, so the claim that g is a biologically-grounded entity is philosophically untenable. Real biological entities have specific, delimited roles, like the role of hemoglobin in the transportation of oxygen. But g is proposed as a single, unified factor that explains ALL cognitive abilities. So the g concept is vague and lacks the specificity expected of real biological entities.
Hemoglobin can be measured in a blood sample but g can’t be directly observed or quantified outside of the statistical framework of IQ test correlations. Factor analysis derives g from patters of test performance, not from an independent biological substrate. Further, intelligence encompasses distinct abilities, as I have argued. g cannot coherently unify the multiplicity of what makes up intelligence, without sacrificing ontological precision. As I argued above, real entities maintain stable, specific identities—g’s elasticity, which is stretched to explain all cognition—undermines it’s claims to be a singular, real thing.
Now I can unpack the argument like this:
(P1) A concept is valid if, and only if, it corresponds to an independently verifiable reality.
(P2) If g corresponds to an independently verifiable reality, then it must be directly measurable or observable beyond the correlations of IQ test scores.
(P3) But g is not directly observable beyond the correlations of IQ test scores; it is constructed through the deliberate selection of subtests that correlate with the overall test.
(C1) Thus g does not correspond to an independently verifiable reality.
(C2) Thus, g is not a valid concept.
Conclusion
The so-called evidence that hereditarians have brought to the table to infer the existence of g for almost 100 years since Spearman clearly fails. Even after Spearman formulated it, it was quickly falsified (Heene, 2008). Even then, for the neuroreductionist who would try to argue that MRI or fMRI would show a biological basis to the GFI, they would run right into the empirical/logical arguments from Uttal’s anti-neuroreduction arguments.
g is not a real, measurable entity in the brain or biology but a reified abstraction shaped by methodological biases and statistical convenience. g lacks the ontological coherence and empirical support of real biological entities. Now, if g doesn’t exist—especially as an explanation for IQ test performance—then we need an explanation, and it can be found in social class.
(P1) If g doesn’t exist then psychometricians are showing other sources of variation.
(P2) The items on the test are class-dependent.
(P3) If psychometricians are showing other sources of variation and the items on the tests are class-dependent, then IQ score differences are mere surrogates for social class.
(C) Thus, if g doesn’t exist then IQ score differences are mere surrogates for social class.
We don’t need a mysterious factor to explain the intercorrelations. What does explain it is class—exposure to the item content of the test. We need to dispense with a GFI, since it’s conceptual incoherence and biological implausibility undermine it’s validity as a scientific construct. Thus, g will remain a myth. This is another thing that Gould got right in his book, along with his attack on Morton.
Gould was obviously right about the reification of g.
HBD and (the Lack of) Novel Predictions
2250 words
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.
I have previously compared and contrasted hereditarian explanations of crime with the Unnever-Gabbidon theory of African American offending (TAAO) (Unnever and Gabbidon, 2011). I showed how hereditarian explanations of crime not only fail, but that hereditarian explanations lack novel predictive power. On the other hand, Unnever and Gabbidon explicitly state hypotheses and predictions which would follow from. The TAAO, and when they were tested they were found to hold validating the TAAO.
Conclusion
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.
I have provided a theory (the TAAO) which does make novel predictions. If the predictions wouldn’t have held, then the theory would have been falsified. However, tests of the theory found that they hold (Burt, Simons, and Gibbons, 2013; Unnever, 2014; Unnever, Cullen, and Barnes, 2016; Herda, 2016, 2018; Burt, Lei, and Simons, 2017; Gaston and Doherty, 2018; Scott and Seal, 2019). The hereditarian dream of having the predictive and explanatory power that the TAAO does quite obviously fails.
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.
A Critical Examination of Responses to Berka’s (1983) and Nash’s (1990) Philosophical Inquiries on Mental Measurement from Brand et al (2003)
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Introduction
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
This is critical for any claim that X is a measure and that Y is a measurement device—one needs to articulate the specified measured object, object of measurement and measurement unit for what they claim to be measuring. To quote Berka:
“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 is False: A Theory of Knowledge Acquisition
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Introduction
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.
Fagan and Holland (2002) explain their study:
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.
Intelligence without IQ: Towards a Non-IQist Definition of Intelligence
3000 words
Introduction
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,
Richardson (2017: 273) writes:
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 social interactions.
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” g in 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.
IQ, Achievement Tests, and Circularity
2150 words
Introduction
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 psychological tools 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.
Dissecting Genetic Reductionism in Lead Litigation: Big Lead’s Genetic Smokescreen
2300 words
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:
There is a large body of studies which show that there is no safe level of lead exposure (Needleman and Landrigan, 2004; Canfield, Jusko, and Kordas, 2005; Barret, 2008; Rossi, 2008; Abelsohn and Sanborn, 2010; Betts, 2012; Flora, Gupta, and Tiwari, 2012; Gidlow, 2015; Lanphear, 2015; Wani, Ara, and Usmani, 2015; Council on Environmental Health, 2016; Hanna-Attisha et al, 2016; Vorvolakos, Aresniou, and Samakouri, 2016; Lanphear, 2017). So the data is clear that there is absolutely no safe level of lead exposure, and even small effects can lead to deleterious outcomes.
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.
Mind, Culture, and Test Scores: Dualistic Experiential Constructivism’s Insights into Social Disparities
2450 words
Introduction
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.
My framework is similar to some other frameworks, like constructivism, experiential learning theory (Kolb) (Wijnen-Meyer et al, 2022), social constructivism, socio-cultural theory (Vygotsky), relational developmental systems theory (Lerner and Lerner, 2019) and ecological systems theory (Bronfenbrenner, 1994).
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.
I can also make this argument:
(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.
Walter Lippmann’s Critique of IQ Tests: On Lippmann’s Prescient Observations
2100 words
Introduction
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.
NotPoliticallyCorrect 