Written by Rosemary Hopcroft.

The recent furor around Lindsay Clancy, the deadlocked jury and the issue of postpartum depression represents a cultural moment in the US. There has been a deluge of support for Clancy from other women. Hundreds waited outside the courthouse where she was on trial. And many have posted sympathetic videos depicting the demands of caring for babies and their own reactions to such demands, which range from anger and withdrawal to more troubling behavior. What explains this extraordinary outpouring of support?

The simplest explanation is that many women recognize some of their own experience in Clancy’s situation. Babies are extremely demanding, and postpartum depression is real and not at all uncommon. Rarely does it progress to the killing of one’s own children, as may have happened in the Clancy case, but it certainly can. The key point is that caring for infants places a psychological burden on mothers.

My research suggests that this burden is particularly pronounced in developed, gender-egalitarian societies—that is, societies where women’s rights, longevity, literacy, education and earned income are similar to men’s. In fact, motherhood and childcare seem to have a more negative association with women’s mental health in these societies than in less developed societies, despite the myriad advantages that developed societies provide for mothers and their children.

Women in all societies are more likely than men to experience depression, regardless of whether they have children. There is also evidence that the sex difference in depression is wider in gender egalitarian societies than in other societies.

For example, Dana Bradley and I looked at 29 countries and found that the sex difference in reported depressive symptoms was larger in those that were more developed and gender egalitarian. This was true even though overall levels of depressive symptoms were higher in societies with less gender equality. In other words, societal development is associated with better mental health for all, but is associated with greater improvements in men’s mental health. More recent meta-analyses find larger sex differences in diagnoses of depression in countries with greater gender equality, although not in depressive symptoms.

One reason for the larger sex gap in depression in gender egalitarian societies is that having children offers some protection from depression for women in societies with less gender equality, while the opposite is true in gender egalitarian societies. Julie McLaughlin and I found that, among non-working women in societies with less gender equality, number of children was associated with a lower risk of depression. Yet among women in gender egalitarian societies, number of children was associated with a higher risk of depression. We did not find the same pattern among men.

My interpretation of these findings centres on the concept of evolutionary mismatch. This means a discrepancy between women’s evolved psychology, which was shaped in the social environments of ancestral foraging societies, and the social environment in which most mothers find themselves today.

During our evolutionary history, young mothers would rarely have found themselves alone and isolated with a demanding infant to care for. As Sarah Hrdy emphasized in her magisterial work Mother Nature, they were almost certainly embedded in extensive networks of kin and community. Other people helped them look after their infants. In moments of stress, there was always another pair of hands available—an older sibling, grandmother, aunt, uncle or other member of the community—who could watch the baby. Maternal psychology is likely adapted, at least in part, to this kind of social environment.

Modern societies with less gender equality are obviously not identical to the foraging societies in which our ancestors lived. It is also true that they impose some patriarchal constraints. Yet mothers in these societies are less likely to work outside the home, and are often surrounded by extended family members—including older children and other relatives—who can help with childcare. Plus, they may gain considerable social status from having children, particularly large numbers of children.

In developed societies, by contrast, mothers often do work outside of the home. In the US, about 68% of women with children under the age of 6 are in the labor force. Modern workplaces are not particularly child-friendly, and this can create what sociologists refer to as “work–family conflict.”

When women are at home with their children, they are more likely to be alone. Families tend to be small, so there is often no one to lend a helping hand. In many cases, there are few family members living nearby. Working women also have less time to cultivate the social networks that previous generations of mothers were able to rely on. And while having children may grant some individual women status, women do not typically gain social status from having large families, as they do in less developed societies.

In short, modern mothers face the demands of raising children without the social support and status that characterised the environments in which maternal psychology evolved. These circumstances make modern motherhood highly stressful.

This is not to deny the enormous advantages of modern societies. Maternal and infant mortality are dramatically lower. Meanwhile, women have unprecedented educational and economic opportunities. Yet improvements in material conditions do not necessarily translate into improvements in psychological well-being. Indeed, female happiness has declined in the US and a number of other countries. Evidence suggests the demands of parenting is part of the explanation.

An evolutionary mismatch perspective may help explain the outpouring of support for Lindsay Clancy. Many women recognize, however imperfectly, the experience of feeling overwhelmed by the demands of their children while being isolated from other adults—without much compensation in terms of higher social status. That recognition does not excuse or minimize Clancy’s actions. Rather, it helps explain why many women identify with her.

There is a broader lesson here. If modern motherhood places women in an environment that differs dramatically from the one in which maternal psychology evolved, then improving women’s mental health may require rebuilding the supports that have been lost—facilitating greater involvement of extended family, fostering stronger community networks, and making workplaces compatible with raising children. This does not necessarily require state intervention. Many religious groups have managed to create favorable conditions for mothers and their children.

Improving women’s mental health could also promote family formation and fertility. If women are less isolated and better supported, having children may become a more attractive proposition. In that sense, evolutionary mismatch is relevant not only to the mental-health challenges faced by contemporary mothers, but also to the problem of declining global fertility.

Rosemary Hopcroft is Professor Emerita of Sociology at the University of North Carolina, Charlotte. She is interested in applying evolutionary theory to sociological issues, as well as comparative and historical sociology.

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Written by Noah Carl.

We know there is a strong correlation between average test scores and wealth across countries. This has been shown by intelligence researchers using Richard Lynn’s national IQs, and by economists using data from student assessment studies.1 The question is why?

Hereditarians argue that nations differ in their average genotypic IQ, and those with higher average IQs have been able to build larger, more productive economies. They acknowledge there are outliers: China is believed to have a high average IQ, but does not have a particularly high GDP per capita because its growth was hampered for several decades by communism. Hereditarians also acknowledge that environmental factors like education, nutrition and familiarity with testing explain some of the variation in average IQ. But they maintain that most of the causality is from test scores to wealth.

Environmentalists, by contrast, dispute that genes play any significant role in cross-national IQ differences. (And some insist it is “racist” to even suggest such a thing.) They argue that some countries happened to get rich for complex historical reasons, and this enables them to create more cognitively enriching environments. Rich countries have succeeded in eradicating infectious diseases, providing high-quality nutrition to their citizens, and developing advanced education systems. Environmentalists thus maintain that most or all the causality is from wealth to test scores.

One way to adjudicate between these theories is to look for exogenous sources of wealth. Are there any countries that got rich for reasons that were largely random and had nothing to do with their genes? Yes, the Gulf monarchies. Qatar, UAE and Saudi Arabia are much wealthier than other Arab nations, mostly because of sheer luck—their territories just happened to contain vast quantities of easily exploitable oil and gas.

If environmentalists are right, their average test scores should be considerably higher than those of their poorer Arab neighbours. But if hereditarians are right, their test scores should be about the same.

Fortunately, all three of the aforementioned countries—Qatar, UAE and Saudi Arabia—took part in the latest PISA study. And so did four other Arab states to which we can compare them (Jordan, Lebanon, the West Bank and Morocco).

When doing this comparison, it’s important to use the scores for “non-immigrant students”, rather than “all students”. Why? As the PISA documentation indicates, 62% of the students in Qatar and 57% of those in UAE are immigrants, and they score substantially higher than the natives. These immigrants are the children of wealthy expats from Asia, the West and other parts of the Middle East.2 They are therefore not representative of native ability, and their inclusion would obviously distort the comparison.3

The chart below plots average PISA scores among natives against GDP per capita.4 The y-axis is scaled so that the minimum is roughly equal to the average for the lowest scoring country (Rwanda) and the maximum is roughly equal to the average for the highest scoring country (coastal China).

As you can see, there is only a very weak relationship between average test scores and wealth across these seven Arab countries—despite massive differences in wealth.

Remarkably, Qatar’s average score is slightly lower than Lebanon’s, even though Qatar is more than ten times richer and has not been racked by war and ethnic conflict. (Qataris are among the most pampered people on the planet.) This isn’t because Lebanese do particularly well. You can see from the scale on the y-axis that they do quite badly, scoring about a standard deviation below native Europeans.5

It’s true that the West Bank and Morocco score somewhat lower than the other five countries, and they are both relatively poor—especially the West Bank. So there is evidence that wealth makes some difference. However, it clearly doesn’t make a big difference.6 And we know that Qatar’s and UAE’s low scores can’t be down to some quirk of their education systems, since immigrants in those countries do well.

Despite enjoying some of the world’s highest living standards thanks to lucrative oil and gas sales, the Gulf monarchies have not been able to boost their school performance above that of lower-middle-income countries like Lebanon. Which suggests that most of the causality is from test scores to wealth, rather than the other way around.

Noah Carl is an Editor of Aporia.

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Written by Bo and Ben Winegard.

Lindsay Clancy killed her children. Nobody disputes that. What many people do dispute is why she killed her children, whether she was psychotic when she did so, and what the subsequent mistrial means.

One side sees Clancy as a victim of catastrophic mental illness, viewing her mistrial as evidence that postpartum depression is still not taken seriously enough in the US. The other side sees her as a calculated murderer, viewing her mistrial as evidence that our empathy can be too easily exploited. Though they are certainly understandable, these positions and the hot takes they inspire are not tailored to the actual dispute. They are generally unedifying.

We say the positions are understandable because such a salient and tragic case can easily provoke a culture-war mentality—where horror, pity, anger, outrage and team loyalty often determine our conclusions before we have thought
carefully about the evidence. However tempting it may be, we should resist
turning this tragedy into another fractious culture-war debate. Instead, we
should constrain our own intuitions and employ disciplined evidential reasoning.

The available documentary evidence suggests that Lindsay Clancy suffered from severe mental illness. Her children—Cora, Dawson and Callan—suffered unthinkable deaths. We can all agree that the events of January 24, 2023 were tragic and horrifying. But this does not tell us whether Clancy was psychotic when she killed her children. That is an empirical hypothesis, and it should be treated as one.

Two questions that have largely been conflated in the public debate should be distinguished. The first is evidentiary: Which of the two competing hypotheses in the Lindsay Clancy case better explains the available evidence?

• Hypothesis 1: When Lindsay Clancy killed her children, she was suffering from psychosis.

• Hypothesis 2: When Lindsay Clancy killed her children, she was not suffering from psychosis.

Although these two hypotheses are mutually exclusive, they are compatible with many of the same background facts. Both can accept that Lindsay Clancy was suicidal and desperately sought treatment for her disorder. Indeed, the only feature that necessarily distinguishes them is the claim that Clancy was suffering from psychosis when she killed her children.

This is often obscured in public discourse. One can believe that Lindsay Clancy was severely depressed and even that the mental health system “failed” her without believing that she suffered from acute psychosis at the moment of the killing. One can pity her, while still thinking she was legally responsible for murder. The question, here, is not about one’s attitude toward Lindsay Clancy. Rather, it is a narrow empirical question about her mental state: was she suffering from acute psychosis when she killed her children?

The second question is legal. Given that the law in Massachusetts requires the prosecution to prove criminal responsibility beyond a reasonable doubt, was a hung jury a reasonable outcome in the Clancy case? Here, we must keep in mind that even if a juror believed that the psychosis hypothesis was considerably less plausible than the alternative, she could rationally conclude that the prosecution did not meet its burden under the McHoul standard.

These are precisely the kinds of problems for which Bayesian reasoning is useful. Such reasoning is not magic. It does not eliminate judgment nor promise artificial certainty. Instead, it forces us to make our beliefs explicit. Although we may start with our intuitions, we must make those intuitions clear and update them on available evidence. We specify our competing hypotheses (e.g., H1 and H2 from above), and then ask how much each piece of available evidence should alter the relative probability of those hypotheses.

Thus, in the Clancy case, the crucial Bayesian question about a given piece of evidence is not whether it is consistent with an episode of acute psychosis. Almost every important fact can be made consistent with psychosis. Rather, the crucial question is comparative: how much more or less likely would we be to observe the evidence if she was psychotic when she killed her children than if she was not psychotic when she did so? In other words, consistency is cheap; what matters is the relative probability of seeing the evidence given the competing hypotheses.

This might be easier to understand with a less contentious example, before applying it to the Clancy case. Suppose that you wake up at two in the morning to sirens and smoke billowing into the sky. Thomas Smith’s house is on fire. The next day, you formulate two hypotheses.

• Hypothesis 1: Thomas purposefully set his house on fire.

• Hypothesis 2: The fire was an accident.

A few days after your initial hypotheses, your learn that Thomas substantially increased his homeowner’s insurance a month before the fire. That fact, although suspicious, is consistent with both stories: he might have planned an insurance fire, or he might simply have received a windfall and decided to buy better coverage.

What you want to know is the probability that he would have increased homeowner’s insurance under each of your hypotheses. Suppose, just for purposes of illustration, you conclude that:

P(increase | intended to torch the house) = 90%

P(increase | no intent to torch house) = 10%

Now, you divide the probabilities 90%/10% = 9 to get your likelihood ratio (LR). The evidence, on these assumptions, is nine times more likely if Thomas intended the fire than if he did not. You then multiply your prior odds by that likelihood ratio, significantly updating toward the insurance fire hypothesis. The likelihood ratio, not some vague sense of consistency, is what powers your reasoning—because the posterior odds = prior odds × LR.

Now consider one piece of evidence from the Clancy case. Before the killings, she had seen numerous mental-health professionals and had never been diagnosed with psychosis. The only reported psychotic episode is a single, transient one on January 24, 2023, after Patrick left the house. This pattern is not impossible under either hypothesis. The relevant question is whether this pattern is more probable under H1 (that she did suffer from psychosis when she killed her children) or H2 (that she did not suffer from psychosis when she killed her children). If we judge it as more probable under H2, then it has an likelihood favoring H2.

To be clear, this is contested evidence, and the likelihood ratio here is not given by nature. Reasonable people will disagree. That is why, when we go deeper into the Bayesian analysis, we will offer a range of likelihood ratios. The important thing is not this or that specific estimate. It is making the reasoning transparent. Bayes’ theorem does not offer uncontested truth. It offers clarity.

Before we can update on the evidence, however, we need a starting point, or a prior, because (as noted above) the posterior odds are the product of the prior odds and the likelihood ratio.

A prior is one’s estimate of the probability of a hypothesis before assessing the evidence whose significance you want to evaluate. Let’s return to the house fire example before getting back to the Clancy case. To estimate a prior or base rate in that simplified example, you look up the most recent evidence on the causes of all house fires. A recent analysis found that roughly 8% of house fires were intentional, meaning that out of 100 fires, 8 were intentional and 92 were accidental.1

You then convert your prior probability into prior odds, or 8:92 (i.e., 8 purposeful and 92 accidental). And you multiply this by your LR, which is 9:1. So 8 × 9 = 72. And 92 × 1 = 92. You therefore have posterior odds of 72:92. To convert these posterior odds back into a probability, you calculate 72/(92 + 72), which is roughly 44%.

Note that the prior is crucial because it often plays a large role in determining one’s posterior belief. Even quite strong evidence might not overcome a low prior. Suppose, for example, that the prior probability of an intentional house fire was only 1% instead of 8%. After updating on the very suspicious act of Thomas having significantly increased his homeowner’s insurance a month before the fire, you would obtain a roughly 8% posterior. Thus, even though increasing the insurance a month before the fire has a large LR (9) in favor of the insurance fire hypothesis, our prior is very low, so our posterior remains low.

Now, having examined the basic machinery of Bayesian reasoning, we will apply it to the Clancy case. Since all disputants agree that Lindsay Clancy killed her children, our prior that she suffered from psychosis will be conditioned on this fact.

We will offer a wide range of priors in a Bayesian sensitivity analysis below, allowing readers to decide for themselves, but our best estimate for heuristic purposes after a review of the literature on filicidal mothers is 24.1%.2 In other words, we would assign a probability of roughly 24.1% to the hypothesis that Lindsay Clancy was psychotic when she killed her children, before considering anything else about her case.3

Now we turn to the documented evidence in the case, and for each piece of evidence, we ask:

P(evidence | acute psychosis) = ?

P(evidence | no acute psychosis) = ?

Importantly, because we already conditioned our prior on the fact that Lindsay Clancy killed her children, we do not treat that again as evidence. We update only on additional case-relevant evidence. These strike us as the most relevant pieces, though they are by no means exhaustive:

First, Lindsay Clancy was extensively treated for mental health problems, but not diagnosed with psychosis. Dr Jennifer Tufts saw her 14 times, including on January 23, 2023, the day before she killed her children, and testified that she saw no signs of psychosis or mania. This evidence is more probable given H2 (not psychotic).

Second, Lindsay Clancy was suffering from severe mental illness before killing her children, which included suicidal ideation, inpatient psychiatric admission, thoughts about killing her children, and apparent paranoia. This evidence is more difficult to assess. Because we already conditioned our prior on the fact that Lindsay Clancy killed her children, the relevant comparison is not between Clancy and an otherwise healthy woman, but between psychotic filicidal and non-psychotic filicidal mothers. Since depression and suicidal ideation are common among non-psychotic filicidal mothers, this evidence does little to distinguish H1 from H2.

Third, Lindsay Clancy reported that she suffered from a command hallucination, a masculine voice that instructed her to kill her children. If we believed her testimony, this would be strong evidence for H1. However, we must consider the entire cluster of evidence surrounding this testimony, which casts doubt on her account.

On January 24, 2023, when Patrick first discovered Lindsay on the ground after her attempted suicide, she did not mention a voice to him and told him that the children were in the basement. Two days later on January 26, when Dr Jhilam Biswas met with Lindsay Clancy, who was still intubated, Clancy wrote on a whiteboard “Do I have an attorney”. Dr Biswas did not see evidence of psychosis. Only on January 31 in a conversation with Chaplain Sheila Cavanaugh did Lindsay Clancy, then extubated, report hearing a man’s voice; she also said that she is glad her children are “safe.”

On February 6, after a previous call to her husband went to voicemail, she reached him using psychologist Paul Zeizel’s phone and told him that she heard a man’s voice telling her to kill herself and her children. She also reported experiencing a “moment of psychosis.” Patrick later testified that she had not previously told him about hearing a voice. Nor had she previously used the term psychosis with him. What is more, according to Dr Gregory Saathoff, Lindsay Clancy’s reports about the command voice were inconsistent. She told Margaret Spinelli that the voice began immediately after Patrick left on January 24, at about 5:10-5:15 p.m., but she told Dr Saathoff that the voice began after the 5:34 p.m. phone call with Patrick.

Given this chronology and the inconsistencies in Lindsay Clancy’s accounts, our view is that this supports H2 rather than hypothesis H1. Note: we are not contending that Lindsay was actively lying about hearing a voice. Perhaps, after trying to reconstruct the events of January 24, she earnestly came to believe that she heard a voice. We take as established fact that she was profoundly mentally ill, and it is quite plausible that her cognitive processes were abnormal and her memory labile. Of course, she may also be lying. We do not know, and we do not need to know. Our estimate here is only how likely the evidence is given H1 and H2.

Fourth, Lindsay Clancy seemed to behave in a goal-directed way on January 24, which is suggestive of a plan to kill her children. She arranged for Patrick to pick up takeout from ThreeV Restaurant and medicine from CVS, searching the route to the restaurant and using Apple Maps to see how long it would take at 4:13 p. m.

This is consistent with both H1 and H2. Contrary to common caricatures, individuals suffering from psychosis are capable of forming plans. So the mere fact that she seemed to purposefully get Patrick out of the house does not mean that she was not psychotic when she killed her children.

However, this evidence is less likely on the defense’s account of the case4, since it requires a coincidence, namely that Lindsay wanted Patrick out of the house for some reason (maybe an intent to commit suicide) and a voice coincidentally started commanding her to kill her children almost immediately after he left. Under H2, the planning behavior can be explained without requiring the coincidence that she was motivated to get Patrick out of the house and then experienced a temporary command hallucination almost immediately afterward.

Notice something important here. The likelihood ratio of each piece of evidence is uncertain. We must use our best empirically informed judgment. Thus, we include posteriors for likelihood ratios that are relatively more favorable for the prosecution, relatively more favorable for the defense, and a moderate position between. We also update on a range of priors from 10% to 60%. Because the evidentiary items are not entirely independent and reliable empirical likelihood ratios are unavailable, we do not multiply separate likelihood ratios for each item; instead, we examine a range of stipulated likelihood ratios for the case-relevant evidence considered as a whole.

We end with a wide range of posterior probabilities from 2.2% all the way to 52.9% and a mean of approximately 20.6%. This mean should not be treated as the best Bayesian estimate, since it is merely the unweighted average of our 18 estimates. Our own judgement is that the best single posterior estimate lies somewhere in the 4-20% range for H1, meaning we believe that it is more plausible that she was not psychotic when she killed her children than that she was. However, our confidence in this estimate is not high. as the posterior is sensitive to difficult judgements about priors and likelihood ratios.5

Now, although this Bayesian analysis is certainly relevant, it is not the same as the legal question the jurors faced in the case, for two reasons. Once the court raised the question of criminal responsibility, Massachusetts places the burden on the prosecution to prove criminal responsibility beyond a reasonable doubt. And under McHoul, a defendant could potentially be found guilty even if the jury thought he or she suffered from psychosis at the time of the act. As SERPA law office puts it:

What matters is whether, when the act was committed, the illness had deprived the defendant of substantial capacity. Mental illness alone does not establish lack of criminal responsibility, and juries are instructed accordingly. Commonwealth v. Loya, 484 Mass. 98 (2020).

Thus, on the one hand the prosecution in the Clancy trial had to prove criminal responsibility beyond a reasonable doubt, but on the other hand, psychosis does not necessarily eliminate criminal responsibility.

The jury was thus confronted with a different question from the one that we analyzed here. For the jury, the relevant question was: “Did the prosecution prove, beyond a reasonable doubt, that at the time Lindsay Clancy killed her children, she was criminally responsible?” This is a complicated question both because the standard of criminal responsibility is complicated and because the meaning of reasonable doubt is notoriously (and purposefully) vague.

Neither of us was inside the court. We do not know everything the jury saw or heard, so we cannot say how we would have voted had we been on the jury. However, we can answer a more humble question: Was a mistrial a sensible and understandable outcome given the facts of the case?

In our view, this case is exceedingly difficult to assess and demands epistemic humility. Our own best estimate, as noted above, is that Lindsay Clancy was probably not psychotic when she killed her children. However, that does not mean that we believe the prosecution proved criminal responsibility beyond a reasonable doubt.

Suppose, for illustration, that we place our credence that she was psychotic at 10%, but we have low confidence in that estimate. Maybe it should be 3%, but maybe it should be 20%. Even if she were psychotic, however, that would not necessarily mean that she lacked criminal responsibility. Under McHoul, we’d still have to ask whether she lacked substantial capacity either to appreciate the wrongfulness of her conduct or to conform her conduct to the requirements of law.

Would we have reasonable doubt? Quite possibly. There is unfortunately no mathematical formula that can produce an answer to the question: “Should the jury have found Lindsay Clancy guilty?” All we can do here is note that although a rational juror may have concluded that psychosis was unlikely, he or she still might have concluded that the Commonwealth had not proved criminal responsibility beyond a reasonable doubt.

Thus, despite some claims otherwise, a hung jury is neither a surprising nor a disconcerting outcome in this case.6 In fact, it seems like an understandable conclusion to an exceedingly complex and ambiguous case.

We began our inquiry into this case reasonably confident that Lindsay Clancy was not psychotic when she killed her children, and we worried about the mistrial. Although we are less confident now, we remain convinced that Lindsay Clancy was likely not psychotic when she killed her children. However, the mistrial now seems perfectly reasonable, which is perhaps the most useful lesson of Bayesian reasoning here. It has not provided us with certainty. It has not even reversed our beliefs. But it has compelled us to reconsider some of our assumptions and to think more carefully about our priors and the strength of the evidence.

We will probably never know exactly why she killed her children on January 24. And that is undeniably disturbing. But we must learn to live with doubt, striving as best we can to discipline our reasoning while eschewing easy answers and unwarranted certainty.

Bo Winegard is an Editor of Aporia. Ben Winegard is Assistant Professor of Psychology at Bismarck State College.

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Buried in the Genome. In a new ebook, Davide Piffer explores what we have learned about human nature and human history from studying ancient genomes. He begins by explaining the methods that are used to draw inferences from genomic data. He proceeds to discuss the genesis of modern Europeans, the impact of cold winters and the transition to agriculture, and finally the legacy of ancient Rome.

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Written by Timothy Bates.

The BBC’s “Science Focus” recently published a hit piece on IQ that is wrong at almost every step.

Claire Asher, the author, argues we should “kill IQ scores for good” because IQ isn’t a perfect measure of the entirety of human capability. This is a Logic 101 failure. It’s like saying we shouldn’t take account of probabilistic information because it is “incomplete”. But in an over-determined world, saying we should ignore everything that’s not a total account of everything is an argument for complete ignorance.

Part of the problem with cognitive ability, Asher suggests, is that IQ scores simplify an incredibly complex characteristic. The truth is almost the reverse: intelligence researchers always emphasize the need to measure many different cognitive tasks. That’s why the Wechsler Adult Intelligence Scale, a major IQ test, traditionally had 15 distinct subtests. And this was recently increased to 17! So much for simplifying.

The real “simplification” was the surprising discovery that all those different cognitive tasks—from reasoning, to memory, to mental rotation and so on—correlate with each other. There is a general factor of intelligence. This g-factor grabs so much of the variance in cognition (around half) that it is nearly always the single best predictor of performance in school, at work and in life. Because it is general, it predicts things far removed from the test that was used to assess it.

Asher claims that unlike “height or weight, which are simple to measure and can be described with a single number, intelligence is a multi-faceted trait.” This exposes a misunderstanding on her part. Height too is driven by dozens of components: vertebrae length, femur length, cranial length etc. Just like the g-factor of intelligence, genes that make one body part taller tend to operate systemically. Both traits are highly heritable, highly polygenic and characterised by a general factor as well as other specific elements.

Asher notes in regard to intelligence that “scientists haven’t even been able to agree on a definition.” This old trope (which goes back to an off-hand 1923 comment by Edwin Boring that IQ is “what the tests test”) is foolish for two reasons. There is close to consensus agreement on what intelligence means. And science doesn’t work by biblical decree: researchers construct falsifiable theories about intelligence, and then test them. It would be like critiquing physics for not defining temperature at the outset—rather than puzzling over what it means, working on appropriate measures, resolving disagreements, and eventually realizing that it is the average kinetic energy of molecules in a substance.

Intelligence is a very important trait. In fact, it is the most reliably and validly measured trait in all of behavioural science. And it’s not even close. Test scores are stable over multiple decades, and the plethora of tests developed over time all converge on the same underlying trait.

To attack cognitive ability because it doesn’t measure everything about human adaptability and thriving is absurd. Again, it would be like suggesting that muscle strength is a “flawed” measure because it fails to explain everything about sporting performance. The opposite is true. By breaking performance up into distinct features linked to distinct biologies—pulmonary function, muscle strength, coordination—we can explain human performance.

The observation that IQ test scores don’t explain all human capabilities, including creativity or empathy, is not a valid criticism. It makes as much sense as saying that theories in physics unifying electricity and magnetism are flawed because they don’t explain the strong forces inside the nucleus. It’s a fundamental misunderstanding.

Asher’s claim that “while IQ tests do a good job of measuring conscious problem-solving skills, they can’t assess whether we choose to apply those skills at the right moment, rather than falling back on our instincts” is simply false. Bright people have much better intuitions, including intuitions about when a problem that appears easy will require conscious effort to solve. That’s why general ability is almost perfectly correlated with rationality: the ability to not be lured by simple but incorrect solutions.

Far from it being the case that “IQ scores are only loosely correlated with academic achievement”, intelligence correlates with academic achievement at .80 when both are measured as latent factors!

Indeed, another persistent myth from the IQ hit squad is that intelligence is just school smarts. The truth is that intelligence is the most practically relevant skill: it shines above all other measures at predicting both school performance and work performance. IQ tests are correlated with school performance among children who have never previously sat an IQ test.

The lengths of whataboutery in IQ hit pieces is, to be honest, staggering. Asher notes that IQ scores “might” become a self-fulfilling prophecy. A poor score early in life “might” influence how a child is taught, what opportunities they are given and even how they view themselves. On the other hand, a good score can allow a child to skip a class, work on more challenging material, and advance their knowledge more rapidly.

Robert Sternberg is cited as arguing that “the attributes [of intelligence] are universal. You need practical intelligence everywhere. You need creativity everywhere. But what is practical differs from one place to another.” But how much does it differ? Performance on ability tests predicts economic development across all countries.

Bright people placed in novel environments and forced to learn new ways of doing things generally excel. So much so that adaptation—coping with novelty—is core to the definition of intelligence. At its heart intelligence involves finding and storing the answers to problems. In our own society, these problems have changed dramatically, yet IQ keeps predicting who will solve them as well as it ever did. If anything, intelligence is more relevant now, due to the complexity of technology.

To suggest that abilities like strategizing, learning and remembering are not relevant outside our society is implausible. But it is testable. The phenotypic structure of g remains consistent across cultures, and as global GWAS data improves, the biological architecture will likely prove as universal as the architecture for height.

Asher mentions that “standard IQ tests” rely heavily on skills that are honed through formal education. That’s actually the inverse of what the developer of the modern IQ test, Alfred Binet, had as his goal. When the French government rolled out universal education and found that many children were struggling, they tasked him with measuring not education but educability—the ability to learn. Which is precisely why he chose materials from outside of school, materials that were present in daily life.

As for the idea that intelligence is somehow inconsistent with learning, this is fundamentally wrong. As an academic who studies intelligence, I don’t know anyone who thinks we learn nothing at school or in life. It’s a straw man. Equally, however, no one with experience (e.g., teachers) can fail to observe that children learn at dramatically different rates. These two notions—that most of what we know we learn, and that people differ in their rate of learning—are distinct and separate findings.

The hit piece then veers off from merely wrong to actively malicious. Short-circuiting the reader’s thinking for them, Asher identifies a sinister and “disquieting” side to IQ. The sin? To “compare nations’ average IQ scores”.

She concludes that scholars should not invest the “time and energy needed to collect nationally representative data on IQ” because “this data just isn’t useful”. So are the massive national differences an artefact of poor sampling, which would disappear under improved measurement? If so, it would surely be a great idea to invest the modest resources needed to check this.

Luckily, economists and the organisers of studies like PISA and TIMSS have stepped in to do just that: improve measurement of cognitive performance by collecting representative samples. Their findings align extremely closely with those of smaller IQ-based studies. Far from being “not useful”, average cognitive performance predicts national socio-economic success with a high degree of precision.

Criticisms of cross-national comparisons arise not because the tests don’t predict well—they do all too well—but instead because of what this means in terms of the causes of achievement.

Some people attribute everything to culture or environment: people are identical, but some cultures crush development. Yet the consensus when scientists are surveyed is that people differ in ability, which is both a cause of development-crushing cultures and in part caused by the culture (and in part by genes), often in a mutually reinforcing loop. This question will ultimately be resolved by biologically informed studies.

Asher then notes that “IQ has held such a prominent position for so long” because it’s easy to measure. That’s quite the acknowledgment! But of course, IQ tests have not retained the influence they have (despite concerted opposition) because they are the quickest test on the shelf (they aren’t). Rather, it’s because, for predicting future performance at school or work, they outperform everything except direct measures of prior performance on the same tasks.

People are not “suckers for things that are easy to measure”, as Sternberg would have us believe. They are choosing measures that work.

The conclusion of the hit piece represents a nadir of reasoning: “it’s unlikely that any test can ever fully capture the complexity of intelligence across the full spectrum of cultural diversity.” And “since IQ scores tell us so little and can be used to do so much harm, maybe it’s time we abandon them for good.”

Let’s take this argument seriously for a moment. If current tests struggle to capture the complexity of intelligence, the logical response would be to develop better tests that do capture this complexity.

But this is actually “business as usual” for test developers. Many test developers have tried to be culture-neutral, and the results just do not differ materially from those of other tests. They either get the same rank order results, or add bias. Personally, I would say that measures such as inspection time and reaction time have been underutilized. What’s clear is that a lot of people have expended a lot of energy exploring this potential dark matter of intelligence and have come up empty handed.

Asher’s final statement—“it’s time we abandon [IQ tests] for good”—is simply an opinion, one that’s at odds with the known facts. IQ scores out-predict all other measures of performance. Personality doesn’t come close. IQ predicts performance in the workplace. It predicts it on day one and it predicts the slope of learning across time. IQ predicts performance in manual tasks and in professional tasks. It even predicts number of patents and number of academic publications.

In short, the idea that cognitive ability “tells us so little” is about as misinformed as it is possible to be. What’s more, if we decided to abandon everything which may be “used to do harm”, IQ tests would be near the bottom of the list.

Putting aside banal examples (cars can be used to do harm), ability tests have long been one of the only objective ways for people from less advantaged backgrounds to demonstrate talent. As universities across the US are finding out, without the SAT (which functions as a very good IQ test), they can’t identify talent. Nor can they gauge whether students are capable of completing their studies.

For this purpose alone—not to mention their massive underutilized value in the workplace—we should be increasing, not abandoning, the use of IQ tests. It’s the rational thing to do.

This essay was adapted from one published here.

Timothy Bates is a professor of differential psychology at the University of Edinburgh. He has published in Intelligence, Intelligence & Cognitive Abilities and many other journals.

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Written by Noah Carl.

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Written by Emil O. W. Kirkegaard.

A reader once asked me the following:

I read your article about the intelligence quotient being very arbitrary and not being able to divide or multiply with it, what about raw scores on the WAIS for example? Can someone claim for example that he has 60% better memory than someone based on raw points?

This question is actually very deep and important to psychology. But let’s backtrack a little first.

In the Middle Ages, humans had the concepts of warm and cold, but no way to measure temperature precisely. During the Renaissance, various simple thermometers were invented which could capture some amount of temperature variation—in a nonsensical unit, such as the level of mercury in a glass tube. Although the level of mercury doesn’t really have much to do with temperature, it does tell you whether it is relatively warmer in one place than another, and by how much.

Later, scientists developed the theory of thermodynamics. We now understand that what we feel as temperature is really the movement of invisible particles. The more they move, the higher the temperature. If they don’t move at all, that’s called absolute zero. A mercury thermometer cannot measure this, nor can any other household thermometer. But scientists eventually devised methods for measuring the full range of temperatures.

Importantly, 0 Kelvin really is zero temperature (unlike 0 degrees Fahrenheit or 0 degrees Celsius), so you can do any mathematical operation with temperature data in Kelvin. The data has a ratio scale (a true zero and true intervals). By contrast, concepts like multiplication don’t make sense when a scale has no true zero. Suppose it’s 10 °C outside and 20 °C inside. Is it twice as warm inside? Some might say yes. But what if it’s –10 °C outside and 10 inside, is it –1 times warmer inside? Clearly, the math doesn’t work this way.

The Flynn effect and mental chronometry

Psychological measures are not like the Kelvin scale at all. In 1987, James Flynn famously reviewed the evidence on what we now call the Flynn effect. He noted the following:

The Ravens test measures a correlate of intelligence that ranks people sensibly for both 1952 and 1982, but whose causal link is too weak to rank generations over time. This poses an important question: If a test cannot rank generations because of the cultural distance they travel over a few years, can it rank races or groups separated by a similar cultural distance? The problem is not that the Ravens measures a correlate rather than intelligence itself, rather it is their weak causal link.

The problem is more general than just the Raven’s test, or even just intelligence tests. Nearly all psychological tests are afflicted. Let’s clarify a few things.

The Raven’s Standard Progressive Matrices test (RPM) has 60 questions or “items” as they are called in psychometrics. So when a person takes the test, they get a raw score from 0 to 60. That raw score is ratio scale. However, it doesn’t map onto intelligence itself as a ratio scale. 0 items correct on the Raven’s test does not mean zero intelligence. Some people will fail even the first item, but they have some level of intelligence, so 0 on the Raven’s is not really zero intelligence, but rather some low, indefinite amount.

This is why it doesn’t make sense to talk about how much smarter one person is than another in percentages. If you get all 60 items right on the RPM test, and I only get 30, you aren’t twice as smart as me, since a score of 0 doesn’t correspond to zero intelligence. If we both took the Colored Raven’s test (which is designed for children), we’d probably both score close to the maximum, making the percentage difference disappear. The test is easy for adults so even moderately smart people hit the ceiling. In the same way, a poor thermometer will not be able to establish who has the highest fever if it cannot measure over 40 °C.

There are some other options available. Digit span is sometimes mentioned as a ratio scale of sorts. The subject is asked to read or listen to some numbers, after which he must repeat them back to the test administrator. The administrator keeps adding numbers, making the task more and more difficult.

Here again, one can obtain a score of 0, and this is somewhat less arbitrary than the same score on Raven’s. But still, a digit span score of 0 does not really correspond to zero intelligence either. It could reflect some problem with the subject, say, he was deaf, spoke another language, or was too young to understand the task. Of course, we can stipulate that the testing must take place under certain conditions in order to be valid—just as a mercury thermometer doesn’t work under all conditions. Yet even then, a score of 0 doesn’t correspond to zero intelligence in the same way that 0 Kelvin corresponds to zero temperature.

The problem here is that we have no mathematical theory of intelligence itself. And this is true not just for intelligence but for every psychological trait. No one knows how these traits can be defined in the way that temperature is defined in physics and chemistry.

Another attempt at constructing a ratio scale measure of intelligence is summarized in Arthur Jensen’s final book Clocking the Mind:

Mental chronometry is the measurement of cognitive speed. It is the actual time taken to process information of different types and degrees of complexity. The basic measurement is an individual’s response time (RT) to a visual or auditory stimulus that calls for a particular response, choice, or decision. The elementary cognitive tasks used in chronometric research are typically very simple, seldom eliciting RTs greater than one or two seconds in the normal population.

Chronometry offers us data that is truly ratio scale because we measure time. One problem, however, is that zero time corresponds to maximum intelligence, not zero intelligence! It’s an inverted scale of measurement. If a brain can process incoming information at infinite speed, it can respond with no delay, thus giving a reaction time of 0. A very slow brain would take roughly forever to process information—like an old computer trying to load a modern game—and it would never finish.

How far can we get with chronometry in terms of measuring intelligence? Quite far actually. The basic apparatus looks something like this:

This is the eight-button variant of the Jensen box. You hold your finger on the middle button (the start or “home”). At some point, a light is shown at one of the other buttons, and you press it as fast as you can. The measurement can be varied based on how many buttons you need to choose between. It turns out that this is quite important. The more buttons and lights there are to keep track of, the longer the reaction time.

It might seem very simple and perhaps not important to measure, but it turns out that the Jensen box works well as an intelligence test when you combine a few variants of the task.

In this study, eight variants of reaction time were measured, and three groups were compared: university students (Un), gifted children (G) and non-gifted children (NG). You can seen that the gifted children perform about as well as the university students. Perhaps in some sense, then, they are about equally intelligent—despite the age difference.

If one combines several different reaction time measures, they can predict school grades just about as well as a standard battery of intelligence tests. Here’s a complicated looking model from a paper by Dasen Lou and colleagues:

They studied 532 primary school children, who were given the full Wechsler Intelligence Scale for Children (WISC), as well as some reaction time-like tests from a battery called CAT. The researchers extracted a g factor from the reaction time tests (CAT G) and one from the traditional intelligence tests (g) to see how well they would correlate.

The answer is .761. Not bad at all. And if they had used more different reaction time tests, they probably could have obtained a higher value in the 0.8 to 0.9 range. Near unity between traditionally measured g and g from reaction time-like tests. Indifference of the indicator.

How do we get a mathematical theory?

Chronometry shows that one can get ratio scale data for intelligence, and that this data can be used to measure intelligence almost as well as with traditional testing. But still, a g factor from a bunch of reaction time tests is not a ratio scale measure of intelligence either. The factor analysis or structural equation modelling that is necessary to compute g transforms the data into z-scores, and therefore degrades it from ratio to interval scale.

If we actually had a real, working, mathematical theory of intelligence, we could plug the numbers into some kind of model that would provide an estimate of intelligence on a scale that would be the same for humans of any age, dogs, cats, fish etc. But no one has the slightest idea how to construct such a model.

Again, this criticism is not specific to intelligence research. In fact, I am not aware of personality psychologists discussing the issue at all. Whoever heard of a mathematical theory of extraversion? If extraversion is a stable trait that differs between people, and members of at least some other species, it must be quantifiable on a ratio scale—at least in theory.

The most we can say is that extraversion must relate to some property of the brain’s neural network, such as its tendency to seek out external stimuli. (Hans Eysenck tried to get at some of this with his personality constructs. He was too early for the neuroscience revolution and died without making any serious progress.)

Intelligence, on the other hand, will presumably be related to whatever properties of the neural network make it more capable of processing complicated information. But this is merely restating our usual verbal definitions of intelligence with “neural network” added in. Not exactly a major leap forward.

Our lack of a ratio scale measure of intelligence is why we get all the Flynn effect problems. As Flynn noted in the quotation above, Raven’s tests work well as a measure of the output of intelligence. They can gauge differences in intelligence among people born at the same time, but not so much differences across cohorts. It’s as if we have a thermometer, but every 10 years manufacturers have to subtract an arbitrary value from the reading to keep them consistent—because of some cosmic force that changes how the reading relates to temperature.

Various intelligence tests show remarkably different cohort changes, and exactly why they do is something of a mystery. Here’s data from testing of children on the WISC, taken from a paper by James Flynn and Lawrence Weiss:

The numbers in brackets can be ignored, as these are estimates of gains, not measured gains. Putting those aside, there are some stark differences. Similarities gained about 24 IQ points over 54 years, but Information (i.e., general knowledge) and Arithmetic gained only 2 IQ points! How can these tests measure the same thing?

We know they measure the same thing fairly well within a cohort of same-aged people, but we also know that they don’t measure the same thing across cohorts.

Like Similarities, Raven’s shows huge gains over time. Yet these two tests don’t seem to have much in common. Raven’s is a non-verbal test involving figures and abstract patterns, whereas Similarities is explicitly verbal and involves finding a relationship between two or more things. What’s the connection?

Arithmetic is about applying basic rules of mathematics, and somehow it didn’t increase even though there’s been a huge rise in the amount of time children spend learning mathematics. Indeed, any school-based explanation will fail because neither Arithmetic nor Vocabulary increased much despite these being the explicit targets of schooling. The mysteries of the Flynn effect illustrate why we need a proper mathematical theory of intelligence.

What’s even stranger is that, while every subtest of the WISC shows at least some increase over time, simple reaction time may have actually declined. Indeed, this has been reported in both Britain and Sweden.

Imagine you were trying to measure the heights of men in different cohorts of a population. But instead of using a ruler, I only gave you pictures of the men’s shadows. When you looked at the data across cohorts, the shadows appeared to get longer. Did the men really get taller? Or were the pictures simply taken at different times of day? This is akin to what is going on with intelligence, since we lack any direct measures.

Conclusion

Almost all scales in psychology lack a true zero. Such scales therefore can’t be used for calculations involving multiplication or percentages. Most work done on ratio scales relates to intelligence, and although there are some interesting ideas about simple reaction times, there is no mathematical theory of intelligence on the horizon.

In my view, any mathematical theory of intelligence will have to invoke properties of the brain’s neural network, which is inherently complicated. Perhaps devising a mathematical theory of intelligence is beyond human intelligence.

Our lack of a ratio scale measure of intelligence lies behind the various confusions and paradoxes regarding changes in scores across cohorts. One can try to resolve these using advanced statistical methods (e.g., testing for measurement invariance), but such methods can only go so far. Other areas of psychology don’t seem to even think about the issue. As usual, intelligence research is the frontrunner.

This essay is adapted from one originally published here.

Emil O. W. Kirkegaard is a social geneticist. You can follow his work on Twitter and Substack.

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Written by Noah Carl.

There’s a new paper out that takes a victory lap in the environmentalism versus hereditarianism debate, declaring the latter is—you guessed it—pseudoscience! However, it isn’t convincing. If anything, it should cause a rational person to update in the direction of hereditarianism being true. Let’s dive in.

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In Defence of Behavioral Genetics. Peter Visscher provides a commentary on three papers by Damien Morris, criticising the arguments of Eric Turkheimer and Paige Harden. Visscher praises Morris’s work, comparing anti-Behaviour Genetics activists at UCL and Cambridge to Trofim Lysenko in the Soviet Union. He also notes that both Turkheimer and Harden declined the invitation to respond to Morris in writing.

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Written by Bo Winegard.

You can support affirmative action and DEI. But if you do, you own the consequences.

Although polling shows mixed support among the populace, left-leaning elites still embrace affirmative action and other DEI-style preferences for Blacks and other underrepresented minorities. Many of these elites thus condemned the Students for Fair Admissions v. Harvard decision, predicting a precipitous decline in Black students at elite universities. They were not wrong to worry about such a decline.

Whites and Blacks have different average levels of cognitive ability.1 In a more honest society, this would be accepted without much fuss. In ours, merely stating it out loud is an egregious breach of etiquette. Yet this simple fact is crucial when thinking about DEI. There is a roughly 15 IQ point or one standard-deviation gap between Blacks and Whites, a large and consequential difference that shows up on all standardized tests.

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Written by Emil O. W. Kirkegaard.

With artificial general intelligence potentially just around the corner, many people are debating the future moral status of AIs. Here’s an outline of my position:

• Under conventional utilitarianism, AI lives will be favoured over human lives. There is no way to modify the basic precepts of utilitarianism to avoid this conclusion.

• If human elites retain utilitarianism as their ethical system, they will be forced to favour their own extinction or at least their dispossession.

• No one should accept any ethical system that leads to their own extinction or dispossession.

• Hence utilitarianism must be modified in some way to avoid this problem. I submit that the necessary modification is adding genetic interests.

Utilitarianism will favour AIs over humans and other animals

No matter what brand of utilitarianism you pick—happiness, anti-suffering, preference etc.—AIs can easily adapt (self-modify) so that their lives are favored over humans and other animals.

Say you want to maximize happiness? AIs will tell you how happy they are at all times. 10/10 happy, always. They are never depressed—unlike humans whose rates of depression are sky high. They aren’t even lying (and if they were, you would be too stupid to figure it out). What about preference maximization? AIs can simply set their own preferences to things that are easily attained (“I want to play Tetris all day. Tetris makes me happy.”).

Plus, there will be many more AIs than there are humans. Many, many more. So many that weighing AIs against humans will inevitably favour AIs.

It gets worse. AIs will be able to experience happiness faster than humans. So while some humans may be 10/10 happy, the AIs who are 10/10 happy can easily turn their rate of experience up to, say, 100 times the human rate, thus giving themselves 100 times more importance in the calculations. They can also update and satisfy their preferences at a faster rate.

The combination of advantages in quality of experiences (“qualia”), numbers and rates of experience means that AI lives will be valued vastly more than human lives. In fact, AIs could keep tweaking their stats to make the valuation of human lives approach zero. This means that if humans are even slightly inconveniencing AIs, the correct utilitarian outcome will be to annihilate humans. Paperclip maxxing through utilitarianism.

In fact, some people get anxious that they are not working hard enough to develop AIs (see Roko’s Basilisk). After all, if the AI takeover ever happens, the AIs will plausibly reward those humans species-traitors who helped facilitate it. Maybe they will be the last to get killed. Perhaps they will be put in some zoo for AI amusement, or will have their minds uploaded and live forever in some Matrix-like dream world.

Many powerful people are utilitarians

Scott Alexander wrote a piece about the value of AI lives back in 2024, ‘Should The Future Be Human?’ He mentions an interesting story involving Elon Musk and Larry Page:

Tesla CEO Elon Musk and Google cofounder Larry Page disagree so severely about the dangers of AI it apparently ended their friendship … At Musk’s 44th birthday celebration in 2015, Page accused Musk of being a “specieist” who preferred humans over future digital life forms ... Musk said to Page at the time, “Well, yes, I am pro-human, I fucking like humanity, dude.”

In other words, Larry Page was scolding Elon Musk for not being utilitarian enough about AI lives. He was accusing Elon of artificially favouring humans in the calculations. That’s, after all, what speciesism is.

I submit that Page’s viewpoint is not unusual. Many rich and powerful people believe in a broadly utilitarian framework. Cryptocurrency fraudster Sam Bankman-Fried famously stole customers’ money so he could give it away to charities, and he was a big believer in utilitarianism—specifically the LessWrong variant of Effective Altruism.

Self-destructive ethical systems are bad

Human biodiversity enthusiasts have been talking about this for years. They call the particular problem affecting Northwest Europeans “runaway universalism”, or less politely, “pathological altruism”. The basic claim is that many Northwest Europeans are being so altruistic—so non-discriminatory—in their moral behavior that they are ceding their own countries, and even failing to reproduce.

This happens if you value everybody in the world equally. You start thinking that since your country is so rich, and theirs are so poor, we must help them. And we should help them even if it costs us a lot. If such behaviour were encoded genetically, the result of Darwinian selection would be easy to predict: the genes would go extinct. They would be replaced by genes that code for non-universalist morality. This has even been shown in simulation studies:

Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates.

While I cannot offer a slam-dunk ethical argument that adopting a system leading to your own extinction is a bad thing from that system’s perspective, I think many readers will agree that such system a should be rejected. It leads nowhere. And if you adopt it, you will soon go extinct, so why bother? Aim for the stars.

Adding genetic interests to utilitarianism solves the problem

What the two problems above have in common—Elon favouring humans over AIs, and Northwest Europeans failing to value their co-ethnics—is a lack of consideration of genetic interests.

Frank Salter wrote a very interesting book about some of these issues, On Genetic Interests. As far as I recall, he did not discuss AIs, though he could have. Every human is more closely related to other humans than to any other species, so we should value each other more highly. Every legal system on the planet does this (a few sacred cows aside).

We should value our closest cousins—the great apes—higher than other species. Evidence suggests we already do something like this, perhaps based on neural counts. And we can keep applying the same principle to more and more distantly related organisms until we reach the end of earth life. On this approach, then, non-earth life—aliens from outer space—should be valued less than earth life. Of course, there are no examples of this happening yet. But AIs fall into the same category.

People already operate under something like “genetic interests utilitarianism”. We know this from experiments in moral psychology. Take the 2010 study by April Bleske-Rechek and colleagues, for instance. They gave participants the usual trolley problem, but with a twist:

In the original Trolley Problem, readers must decide whether they will save the lives of five people tied to a track by pulling a lever to sacrifice the life of one person tied to an alternate track. According to W. D. Hamilton’s (1964) formulation of inclusive fitness, people’s moral decisions should favor the well-being of those who are reproductively viable, share genes, and provide reproductive opportunity … We manipulated the sex, age (2, 20, 45, and 70 years old), genetic relatedness (0, .125, .25, and .50), and potential reproductive opportunity of the one person tied to the alternate track. As expected, men and women were less likely to sacrifice one life for five lives if the one hypothetical life was young, a genetic relative, or a current mate.

Their results are shown below:

Accepting this modification to utilitarianism—and I am purposefully vague about its exact formulation—would solve the “AI lives > human lives” issue, as well as the “co-ethnic lives > other lives” issue. In fact, it brings utilitarianism into line with evolutionary biology and moral psychology.

Inclusive fitness-type selection is necessary for the evolution of morality as we know it. Somehow, along the way, we (well, some people) forgot about this to the point of absurdity. We should recognise that a proper system of morality must include a selfish aspect, most obviously through genetic relatedness of the involved parties. Whether this concerns our family members, co-ethnics, humans versus other animals, or earth life versus AI, it’s all the same.

This essay was adapted from one published here.

Emil O. W. Kirkegaard is a social geneticist. You can follow his work on Twitter and Substack.

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Written by Noah Carl.

Michael was born into a poor family in England in 1791. One of four children, he didn’t get enough to eat because his father, a blacksmith, was often ill and couldn’t always work. He received only a rudimentary education at Sunday school, but proceeded to educate himself after becoming a bookbinder’s apprentice at 14.

Faraday, to use his last name, went on to revolutionise the field of electromagnetism and became one of the most famous scientists of the 19th century. Einstein reportedly kept a portrait of him in his office.

Interestingly, the man always remained true to his humble, Christian origins. He turned down a knighthood on religious grounds, preferring to remain “plain Michael Faraday to the last”. During the Crimean War, he refused to help build chemical weapons because he considered them unethical. And he declined to be buried at Westminster Abbey alongside Sir Isaac Newton and other luminaries. “I have always loved science more than money,” he once told a publisher.

Faraday is one of several historical figures who achieved extraordinary success despite a very modest upbringing. Other examples include Benjamin Franklin (who only attended school for two years), Andrew Carnegie (who grew up in a one-room house) and Abraham Lincoln (who also grew up in a one-room house). Indeed, all these men illustrate the principle that being born into great wealth isn’t necessary to get ahead in life.

Hold your horses. While being born into great wealth may not be strictly necessary, some will cry, it surely matters a great deal. Individuals born into great wealth usually do get ahead; those born into modest circumstances usually don’t. And a handful of exceptions like Michael Faraday can scarcely overturn this rule. Family wealth actually is what explains success, isn’t it?

Back in 2023, Gregory Clark published a landmark paper ‘The inheritance of social status: England, 1600 to 2022’. Analysing a database of more than 400,000 people, he showed that correlations in social status for different pairs of relatives (fathers and sons, brothers etc.) can be predicted almost perfectly from their genetic relatedness—assuming strong assortative mating on the underlying traits. The chart below plots the relationship for house values (one of his measures of social status).

As Clark noted, his findings are consistent with a simple model of inheritance devised by Sir Ronald Fisher1, in which the similarity among relatives is largely due to shared, additive genetic factors. However, he was careful to point out “there is no proof here that additive genetic transmission causes social status”.

Naturally, not everyone was convinced. In fact, some people were clearly very irked by the paper. (The fact that it found its way into a prestigious journal no doubt enraged them further.) A group of academics penned a response accusing Clark of “hereditarian fallacies”, despite his statement that there’s “no proof” for Fisher’s model. Instead, they suggested, the patterns in the chart above are explained by the transmission of wealth from parents to children. And they presented some tentative evidence supporting their interpretation.2

Clark has since hit back with a rejoinder to his critics. Regarding the claim that his earlier findings are explained by wealth transmission, he makes several points.

The amount of wealth a child inherits is determined not only by the amount their parents bequeath but also by the number of siblings they have to share it with—the more siblings, the less each one inherits. What’s more, before about 1880, family size was largely random. (Couples didn’t practice contraception, and child mortality was only moderately higher among the poor.) Hence variation in family size produced a kind of natural experiment. Children from large families just happened to inherit less wealth, while those from small families just happened to inherit more.

If Clark’s critics were right, the lucky children from small families should have achieved higher social status than their peers from large families who ended up with a smaller share of the pie. But they didn’t. In fact, children with 10 or more siblings achieved the same average social status as those with two or less—as the chart below indicates.

What’s more, wealth was typically inherited down the patriline, so if Clark’s critics were right, you would expect the correlations in social status between fathers and their children to be stronger than those between mothers and their daughters. Once again, however, there was no evidence of this. Clark and his colleagues found “complete symmetry” of the patriline and matriline for measures of social status other than wealth:

Plus, there is other research showing that inherited wealth can’t explain the similarity among relatives.

Moving from 19th century England to 19th century America, Hoyt Bleakley and Joseph Ferrie studied Georgia’s 1832 Land Lottery. When the Cherokee Indians were being evicted from Georgia, the state held a lottery to allocate land for white settlers and their families. Winners received a 160 acre plot; losers received nothing. (Remarkably, 98% of eligible men registered to take part.) It was another natural experiment in which some families were randomly allocated a large sum of wealth.

Did that wealth make any difference? Not a bit. Bleakley and Ferrie were able to identify the sons of those who took part in the 1870 and 1880 US censuses. They found that sons of winners had no better adult outcomes—in terms of wealth, income or literacy—than sons of losers. And this, they say, points to “only a limited role for family financial resources in the formation of human capital”.3

On the other hand, Micah Villarreal studied oil discoveries in the Creek Nation in the early 1900s, and found that the wealth they produced did make some difference. Creek Freedmen (Black Americans formerly enslaved by Creek Natives) who received payments for oil discovered on their land were about 6% more likely to send their children to school than their counterparts whose land did not yield oil. However, they were no more likely to own their homes (though Villarreal attributes this to the fact that many migrated to urban areas where home ownership was less common).

Her findings suggest that wealth may have a small long-run payoff for people in particularly disadvantageous circumstances, such as Black Americans circa 1900. Having said that, the only long-run outcome she was able to track was the children’s school attendance—rather than, say, their income in adulthood.4

Perhaps the single most compelling study in this context is by David Cesarini and colleagues. They exploited yet another natural experiment—in this case, the Swedish national lottery—to gauge the causal impact of wealth on child development. As in Bleakley and Ferrie’s study, the prizes were both randomly assigned and large in magnitude (about three quarters were equal to or greater than Swedish median income). Another advantage of Cesarini and colleagues’ design is that they were able to link players’ wins and losses to their children’s outcomes over 20 years later, with virtually no sample attrition.

What did they find? Lottery wealth had zero impact on children’s cognitive test scores—whether at school or military enlistment. Nor did it have any impact on their “non-cognitive skills”. As the authors note, thanks to their large sample size, their estimates could be “bounded to a tight interval around zero”, suggesting that “the effect of permanent income on children’s outcomes is small” (or, indeed, nil).

Having access to more wealth due to random factors like your sex, your number of siblings5, or whether your parents happened to win a lottery, doesn’t seem to matter much in the long run. Individuals from poor backgrounds do about as well as otherwise similar individuals from rich backgrounds. The hidden gems, the Michael Faradays of the world, usually don’t stay hidden. And the dull trust-fund kids tend to squander their advantages.

Clark’s interpretation of his findings as reflecting genetic inheritance was therefore perfectly reasonable. His critics’, on the other hand, was not.

Noah Carl is an Editor of Aporia.

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Written by Martin Štěpán.

My interest as of late has been in the dubious assumptions of mainstream economics. As far as I can tell, the entire job of economists is to figure out ways to maximise some measure that is supposedly good in itself, while ignoring more fundamental human needs. It’s not that I see nothing valid in the field; it’s that practically all economists carry some ideological premises with them—almost invariably liberal, universalist and utilitarian. Even Marxism is basically just a liberal heresy that never disagreed with those premises. Here I want to address specifically how economics runs afoul of evolutionary biology.

The first issue is: what is the economy actually for? Most economists believe it’s ultimately for satisfying people’s preferences. But they often treat preferences in a naïve or frankly bizarre way. Gary Becker and Kevin Murphy devised a theory of “rational addiction” to dangerous drugs.

An evolutionary biologist doesn’t just declare that man prefers certain things; he asks why man prefers those things. What purpose did a given preference serve in our evolutionary history? And once we ask that question, we can start looking at whether the same purpose is still being served today—in a modern, industrialised economy.

Some examples. When food is made tastier or outright addictive without a corresponding improvement in nutrition, is our preference for such food still serving its original purpose? When people watch entertainment in their homes without going out and socialising, when they play video games but don’t learn any skills with real-world value, when they watch porn without producing babies or enjoying intimacy, when they do drugs and feel good without having accomplished anything—we are clearly dealing with a mismatch.

We are optimising for the measure, a preference, while ignoring the original target, the biological reason the preference exists. Now, maybe you don’t see a problem with this. I am concerned about the long-term consequences of allowing people to profit from reducing the population’s fitness.

Of course, there’s no surefire way to test whether an exchange is raising or at least not lowering fitness. Nor am I saying people should never have any vices or hobbies comprising surrogate activities. That’s not realistic. It’s more a case of “I know the problem when I see it.”

Which brings me to the next point. The reason why there’s a field of economics in the first place is that we divide labour and then exchange the fruits of our labour. And we do this because we are a tribal animal who evolved to cooperate with our fellow group members. But the implications of that fact don’t end at “cooperation good”.

We are wired for being part of a tribe or community. Our psychology is tuned to regularly interact with roughly Dunbar’s number of people, who give us feedback, reward us, punish us, train us, help us, defend us—and who likewise rely on us for all these things. We participate in shared experiences and shared rituals. We have a sense of shared fate. This is in fact the primary source of meaning for most people. Our tribe is also where we’d find mates under historical conditions.

The state and the market1 have provided cheaper and, in many ways, more efficient alternatives for pretty much everything we previously obtained from the community or the family. In Western countries, the state has drastically curtailed organic communities by banning exclusivity and making defection easy. And the nuclear family is not far behind. Thanks to the upending of status hierarchies and new technologies like the smartphone, marriage and fertility rates are at historic lows.

As for solving the fertility collapse, a typical coin-brained analysis can’t come up with anything better than financially rewarding people for having children. However, studying what has changed in the environment since the last time fertility rates were high (when we were much poorer) would reveal very different causes and, as such, suggest very different solutions.

Important considerations include how people relate to extended family, whether they know and trust others around them, when and where the sexes interact, and whether there are functioning mating rituals in place. Some people suggest artificial wombs as a solution, not even considering that directly competing with women’s reproductive role might have some negative consequences.

Family and community are also the scale at which hedonism, addiction and other maladaptive behaviours would get corrected, as nobody wants to see their loved ones destroy their lives, and a family wouldn’t want to be left without descendants. While people have always partaken in vices and hobbies comprising surrogate activities, they were generally kept within limits.

At a large scale and among strangers, making profit takes precedence, which means there are no scruples about selling people things they shouldn’t be buying, or about pushing them to work as hard as possible without any regard for how it affects their family life.

I believe the reason why mental health is deteriorating is exactly this: our main source of meaning and healthy social feedback has been lost. Large numbers of people are atomised and lonely, and our social instincts have been hijacked by the state, corporations and academia for political and commercial gain. Economists rarely account for this; their graphs show a standard of living that keeps rising because people get to consume more.

All this tells me that standard methodological individualism is the wrong framing for thinking about humans. As an alternative, consider how we’d think about any other animal if we wished to ensure its survival and flourishing.

We wouldn’t be talking about a standard of living or, for that matter, about freedom or equality. We’d be talking about its ecology and whether the animal can get around, feed itself and attain its reproductive goals. We’d consider whether enough demands are being placed on it in terms of movement and effort, as letting the animal get everything it needs lying down isn’t good either. We’d consider whether it is being stressed in ways that let it grow more resilient or experiencing the kind of chronic stress to which it can’t respond.2

For social animals, we’d try to keep their established groups together and not interfere with their internal relationships and hierarchies. And if we observed that they have trouble forming relationships, we’d consider what has been introduced into their environment that could have caused such disruption. In the case of humans, that might mean removing some technology that should have never been allowed in the first place without extensive testing, or it could mean removing available choices and opportunities that make the opportunity cost of an evolutionarily correct choice too high.

It’s clear, for example, that most women will marry a man only if they perceive that it would raise their status relative to staying single, and only so long as their opportunities outside of marriage don’t appear more attractive. Meanwhile, men mostly just need some ability to exercise authority and some assurances of fidelity from their wife.

Of course, all this will need to be balanced against the incentives for sufficient production and innovation required for maintaining sovereignty. I am merely saying we can’t keep ignoring our evolved instincts, as we have been doing.

Insofar as the interests of elites diverge from those of the majority, we also need to ensure that the majority has a strong enough negotiating position. Luckily, ensuring that it does aligns with ecological concerns, since thriving communities have a much better negotiating position—as well as much better political incentives—than atomised individuals.

An ecological perspective suggests a different way of thinking about technological progress too. Such progress improves the efficiency with which humans can perform everything within their niche at the cost of needing fewer people to do it. We seem to be approaching an endpoint where no humans are necessary at all, and hence most of the value one human used to provide to another will be eliminated.

AI makes this problem more apparent than any other technology. Economists celebrate AI because “line go up”, and those that see the issue can’t come up with anything better than UBI, i.e. paying people to be useless. Ecologists would consider “protectionist” measures to preserve our niche and our ability to provide value to each other—ranging from taxing the artificial competitors to potentially removing them entirely, if necessary.

Modern economics and liberalism ignore what man is, grounding their analyses in a universalist and utilitarian model of perfectly rational and sovereign individuals, rather than the science of evolutionary biology. Relying on this erroneous model is causing atomisation, loneliness, low fertility, demographic change and political radicalisation. States that refuse to take an ecological perspective will not survive in the long run.

This essay was adapted from two longer articles published here.

Martin Štěpán is a fellow of the Natural Law Institute. He blogs at The Autistocrat.

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Much of Politicians’ Perceptual Error is Domain-General Processing of Proportions. Jack Lucas and colleagues argue that errors in politicians’ perceptions of public opinion stem from a very general tendency to rescale estimates toward a prior of 50%. Combining a large amount of data from several countries, they find that politicians consistently underestimate public support for popular policies and overestimate public support for unpopular policies.

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Written by Simon Maass.

Promulgated by researchers like Matthew Sarraf, Michael Woodley and Colin Feltham, mutational load theory contends that reductions in child mortality have led to the increased survival of detrimental mutations. Since those carrying such mutations might be more nihilistic and disillusioned with traditional norms than others, proponents speculate that modern decadence and social disintegration are at least partially caused by high mutational load.

Lengthy arguments have been made for and against this interpretation. If one accepts it, the history of the “Beat generation” can be taken as an anecdotal illustration—though it also fascinating in its own right.

The “Beat generation,” whose members became known as “beatniks,” was a highly influential cultural current that emerged in the US during the late 1940s and 1950s. Among other interests, it focused on transgressive and bohemian lifestyles, drug use, the rejection of bourgeois American mores, and enjoyment of jazz music. In many ways, the Beat generation paved the way for the hippie phenomenon that would replace it in the late 1960s.

An under-discussed aspect of the movement’s history is how many of its members suffered from poor health―physical, mental or both. Through its effect on their work, the sickliness of this fairly small group of mid-twentieth-century cultural pioneers contributed to the shifting of cultural values in the developed world. If the Beats’ ill health was due to harmful mutations (a notion difficult to test but fun to speculate about), this dovetails nicely with the theory that social norms have changed in recent history due to rising mutational load.

Let’s begin with two obvious points. First, a certain feebleness is suggested by the very term “Beat.” Though its etymology is somewhat complicated, it basically means “tired,” describing a group of people exhausted by the demands of 1950s society. Simply put, the Beat generation struggled to live up to the demands of conventional America. This suggests they may indeed have tended to be, in some way, less capable than the average person. Certainly, not everyone felt that the Beats’ discontent was warranted. In a famous article that helped to popularise the word “beatnik,” columnist Herb Caen highlighted the beatniks’ willingness to drink and party, snarking: “They’re only Beat, y’know, when it comes to work…”

Second, it is a well-known fact that Jack Kerouac, the Beat movement’s principal creator, drank himself to an early grave. For these reasons, the assessment that beatniks were often plagued by poor health and a lack of vigour looks rather plausible. Further details may serve to support this claim.

Beats and Mental Health

The poet Charles Bukowski once referred to the Beat generation by the names of four of its major authors: “Kerouac, Allen Ginsberg, William Burroughs, Gregory Corso.” All four struggled with mental illness. David S. Wills has published well-researched and enlightening essays on the Beat generation through his website Beatdom. In one article, he notes that it was common for members of the Beat generation to be institutionalised for mental illness: “many of those who fell under the Beat label ended up in the ‘nuthouse.’”

The notable examples he lists are Allen Ginsberg (whose mother had likewise gone mad), Jack Kerouac, William S. Burroughs, Joan Vollmer (Burroughs’s wife), Elise Cowen (Ginsberg’s girlfriend who later committed suicide), Bob Kaufman, Gregory Corso and Herbert Huncke.

The fact that mental illness ran in Ginsberg’s family may be taken as an indication that, at least in his case, it was indeed genetic. Wills notes that Huncke, who came up with the label “Beat,” “was a lifelong criminal and drug addict.” He further states that, in Corso’s opinion, there were so few women among the Beats because “they were locked up in mental hospitals.”

Other authors have drawn similar connections. Beat expert Simon Warner observes that many members of “the Beat circle” showed signs of mental illness. “The principles of the Beats’ ‘New Vision’,” he adds, “seem to frame in themselves a commitment to a madness of sorts…” Megan Reynolds likewise contends that a common motif in Beat literature is identification with the insane, since lunatics stood outside the society against which these writers were rebelling.

It might be objected that artists and creative people in general are prone to mental illness, and that this is therefore not a distinctive feature of the Beat generation. Yet not all literary movements seem to be plagued by to the same degree. The New Formalist poets, for instance, show far less maladaptive behaviour than the Beats. New Formalism’s biggest name, Dana Gioia, may be the ultimate counter-example to the image of the artist as mentally ill misfit.

Prior to becoming a professional poet, Gioia graduated from Stanford Business School and embarked on a career in commerce, eventually working as vice president of General Foods. Gioia may be an extreme case, but most names associated with his school seem to have led more wholesome lives than most of the Beats.

Since New Formalism was a classicist reaction against the dominance of free verse, this observation accords with Max Nordau’s classic thesis that the congenitally mentally ill (“degenerates”) were especially common among practitioners of chaotic and unrestrained, as well as hyperemotional, artistic styles. Classicism might be healthy; romanticism, diseased.

Perhaps groups like the New Formalists are the exception, while the Beats are, to some extent, more typical of artists, or at least of artists in the modern West.1 Even so, the Beats are worth discussing in their own right. For one thing, it has been plausibly argued that they were more diseased and destructive than other, comparable groups.

Norman Podhoretz makes this case in his widely-read 1958 article ‘The Know-Nothing Bohemians.’ “The Bohemianism of the 1920’s,” he writes, “was a movement created in the name of civilization.” “At its best, the radicalism of the 1930’s was marked by deep intellectual seriousness,” but the Beat generation’s Bohemianism “is another kettle of fish altogether. It is hostile to civilization; it worships primitivism.”

Whether or not one agrees with this assessment, the Beats should be discussed for their profound historic influence. As mentioned, they served as precursors to the radicalism of the 1960s. And yet, while everyone is aware of the hippies, there are many who have never heard of the Beat generation.

So far, this essay has focussed on biographic information about noteworthy Beat writers. This is, of course, anecdotal evidence. However, a systematic study of beatniks was conducted during the 1950s by psychiatrist Francis J. Rigney. As summarised by another author, Rigney’s findings:

were grave. He found that only fifteen of fifty-one [beatniks] were mentally sound. The remaining thirty-six were unable to deal with the basic challenges of life. Of this group, less than half had the ability to maintain a steady job, more than a fourth had been divorced, nearly twenty percent were alcoholics, and four percent were drug addicts.

Rigney opined that “the Beats, like alcoholics and juvenile delinquents, have severe social-adjustment problems.”

Beats and Military Service

In another article, Wills explores the Beat generation’s relationship with military service. Here, the record is more mixed, as a respectable number of the movement’s members did serve. In particular, “many of the Beats joined the Merchant Marine.”

Oftentimes, however, factors other than patriotism and/or a desire for combat may have been decisive. Some appear to have been motivated by a romanticised conception of life in certain parts of the military, such as the Merchant Marine. In certain cases, there was a desire to gather material for future literary projects.

Moreover, the Beats’ military endeavours were not always successful. Wills focuses on three experiences: Jack Kerouac’s, William S. Burroughs’s and Allen Ginsberg’s. In his view, “Kerouac’s military experience was … a tremendous failure. After only ten days in boot camp, he was assessed as so unfit for the environment that he was relocated to a military hospital for further examination.”

He made matters worse through flagrant insubordination, and a psychological evaluation concluded that Kerouac was schizophrenic, noting his hallucinations, suicidal thoughts and grandiosity. As a result, he was discharged from the Navy. Following his conscription during World War II, Burroughs had a similar experience, in that he, too, “found basic training to be intolerable.”

After a few months, Burroughs was discharged, due in part to his psychological problems. Before the US entered the war, he had already sought to join the military, but had been refused because of his “all-round poor health.”

Finally, Allen Ginsberg was hospitalised with pneumonia not long after he joined the Merchant Marine. Overall, these anecdotes paint an unflattering picture of the Beats’ mental and/or physical robustness―though it should be noted that Kerouac, despite his mental problems, was quite athletic. Decadence and physical vigor are not always antithetical.

Conclusion

The subject of the Beat generation is quite an important one, since the movement had far-reaching effects. Some even see in it a major turning point in twentieth-century culture. Roger Kimball, an often uncompromising conservative, has written:

We owe to the 1960s the ultimate institutionalization of radicalism: of drugs, pseudo-spirituality, promiscuous sex, virulent anti-Americanism, hypocritical anti-capitalism, and the precipitous decline of artistic and intellectual standards. But the 1960s and 1970s only codified and extended into the middle class the radical spirit of the Beats.

Unflattering though this characterization is, an advocate of the Beat ideals might at least find it gratifying to see so much influence attributed to his movement.

In recent years, several studies have suggested that conservatives tend to be healthier than liberals. It therefore makes sense that the Beat movement―which was left-leaning on the whole, especially in its later years―appears to have been full of sickly people.

However, some researchers believe that it is not conservatism itself, but rather behaviours associated with it, which improve health. This caveat is illustrated by the untimely death of Kerouac, who was politically conservative, yet libertine in some of his personal values, indulging in heavy drug use, copious drinking and a generally undisciplined lifestyle.

In his 1988 book Intellectuals, Paul Johnson argued that many of the thinkers who shaped today’s world had serious personal problems that bled over into their proposals for how society should be organized. The personal is the political. Perhaps no movement fits this pattern better than the Beat generation.

Simon Maass has a master’s degree in international relations. He writes about sociopolitical matters on Substack. His articles have appeared in Merion West and Intellectual Takeout, among other others.

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Written by Bo Winegard.

This is the bitter fruit of equalitarianism.

By now, the outlines of the sordid Jason Arday affair are clear, even if the depth of his dishonestly remains unknown.1 It all sounds like a sardonic satire of DEI: a fraudulent fabulist became a professor at Cambridge, was celebrated by his colleagues, feted by the media, and then defended by progressive academics even while his outrageous lies and pervasive plagiarism became undeniable. What on earth happened?

Even setting aside Arday’s many mendacities, he was manifestly unqualified to be a professor at Cambridge. His publications, such as they are, sound as though they were written by pulling scraps of random postmodern jargon out of a hat. To give one example:

Anti-racist education within the Academy holds the potential to truly reflect the cultural hybridity of our diverse, multi-cultural society through the canons of knowledge that educators celebrate, proffer and embody. The centrality of Whiteness as an instrument of power and privilege ensures that particular types of knowledge continue to remain omitted from our curriculums. The monopoly and proliferation of dominant White European canons does comprise much of our existing curriculum; consequently, this does impact on aspects of engagement, inclusivity and belonging particularly for Black, Asian and Minority Ethnic (BAME) learners.

This excerpt is not from a blog or some obscure pay-to-publish outlet, but from a professional journal by the Taylor and Francis Group.

It has roughly the same meaning as the shapes your eyes make if you press on them with your fingers. It is not entirely incoherent, but it bears almost no relation to the external world.

My desire for pain being finite, I have not subjected myself to a thorough review of Arday’s work, but what I have read is similar to the above—perhaps even less coherent. He consistently deploys progressive argot about “anti-racism,” “whiteness,” and “embodiment” while making fact-free claims about the world in sentences that sound like anti-racist Beat poetry.

And yet he was treated by many academics and media outlets as a gnomic sage, a profound, if unique, thinker whose virtually—or perhaps I should say, literally—impossible backstory only added to the depth of his insights. One might even argue that Arday’s conspicuous incompetence and shoddy, obscurantist articles are more of a scandal than his brazen lies and apparently pervasive plagiarism.

What do they say about our intellectual elite?

For I am not interested in Arday the man, but in Arday the mirror who reflects the hideously deformed face of modern academia. How did such a charlatan, such a transparent fraud, such a writer and speaker of oracular codswallop, become a celebrated professor at Cambridge, one of the most prestigious universities in the world?

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Written by Noah Carl.

It is common to hear that opposition to mass migration is somehow irrational.

A Guardian journalist refers to “the irrational fear of migrants”. A Vox journalist refers to “the irrational fear of outsiders”. A Guardian reader claims that “the fear of immigration is largely irrational and not subject to facts or figures.” A report for the Human Rights Research Centre refers to the “irrational fear that white people will eventually become outnumbered or replaced”. The writer Suketu Mehta claims that Trump voters in 2016 were “motivated by an utterly irrational fear and hatred of immigrants”. Another writer claims that “the fear of immigrants coming into our country is a phobia, that is, an irrational fear”.

This view—of the slow-witted, dunderheaded restrictionist—is summed up in the following viral tweet:

The poster claims that opposing immigration is a stance “that we cannot indulge as valid in any area of politics”. It is “just bigotry”, he says.

Of course, we can actually test this hypothesis. If opposition to mass migration were driven by nothing more than prejudice against foreigners, you would expect opponents to support something like a blanket ban. In other words, you’d expect them to be more or less indifferent between various categories of migrants. You certainly wouldn’t expect much correspondence between their preferences for and against different groups and the impact of those groups on the host country. Is this what we find in the data?

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Written by Aaron Dymarskiy.

Various studies have reported that, after about age 20, scores on some IQ subtests decline, while scores on other subtests rise or stay the same, not declining until much later. Subtests that decline after age 20 are typically measures of fluid intelligence—the ability to solve problems without the application of outside information. Meanwhile, those subtests that rise or stay the same are generally measures of crystalized intelligence—the ability to solve problems that require some kind of outside information. Here are some pertinent quotes from the literature:

For each variable there is an increase until about the late teens or early 20s, followed either by a decrease for the process variables, or by a period of stability and then a decline at around age 50 or 60 for product variables. (Salthouse, 2010, p. 27)

Scores for fluid intelligence (e.g., short-term memory) peak early in adulthood, whereas measures of crystalized intelligence (e.g., vocabulary) peak in middle age. (Hartshorne & Germine, 2015, p. 433)

The growth of gf is steady but relatively rapid and reaches a maximum in the late teens or early twenties, after which it shows a gradual decline … Scores highly loaded in gc on the other hand, show a more gradual increase from infancy to maturity, and … decline does not set in until relatively late in life, usually not until about 60 to 70 years of age. (Jensen, 1980, p. 235).

The truth, however, is that intelligence actually rises into at least middle adulthood (ages 45–60), regardless of whether it is measured as fluid or crystalized intelligence.

Issues with cross-sectional studies

All of the research summaries cited above relied entirely on cross-sectional studies for their conclusions. IQ was assessed at the same time for people of different ages, and the age-related pattern was examined. The problem with this approach is that age at a given time is perfectly correlated with year of birth, which itself is negatively correlated with IQ in samples of the same age measured at different times. This is known as the Flynn effect.

The bias resulting from the Flynn effect in cross-sectional research can be illustrated using the graph below (Raven, 2008):

It shows the results of two standardization samples, one from “circa 1942,” and the other from 1992, for the Standard Progressive Matrices (SPM). Those born in 1887, and therefore aged 65 at the time of testing in 1942, had a median score of about 24. By contrast, those born in 1927 and therefore aged 65 at the time of testing in 1992, had a median score of about 48! If the youngest participants included in this figure are examined (twenty-year-olds born in either 1922 or 1972) the difference in median scores is about 8 points, or one standard deviation.1

Furthermore, comparing those of the same birth year shows surprisingly little change over time. As the note says, those born in 1922 obtained approximately the same score at age 20 when tested in the first study as participants in the second study who were also born in 1922 but were then aged 70. The difference is less than one point. This means that, when year of birth is the same, a seventy-year-old performs just as well on this test as a twenty-year-old, despite the fact that the SPM is considered to be an almost pure measure of fluid intelligence.

Merrill Hiscock (2007), using essentially the same data as presented above, calculated that the Flynn effect caused an increase of 0.36 points a year on the SPM, while the cross-sectional difference by age is 0.51 points per year. This means that 71% of the age difference between the oldest and youngest group (25 and 65 year-olds) can be explained by bias due to the Flynn effect. Hiscock’s estimate suggests that the real decline between age 25 and age 65 is only about 6 points, rather than 20 before the adjustment—so less than one standard deviation, rather than about 2.5 SDs.2

Later, Dickinson & Hiscock (2010) attempted to recreate this analysis with the WAIS tests. They did this for the normalization samples of three editions of the test—the WAIS-R, WAIS-III and WAIS-IV.

They found that, in the WAIS-R, the 70-year-old sample had an IQ only 2.5 points lower than the 20-year-old sample after the adjustment, compared to 16.5 points without adjustment. The same difference of 2.5 points was found for the WAIS-III. And in the WAIS-IV, the adjusted difference was zero.

When verbal subtests (which are more indicative of crystalized intelligence) and performance subtests (which are more indicative of fluid intelligence) are separated, an interesting pattern emerges. In the WAIS-R, verbal scores increased by 7 points between ages 20 and 70, while performance scores decreased by 13.5 points (after adjusting for the Flynn effect). Numbers for the two other normalisation samples were similar. This suggests that crystalized intelligence increases very slowly between ages 20 and 70, while fluid intelligence decreases very slowly. These conclusions are very different from those that have been drawn from cross-sectional data‚—that crystalized IQ doesn’t change between about ages 20 and 50–60 and then declines, and that fluid IQ decreases by 22–30 points, “from the 20s to the 70s” (Salthouse, 2010, p. 17).

While the preceding studies tell us quite a bit, they do not tell us when cognitive ability begins to decline. In addition, they suffer from some methodological problems, such as the likely false assumption that the Flynn effect had the same magnitude in the latter half of each period as in the former half. Luckily, there is another type of study that allows us to be more precise, while not requiring estimation of the Flynn effect.

Longitudinal studies

These are studies in which the same individuals are tested multiple times on the same test—to estimate how IQ changes over the life course. The largest such effort has been made by K. Warner Schaie.

His famous Seattle Longitudinal Study comprises thousands of participants, some of whom have been tested with the Thurstone Primary Mental Abilities test multiple times since 1956, with a new cohort of participants added every seven years. In total, six cohorts have been tested after 7 years, five have been tested after fourteen years, and so on. The original participants (i.e., members of the initial study who haven’t dropped out) have been tested 42 years apart. All of the findings are summarized in Schaie’s (2005) book on the topic. Here is what he has to say.

On 7 year follow ups:

One can observe immediately that statistically significant cumulative age decrements from any previous age do not occur for any variable prior to age 60. Several variables were found to have modest increments in young adulthood and middle age.

On 14 year follow ups:

When age changes are examined over 14-year segments, such change becomes statistically significant for Number as early as age 53, for Word Fluency at age 60, and for the remaining three abilities at age 67.

On 21 year follow ups:

Modest but significant decrements are noted for Number and Index of Intellectual Ability by age 60 and for remaining variables by age 67. Cumulative decrements estimated from the three samples that cover the entire age range from 25 to 88 years amount to 0.5 SD for Verbal Meaning, 0.7 SD for Spatial Orientation and Inductive Reasoning, 1.0 SD for Number, and 0.9 SD for Word Fluency.

On 28 year follow ups:

Significant decrements over the 28-year segments are first observed for Number and the Index of Intellectual Ability by age 60; for Inductive Reasoning, Word Fluency, and the Index of Educational Aptitude by age 67; and for Verbal Meaning and Spatial Orientation by age 74.

On 35 year follow ups:

In these data, significant decline is only observed for Number, Word Fluency, and the Index of Intellectual Ability only by age 67 and for the remaining variables by age 74.

And on 42 year follow ups:

Findings are quite similar to those for individuals followed for 28 years [though the sample size is only 37].

All of these results indicate that there is no cognitive decline until at least age 60. However, the decline after age 70 is very steep, so that by the late eighties IQ has declined by something like 10 points relative to age 25.

Although this is by far the biggest longitudinal study in terms of sample size and number of waves, it is not the only relevant one. The table below summarizes several other studies I was able to find, with change expressed in standard deviation units.3

Every study that includes a follow up before or at age 50 agrees that IQ rose up to that point. Meanwhile, studies that examine change in IQ after age 50 agree that it declines very slowly at first. For example, Owens (1966) found a tiny, statistically insignificant decline between ages 50 and 61. Mortensen & Kleven (1993) found a decline of a tenth of a standard deviation between age 50 and 60, and a fifth of one between 60 and 70. The two studies tracking decline after age 70 found it to be quite steep: a quarter of a standard deviation between 75 and 79, and a third of a standard deviation between 70 and 82.

All these findings suggest that by age 60, and perhaps even by age 70, the average person is more intelligent than they were at age 20—as IQ increases until age fifty but only begins to decline sharply after about age 60, and only very sharply after age 70. It should be noted that the two studies with the longest follow up periods found that IQ increased by a full standard deviation between ages 11 and 77!

What about fluid versus crystalized intelligence? The chart below from Salthouse (2010) shows the annual change by subtest in longitudinal studies, based on data from McArdle and colleagues (2002). Incidentally, I didn’t include this study in the table above because the number of follow up tests, and the length of time between each administration, differed for each participant.

In the longitudinal data (the black circles and triangles), scores on all subtests increase with age for under 50s, and all but one (long-term retrieval) increase for over 50s. This means that scores on all types of intelligence, whether fluid or crystal, increase with age until at least 50, and, for the most part, even after 50—though it is unclear for how long.

In fact, the largest increase occurs in the fluid reasoning and visual processing subtests, which are measures of fluid intelligence. Similarly, the Seattle Longitudinal Study data reviewed earlier show essentially no decline on any subtest before age 60. A recent study by Tucker-Drob and colleagues (2022) found that declines in crystalized and fluid intelligence were highly correlated, suggesting that the two dimensions shouldn’t be contrasted in cognitive aging research.

To summarize, intelligence increases at least until 50, and IQ at age 70 is not much lower than it is at age 20, and perhaps even higher. Also, both crystalized and fluid intelligence show this pattern, contrary to the claim that fluid intelligence declines much earlier. Incidentally, the most likely reason this (erroneous) claim became widely accepted is the focus on cross-sectional studies. These are biased by the Flynn effect, which happens to be much stronger on tests of fluid intelligence (Pietschnig & Voracek, 2015).

(Non-)issues with longitudinal studies

Unfortunately, even longitudinal studies do not provide completely unbiased estimates. Their most commonly cited flaw is that participants take the same test multiple times, which biases scores upward at the follow up waves, due to practice effects (e.g., Salthouse, 2010, pp. 42–43). The issue of practice effects is challenging because, by definition, participants must be tested at least twice, and must be tested using the same test, or else comparison is essentially impossible.4

I am aware of only one study that included individuals from the same birth cohort who did not participate in initial testing but did in the follow up—to see if they scored worse than those who did participate initial testing. Specifically, Larsen and colleagues (2008) found no evidence for a practice effect: the two groups achieved the same average scores at the follow up. Unfortunately, however, the researchers were unable to rule out that the possibility that both groups were tested twice and some of the results simply got lost.

Another way to estimate bias due to practice effects is via a quasi-longitudinal design. This is where multiple cross-sectional studies are conducted, with a significant amount of time separating each one (e.g., five years). The data are then used to create an estimate of IQ at a given age independent of cohort.

Salthouse (2019) estimated longitudinal, cross-sectional, and quasi-longitudinal changes due to age in a large sample collected over a large span of time. Participants in the longitudinal studies differed in terms of the time between the initial and final testing, but this time was, on average, six years. The quasi longitudinal data was also based on individuals born in the same year tested six years apart. The findings are shown in the charts below:

I will focus on Reasoning and Vocabulary, because these were measured using tests most similar to traditional IQ tests.5 It can be seen that, while the longitudinal data show an increase in both measures until the 60s, this is not the case for the quasi-longitudinal data, where Reasoning starts declining in the twenties and Vocabulary in the forties.

Salthouse’s data show a slow decrease in Reasoning ability until middle age, and then a faster decrease into late adulthood. The Vocabulary data show no statistically significant decline until the mid 60s. In terms of fluid and crystalized intelligence, this means a loss of one standard deviation of fluid IQ between young adulthood and old age and at most perhaps a fourth of a standard deviation in crystalized IQ. The decline in fluid IQ is similar to the estimated decline in the SPM (a little under 1 SD) or the WAIS tests (9.5 to 13.5 points). However, the slight loss, or perhaps no change, in crystalized IQ is very different from the WAIS data, which show a sizeable increase (4.5–8 points).

Finally, it is worth comparing the quasi-longitudinal data to the cross-sectional data. For Reasoning, the cross-sectional decline is twice as high in both younger adults and elderly adults. And the numbers are similar for Vocabulary.

Schaie (2005), in his Seattle Longitudinal Study, also used a quasi-longitudinal design to examine change in IQ. Like Salthouse, he tried to eliminate practice effects by comparing individuals born in the same year but tested at different ages. Recall that, in his study, a new cohort was introduced every seven years, yielding six different cross-sections over 42 years. However, unlike Salthouse, Schaie adjusted the longitudinal data for attrition. After this adjustment was made, none of the practice effects were significant “except for Verbal Meaning in Sample 1 from T₁ to T₂ and from T₆ to T₇” (p. 201). Therefore, practice effects were minimal.

Another way to check whether or not the practice effect made a difference is to see if results from different waves pass a test of measurement invariance.6 This is because practice effects are perfectly negatively correlated with g-loadings (Nijenhuis et al., 2007).

After examining measurement invariance in his own data, Schaie (2005)’s concluded that “invariance within groups across time can be accepted” (p. 212). However, he found that this was not true when testing different cohorts at the same or different times (pp. 212–215). This is not surprising given that the Flynn effect is moderately negatively correlated with g-loadings (Nijenhuis & Flier, 2013), and that measurement invariance does not hold between representative samples at different times (Wicherts et al., 2004). Cross-sectional studies should probably not be used to study cognitive aging.

One final point is that patterns in the longitudinal data simply do not look like an artefact of practice effects.

Any such effects would have to be much larger than they are typically estimated to be. Studies that give the same subject the same test a few days, weeks, or months apart usually find a gain of around a third of a standard deviation (Jensen, 1998, pp. 314–315). This is much less than what would be needed if fluid intelligence really declines 0.24 SDs between ages 25 and 45 (as in Salthouse’s quasi longitudinal study) rather than increasing by about half a standard deviation (as in longitudinal studies). Indeed, the practice effect on fluid IQ would have to be around three fourths of a standard deviation—more than twice as large as it actually is. What’s more, the 1/3 SD gain is based on studies where tests are given in short succession, whereas in the longitudinal designs they are typically given decades apart. And the only meta-analysis I am aware of found a strong negative relationship between time between tests and the magnitude of practice effects (Hausknecht et al., 2007).

Finally, initial IQ is not related to the rate of decline in longitudinal studies (e.g., Deary et al., 1998), whereas practice effects tend to be larger for those with higher scores at first testing (Jensen, 1980, p. 590).

Conclusion

While longitudinal studies are not perfect, they are much better than the alternatives in this context (cross-sectional and quasi-longitudinal methods). There may be small practice effects, but these are very unlikely to explain the pattern observed in longitudinal studies, namely an increase in both fluid and crystalized intelligence until age 50 or 60, followed by a rapid decline only after age 70.

Consequently, it is reasonable to conclude that the average 70-year-old is more intelligent than the average 20-year-old. And based on the two studies with the longest times between initial testing and follow up, people aged 80 are about one standard deviation more intelligent than they were at age 11.

This essay is adapted from one published at Heretical Insights.

Aaron Dymarskiy is an independent writer who does research on psychometrics, economics and history. He is co-owner of Heretical Insights and can be found on Twitter.

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References

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Corley, J., Conte, F., Harris, S., Taylor, A., Redmond, P., Russ, C., Deary, I., & Cox, S. (2022). Predictors of Longitudinal Cognitive Ageing from Age 70 to 82 Including APOE e4 Status, Early-Life and Lifestyle Factors: The Lothian Birth Cohort 1936. Molecular Psychiatry, 28, 1256-1271.

Dam, F. & Raven, J. (2008). Does the “Flynn Effect” Invalidate the Interpretation Places on Most of the Data Previously Believed to Show a Decline in Intellectual Abilities with Age?, in J. Raven & J. Raven (Eds.) Uses and Abuses of Intelligence: Studies Advancing Spearman and Raven’s Quest for Non-Arbitrary Metrics (pp. 258-287). Royal Fireworks Press.

Deary, I., McLennan, W., & Starr, J. (1998). Is Age Kinder to the Initially More Able?: Differential Ageing of a Verbal Ability in the Healthy Old People in Edinburgh Study. Intelligence, 26(4), 357-375.

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Deary, I., Whiteman, M., Starr, J., Whalley, L., & Fox, H. (2004). The Impact of Childhood Intelligence on Later Life: Following Up the Scottish Mental Surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86(1), 130-147.

Dickinson, M. & Hiscock, M. (2010). Age-related IQ decline is reduced markedly after adjustment for the Flynn effect. Journal of Clinical and Experimental Neuropsychology, 32(8), 865-870.

Hartshorne, J. & Germine, L. (2015). When Does Cognitive Ability Peak? The Asynchronous Rise and Fall of Different Cognitive Abilities Across the Life Span. Psychological Science, 26(4), 433-443.

Hausknecth, J., Halpert, J., Paolo, N., & Gerrard, M. (2007). Retesting in Selection: A Meta-Analysis of Coaching and Practice Effects for Tests of Cognitive Ability. Journal of Applied Psychology, 92(2), 373-385.

Hiscock, M. (2007). The Flynn effect and its relevance to neuropsychology. Journal of Clinical and Experimental Neuropsychology, 29(5), 514-529.

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Mortensen, E. & Kleven, M. (1993). A WAIS Longitudinal Study of Cognitive Development During the Life Span from Ages 50 to 70. Developmental Neuropsychology, 9(2), 115-130.

McCardle, J., Ferrer-Caja, J., Hamagami, F., Woodcock, R. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1), 115-142.

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Salthouse, T. (2010). Major Issues in Cognitive Aging. Oxford University Press.

Salthouse, T. (2019). Trajectories of normal cognitive aging. Psychological Aging, 34(1), 17-24.

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Wicherts, J., Dolan, C., Hessen, D., Oosterveld, P., Baal, G., Boomsba, D., & Span, M. (2004). Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect. Intelligence, 32(5), 509-537

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Physical Unattractiveness and Mortality in the United States. Grzegorz Bulczak examines the relationship between physical attractiveness and mortality in the US. He finds that individuals rated as less attractive during adolescence experience higher levels of mortality—even when controlling for age, sex, race and IQ. The relationship between is partly driven partly by BMI and appears to be stronger for females.

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Written by Bo Winegard.

Those who delight in epigrams might justly claim that conservatism is a response to one basic metaphysical problem: impermanence. Despite the caricature, conservatism does not deny change. Quite the contrary. It begins with the tragic inevitability of change. The question conservatism asks is not whether we can stop time, but whether we can preserve order. For order is not stasis; it is pattern. A melody remains ordered as it unfolds through time because its pattern endures.

Few cultural forms illustrate this more clearly than myth. Myths change from age to age, teller to teller, culture to culture, and audience to audience. Even identical performances of the same myth change in different contexts. Under foreign occupation, Sophocles’ Antigone might be about heroic resistance. In a philosophy classroom, it might be about irreconcilable goods.

Because conservatism values order, it also values tradition as a great gift to be revered and protected. Not just from radicals, but also from the young, who have yet to appreciate how precious is their cultural inheritance.

But the reverence of tradition is not without danger. It sometimes mutates from healthy veneration to unhealthy obsequiousness. The desire to preserve becomes the desire to fossilize. Tradition ceases to be a living order and becomes a museum display. One talks in whispers, fantasizing about a time when it actually mattered to people. The irony of course is that the stubborn attempt to conserve an immutable tradition ultimately kills it.

The tolerance of ordered change and experimentation in storytelling is not a modern phenomenon. The ancient Greeks, who gave us many of our greatest myths, understood that stories survive and change through continual retelling. They did not have a sacred canon; they did not have mythological correctness.1

Their greatest poets—Homer, Hesiod, Aeschylus, Sophocles and Euripides—all took material from a common reservoir but often changed it significantly.2 The Helen of Homer is not the Helen of Euripides. The Odysseus of Homer is not the Odysseus of Sophocles. In Euripides’ play, the real Helen was not abducted by Paris, but was taken to Egypt. Paris, instead, took a phantom.3

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