The Unz Review - Mobile
A Collection of Interesting, Important, and Controversial Perspectives Largely Excluded from the American Mainstream Media
Email This Page to Someone

 Remember My Information

 James Thompson BlogviewTeasers

Olivia Manchester victim

After the slaughter of innocents, the ritual of abnegation. While parents pleaded for news about the pieces of their children, the citizens of Manchester met in a public show of solidarity, in an all faith meeting to show that “the bombers would not win”. We have plenty of experience of organizing those. Commendable, very Christian, but hardly proportionate. The plan, such as it is, appears to be to keep on taking casualties in the hope that a policy of massive unselective racial and cultural immigration can be made to work. Sometime. Like actors in a tragedy foretold, the politicians have issued statements bemoaning the events, though one of the contenders to become Prime Minister remains a prominent supporter of the IRA and will not condemn their bombing campaign even years after the event (he condemns all violence, he says), so his statement must have involved some selective reinterpretation of his own deepest beliefs.

The Police are on alert, or heightened alert, or something like that, and have made an arrest, which will usually be followed four days later by the arrested person being discharged for lack of evidence. The emergency staff have been applauded. Parents and friends have remembered their dead, ordinary and lovely lives rendered into a pulp. After a delay, the perpetrator is named, to no great surprise. This one was a 22-year-old Libyan, Salman Ramadan Abedi, born in Manchester, a college dropout who liked cannabis and football, and attended the Didsbury mosque, which is described as moderate, modern and liberal.

Feelings are not a proof of accuracy in judgment, but they are a guide to examining new actions. The Islamic terrorists are making a clear statement: We can kill you whenever we like; we can kill your children; we can kill you in your most important public places; we can make your leaders hide behind bodyguards; we can make you partly undress every time you go through an airport; we can make fools of you by using your laws against you; we can make you scared of making fun of our religion even while you make fun of what remains of your own; we can have more children than you do and get you to pay for them; we can breed resentful losers in such large numbers that any one of them can become a murderer, and it is unpredictable which Mohammed will take the predictable step of murdering you; and you will accept our excuses that we had no idea that that particular Mohammed would take the logical step of biting the hand that fed it, in the sure and certain knowledge that you will feed us once more.

Meanwhile, at a desk somewhere, a young intelligence officer is doing a simple calculation: we have over 3000 Mohammedans with an urge to kill us, embedded in almost 3 million Mohammedans with no particular urge to kill anyone, but with some reluctance to denounce a fellow religionist on the basis of no particular feeling other than that they are taking their religion too seriously, if that is possible. Any of those 3000 hard line militants can murder, “don’t know where, don’t know when, but we’ll meet again some day” and that is too many to keep under constant surveillance, so one or two or three of them can go pop at any moment. The fact that a bomb was used in this mass murder makes it likely that other people were involved, since although making a bomb is not difficult, it usually requires more logistics, and therefore more chance of finding co-conspirators.
The general outline on the link between Muslims and terrorism was given by Noah Carl

This paper examines the relationship between percentage of Muslims in the population (logged) and two separate measures of Islamist terrorism for a large cross-section of countries (n= 168). The first measure of Islamist terrorism is the number of Islamist terror attacks 2001–2016 (logged); the second is the number of casualties from Islamist terrorism 2001–2016 (logged). Percentage Muslim was strongly associated with both measures of Islamist terrorism (β = .49–50). These associations were not disproportionately driven by co-variation within one or two global regions: positive associations were found within Sub-Saharan Africa (β = .35–38), South & East Asia (β = .49–50), Eurasia (β = .31–37), and the West (β = .32–50).
The raw associations within Latin America & Caribbean (β = .06–13) were very weak, and those within Middle East & North Africa were negative (β = –.18–21). Yet the results for Middle East & North Africa were attributable to Israel being a major outlier; when Israel was omitted, strong positive associations emerged (β = .65–69). In a multiple regression analysis, both associations were robust to controlling for region fixed-effects, land area (logged), absolute latitude, average elevation, terrain roughness, legal origin, GDP per capita (logged), democracy, and ethnic fractionalisation (β = .32–33). Consistent with a previous study, both percentage Muslim (β = .21–56) and indicators of military intervention in the Middle East (β = .17–80) were associated with Islamist terrorism across Western countries.

The UK terror threat level has been raised to “critical” which apparently has only happened twice before. Prior to that it was “severe”. I think that the other levels are “irritating” “a nuisance” and “very tedious”. At some stage the powers that be will have a little meeting, and discuss whether they might consider telling the 3000 most dangerous Mohammedans that their presence in the United Kingdom “is no longer conducive to the public good”. The Home Office used to do that decades ago. However, I doubt that such a proposal would ever be accepted, on the grounds that it would antagonize the Muslim “community”. Antagonizing them might provoke them into bombing us even more.

Keep alert, but do not panic, and whatever you do, do not question the wisdom of unselective mass immigration. There are some values we hold dear, and after many sacrifices we will prevail a bit.

• Category: Science 

Brain mapping connections and IQ

No sooner do I return from my own intelligence conference, about which more later, than I note, courtesy of another scholar, a fascinating new paper showing that 40% of the variance in IQ can be accounted for by a new measure of brain networks. This is strong stuff, so with a spinning head I tried to make sense of the new work.

Morphometric Similarity Networks Detect Microscale Cortical Organisation and Predict Inter-Individual Cognitive Variation, Jakob Seidlitz et al
Email: or

doi: bioRxiv preprint first posted online May. 9, 2017;

Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

Unpicking this abstract takes some time. Morphometric similarity networks need to be mapped out, and then the resultant nodes calculated and correlated with the IQ measures.

How does one get the “feel” of a paper? I am reassured by the first paragraphs being cautionary in tone. Those of us whose understanding of scanning is restricted to being in the prone position, fighting claustrophobia, are prone to accepting the pictures we are shown at the end of the process as the cartographic truth. For many clinical purposes these are good enough, and two orders of magnitude better than anything available 20 years ago. We live in good times. However, when going deeper into the matter of which signal goes from which part of the brain to the other, forming any picture involves using approximations. Signals cannot be detected for long distances, and although even better scanners are in the pipeline, the bottleneck is interpretative power, not just detection. Even the prettiest pictures contain assumptions.

The mapping technique they used is innovative and fascinating. The full details are in their paper, but they integrated three approaches:

First, there is histological evidence from non-human primates that axo-synaptic connectivity is stronger between micro-structurally similar cortical regions than between cyto-architectonically distinct areas.

Second, there is encouraging evidence that conventional MRI sequences can serve as proxy markers of cortical microstructure. Cortical MRI metrics –such as magnetization transfer (MT), a marker of myelination -show spatial gradients in humans which align closely with known histological gradients in non-human primates

Third, there is emerging evidence that structural properties of the human cortex are more precisely estimated by the combined analysis of more than one MRI morphometric index at each region e.g. cortical thickness and sulcal depth, cortical thickness and myelination or cortical thickness and grey matter volume. On this basis, we predicted that morphometric similarity mapping with multiple MRI morphometric indices could provide a new way of estimating the linked patterns of inter-regional histological similarity and anatomical connectivity within an individual human brain.

Although the techniques and the results are exciting, further cautionary words are required. The mapping was done individually on the MRIs of 296 healthy young people, so this gives us the predictive measure to be tested. This involved measuring 10 shape variables in 308 cortical regions. They moved from the sample of discovery to the sample of testing, which was 124 other people. They also tested the accuracy of their mapping by looking at genes closely related to brain architecture, and found a mild but positive correlation between such genes and their mapping measure. They confirmed that the genes most involved in brain shape and signalling were far more likely to be involved than control genes taken at random, and that the random deletion of the most important genes had a disproportionate effect on the correlation. It seems likely that the mapping measure is somewhat related to genetic measures. The authors also applied their measures to the MRIs of 31 juvenile macaque monkeys and found:

Taken together, these findings indicate that the morphometric similarity of two cortical regions is directly related to the strength of monosynaptic axonal connectivity between them.

Then they were in a position to compare their mapping measure with IQ.

We predicted that IQ should be positively associated with integrative topological features that promote efficient information transfer across the whole network. High degree hub nodes are crucial to the global efficiency of the connectome and preferentially impacted by clinical brain disorders associated with cognitive impairment

They went back to their original sample, given here as 292 individuals, in order to test their hypothesis. That is, they went back to their original sample of discovery, which of course increases the possibility that the prediction fits only that sample, or that it fits that sample much better than it would fit any other sample. This is a limitation.
The IQ measures are good, though brief. MRI tests are expensive, and intelligence testing is far cheaper. The Wechsler Vocabulary and Matrix Reasoning test are a very good choice if you are in a hurry, but having done all this work it would have been much better to have a few more intellectual measures. For example, Coding takes 2 minutes, is a proper ratio scale measure, and is likely to depend on speed of connection between brain areas. An opportunity missed. Perhaps it was just part of the collaborative data set, but why not spend a little more time assessing intelligence, the best predictor of human outcomes?

We assessed the relationship between individual differences in IQ and individual differences in nodal degree of each of 308 regions in each of 292 individual MSNs using the multivariate method of partial least squares (PLS) regression, as in Whitaker and Vértes et al. (2016) and Vértes et al. (2016). This dimensionality reduction technique seeks to find the latent variables or PLS components which maximise the correlation between a set of collinear predictor variables and a set of response variables.

• Category: Science • Tags: Brain Scans, Brighter Brains, IQ 

african-teenagers3 (1)

There is nothing like sex differences in intelligence to put you on the wrong side of half the population. The story so far is that the standard academic opinion on sex differences in intelligence is that there aren’t any, or that they are small, or that the few that exist counterbalance each other. Women are a bit more verbal, men are a bit more mechanical and spatial, but these differences are nothing to write home about, not that men are likely to write home to anybody, tending to leave that pastime to women.

There is agreement among these same researchers that despite equality in means there is a sex difference in standard deviations: women are somewhat more closely clustered around the mean, men more scattered to the world’s imagined corners, with all the variety of an untidy room. There are more extremely foolish and extremely bright men than extremely foolish and extremely bright women.

Like all agreed positions, there are some discrepant findings. For example, it may be a sampling issue, but the usual sex difference in standard deviations does not show up in Romania.

Furthermore, as you may have read in my last post “Women’s brains”, when a large sample of people have their brains scanned, men are 3.75 IQ points brighter than the women, but there is no difference between the two on the standard deviations of intelligence, so that goes against the general pattern of the findings.

Richard Lynn (1994) argued that some of this confusion arises because so many tests of intelligence are carried out on school age children, and since girls mature faster than boys, so they lead in intelligence initially, but when boys finally mature at roughly 15 year of age, men end up a little brighter than women, by about 4 IQ points. This finding has been supported by various studies, though some find male advantage sooner in child development.

Now a new study has been published which shows a male advantage appearing by the age of 10 in Nigeria.

Testing Lynn’s Theory of Sex Differences in Intelligence in a Large Sample of Nigerian School-Aged Children and Adolescents (N >11,000) using Raven’s Standard Progressive Matrices Plus
Yoon-Mi Hur, Mokpo National University, Jeonnam, South Korea
Jan te Nijenhuis, University of Amsterdam, The Netherlands,
Hoe-UK Jeong, Mokpo National University, Jeonnam, South Korea
MANKIND QUARTERLY 2017 57:3 428-437

Corresponding author: Yoon-Mi Hur, PhD,

The authors say:

However, Liu and Lynn (2011) observed a consistent male advantage in the Full Scale IQ scores at ages as young as 5 to 6 years in a Chinese sample, and on spatial ability tests of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) among children aged four and five years in China, Japan, and US. The magnitudes of sex differences were inconsistent as well. While Lynn and Irwing’s meta-analysis demonstrated an average sex difference of 0.33d, two large-scale studies (Lynn & Kanazawa, 2011; Rojahn & Naglieri, 2006) converged to indicate that although sex differences followed the developmental pattern as Lynn (1994, 1999) suggested, the differences after puberty were less than 0.12d and thus concluded that the differences were practically insignificant.

While sex differences in cognitive abilities have been extensively studied in Europeans, Americans, and Asians, there are only a few reports on sex differences in cognitive abilities among Africans. Lynn (2002) administered the Raven’s SPM to 3,979 15- to 16-year-olds in secondary schools in South Africa and found that males obtained a higher mean equivalent to 0.16d among 15-year olds and to 0.31d among 16-year-olds, suggesting that the sex difference increases with age. However, these differences were not consistent across ethnic groups in the study sample. More recently, Bakhiet et al. (2015) analyzed scores of the SPM in 7226 students aged from 6 to 18 years in Sudan. Females tended to perform slightly better than males on the total score up to age 11 years, with a highest d of -.12. From the age of 12 years onwards, however, a male advantage began to appear even though the magnitudes of sex differences were generally moderate ranging from d = .10 to d = .20 with an exception of d = .66 for 17-yearolds.

The present study consisted of 11,164 students (mean age = 13.5, SD = 2.6 years) drawn from three separate samples in the Nigerian twin-and-sibling studies. This sample is far larger than all previous intelligence test results put together, indeed, almost 7 times as large as the best studies previously available. More attention was paid to sampling than ever before. Currently, it is the definitive study of school age Nigerian intelligence.

The children were drawn from public schools, and education officials were involved in picking representative ones, and this was done in Lagos and in Abuja Capital Territory. It may have left out both elite schools and the unschooled, so the exclusions are probably counter-balanced. Intelligence was measured using Raven’s matrices, the best validated and widely used non-verbal intelligence test.

The SPM+ consists of 60 matrix items divided into five sets (A, B, C, D, & E) constructed to become progressively more difficult moving from set A to E. Validity and reliabilities of the SPM+ have been well established (Raven, 2008). As the SPM+ is a non-verbal test, it has commonly been used to assess sex differences in cognitive abilities in diverse populations with different languages and cultural backgrounds.

By the way, if you look at item difficulties on SPM overall, children of all genetic backgrounds have difficulties with the same items (with only 3 items out of 60 showing slight deviations, in two cases Africans having somewhat more difficulty and in the last case Europeans having more difficulty). This means that different genetic groups can have almost identical error patterns while having very different total scores. It is a power difference, not an operating system difference. We can look at those arguments later, in another post.

Anyway, here are the scores for boys and girls in Nigeria.

Nigerian sex differences

The pattern of sex differences is a little hard to see, so here it is in a simple graph:

Nigerian boys and girls graph

• Category: Science • Tags: Africans, IQ, Nigeria 

old person pushups

My attitude to exercise was best summed up by cartoonist Paul Terry:

When I feel like exercising, I just lie down until the feeling goes away.

However, I am not deaf to the cacophony of advisers recommending that people should keep active, particularly the over 50s. The notion seems to be that the elderly serve some undefined but useful purpose which could be prolonged by physical exertion. I find this proposition doubtful on all grounds.

Nonetheless, even morning radio programs which normally deal with high matters of State have propagated the latest finding, that the over 50s should do at least 150 minutes of exercise per week, because a recent study, a meta-analysis no less, has shown that cognitive function was better preserved in those who took exercise.

At this stage we must make a detour. Health and longevity can be predicted by the 11+ IQ test, so ideally participants in these sorts of studies should be restricted to those on whom we have early life assessments of intelligence. Of course, this is impracticable, and if the papers are on properly randomized samples, they can still detect the effects of extra exercise. However, when control is made for early life assessments of intelligence and health there is little evidence that variations in self-reported exercise have any influence on mental status in the elderly. There is certainly plentiful evidence that lower ability (as tested at 11) is associated with greater hazard ratios. That is, brighter people live longer (right hand figure), and psychologically calm persons live longer than worriers (left hand figure). It would be good if more researchers paid attention to these findings, and included even brief measures of ability and personality in their assessments.

Intelligence and neuroticism and hazards of life

However, we cannot ignore a good quality meta-analysis implying that exercise can mitigate the impact of ageing on cognitive function. Additionally, I warm to this paper because they have not been prissily prescriptive about what constitutes exercise. Good on them. Any stirring of the body parts is worthy of commendation.

Joseph Michael Northey, Nicolas Cherbuin, Kate Louise Pumpa, Disa Jane Smee, Ben Rattray. Exercise interventions for cognitive function in adults older than 50: a systematic review with meta-analysis. British Journal of Sports Medicine, BMJ.

Here are their methods:

The search returned 12 820 records, of which 39 studies were included in the systematic review. Analysis of 333 dependent effect sizes from 36 studies showed that physical exercise improved cognitive function (0.29; 95% CI 0.17 to 0.41; p<0.01). Interventions of aerobic exercise, resistance training, multicomponent training and tai chi, all had significant point estimates. When exercise prescription was examined, a duration of 45–60 min per session and at least moderate intensity, were associated with benefits to cognition. The results of the meta-analysis were consistent and independent of the cognitive domain tested or the cognitive status of the participants.

The effect size is respectable, equivalent to 4.4 IQ points. However, they haven’t measured intelligence, and don’t actually find that overall cognition (measured by the mini mental state assessment) is preserved by such exertions. They do pick up some very promising effects for executive functions and memory. I cannot find which tests were used, nor the length of the follow-ups in the supplementary materials.

Neuropsychological tests were classified according to the domain of cognition being assessed, similar to previous reviews. The domains considered were global cognition (eg, The Mini-Mental State Examination), attention (sustained alertness, including the ability to process information rapidly), executive function (a set of cognitive processes responsible for the initiation and monitoring of goal-orientated behaviours), memory (storage and retrieval of information) and working memory (short-term manipulation of encountered information).


The effect of exercise on cognition was statistically significant for all domains, except global cognition. As prior reviews have indicated the effects of exercise on cognition may vary depending on the mode of exercise and cognitive domain, we included both these moderators as an interaction term in a separate model. Studies of resistance training had significant interaction effects on executive function (SMD=0.49, 95% CI 0.20 to 0.78; p<0.01), memory (SMD=0.54, 95% CI 0.23 to 0.85; p<0.01) and working memory (SMD=0.49, 95% CI 0.16 to 0.82; p<0.01). There was also a significant tai chi x working memory interaction (SMD=−0.70, 95% CI −1.21 to −0.19; p=0.01). All other interaction terms were non-significant.

Here are the main results on cognition after exercise:

exercise age and cognition table 1

Tai Chi seems to do well, though the number of studies is smaller than other forms of exercise. The frequency of taking exercise shows a dose-response relationship, but less for intensity and duration and length, which is a little surprising. Moving about a bit every day seems the best policy. More socially active control groups seem almost as good as exercise, as does the sham exercise of stretching, so this is somewhat of a worry for the “exercise saves your wits” hypothesis.

The type of control group was associated with differences in the statistical significance of the effect size estimated. When the control group involved either no contact (eg, waiting list, usual care; p<0.01) or education (eg, computer course, health lectures; p=0.01) the estimate was statistically significant. Where the control condition was exposed to an active control (eg, stretching; p=0.17) or social group (p=0.62), the effect size was still positive but no longer statistically significant.

This is a quibble, but when the authors say “the effect size was still positive but no longer statistically significant” what they in fact mean is “there was no effect”.

This is a very useful meta-analysis of intervention studies. Although we do not have prior measures of ability and personality, we can hope that random allocation to experimental groups should have balanced out those factors, though volunteers in such studies tend to be brighter than average. We do not know how long the follow-ups were, nor much about the previous health and nothing about the intellectual levels of the participants. However, those without mild cognitive impairment have done better than those already mildly cognitively impaired. I would like to see which executive function and memory tests were used, but all of them are moderately correlated with general ability. Reaction times, grip strength and a process measure like digit-symbol would be very welcome additions.

• Category: Science • Tags: Exercise 
Brain size and intelligence


Here is a very interesting paper on sex differences in brain size and intelligence, notable for linking people’s brain scans with their detailed intelligence test results. It has been accepted for publication in Intelligence.

Sex differences in brain size and general intelligence (g)

Dimitri van der Linden, Curtis S. Dunkel, Guy Madison


Utilizing MRI and cognitive tests data from the Human Connectome project (N = 900), sex differences in general intelligence (g) and molar brain characteristics were examined. Total brain volume, cortical surface area, and white and gray matter correlated 0.1 – 0.3 with g for both sexes, whereas cortical thickness and gray/white matter ratio showed less consistent associations with g. Males displayed higher scores on most of the brain characteristics, even after correcting for body size, and also scored approximately one fourth of a standard deviation higher on g. Mediation analyses and the Method of Correlated Vectors both indicated that the sex difference in g is mediated by general brain characteristics. Selecting a subsample of males and females who were matched on g further suggest that larger brains, on average, lead to higher g, whereas similar levels of g do not necessarily imply equal brain sizes.

Sex diffs in brain size Madison

Men’s brains are bigger than women’s, even when controlling for bigger body size, which means they should have higher intelligence, though the evidence for that is conflicting. Most researchers find no notable differences overall, saying that different strengths and weaknesses balance each other out, but Lynn and Irwing (2002, 2004) argued that adult males are almost 4 IQ points brighter than adult females. The authors of the present paper have found one of the largest MRI samples available, each scanned person having done 10 cognitive tests, which is what makes this study particularly interesting. The tests included: Penn progressive matrices, Peabody vocabulary, reading recognition, working memory, pictorial episodic memory, spatial orientation, card sorting, verbal episodic memory, and the Flanker task of inhibition and sustained attention.

First, here are the correlations between brain measures and overall mental ability.

Sex diffs brain size Madison corr with g

The tests were used to create an overall g score. Correlations with this overall g measure and brain measures are not large, but for both males and females the highest correlations are with gray matter volume. It seems that Agatha Christie’s fictional Belgian detective Hercule Poirot was right, when he said that crime detection and problem solving depended on “the little gray cells”.

Here are the scores for the individual mental ability tests:

Sex diffs brain Madison on particular cognitive tasks

Once again, I recommend that men pay close attention to the largest sex difference, which plays out in their favour: spatial orientation, in which they have a 6 IQ points advantage. I recommend that women play close attention to Episodic memory in which they have an advantage of 4 IQ points, giving women the upper hand when remembering male transgressions. Those particular findings hold up even when you control for g, so they are very real cognitive sex differences, and are mostly across the board of the abilities measured.

The spatial male advantage shows up in the first year of life.

The authors conclude:

there was a significant sex difference in g in this sample, with an effect size of one quarter of a standard deviation. This corresponds to approximately 3.75 IQ points, which is a similar sex difference in general intelligence as reported in previous large population studies (Lynn & Irwing, 2004) and meta-analyses (e.g., Madison, 2016; Irwing & Lynn, 2005).

Analyses of the cognitive subsets used to extract g showed that the sex differences were not related to extremely high scores of males on a limited number of particular tasks, but tends rather to reflect a more general pattern.

the central point of the present study, is that the various statistical methods applied seem to suggest that sex differences in brain characteristics indeed mediate sex differences in g. Direct support for this notion came from the mediation analyses, indicating that brain volume measures could account for roughly half of the sex differences in g.

They note:

It is an interesting observation that in the nineteenth century the consensus was that sex differences in brain size exists, leading to a slightly higher average of males in general intelligence (e.g., Darwin, 1871). However, improved psychometric and brain imaging techniques have led to a new wave of studies and have reactivated the debate on this topic. Regarding this, the present study may contribute to this field by applying a combination of newer and more traditional methods. Overall, we agree with the conclusion of Burgaleta et al. (2012) and Escorial et al. (2015) that within subgroups or at the individual level, larger male brains do not necessarily have to be accompanied with higher general intelligence. Nevertheless, the present study also clearly indicates that, at the group level, there is a sex difference in g and that differences in brain size likely play a relevant role in this. Given that those conclusions were based on the results of one of the larger MRI studies available, it can be expected that the effect sizes provide reliable estimates of the relations and can be regarded as benchmarks in the literature in this area.

This study supports the minority position of Lynn and Irwing, that men are about 4 IQ points brighter than women, an across-the-board advantage, plus better spatial ability, and that part of this difference may be attributed to brain size. Here is Prof Richard Lynn lecturing about it:

You will see that male advantage shows up in the Wechsler standardisation samples, though Wechsler will not agree to this being acknowledged in print, though they have passed on the results privately. An odd situation, to say the least.

As usual, a small difference in means has larger consequences at the extremes. If one assumes a 4 point difference straddling the mean, then women will be 98 to men’s 102. Keeping the standard deviations to 15 for both sexes, and setting the cutoff point at IQ 130 then 3.1% of men and 1.6% of women pass the threshold, meaning 65% of the brightest people will be men.

• Category: Science • Tags: Gender, Gender Equality, IQ 

wealth actual and ideal, details

There was a time when boys played games of marbles following strict playground rules: contestants had to stand a prescribed distance away from the little pyramid of marbles, and chuck only marbles of the prescribed size. Rules ruled. Piaget was intrigued by the explanations children gave for moral judgements, and the playground is the arena in which the concept of fairness is honed.

Piaget followed a model which is rare nowadays. He observed his own children in great detail as they grew up. His was the least representative sample in the history of psychology. Nonetheless he launched the study of the development of morality, and the conception of fairness.

The majority of experimental studies done in psychological laboratories seem to show that even young children prefer equal shares rather than unequal shares. This would suggest that people have an innate preference for socialism and the re-distribution of wealth.

In fact, this is true only if people are asked to distribute goods between people who are unknown to them, and who have not behaved in any particular way which would make them consider that some were more worthy and deserving than others.

The moment you show that one person has been more helpful than another, or has worked harder than another, then judges believe that, as a matter of fairness, the more energetic and helpful person should get a greater share.

That is fair, after all, because those who were hard-working and helpful have deserved it because of their efforts. So although there are many studies suggesting that people do not like inequality, it turns out that what they most dislike is unfairness.

Once it can be shown that a distribution is fairly based on effort then respondents will tolerate and indeed require that the distribution of wealth is proportionate to effort and not just based on the mere fact of existing. People prefer unequal societies for the reason that they in fact they do not mind inequality if it is based on rewards for effort.

Why people prefer unequal societies. Christina Starmans, Mark Sheskin & Paul Bloom Nature Human Behaviour 1, 0082 (2017)doi:10.1038/s41562-017-0082

Unusually for a scientific paper, it is a good read. What really matters in these experiments is context, and once context is provided then it is clear that people accept unequal societies so long as they are based on a fair allocation of rewards, proportional to contribution.

The authors say:

There is immense concern about economic inequality, both among the scholarly community and in the general public, and many insist that equality is an important social goal. However, when people are asked about the ideal distribution of wealth in their country, they actually prefer unequal societies. We suggest that these two phenomena can be reconciled by noticing that, despite appearances to the contrary, there is no evidence that people are bothered by economic inequality itself. Rather, they are bothered by something that is often confounded with inequality: economic unfairness. Drawing upon laboratory studies, cross-cultural research, and experiments with babies and young children, we argue that humans naturally favour fair distributions, not equal ones, and that when fairness and equality clash, people prefer fair inequality over unfair equality. Both psychological research and decisions by policymakers would benefit from more clearly distinguishing inequality from unfairness.

The authors review a long series of experiments which seem to show that children prefer absolute equality in the sharing of rewards. Inequality is certainly a focus of political concern. It attracts those who make bold complaints of the form “The top 1% of people own XX% of the wealth” where the implication is that the owned wealth should be 1% but for foul reasons is much higher than that. This statistic contains several errors, and tends to mislead.

By the way, it amuses me that people who strongly object to a person’s general level of ability being represented “by a single figure” have no qualms about wealth being represented “by a single figure” despite it being based on chattels, residential property (sometimes minus mortgages, sometimes not), stocks and shares, bank accounts, pension rights totals (say, at 20 times annual payments), and other quasi-monetary benefits. Such critics should relax: although wealth estimates have methodological shortcomings, an overall figure gives a reasonable estimate for comparative purposes (as do estimates of general intelligence).

The Gini coefficient (0 is equitable distribution, 100 is outrageous inequity) is well-known, and usually widely quoted without comment, since the manifest goodness of equality is assumed to be agreed by all. Laboratory studies seem to confirm that people have a deep preference for equality.

So, when people are asked to distribute resources among a small number of people in a lab study, they insist on an exactly equal distribution. But when people are asked to distribute resources among a large group of people in the actual world, they reject an equal distribution, and prefer a certain extent of inequality. How can the strong preference for equality found in public policy discussion and laboratory studies coincide with the preference for societal inequality found in political and behavioural economic research?

We argue here that these two sets of findings can be reconciled through a surprising empirical claim: when the data are examined closely, it turns out that there is no evidence that people are actually concerned with economic inequality at all. Rather, they are bothered by something that is often confounded with inequality: economic unfairness.

We suggest that the perception that there is a preference for equality arises through an undue focus on special circumstances, often studied in the laboratory, where inequality and unfairness coincide. In most situations, however, including those involving real-world distributions of wealth, people’s concerns about fairness lead them to favour unequal distributions.

Anyone looking for evidence that people have a natural aversion to inequality will find numerous laboratory studies that seemingly confirm their view. For example, studies have found “a universal desire for more equal pay”, “egalitarian motives in humans”, “egalitarianism in young children”, and that “equality trumps reciprocity”. A Google Scholar search for “inequality aversion” yields over 10,000 papers that bear on this topic.

Furthermore, people appear to view the equal distribution of resources as a moral good; they express anger toward those who benefit from unequal distributions.

wealth actual and ideal, details

• Category: Economics, Science • Tags: Inequality, Psychology 

It is in the spirit of human intercourse, untrammelled by paywalls and anonymous peer reviews, to freely exchange ideas and reflect on life. In that light I reproduce here, for greater public attention, comment No 41 on my post “Intelligence and General Knowledge”. Good books have been based on less.

“This is true in my experience – also true is that women (90% of my colleagues, and 100% of my mentors in long career as man in human services/education) enjoy a very similar advantage over men when it comes to communication (in terms of affective comprehension, attentive consultation, co-operation and consensus-building).

A happy and long-lasting marriage is a reflection of this simple biological fact of life. In a successful (increasingly rare) partnership, a man and a woman complement one another in using their differing intellectual and social problem-solving skills together to cope with whatever life throws at them.

It is a commonplace among men that women “talk too much”, but a wise husband learns to tune out some of his wife’s ‘distracting’ verbalization, because he has learnt that it is an absolutely necessary component of female ratiocination and genius – just as his wife has learnt to overlook his apparent self-absorption and obsession with factoids and hobbies.

Arguments caused by these differences are crucial tests of both partners’ ability to learn about their own abilities and limitations, as well as their ability to appreciate another person’s perception of reality under stress – to support one another, to ‘deepen their love’ and to continue to build their lives together. It takes much patience, giving, and forgiving of oneself – as much as of the other person, so that that person does not become a stranger.

Unfortunately, too many people of both sexes fail this test, resorting to old needful and selfish childhood attitudes and behaviours that are fatal to an adult relationship. Hence our depressing divorce rate of 50%, and its doleful effect on everyone involved, especially children caught in the playground crossfire.

Infantile social ‘justice’ warriors (whatever their emotional motivations for playing on these differences – mommy/daddy problems?) will get nowhere with their absurd gender theories – which are actually a denial of biological and evolutionary science.

If there is a Divine Plan, there is no better proof of it than in the magnificent and complementary differences between males and females of our species, evident everywhere in Nature. It is the Truth, it is pure Genius in our lives, and it is why we’re still on this planet, despite all the odds against us.”

I am a much better person, and man, for having learnt so much from the women with whom I have worked – and those I have served in my career.



It is a measure of the quality of British life that one of its longest running TV programs is “University Challenge”, a quiz show for university students. Yes, it has always been a minority interest, but it is a showcase of talent, an astounding example of what bright young people can get to know in roughly 25 years.

My introduction to this phenomenon was at the University of Keele, which won the contest in 1968, the year of my graduation. I knew team member Pam Maddison (Groves), who studied Psychology in the year below me, and once chatted with me about the estimated number of objects in the universe, a concept I found mind-blowing. I think she added that she had checked her calculations with those of her boyfriend, and found them a reasonable match. It is good to meet bright people.

Unusually, University Challenge has stuck to the same format since inception in 1962, and that means that the long series of results is broadly comparable in the best sense of being an open competition following the same rules. Each contesting university achieves a score against a competitor, and at the final the winner beats the runner up, and their scores show the winning margin. In more detail, some contestants are better than others, and answer more questions, which gets them fan club status, like Eric Monkman (pictured above). Universities field their best candidates, having selected them in qualifying rounds, and then train up a small group before the chosen 4 go forward to compete. The winning team in each round is the one with the most correct answers and fewest incorrect interruptions. The subject matter is extremely broad, but the results of each competition are on ratio scales with true zeros and equal intervals.

Naturally, only the brighter people get to university, and presumably only the brighter of those get onto the university team. Many of the contestants have gone on to notable achievements in public life. The competition tests knowledge, plus the capacity to quickly judge from the question what answer is being looked for, and whether it is worth jumping in with the likely answer in order to obtain bonus points. In the psychometric jargon this is mostly about crystallized intelligence rather than fluid, on-the-spot problem-solving, intelligence, of the sort involved in mental calculations as showcased in another TV program, Countdown.

So, although University Challenge may not be the hardest test of raw intellectual power, it certainly demands very high ability. Adrian Furnham and colleagues found that IQ was the best predictor of general knowledge, but that Openness to Experience, a weak proxy for intelligence included in Five Factor personality assessments, made an additional contribution.

Cognitive ability, learning approaches and personality correlates of general knowledge
Adrian Furnham , Viren Swami , Adriane Arteche & Tomas Chamorro‐Premuzic Pages 427-437 | Received 05 Jul 2007, Accepted 03 Oct 2007, Published online: 20 May 2008

So, this is a showcase of talent, and it is great that it has built up a loyal following, and entered into British popular culture: “Fingers on buzzers” “Your starter for 10”. That is real fame, and I hope those phrases last. So, to use another phrase, though not from this TV program “What’s not to like?”

Michael Hogan, writing for “The Telegraph” (on the political Right) says:

Gender balance needs to be tackled next series

The all-male line-up for this final has sparked a sexism debate over the past week. The statistics are indeed pretty damning. One-third of this year’s teams had no women, only 22 per cent of the contestants this series were female and just five per cent of finalists over the last five years have been women. Equality quotas are a tricky topic but perhaps the team selection process within universities needs to be looked at and guidelines issued. Eight males on-screen in this showcase final – 10 if you include Paxman and Hawking – simply just doesn’t send out the right message.

Eve Livingston writing for “The Guardian” (on the political Left) says:

The year is 2017 and at 8pm on Monday 10 April, televisions across the country switch on to the BBC: four men from Oxford face four men from Cambridge in a combative race to prove their superior intelligence. Verbose questions and bellowed answers are punctuated only by sneering quips from a white male Cambridge alumnus. No, it’s not a parliamentary debate, but the final of University Challenge, a stalwart of middle-class British culture since the early 1960s.
The all-male contest ended a series in which just 22% of competitors were women, a fact that hasn’t gone unnoticed by viewers and campaigners alike.
University Challenge is, of course, hardly at the pinnacle of gender inequality issues facing women every day. And the show’s problems with representation don’t stop at gender – notably the under-representation of black students and the elitism inherent in questions about classical composers, Greek mythology and Renaissance literature, which see various iterations of Oxbridge colleges dominate year on year.
a conversation about representation is worthwhile. The BBC has previously said that institutions are responsible for selecting their own teams, effectively laying responsibility at the door of universities (and some do voluntarily put in place quotas), but there is nothing to stop the broadcaster from issuing guidelines or conditions for entry. In the meantime, an acknowledgement of the problem from both parties and a meaningful commitment to tackling it would be a good starter for 10.

It would appear we have a consensus that something has gone wrong, and both ends of the political spectrum are asking for quotas. I have not checked these figures, but the final winning teams since inception number 184 contestants, of whom only 16 were women, so their representation is roughly 9%.

I am not writing for a national newspaper, but I take a more measured approach than to ask for quotas. What do we know about general knowledge and sex differences outside this particular TV format?

Sex differences in general knowledge, semantic memory and reasoning ability
Richard Lynn and Paul Irwing, British Journal of Psychology (2002), 93, 545–556.

This paper has the three objectives of attempting to replicate a previous study in which it was found that males have substantially greater general knowledge in most fields or domains than females, and of determining how far sex differences in general knowledge are a function of differences in either Gf (fluid intelligence), or experience. The results confirmed the previous study to the effect that males have higher means in a general knowledge factor of approximately .50d (half a standard deviation). It was found further that there was no significant sex difference in Gf measured by Baddeley’s Grammatical Reasoning Test, and only a low correlation between general knowledge and Gf. Analysis of covariance showed that differential experience as indicated by ‘A’-level points and socio-economic status had only a marginal impact on the observed sex difference. The results are interpreted as showing that sex differences in general knowledge cannot be explained as a function of differences in either Gf or experience. It is proposed further that general knowledge should be regarded as a new second-order factor and designated as semantic memory.

• Category: Science • Tags: Intelligence, IQ 
Estimating blogger productivity


This is not about baseball, but about blogging, but times are hard for some columnists, so I needed to get your attention.

Steve Sailer has put up his March statistics (More records for iSteve, April 3) showing that last month his posts generated 19,707 comments containing a total of 1,485,295 words. By any standards, this is a considerable achievement. It raises the question as to how he managed to write an extraordinary 159 posts in March. Very many people read them and were motivated to comment. First, some very minor analysis. On average (probably a skewed distribution, as inspection of the actual comments will reveal) comments were 75 words long, which is not verbose. On a per post basis, an iSteve post generates 124 comments.

By university standards, he is an entire department. Ability and application have combined fruitfully. He generates a slipstream in which other scribblers can catch a favourable breeze which tows them along, present company not excepted.

Universities are fond of statistics, while pretending to ignore them. The Science Citation Index is an important metric of productivity, but has been surpassed by the more complicated but supposedly more comprehensive Hirsch index, that measures both the productivity and citation impact of an academic. Whatever the precise formula calculated, staff are rated by publications: their number (4 a year is a ballpark figure); the impact factor of the journals in which the publication appeared; the impact factor being derived from how much they are read; the amount they are read being derived from how long they were established and how many hopeful papers they receive and reject; and then in addition to publications, the amount of grant money the scholar has brought in, multiplied a little by the difficulty of getting money out of a particularly prestigious government body, and sometimes divided by the number of people who obtained the grant.

I have simplified somewhat, but I am told the real metric is simpler: how much money. If you have raised less than a million you have not understood how the system works. Against all that, blogging is a harmless activity. On the other hand, perhaps the assessment of bloggers is as merciless: until a blogger gets over a million comment words a month, he is a mere scribbler.

To put Steve Sailer’s stellar output into context, as a new columnist, last month I was proud to get 801 comments containing a total of 107,417 words. So, Steve has generated 25 times more comments and 14 times more comment words. Even allowing for the lower productivity expected of columnists, and reasonable period for apprenticeship, the rest of these remarks are a mere postscript to this glaring disparity. However, even as I seek to emulate his achievements, I am not without guile. From a very weak position, similar to most marginal university departments, I can assemble some statistics to avoid being relegated to well-deserved obscurity.

Steve: 159 posts; 19,707 comments; 1,485,295 comment words; 9,341 per post
James: 6 posts ; 801 comments ; 107,417 comment words ; 17,902 per post

Could you please comment on this post? You may develop your arguments at length.

• Category: Science 
Lead poisoning reduces social mobility

Lead_poisoning There are still many people who believe that intelligence does not exist or that it cannot be measured, particularly if the summary result is given as a single figure. The argument seems to be that single figure cannot possibly represent their myriad abilities. Quite so.

What are they to make of a recent finding by the Dunedin study team? This is an epidemiologically based study of child development in Dunedin, New Zealand, and it suggests that lead ingested in childhood is bad for intelligence and for later social mobility. If you maintain that intelligence does not exist, you need not be concerned.

March 28, 2017
Association of Childhood Blood Lead Levels with Cognitive Function and Socioeconomic Status at Age 38 Years and With IQ Change and Socioeconomic Mobility Between Childhood and Adulthood
Aaron Reuben; Avshalom Caspi; Daniel W. Belsky; et al

The authors say:

A prospective cohort study based on a population-representative 1972-1973 birth cohort from New Zealand; the Dunedin Multidisciplinary Health and Development Study observed participants to age 38 years (until December 2012).
Childhood lead exposure ascertained as blood lead levels measured at age 11years. High blood lead levels were observed among children from all socioeconomic status levels in this cohort.
The IQ (primary outcome) and indexes of Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed (secondary outcomes) were assessed at age 38 years using the Wechsler Adult Intelligence Scale–IV (WAIS-IV; IQ range, 40-160). Socioeconomic status (primary outcome) was assessed at age 38 years using the New Zealand Socioeconomic Index-2006 (NZSEI-06; range, 10 [lowest]-90 [highest]).
Of 1037 original participants, 1007 were alive at age 38 years, of whom 565 (56%) had been lead tested at age 11 years (54% male; 93% white). Mean (SD) blood lead level at age 11 years was 10.99 (4.63) μg/dL. Among blood-tested participants included at age 38 years, mean WAIS-IV score was 101.16 (14.82) and mean NZSEI-06 score was 49.75 (17.12). After adjusting for maternal IQ, childhood IQ, and childhood socioeconomic status, each 5-μg/dL higher level of blood lead in childhood was associated with a 1.61-point lower score (95% CI, −2.48 to −0.74) in adult IQ, a 2.07-point lower score (95% CI, −3.14 to −1.01) in perceptual reasoning, and a 1.26-point lower score (95% CI, −2.38 to −0.14) in working memory. Associations of childhood blood lead level with deficits in verbal comprehension and processing speed were not statistically significant. After adjusting for confounders, each 5-μg/dL higher level of blood lead in childhood was associated with a 1.79-unit lower score (95% CI, −3.17 to −0.40) in socioeconomic status. An association between greater blood lead levels and a decline in IQ and socioeconomic status from childhood to adulthood was observed with 40% of the association with downward mobility mediated by cognitive decline from childhood.
In this cohort born in New Zealand in 1972-1973, childhood lead exposure was associated with lower cognitive function and socioeconomic status at age 38 years and with declines in IQ and with downward social mobility. Childhood lead exposure may have long-term ramifications. JAMA. 2017;317(12):1244-1251. doi: 10.1001/jama.2017.1712

The big advantage of this sample is that it is very well drawn up to be representative of the New Zealand population, and by implication representative of European populations. It has been closely studied and assiduously followed up. Happily, we also have a good genetic study of the participants, so it is apposite to give those results. Sorry for this nested parenthetical approach, but it provides context.

The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development.

Daniel W. Belsky, Terrie E. Moffitt, David L. Corcoran, Benjamin Domingue, HonaLee Harrington, Sean Hogan, Renate Houts, Sa Ramrakha, Karen Sugden, Benjamin S. Williams, Richie Poulton, Avshalom Caspi.

June 1, 2016. Psychological Science Vol 27, Issue 7, 2016

A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.

Of course, this is the sort of snappy title and genetic finding which drives some people to doubt the existence of intelligence. It is against this background that the neurotoxic effects of lead are interesting. Crucially, lead exposure in New Zealand in the 1980s did not follow a social class gradient. It mostly came from car exhausts, and was thus an equal opportunity toxin.

Of 1037 participants in the original cohort, 1007 were still alive at age 38 years, 565 (56%) of whom had been lead tested at age 11 years (303 [54%] male; 525 [93%] white). Participants alive at age 38 years with childhood blood lead data (n=565) and without childhood blood lead data (n=442) did not differ to a statistically significant extent from each other in terms of their mothers’ IQ scores or their social class origins, but those without blood lead data did have lower mean childhood IQ scores as a group.

The differences are not big, but they are linear, suggesting a dose-response relationship.
Lead levels IQ and social mobility

The correlation between childhood blood level and age 38 IQ is merely -.11 which is the sort of size I usually ignore, yet the mean differences are instructive, as shown above.

Lead and child and maternal IQ

• Category: Science • Tags: IQ, Lead Poisoning 
James Thompson
About James Thompson

James Thompson has lectured in Psychology at the University of London all his working life. His first publication and conference presentation was a critique of Jensen’s 1969 paper, with Arthur Jensen in the audience. He also taught Arthur how to use an English public telephone. Many topics have taken up his attention since then, but mostly he comments on intelligence research.