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Edinburgh was rightly chosen for the ISIR conference this year, since it must now rank as the world leader in intelligence research.

120 delegates gathered in the grand surroundings of the Royal Society of Edinburgh, bathed in the almost perpetual Northern sunlight of this noble city, to start at the beginning, which was 530 million years ago with a synapse complexity expansion, which itself was followed by a genome duplication event 30 million years later. It was the mutation of all mutations, since the duplication then duplicated again, and it is from that freak 4 duplicated genomes that all vertebrate life descends. Truly, our ancestry depends upon a mistake, and we are the spawn of error.


Seth Grant explained how the synapse functioned. Synapses are the junction boxes through which brain cells communicate with each other. One cell has a long connecting axon coming out of it which ends in several synapses right next to other brain cells and clasps them together. The transmission has to leap a tiny synaptic gap by chemical means. Most drugs operate by affecting this synaptic gap. I confess that when I learned physiology I was a very poor student and thought of it as no more than a messy junction box, and would have preferred it to be made of copper wires rather than a chemical exchange soup. Wrong, wrong, utterly wrong. In fact, the synapse takes the digital signal from the nerve and converts it into a highly informative analogue signal depending on the time intervals of the arriving digital signals. The synapse counts, and then draws a graph. This exquisitely informative shape provides patterns, and even a 9 by 9 array of synaptic receptor will provide a rich mental map of every move an organism takes, like a film. It is a Youtube of events registered at the synaptic level. Given that there are so many receptors, each with different triggering sensitivities, complex computation can be carried out at the synaptic level. In short, it was good to hear all this laid out before us. The relays are doing a lot of the thinking.

Seth Grant went on to discuss a puzzle: why does schizophrenia have such a late onset? Autism reveals itself very early. Attention deficits and other problems are detectable at 3 years of age, and by 11 years of age most of the psychological problems a child may have are already evident. Schizophrenia, on the other hand, seems to come almost out of the blue in the 17 to 25 year period. How can the heritable susceptibility lie dormant so long, and what triggers the breakdown?

Skene, NG, Roy, M & Grant, SG 2017, ‘A genomic lifespan program that reorganises the young adult brain is targeted in schizophrenia’ eLIFE, vol 6. DOI: 10.7554/eLife.17915

The genetic mechanisms regulating the brain and behaviour across the lifespan are poorly understood. We found that lifespan transcriptome trajectories describe a calendar of gene regulatory events in the brain of humans and mice. Transcriptome trajectories defined a sequence of gene expression changes in neuronal, glial and endothelial cell-types, which enabled prediction of age from tissue samples. A major lifespan landmark was the peak change in trajectories occurring in humans at 26 years and in mice at 5 months of age. This species-conserved peak was delayed in females and marked a reorganization of expression of synaptic and schizophrenia-susceptibility genes. The lifespan calendar predicted the characteristic age of onset in young adults and sex differences in schizophrenia. We propose a genomic program generates a lifespan calendar of gene regulation that times age-dependent molecular organization of the brain and mutations that interrupt the program in young adults cause schizophrenia.

Schizophrenia tends to run in families and it is likely that different combinations of faulty genes that affect the connections between nerve cells increase the chance of having the disease. Until now, scientists have assumed that certain situations and environmental factors trigger the condition, but it was unknown if genes could influence the age at which the disease will begin.

To explore whether genes in the brain change at certain time points, Skene et al. examined how the genes are turned on and off across the lifespan of healthy mice and humans. The results showed that in both mice and humans, a ‘genetic lifespan calendar’ controlled every cell type in the brain and directed the way they worked at different ages. The timing was so precise that it was possible tell the age of a mouse or a person simply by looking at the way the genes were expressed in a tissue sample.

Skene et al. then studied how the genetic lifespan calendar controlled the genes damaged in schizophrenia, and found that the calendar caused a major reorganization of the genes at the time when the symptoms started. This suggests that the genetic lifespan calendar is a crucial factor that can determine at what age the disease will start.

The next step will be to study how the genetic lifespan calendar programs changes throughout the brain and to explore if it could be manipulated to change how the brain ages. This could help to develop new types of treatments for schizophrenia and other conditions of the brain.

It could be that mutation load accumulates in post-synaptic proteins, and gene regulation changes a lot during that period, so that brains also change considerably, and it is enough to trigger a major disorder in the vulnerable 1%. Since therapies might not be able to target all the specific causes of schizophrenia, it might be better to find a pharmacological way (for genetically vulnerable teenagers) of slowing down the general gene regulation changes until such time as the person is more resilient. I likened it to being a good mountaineer, but lacking oxygen as you get experienced enough to climb the highest peaks.

What this lecture shows is that real subjects develop and are altered by discoveries in related fields. Essentially, we are studying the brain, and doing so with the best tools available. The tool set has expanded considerably in the last decade. This should boost our understanding, as when a walker in a city discovers that the edge of their familiar neighbourhood links to other neighbourhoods in ways they had not realized. I call it the London Underground Pop Up phenomenon. Places formerly seen as disparate countries with their tube station capitals are revealed, by one long intrepid walk, to be adjoining principalities in a grander continent.

• Category: Science • Tags: Genetics, Schizophrenia 
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From time to time some commentators say that there is no agreement on what constitutes intelligence.
In fact, there is a widely agreed statement drawn up by Linda Gottfredson, which you can read below.

Many people have added variants. I will be going to the International Society for Intelligence Research meeting at Edinburgh University next week.

Here is the program:

If you have your own additions I will discuss them with participants, and see whether the statement needs updating.

I hope to tweet about the conference papers, using the tag #ISIR2018

You can also read the best introductory text:

Or an older summary of the topic in 2000 words.

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sternberg intell

Publisher: Cambridge University Press
Online publication date: January 2018
Print publication year: 2018
Online ISBN: 9781316817049

I do not wish to quote myself too often, but in my 2013 review of Sternberg’s Handbook of Intelligence I raised an eyebrow about how often he quoted himself, and by means of an internal citation count questioned whether his choice of authors constituted a fair representation of the field. In a reply, Sternberg said that since he was developing the field beyond general intelligence, it was natural that he should be quoting the new approaches he had initiated.

Sternberg begins his latest volume with an explanation: he has invited the 19 most cited psychometricians to contribute (the late lamented Buz Hunt was too ill to participate). This method is good, and will set a standard for other editors to follow. Respect.

  1. Intelligence as Potentiality and Actuality. Phillip L. Ackerman
  2. Hereditary Ability: g Is Driven by Experience- Producing Drives. Thomas J. Bouchard, Jr.
  3. Culture, Sex, and Intelligence: Descriptive and Proscriptive Issues. Stephen J. Ceci, Donna K. Ginther, Shulamit Kahn, & Wendy M. Williams
  4. The Nature of the General Factor of Intelligence. Andrew R. A. Conway & Kristof Kovacs
  5. Intelligence in Edinburgh, Scotland: Bringing Intelligence to Life. Ian J. Deary & Stuart J. Ritchie
  6. Intelligence as Domain-Specific Superior Reproducible Performance: The Role of Acquired Domain- Specific Mechanisms in Expert Performance. K. Anders Ericsson
  7. Intelligence, Society, and Human Autonomy. James R. Flynn
  8. The Theory of Multiple Intelligences: Psychological and Educational Perspectives. Howard Gardner, Mindy Kornhaber, & Jie-Qi Chen
  9. g Theory: How Recurring Variation in Human Intelligence and the Complexity of Everyday Tasks Create Social Structure and the Democratic Dilemma. Linda S. Gottfredson
  10. Puzzled Intelligence: Looking for Missing Pieces. Elena L. Grigorenko
  11. A View from the Brain. Richard J. Haier
  12. Is Critical Thinking a Better Model of Intelligence? Diane F. Halpern & Heather A. Butler
  13. Many Pathways, One Destination: IQ Tests, Intelligent Testing, and the Continual Push for More Equitable Assessments. Alan S. Kaufman
  14. My Quest to Understand Human Intelligence. Scott Barry Kaufman
  15. Mapping the Outer Envelope of Intelligence: A Multidimensional View from the Top. David Lubinski
  16. The Intelligence of Nations. Richard Lynn
  17. Intelligences about Things and Intelligences about People. John D. Mayer
  18. Mechanisms of Working Memory Capacity and Fluid Intelligence and Their Common Dependence on Executive Attention 287 Zach Shipstead & Randall W. Engle
  19. Successful Intelligence in Theory, Research, and Practice. Robert J. Sternberg

First up is Phillip Ackerman, distinguishing between intellectual potentiality and actuality. I don’t agree with many of his arguments, so let me explain them. Ackerman is good at distinguishing between sheer general problem-solving brain power and accumulated, skilled knowledge. Intelligence testing includes plenty of the former, and a selection of the common denominator of the latter. He notes that when you get into wide ranging content areas, men and women differ considerably, and argues that the only reason we don’t have separate normative data for the sexes is that Lewis Terman preferred that the sexes be declared equal on the Stanford Binet, and achieved this by counter-balancing items so they appeared the same. One learns something every day. Academic domains of general knowledge usually show a male advantage, but women are ahead in health matters. Ackerman proposes that effort is a big part of actual human achievement, and who would quibble with that, save some of the facts? Practice makes one third perfect.

His chapter has one data table, and one theoretical figure.

Thomas Bouchard next, on hereditary ability. He immediately counters (and refines) Ackerman’s effort argument with Darwin’s admission that Galton has convinced him that intelligence is hereditary and of major importance:

You have made a convert of an opponent in one sense, for I have always maintained that, excepting fools, men did not differ much in intellect, only in zeal and hard work; and I still think there is an eminently important difference.

It is worth emphasizing Darwin’s astute comment that there is a difference between intelligence and motivation (zeal) and effort (hard work), and that the difference is important. Galton himself was well aware of the difference and argued that all three were influenced by heredity. “The triple event, of ability combined with zeal and with capacity for hard labour[,] is inherited”. Galton’s speculative proposal has been nicely confirmed. We now know that virtually all traits (human and nonhuman, psychological and otherwise) are influenced by heredity.

Bouchard goes through a scrub-clearing exercise on the hoary objections about what intelligence is (Gottfredson’s explanation is perfectly good); the reality of g: g is inevitable if the range of tests and range of intellects is wide enough and sample sizes big enough; the notion that at some threshold higher intelligence doesn’t matter: it is monotonically effective;

This chapter is more evidence based, in my view, but that may be because it is treading a path I am in favour of. This chapter gives one figure with data.

Chapter 3
Culture, Sex, and Intelligence: Descriptive and Proscriptive Issues
Stephen J. Ceci , Donna K. Ginther , Shulamit Kahn , & Wendy M. Williams

Lots of data and figures on sex differences, stereotypes and a growth mindset.

Chapter 4
The Nature of the General Factor of Intelligence
Andrew R. A. Conway & Kristof Kovacs

About the relationship between working memory, executive attention, and intelligence. This line of work has culminated in a new theory of the positive manifold of intelligence and a corresponding new model of the general factor, g. We refer to this new framework as process overlap theory (POT) (Kovacs & Conway, 2016b ).

Chapter 5
Intelligence in Edinburgh, Scotland: Bringing Intelligence to Life
Ian J. Deary & Stuart J. Ritchie

People who tend to be good at one mental ability tend to be good at others also; these include remembering things, manipulating information, working out general principles from a set of examples and then applying them more broadly, thinking quickly, organising mental work, working things out in two or three dimensions, knowing word meanings, and knowing facts about the world.

These are action packed pages, written with aplomb by leading researchers in the business.

Chapter 6
Intelligence as Domain- Specific Superior Reproducible Performance
The Role of Acquired Domain- Specific Mechanisms in Expert Performance
K. Anders Ericsson

• Category: Science • Tags: Intelligence, IQ 
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Norwegian family flynn effects

I have good memories of 1975. I got my first secure job, a Lectureship in Psychology at the Middlesex Hospital Medical School, part of the University of London. It was a glorious summer, followed the next year by an even better and drier one, and I finally finished my PhD. Little did I realise that we had reached peak intelligence, and after that it would be downhill all the way. In my defence, it takes time to notice that a peak has been passed, and all this relates to Norwegian data, but nonetheless, the endullment of Western society was underway, and any subjective concerns I had about the increasing foolishness of the world have now been amply confirmed. 1975 turns out to have been a pivotal year, and a new paper says that the reasons are within the family.

Flynn effect and its reversal are both environmentally caused
Bernt Bratsberg and Ole Rogeberg


Population intelligence quotients increased throughout the 20th century—a phenomenon known as the Flynn effect—although recent years have seen a slowdown or reversal of this trend in several countries. To distinguish between the large set of proposed explanations, we categorize hypothesized causal factors by whether they accommodate the existence of within-family Flynn effects. Using administrative register data and cognitive ability scores from military conscription data covering three decades of Norwegian birth cohorts (1962–1991), we show that the observed Flynn effect, its turning point, and subsequent decline can all be fully recovered from within-family variation. The analysis controls for all factors shared by siblings and finds no evidence for prominent causal hypotheses of the decline implicating genes and environmental factors that vary between, but not within, families.

This is an interesting and quite complicated paper, which argues that because the Flynn effect is of equal magnitude in older and younger sons within the same family, then it was probably caused by unknown factors affecting all family members, and cannot have been caused by differences between families. For example, if poor families have more children, and these tend to be dull, then there will be differences between families, not within them.

The method seems to be simple: every year test the intelligence of the first born son and compare it with the intelligence of the second son within that same family, the second son of course being born and being tested some time later. If both sons show the same pattern of rising or falling intelligence, then the Flynn effect, whatever its cause, is due to things which affect all families in the same way. Clean water, good food, home computers, healthy living can boost all family member’s ability levels. Genetic differences would favour some families over others, but that is not detected in this study, so is probably unlikely.

To narrow down the set of hypotheses, we examine the extent to which we can recover observed Flynn effects from within-family variation in large-scale administrative register data covering 30 birth cohorts of Norwegian males. Within-family variation will only recover the full Flynn effect if the underlying causal factors operate within families. Notably, if within-family variation fully recovers both the timing and magnitudes of the increase and decline of cohort ability scores in the data, this effectively disproves hypotheses requiring shifts in the composition of families having children. This set of disproved hypotheses would include dysgenic fertility and compositional change from immigration, the two main explanations proposed for recent negative Flynn effects

A metareview of empirical studies argues that the positive Flynn effect relates to improved education and nutrition, combined with reduced pathogen stress. Turning to the negative Flynn effect, the metareview notes a deceleration of IQ gains in some studies and suggests that these may relate to (i) decreasing returns to environmental inputs (saturation) or (ii) the picking up of effects that cause IQ decreases and may ultimately reverse the Flynn effect, such as dysgenic fertility. Dysgenic fertility is also the favored hypothesis in a recent literature review on reversed Flynn effects, where the authors conclude that dysgenic trends are the simplest explanation for the negative Flynn effect. A negative intelligence–fertility gradient is hypothesized to have been disguised by a positive environmental Flynn effect, revealing itself in data only once the ceiling of the Flynn effect was reached.

So, this paper confirms what was already known, that the secular rise in intelligence test scores has already given way to a fall in those scores (with perhaps a slight rise again?), but attempts to rule out a whole set of possible explanations, saying that there is no need for them. The full effect can be shown to operate within families.Hence the authors’s view of the significance of their findings.

Using administrative register data with information on family relationships and cognitive ability for three decades of Norwegian male birth cohorts, we show that the increase, turning point, and decline of the Flynn effect can be recovered from within-family variation in intelligence scores. This establishes that the large changes in average cohort intelligence reflect environmental factors and not changing composition of parents, which in turn rules out several prominent hypotheses for retrograde Flynn effects.

Now for the detail. These are conscription data for young men. No reason to believe young women would be different. The authors report that the family average tracks the pattern of the first born pretty well. However, as the years went by, lower scoring first borns were unlikely to have had younger brothers who were tested themselves, and this requires a correction factor to be applied. Although the correction for this selection bias is carefully done, it introduces some uncertainty to the estimates, though the authors see this as well within reasonable limits. The authors also argue, in my view convincingly, that the reversal of the Flynn effect is so rapid that it is unlikely to be a dysgenic indicator, since such things happen between successive generation, not within one.

A salvo has been fired against genetic factors being implicated in the apparent drop in intelligence. As a note of caution, it would be good to look at whether this holds true of all components of the intelligence test: numbers, words and shapes. Anyway, the authors say that these data do not allow them to specify what causes the Flynn effect and its reversal, merely to suggest it cannot be genetics.

The strong points of the study are that it is based on conscript age data obtained on almost all Norwegian men from 1962 to 1991, and they restrict themselves to Norwegians with two Norwegian parents, so they will not be cultural or genetic confounders.

They say:

• Category: Science • Tags: Flynn Effect, IQ 
Who? Whom? versus "What? When? Why? How?
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Black-white difference Becker survey

The argument from authority is of questionable merit. Yes, some people know far more than others, but how does one establish that? Happily, there are publication and citation metrics available to help us, and a reasonable case can be made that experts exist. That does not preclude the possibility that they are all wrong. One really good study might conceivably show that they had all missed an important point. Although rare, this does happen from time to time, just to make things interesting.

In 1987 Synderman and Rothman reported on a survey of 1020 intelligence experts, data having been collected in 1984. The experts were in agreement (99.3%) that intelligence involved “abstract thinking or reasoning”. As regards the burning question: “what is the source of the black-white difference in IQ?” 45% said both genetics and environment, 15% entirely environmental, 14% did not respond, and 1% said entirely genetic. So, strong environmentalists were far more common than strong geneticists. Looking at the references in that paper shows you that experts at that time were reading SJ Gould and Leon Kamin, and their arguments may have increased the environmentalist tendency.

Who are the intelligence experts now?

Men, mostly. That 83% of them are male could be because of male standard deviation advantage, in that exceptional ability is more likely in males (as is the exceptional lack of it). Even more precisely, it would fit the hypothesis that men are also 3 points ahead of women. They are of middle age, which is what one becomes after reading all the required literature (same pattern as in 1984). However, if 30 year olds bother to do the reading they can quickly contribute to it. 7 people over 70 are still publishing. The experts are well-published; and left-wing. The last point may come as a surprise. They tend towards liberal rather than conservative opinions. They are left of centre by 2 to 1. They are strongly in favour of gay marriage, in favour of more social democratic policies and of immigration. They are less keen on, though not totally opposed to “strong affirmative action”.

They come from middle class backgrounds, as one would expect of the children of better educated and probably brighter parents. They are mostly European, and often Jewish. They studied psychology, have PhDs, and are mostly in universities. Two thirds of them are not religious. 75% regard themselves as Jensenists, meaning that IQ has a general factor and is heritable.

Experts sometimes talk to the media, but have a generally poor opinion of it. As of 2013/2014 they rated two particular bloggers far more highly. They find the public debates about intelligence are mostly (two thirds) based on ideology. They have often hesitated to give their opinions in public. They also think that intelligence research could be abused in political settings. On balance, they think that 51% of the black-white difference in intelligence in the US is caused by environmental factors.

Figure 10 shows the range of opinions:

Black-white difference Becker survey

Environmental hardliners are nearly three times as common as hereditarian hardliners. Since the Snyderman and Rothman study in 1987 there has been a shift towards accepting genetic components. Unfortunately, these judgments are strongly related to political perspectives r = -.49 as will be seen below.

Black-white difference and politics Becker

One assumes that political perspective came first, and provided a powerful interpretative filter. Age did not have much effect, but gender was almost as powerful r = .48 . There were only 10 women and their average estimate was that 77% of the genetic group difference was environmental, compared to the male average estimate of 39%.

So, here we have another projective test. Is the question of group differences all down to opinion, or do facts matter? It seems to be a case of “Who? Whom?” versus “What? When? Why? How?”. From my perspective, the political affiliations come as a partial surprise. It is informative that this group put the genetic contribution at 49% despite the fact that they lean left. I hope this finding about a willingness to countenance (anonymously) a genetic compontent will not feed them to the wolves.

Having heard the details of the expert’s backgrounds, if you judge people by their demographics, then you may find lots to object about, and might even want to expel those with right wing opinions. If you judge people by their arguments, then you might wish to ignore the revealed political bias, and concentrate on the actual arguments and the supportive evidence, to examine the deeper foundations of the debate. I would favour that approach, but would warn you that, if you are not middle aged now, you will be middle to old aged by the time you have done the necessary reading.

Here is the slide deck presenting the results of the survey.

London18DBSurveyV3 (1)

• Category: Science • Tags: Heredity, IQ, Race/IQ 
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I have held off talking about the Press attacks on the London Conference earlier this year. Triggered by journalist and educationalist Toby Young’s appointment to a UK Governmental education committee, rival journalists fired off a salvo of accusations in a “guilt by association” spoiling operation. I was simply collateral damage, accused of keeping bad company with a group of deplorables, whose secret meetings Toby had briefly attended in order to gather material for a talk he was giving at another intelligence conference. I thought it was better that the participants replied on their own accout, since they drew most of the fire, rather than I as the organizer tried to speak for them. Many of the participants have put their names to a joint paper which is soon to be published in a scholarly journal.

However, for some months I have been sitting on a charming essay by one particular participant, Julien Delhez, who has the great merit of being an Egyptologist. What is such a person doing at a conference on intelligence, otherwise infested by psychologists of a psychometric persuasion? Well, our inclusion criteria are pretty simple: people with interesting ideas who are committed to empirical methods. The notion that one could someday measure the intelligence of the Pharaohs seemed intriguing, and Julien’s ideas developed as he attended the conferences. Originally interested in estimating how illnesses may have diminished the ability of the highest status ancient Egyptians, he now wants to incorporate ancient DNA studies to do intelligence estimates.

I think he gives a very good background to the events, explains the conference content and general procedures, and draws attention to the underlying socio-political problems which arise when research even raises the possibility of genetic components in group differences.

Any comments you make here will be also be looked at by him, so both of us can reply to your remarks.

• Category: Science 
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Cambridge undergrads

Newspaper reports are still discussing the story about the numbers of Africans admitted to Oxbridge, but I have not seen any giving the numbers of AAA students available, or any that mention intelligence. I doubt that Admissions Officers read my blog, or indeed that they would survive in post if they were ever caught doing so, but it is perfectly possible that Admissions Officers have friends, and some of those friends might innocently print out this post for them to read when they call round for coffee.

First, let us start with some data on cognitive assessments carried out in schools. They are not face to face Wechsler tests, nor are they the traditional tests on which we have decades of comparative data, but they give us some idea of student capabilities by ethnic group, and that is what we need in order to contribute to this debate. Indeed, looking at the data has a generality beyond the specific case of Oxbridge, and applies to all university entrance matters where ethnicity is an issue. Here are cognitive scores for ethnic groups in Britain, as assessed by the CAT test at school.

pupil background CAT

In the table below I will concentrate mostly on those groups with more than 1000 subjects and not having “other” in their ethnicity description, since one does not know what ethnicities are included. I simplify the three reasoning measures into one simple average and standard deviation (this is crude but quick). I then give the percentage of each ethnic group who are above IQ 130, on the basis of a requirement that students are IQ 130 and above on the assumption that the population has an average IQ 100 and a standard deviation of 15 points. These are all approximations, since the observed standard deviations are narrower than expected, possibly because the tests may not have been offered to some lower ability children, and they may have omitted private schools with higher ability students. I have rounded the means and standard deviation scores up or down with the usual 0.5 break point, which will have further affected some of the calculations.

130+ by ethnicity

As you can see, on these figures Chinese students are the most likely to get to a good university. They would do so at roughly 3 times the rate of white students. White students would be almost 7 times more likely to get to these universities than Black Caribbean students.

Remember also that category of “3 A’s or better” hides a four point scale, ranging from AAA to A*A*A*, so a demanding university can still pick and choose within the students who achieve the minimal entrance level.

Now back to the actual figures of students getting AAA, which is what the universities must deal with. Since only 6600 applicants get a place at Oxbridge, we can assume that if every candidate with at least 3 As applies, then the success rate is 6600/17,146 which is 38%. The final column shows what the actual figures of placement offers should be for Oxbridge. These would be the admissions made on merit only, as judged by scholastic results. There would only be 1 Black Caribbean for every other 277 students.

Oxbridge entry

I do not frequent these establishments, but if you have a pressing interest in using genetic background as an entry criterion, a prospect which does not please me, you may wish to go around counting undergraduates and rate them according to their apparent ethnicities, and compare them with the various calculation shown above. The final point should be very clear: entrance to university cannot be calculated on population numbers alone.

• Category: Science • Tags: Academia, IQ 
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It is that time of year when Oxford and Cambridge universities are in the doghouse again, accused of being biased against black students. A politician, Mr David Lammy, has called for special measures to be taken to boost the numbers of Africans at those universities. Calls like this seem to be accepted at face value, but universities are tertiary educators, fed by secondary schools. What does the pipeline deliver them?

Well, to get to a good university you need at least 3 A grades, and for the best colleges preferably 5, all in respectable, that is to say, hard subjects. For example, an A in Maths, and another A in Further Maths reassures good universities that the place they offer a candidate is unlikely to be wasted. If one looks at the average offer extended to Oxbridge candidates it is A*AA (three As, one of them being A starred). That is what they must get in their exams to secure a place, the actual subjects depending on what discipline they wish to read.

Here is the official Department of Education summary of the ethnic success rate in most recent results for 3 A grades.

3 A grades or better at A level was achieved by 24% of Chinese students, 11% of Mixed students, 11% of White students, 11% of Other ethnic group students, 10% of Asian students and 5% of Black students.

Chinese students were consistently most likely to achieve 3 A grades or better at A level and Traveller of Irish Heritage students and Gypsy/Roma students were least likely to.

The summary is not entirely clear about mixed students. The detailed tables are hard to display, so I have made a simplified version. By the way, two things should be borne in mind when considering the numbers of ethnic students who gain entry to highly selective universities: the percentage of each ethnic group who reach the basal standard, and the actual size of the ethnic group.

3 As or better

Oxford and Cambridge offer roughly 6,600 undergraduate places in total, and roughly five times as many students apply as are accepted. So, 7600 white students who reach the minimal standard do not get admitted to Oxbridge every year. Tough luck.

Every statistic based on ethnicity is influenced by the immigration history of the nation in question. For example, Black British used to mean “from the West Indies”. These are the group who have had most time to get the benefits of life in the United Kingdom, and are almost all British born, using the NHS from conception and the education system throughout. Now the African population is larger than the Caribbean population, a consequence of recent mass migration. Many will have been born abroad.The Indians in the UK are drawn from particular populations, and India is heterogenous as regards ability.

Many people will find the statistics startling. Can it really be the case that only 62 Black Caribbean students achieve 3 A grades? Here is a very rough calculation: assume 594,825 Caribbeans in the UK. Assume that, as for other populations, only 2.33% of that population are of an age to go to university, and that all apply. Assume that the best estimate of Afro-Caribbean intelligence is 90, and that IQ 130 is the minimal Oxbridge entrance requirement. In that case there will be 53 qualified applicants. This estimate is in broad agreement with the observed figure.

The larger (and very probably pre-selected) African ethnic group seems a more promising pool for recruitment, if the requirement is simply that the candidate be of African genetics. The African group is bimodal in terms of occupational level: lots of professional African immigrants, plus lots who are unemployed. They are drawn from a vast population.

The table is also informative about racial admixture. The children of Whites have gained by mixing with Asian genetic groups (actual group unspecified) and lost somewhat when mixing with Black Africans and more so with Black Caribbeans. Interestingly, the White/Black Caribbean mix of 11% and 3% pass rates results in exactly a 7% pass rate for the mixed group.

It seems that British people do not blink when it is proposed that another genetic group should be granted extra privileges. There is no call for White British candidates to be put on the same footing as Chinese and Indian students. I suppose the supposition is that they are brighter or studied harder, probably both.

It might help the public debate about university entry if more people were to look at the official education statistics. The focus of discussion may one day move to secondary schools. Then, after a while, it may move to primary schools and then kindergartens. Since racial differences in ability can be detected at age 3, expect special measures to be required for kindergartens.

• Category: Science 
118 cm3
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Sex diffs in brain size Ritchie

Pity the poor blogger’s lot: there are more interesting papers being published every week than any essayist, however diligent, can possibly cope with. And there will be more, as the vast genetic databases give up their secrets. No sooner does one team scoop the others with a savage novelty than their rivals counter-attack with their own surprising findings. If you are curious about mankind, it is the best time to be alive. We are likely to learn more about ourselves in the next few decades than was possible in the last few centuries.

So back we go to an old theme, but with a new twist: how do women’s brains work?

To sort out this mildly contentious issue, Stuart Ritchie, up and coming member of the Edinburgh crew and its international affiliates, has provided intrigued men with a map of women’s brains. Smaller, of course, as many a man has surmised in the midst of an unexpectedly heated domestic discussion, but apparently able to function as well, or almost as well, as the male variety. Let us dig deeper into these mysteries, in the calm and measured way which befits this distinguished audience.

Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants
Stuart J Ritchie, Simon R Cox, Xueyi Shen, Michael V Lombardo, Lianne M Reus, Clara Alloza, Mathew A Harris, Helen L Alderson, Stuart Hunter, Emma Neilson, David C M Liewald, Bonnie Auyeung, Heather C Whalley, Stephen M Lawrie ,Catharine R Gale, Mark E Bastin, Andrew M McIntoshIan, J Deary.
Cerebral Cortex, bhy109,
Published: 16 May 2018

The authors say:

Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44–77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.

There is much to discuss here, but my attention was drawn by two phrases “considerable distributional overlap” (which in my experience means that one group is pretty different from another) and “generally greater male variance” (which agrees with most of the observations on sex differences indicating that men are leptokurtic (more variable), women more platykurtic (less variable).

Women are more at risk of dementia, depression, schizophrenia and dyslexia. Men are better than women at mental rotation tasks, and are more physically aggressive; women are more interested in people than in things, are more neurotic and more agreeable.

One of the most interesting sex differences is intelligence. Here is their introduction to the topic:

There is more to sex differences than averages: there are physical and psychological traits that tend to be more variable in males than females. The best-studied human phenotype in this context has been cognitive ability: almost universally, studies have found that males show greater variance in this trait (Deary et al. 2007a; Johnson et al. 2008; Lakin 2013; though see Iliescu et al. 2016). This has also been found for academic achievement test results (themselves a potential consequence of cognitive differences, which are known to predict later educational achievement; Deary et al. 2007b; Machin and Pekkarinen 2008; Lehre et al. 2009a, 2009b), other psychological characteristics such as personality (Borkenau et al. 2013), and a range of physical traits such as athletic performance (Olds et al. 2006), and both birth and adult weight (Lehre et al. 2009a). To our knowledge, only two prior studies have explicitly examined sex differences in the variability of brain structure (Wierenga et al. 2017; Lange et al. 1997), and no studies have done so in individuals older than 20 years. Here, we addressed this gap in the literature by testing the “greater male variability” hypothesis in the adult brain.
We tested male–female differences (in mean and variance) in overall and subcortical brain volumes, mapped the magnitude of sex differences across the cortex with multiple measures (volume, surface area, and cortical thickness), and also examined sex differences in white matter microstructure derived from DT-MRI and NODDI. We tested the extent to which these differences were regionally-specific or brain-general, by adjusting them for the total brain size (or other relevant overall measurement; for instance, adjusting volume differences for total brain volume and cortical thickness differences for mean cortical thickness), and examining whether the differences found in the raw analyses were still present. We tested the extent to which these structural differences (in broad, regional, and white matter measures) mediated sex variation in scores on two cognitive tests, one tapping a mixture of fluid and crystallized reasoning skills (skills previously found to be linked to brain volumes; Pietschnig et al. 2015) and one testing processing speed (previously found to be linked to white matter microstructural differences; see Penke et al. 2012). At the functional level, we also examined large-scale organization of functional networks in the brain using resting-state fMRI functional connectivity data and data-driven network-based analyses.

The study compared 2750 females (mean age = 61.12 years, SD = 7.42, range = 44.64–77.12) and 2466 males (mean age = 62.39 years, SD = 7.56, range = 44.23–76.99). These are extremely large samples, two orders of magnitude larger than the early studies in the 1980s, and way larger than many of the studies that the Press report so frequently. Consider them “Foxtrot Oscar” samples.

The first result is startling: male brains are very much bigger, a colossal 1.4 effect size. 92% of men will be above the mean for women. On average men have 117.8 cm3 more brain than women. All this extra brain must be doing something for men, you might surmise, other than just helping them perpetually contemplate the relative advantages of the more complicated positions adopted during sexual intercourse. Perhaps not. Broadly the same effect of male advantage can be found in all the brain region sub-comparisons. Male brains are both larger, and also vary more in size. Greater male variability seems a fact of nature. If there were a direct relationship between brain size and cognitive ability, there would be many, many more bright men than bright women.

• Category: Science • Tags: Brain Scans, Gender, Sex Differences 
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Genc Figure_4 fewer connections

The ISIR July 2017 meeting in Montreal seems a long time ago, and that feeling is entirely explicable by it being 10 months since I heard the lecture in question. I was chairing the session, which normally diminishes attention to the actual content, but this talk was the exception. It came up with a counter-intuitive finding, and it has been difficult to avoid talking about it. Brighter brains have fewer connections between neurones. Cool.

It has been a real struggle to keep quiet about this remarkable result, and a relief that the embargo has been lifted today, 14 months after receipt of the paper by the publishers. Publish and be damn delayed. Blogging is the future.

As you will see from the author list, particularly the last author, this is a team which has been working on this topic for decades, (with important results from at least 1988) and has always sought to have reliable measures and large sample sizes before publishing anything. In ISIR 2014, tired of reading neuro-bollocks in the media, I lobbed Rex Jung what I thought might be a tricky question: How reliable are your neuro-imaging measures? He replied that he and Rich Haier had always put their subjects into the scanner twice: once briefly so as to get benchmark reliability measures, and then again for the full session. Jung and Haier also held back from publication until they had large sample sizes, although in early years this meant a long wait, since they were mostly working in the odd free spaces between the high priority medical school clinical use of the sole scanner available. Things have got better in recent years.

Another feature of this duo is that when they were offered an celebratory session at ISIR 2017 they chose to invite their critics to knock hell into them. Several did, and I pursue them every now and then to make their P-FIT theory more specific. So, it is great to be able to report some new and very specific findings.

Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Erhan Genç, Christoph Fraenz, Caroline Schlüter, Patrick Friedrich, Rüdiger Hossiep, Manuel C. Voelkle, Josef M. Ling, Onur Güntürkün & Rex E. Jung

Nature Communications volume 9, Article number: 1905 (2018)

The first two authors contributed equally. Take a good look at their reference list, which is a roll-call of the top people in the field, and those one should turn to for further comments on this paper and its implications.

Here is the main finding in full screen size, with the relevant explanations.

Here is the link to the entire paper:

Here is the abstract:

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

“Intelligence is not a function of how hard the brain works but rather how efficiently it works”.

In terms of method, the team collected 259 participants (138 males) between 18 and 40 years of age (M = 24.31, SD = 4.41) which gives the analysis of results sufficient power. Participants had no history of psychiatric or neurological disorders and matched the standard inclusion criteria for fMRI examinations. Each participant completed the matrix-reasoning test and neuroimaging measurements.
To validate the results obtained from sample of 259 subjects, the team downloaded additional data provided by the Human Connectome Project, namely, the “S500 plus MEG2” release. This set includes 506 participants with data suitable for their analyses. The best papers now give what would formerly have been two papers, for the price of one. The first sample is the sample of discovery, the second the sample of validation. Some things in science are getting better.

The measures themselves are a new variant of diffusion imaging analysis. If you will forgive a simplistic analysis: a pipe full of water will show different measures if measured end-on (where all the water in the pipe vibrates with the imposed resonance) as compared to when measured at right angles to the pipe (where only a small amount of water is available for resonance to be detected). In this way you can deduce which way the dendrites run in the brain.

Currently, the most promising technique for the quantification of neurite morphology is a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). This technique is based on a multi-shell high-angular-resolution diffusion imaging protocol and offers a novel way to analyze diffusion-weighted data with regard to tissue microstructure. It features a three-compartment model distinguishing intra-neurite, extra-neurite, and cerebrospinal fluid (CSF) environments. NODDI is based on a diffusion model that was successfully validated by histological examinations utilizing staining methods in gray and white matter of rats and ferrets. In addition, Zhang, Schneider have shown that NODDI is also capable of estimating diffusion markers of neurite density and orientation dispersion by in vivo measurements in humans. Direct validation of NODDI has recently been performed in a study investigating neurite dispersion as a potential marker of multiple sclerosis pathology in post-mortem spinal cord specimens. The authors reported that neurite density obtained from NODDI significantly matched neurite density, orientation dispersion, and myelin density obtained from histology. Furthermore, the authors also found that NODDI neurite dispersion matched the histological neurite dispersion. This indicates that NODDI metrics are closely reflecting their histological conditions.

The point is that this study confirms previous findings, that “measures of neurite density and arborization show negative relationships to measures of intelligence, implicating neural efficiency, particularly within parieto-frontal brain regions, as suggested by the vast majority of neuroimaging studies of intelligence”.

The study also provides a partial confirmation of the P-FIT theory, in that a majority of the observed associations between brain areas and intelligence conform to the predictions from P-FIT as proposed by Haier and Jung, or as further elaborated by Basten. The score could be called a 4 out of 5 area confirmation.

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