Digit Ratios and Mutational Load

“Mutational load” is the idea that organisms contain some number of deleterious mutations. Some mutations will kill you outright, like the one for Tay-Sachs disease; some mutations greatly reduce your fitness but aren’t immediately lethal, like the inability to sweat; and some mutations are potentially problematic but mostly just kind of annoying, like colorblindness.

Random mutations happen all the time as a result of genetic transcription. The obviously bad ones tend to get weeded out of the population pretty quickly, but the ones with only a mild effect on fitness can stick around for a pretty long time. Under harshly Malthusian conditions where organisms compete for limited resources and danger and disease lurk at every turn, deleterious mutations will tend to get weeded out pretty quickly, but increase the food and decrease the danger/diseases, and a far larger % of your population will reproduce, including people who would previously have died.

One of the areas where mutational load seems to play a significant role is in IQ. I commented on a study n the subject back in “Is Genius Fragile?” While obviously a great variety of things go into determining one’s IQ, like whether you were in a good mood when you took the test and if your parents dropped you on your head as an infant, this particular study found that the major difference between extremely-high-IQ kids and normal-to-low-IQ people was that the normal-to-low people had a higher frequency of rare, slightly deleterious mutations. The lower the IQ, the more of these mutations.

Each mutation obviously has only a small effect–you could have several and still come out pretty smart. But to be one of the super smart kids, you had to basically be one of the lucky folks who escaped almost all of them.

IQ is interesting in another way: it is more variable in men than women. People make a big deal out of the greater preponderance of men than women at the very high end of the IQ distribution (especially math ability;) this is, we are frequently told, due to the pernicious evil effects of the patriarchy’s black-magic mind-control rays convincing women that they are bad at math. Strangely, however, we are never told that the opposite effect–the fact that the ranks of the intellectually retarded are also disproportionately male–is also due to the magical effects of the patriarchy.

BTW, if you think it is a problem that the evil patriarchy is preventing girls from getting math PhDs, but have no problem with boys being over-represented among the retarded, you are a horrible person.

No, it’s not the patriarchy. It’s the Y chromosome.

You see, because random unpleasant shit happens, like snake bites and random mutations, nature has built us with a fair amount of redundancy. If something happens to one of your eyes, you’ve still got the other. If something happens to one of your hands, you’ve got an extra. Etc. This is true on the genetic level, too, which is why you can survive even with small, fitness-reducing mutations.

But men have slightly less genetic redundancy than women, because they have an X and Y chromosome instead of two Xes. If a woman has a wonky mutation on one of her Xs, the other X may have a mutation that makes up for it. If a man has a wonky mutation on his X, his Y chromosome may have nothing to counteract it (and likewise, if there’s a wonky one on his Y, his X may have nothing to counteract it.)

Some mutations are good, some are bad, and some are neutral. Height is fairly neutral. The average man is taller than the average woman, but the spread from tallest men to shortest men is bigger than the spread from tallest women to shortest women. All women tend to cluster closer to the female average than men; there are both more “short men” and “tall men” than “short women” and “tall women.”

Likewise with IQ; there are both more male geniuses and retarded than female geniuses and retarded, most likely as a result of men having lower genetic redundancy to counteract the effects of mutational load.

On to digit ratios!

SlateStarCodex recently posted the results of the SSC/Less Wrong survey, which included digit ratios.

(To measure your digit ratios:

1. Place your right hand firmly on the plate of a photocopier or scanner with fingers straight. Close cover of place a sheet of paper over your hand to prevent glare from overhead lights. Ensure that the bottom crease and finger tip can be clearly seen in the photocopy.

2. Use a ruler or calipers to measure the distance from the middle of the bottom crease to the tip of the finger to the nearest hundredth of a centimeter.

3. Once you have the measures for both your ring and index finger, then divide the length of your index finger by the length of your ring finger. The result is 2D:4D (2nd digit divided by 4th digit).

If possible, please give three digits – for example, 0.915. Some people may have digit ratios slightly greater than 1, which is okay.)

Inspired, the husband and I decided to measure ours, too. Since we didn’t have a photocopier on hand, and were lazy, we just used a common tape measure. We measured both hands and checked each other’s work, but both of our hands came out identical.

I got a ratio of 0.971, he got 0.957.

(Note that the closer the ratio is to 1, the closer your fingers are to being the same length. The further the ratio is from one, the further apart your finger lengths are.)

Scott notes that the average male digit ratio in his survey was 0.972; the average female digit ratio was 0.975.

According to Wikipedia, a study of 136 males and 137 females at the University of Alberta found:^[17]

• Males: mean 0.947, standard deviation 0.029.
• Females: mean 0.965, standard deviation 0.026.

People have taken to calling lower digit ratios (further from one) more “masculine,” and higher digit ratios (closer to one) more “feminine.” Which leads to the question of why all of these Rationalist math-nerds, whose community is definitely majority male and whose field is regarded as a stereotypically “Male” thing, should all have such overwhelmingly girly hands.

My first thought was that math nerds are effeminate. Which they are, for certain definitions of effeminate. But mathy women tend to be kind of masculine, which isn’t what this data shows. My second thought was that femininity/masculinity may be additive rather than subtractive–that is, having an extra unit of “masculinity” doesn’t necessarily mean someone must therefore lack a unit of “femininity” in a directly linear fashion. Some people could be very low in both femininity and masculinity, or high in both.

My third thought was that maybe measuring digit ratios is too complicated by measurement error and bias and random noise due to things like “how do your fingers crease?” and “did you actually use a copy machine?” A LOT of social science research doesn’t replicate at all.

My fourth thought was that a large difference between one’s finger lengths sounds a lot like physical asymmetry–which is caused (among other things) by mutational load.

Symmetry has long been recognized as one of the things people look for in a mate. Asymmetric faces (and bodies) are deemed less attractive than symmetric ones. Symmetry is a sign of good health, good lifetime nutrition, few parasites, and low mutational load. Asymmetry is a sign of things gone wrong.

Men display more of the effects of mutational load due to their Y chromosomes, so we’d expect to see a wider range of male digit ratios than female ones–which is indeed what the Alberta study found. Really dumb men probably have very different digit lengths, while really smart men trend toward even fingers. Women, because of their two X chromosomes, are probably just less likely to have really uneven fingers (just as they are less likely to be really dumb.)

The Slate Star Codex and Less Wrong cohorts, on the other end of the spectrum, are very smart people in whom we would expect to see lower mutational load.

The latest study I read on autism found that sufferers have a higher mutational load than the background population; while such an explanation is less fun than “autistics are secret math geniuses,” it is sensible. At any rate, if so, we should find a correlation between autism and divergent digit ratios, which the SSC/LW survey did. (Why autistics tend to be male should be immediately obvious.)

Likewise, if homosexuality is caused by some kind of genetic or parasitic agent, we would expect it to correlate with digit divergence. According to the Wikipedia, lesbians have more divergent digits than heterosexual women, but the jury is still out on gay men.

Interestingly, Wikipedia reports that the Han Chinese (who score very well on IQ tests,) have very even fingers, and that the Jamaicans (who do not do so well on IQ tests,) have very divergent ratios. (However, like much of this digit ratio research, I regard this as speculative.)

Of course, like height, there my also be an androgenic effect, such that men are supposed to (for whatever reasons) have slightly different digit ratios than women. After all, even the SSC/LW sample had more divergent ratios for the men than the women, even though the whole SSC/LW population probably has about equal mutational loads (having been pre-selected for high IQ, which = low mutational load.)

 

Is Race a Social Construct?

People mean a lot of things when they say “social construct.” Mostly they mean “made up.”

Luckily for us, Google is very helpful:

Dear Google and the NY Times: Not only is that not the biological definition of race, it’s not even the biological definition of SPECIES. This is not what laymen mean when they speak of race, not what racists mean when they speak of race, not what blacks or whites or Hindus mean, and definitely not anyone who actually studies human biology and genetics.

The simple folk definition of “race” is “a group of people who look kinda similar and come from the same large area of the world.” This, of course, absolutely exists. Most people in the world look a lot like their neighbors, especially if they live in their ancestral homeland and their country hasn’t been invaded lately.

Now, the exact details of how you racially classify people–are Hindus Caucasian? How about North Africans and Iranians? What about mixed-race people?–are socially constructed. This mean that a word like “black” may mean something different in Russian than it does in the Dominican Republic than it does in the US.

This does not change the underlying reality–the humans referred to as “black” still possess the quality of looking similar to other people from their ancestral part of the world. Reality does not disappear just because people sometimes disagree on exactly how to use words to define it.

The scientific, biological definition of race gets a little more complicated, since it matters whether we are talking abut chromosomal races, fungal races, or humans. A couple of definitions:

Geographical race

A distinct population that is isolated in a particular area from other populations of a species,^[9] and consistently distinguishable from the others,^[9] e.g. morphology (or even only genetically^[4]). Geographic races are allopatric.^[7]

Physiological race

A group of individuals that do not necessarily differ in morphology from other members of the species, but have identifiably different physiology or behaviour.^[10] A physiological race may be an ecotype, part of a species that is adapted to a different local habitat, defined even by a specific food source.^[11]

Notice that neither of these include, “possessing a gene or cluster of genes common to everyone in the race but to no one outside of it.”

But if you don’t like the Wikipedia, here’s what Biology Reference has to say:

The biological definition of race is a geographically isolated breeding population that shares certain characteristics in higher frequencies than other populations of that species, but has not become reproductively isolated from other populations of the same species. (A population is a group of organisms that inhabit the same region and interbreed.) Human racial groups compose a number of breeding units that in the past remained geographically and perhaps temporally isolated, yet could interbreed and produce viable offspring within the species Homo sapiens sapiens.

The Biology-Online Dictionary has some more definitions.

These races are real things, even if biologists disagree about exactly which race a mushroom should belong to.

The reality on the ground:

There are few truly isolated groups in the world, though the Onge (and most likely the Sentinelese) actually fit the NY Times’s wacky definition of a “race” due to thousands of years of isolation on tiny islands in the middle of nowhere:

From Haak et al.

The Onge are the peach stripe between the olive brown section and the purple section.

Major groups in this dataset, running from left to right (excluding the ancient skeletons at the far left):

Light Green: Brazilian rainforest dwellers who may be most closely related to Melanesians

Light Pink: Aztecs and their relatives

Brown: Canadian Indians

Green: Bushmen/San

Rose: North-East Africans

Dusty Blue: Bantus

Light Blue: Pygmies

Magenta: Tanzanian hunter-gatherers

Orange/Blue/Teal: Europeans

Orange/Purple/Teal: Middle Easterners

Olive Brown: Inuit (Eskimo)

Peach: Onge

Purple: PNG/Australia (Melanesians and Aborigines)

Light Green/Teal: India

Yellow/Red: East Asia

Yellow: Taiwan

Red: Siberia

Some of these groups have very mixed ancestries; people from eastern Canada or the middle of Eurasia, for example. Others are quite distinct–there is no doubt that the Eskimo and Pygmies are genetically distinct, physically distinct, geographically distinct, behave differently, and do not generally marry each other.

We may argue about whether Turks should be considered “Europeans” or “Middle Easterners,” or perhaps say that all orange people should be grouped together, or all teal or blue, but here geography does its job: Europeans look genetically like other Europeans; Indians look genetically like other Indians; Middle Easterners look like each other (except for Bedouins,) etc.

We may also argue about how many races we want to distinguish–people usually determine races based on whichever people are around, but obviously the world is more complicated than this. Americans generally think of “African Americans” as part of a broader race that includes all Africans, but we have distinguished here between 5 different groups, some of which are quite distinct–the ancestors of today’s Pygmies and Bantus, for example, split apart about 100,000 years ago, whereas the ancestors of today’s Bantu’s and Koreans split about 70,000 years ago (as far as we know.) Most African Americans are genetically Bantu (with a bit of European admixture,) not Pygmy. We might in a folk-sense refer to both of these groups as “Africans” or “black,” but genetically (and behaviorally) they are distinct.

Of course, you do not have to call them “races.” Most people studying human genetics use terms like “population,” “ethnic group,” “ethny,” or “clade” instead, but the practical meaning is the same.

But the idea that groups that are genetically, physically, behaviorally, or geographically distinct or distinguishable from each other do not exist is pure nonsense.

So why are people Rh-? (part 2)

Part 1 is here.

Unfortunately, Googling “Why are people Rh-?” leads you down one of those fevered rabbit holes full of crazy. See, “Rh” was originally named after the rhesus monkey because some early blood work discoveries were done with monkey blood instead of human blood, probably for obvious reasons related to monkeys being more common lab subjects than humans. Rh+/Rh- blood in humans doesn’t actually have anything to do with rhesus monkeys. But some people have interpreted the Rh+/Rh- distinction as meaning that some people have monkey blood and are therefore descended from monkeys, while other people don’t have monkey blood and therefore aren’t descended from monkeys. They think Rh- folks are descended from reptiles or gods or angels or ancient human breeding experiments or something else.

I’ve got news for you. You’re all descended from apes. Yes, even you.

Can someone explain what, exactly, motivates these fever dreams of alien god blood? “Crazy” seems an inadequate answer, because most of these people can type in complete sentences and even form coherent paragraphs, in contrast to, say, schizophrenics, who as far as I know have difficulty with such tasks. Is it just a side effect of being too dumb to tell the difference between “things scientists believe are reasonably plausible” and “a guy claiming that Rh- people are space aliens with extra vertebrae?” Or maybe a critical percent of them are just 15?

Anyway, back on topic, since it seems basically like Rh- people shouldn’t exist, why do they? There are three basic possibilities:

1. Random chance.
2. Founder effect in some populations
3. Some beneficial effect to being Rh- or heterozygous

If random chance were the solution, we’d expect to find Rh- people distributed in roughly equal quantities throughout the world, or much of it. This is not what we find. Rather, according to Wikipedia, Rh- is most common among the Basque people (21-36% of Basques are Rh-); fairly common among other Europeans (16%); rare among African Americans, who have some European admixture, (7%); occurs occasionally in Siberians (% not given); shows up in about 1% of Native Americans; and is almost totally unknown in Africans and “Asians.” (Remember that this only counts people who are homozygous for the negative allele; due to heterozygosity, approximately 10% of Native Americans have the the negative allele. By contrast, only 1% of “Asians” have the allele.)

If you’ve read a lot of my posts, that list should match a pattern you already know; you can see part of it at the top of the screen, but Haak’s data includes more of the relevant Siberian and Native American groups:

From Haak et al.

Click to get a good look. Unfortunately, different people use different colors on their charts, so “blue” or “yellow” don’t necessarily mean the same things on different charts. Luckily for us, the “dark blue” seems to represent the same thing in both charts.

Dark blue is an ancient, ancestral, shall we say indigenous DNA group that’s found in ancient European skeletons from places like Sweden and Hungary, and is found in large chunks in all modern European populations (Gypsies probably excepted.) Dark blue is also found, in smaller amounts, in some north African populations, west Asian (including the Caucasus and northern Middle East but not really the bulk of the Middle East,) India, and Siberia (the relevant groups here are the Chuvash, Mansi, Even, Selkup, Aleut, Tlingit, Yukagir, Tubalar, Altaian, Dolgan, and Yakut). It’s found in tiny bits in Native American DNA, either because Native Americans brought it with them when they crossed the Bering Strait, or because of recent European admixture. (Or both.)

Interestingly, the Basque have very little of the “teal” (light green in the graph at the top of the blog,) simply because teal was brought in with the Indo-European invasion and Basque aren’t Indo-European. Teal is also very common in India (Indo-European and all that,) but Rh- isn’t common in India.

The “orange” DNA (light blue at the top of the blog) is found throughout the Middle East, where Rh- isn’t, and isn’t found much in Siberia, where Rh- is.

In other words, the Dark Blue people left DNA in approximately the right amounts in all of the relevant people, and the other color-groups in the chart didn’t.

In Africa and Asia, it seems likely to me that the Rh- people actually are the result of random chance. But among the folks with Blue People admixture, I suspect that we are looking at a Founder Effect–that is, when the original band of hunter gatherers who became the Blue People split off from the other tribes, they just happened, by random chance, to have a higher than average percentage of people with Rh- alleles than the rest of the human population.

This happens all the time; if you were to just pick ten random people off the street and test their DNA, you’d likely find that your random population has some genes that are far more common or rarer than in humanity as a whole.

But this does not explain the persistence of Rh-, much less its rather high frequency among the Basque.

First, I want to stop and make a PSA about the Basque:

The Basque are not super people who descended directly from the gods, aliens, Neanderthals, the first primeval man, or whatever. They’re just some guys who, like the Sardinians, didn’t get conquered by the Indo-Europeans, and so never picked up an Indo-European language and held onto a slightly different culture, though they’ve had a ton of cultural contact with the Spanish and French over the years and probably all speak Spanish and/or French these days.

Humans–by which I mean “anatomically modern humans” as they are called–have been around for approximately 200,000 years. About 100,000-70,000 years ago, humans left Africa and spread out across the rest of the world. (We picked up our Neanderthal admixture around this time, so pretty much all non-Africans have Neanderthal DNA, and even the Africans probably have some Neanderthal DNA because it looks like some non-Africans later went back to Africa and intermarried with the people there, because humans have moved around a lot over the past 100,000 years.)

Indo-European, as a language family, didn’t get going until about 8,000 to 6,000 years ago. It didn’t reach France until about 3,000 years ago, and got to Spain even later.

In other words, the Basques are not the sole living descendents of the first peoples from 200,000 years ago, or Neanderthals from 40,000 years ago. They are among the few unconquered descendents of people who lived about 3,000 years ago. You know, about the time the Greeks and Romans were getting going, or maybe the Assyrian Empire. Not prehistory.

Back to our story.

Unfortunately, there isn’t a lot of research on why Rh- exists, but some folks have been pursuing the Toxoplasma Gondii angle. Basically, the idea is that if Sickle Cell Anemia exists because heterozygous sickle cell carriers are protected against malaria, even if folks who are homozygous for SSA die off.

Toxoplasma turns out to be one of the most common parasitic infections, infecting 30-50% of humans. I have yet to find what I consider a reliable-looking map of rates of T. gondii infection world-wide, but it infects about 22% of Americans over 12, and infection rates reach 95% in some places. (And 84% in France, probably due to bad hygiene and raw meat consumption.)

Even though T. gondii likes pretty much any warm-blooded host, they can only reproduce in cats/felids. So I wouldn’t expect any T. gondii in areas with no cats, like Australia before the Europeans got there.

One of the effects of T. gondii infection is slower reactions, so scientists have looked at whether people with Rh- blood or Rh+ blood have slower reactions with or without T. gondii infections.

The conclusions are kind of mixed, and I put this in the “needs more research” category due to some small Ns, but nevertheless, here’s what they found:

Among uninfected people in an ethnically homogenous population, Rh- males had faster reaction times than Rh+ males. However, when infected, the Rh-s become slower than the Rh+s (who showed very little change). But if we break the Rh+ group into homozygous Rh++ and heterozygous Rh+-s, we see something remarkable: the Rh++s have worse reaction times following infection, but the Rh+-s’ reactions times actually decreased!

The only problem with this theory is that T. gondii has probably historically been most closely associated with parts of the world with more cats, and Africa, the Middle East, and India historically had more cats than Europe, and certainly more than Siberia. If the idea is that being heterozygous is supposed to be protective against T. gondii, we’d expect to see more heterozygotes in areas with high rates of T. gondii, just as Sickle Cell Anemia is common in areas with malaria. We wouldn’t expect it in places like Siberia, where there are very few cats.

But perhaps the answer is more straightforward: Rh++ is protective against T. Gondii, but at the cost of lower reaction times. Rh– confers faster reaction times, but sucks against T. Gondii. Rh-s could therefore have an advantage over Rh++s and proliferate in areas with few cats, like Siberia.

But T gondii has had time to adapt to the older variant (Rh++;) Rh+- confuses it, thus offering protection against slower reaction times mostly by accident rather than positive selection for Rh+- people in areas with high levels of T. gondii.

Of course, this is all speculation; maybe folks in the Basque region have actually just had a lot of housecats and so contacted T. gondii more than other people, or maybe we’re just seeing an “Elderly Hispanic Woman Effect” due to the data being split into a lot of categories.

Things being as they are, I’d suggest studying the Basque and seeing if Basques with Rh- alleles have any traits that Basques with Rh+s don’t.

I really wish there were some more research on this subject! I guess we just don’t know yet.

ETA: I just realized something that, in retrospect, seems really obvious. If the French have an 85% T. Gondii infection rate, then the Basques–whose territory is partly in France and partly in Spain–may also have a very high infection rate. The French must have a ton of cats. Infection rates probably have more to do with the density of domesticated cats than of wild cats; the prevalence of Rh- and Rh+- alleles may have nothing to do with ancient cave people, but be a more recently selected adaptation. I don’t know when cats became common in Europe, but I’m guessing that plague-infested Medieval cities invited a fair number of cats. Hey, better T. Gondii than Yersina Pestis. If the Basques have somewhere near an 85% T. gondii infection rate, and have had it for a while–say, since the Middle Ages–their current high rates of Rh- blood may in fact be due to Rh+- folks being protected against the effects of infection.

I don’t know why I didn’t see that earlier.

Now I want to know whether people with T. Gondii are more likely to go on strike or start revolutions.

Why do Rh- People Exist?

Having the Rh- bloodtype makes reproduction difficult, because Rh- mothers paired with Rh+ fathers end up with a lot of miscarriages.*

The simplified version: Rh+ people have a specific antigen in their blood. Rh- people don’t have this antigen.

If a little bit of Rh+ blood gets into an Rh- person’s bloodstream, their immune system notices this new antibody they’ve never seen before and the immune response kicks into gear.

If a little bit of Rh- blood gets into an Rh+ person’s bloodstream, their immune system notices nothing because there’s nothing to notice.

During pregnancy, it is fairly normal for a small amount of the fetus’s blood to cross out of the placenta and get into the mother’s bloodstream. One of the effects of this is that years later, you can find little bits of their children’s DNA still hanging around in women’s bodies.

If the mother and father are both Rh- or Rh+, there’s no problem, and the mother’s body takes no note of the fetuses blood. Same for an Rh+ mother with an Rh- father. But when an Rh- mother and Rh+ father mate, the result is bloodtype incompatibility: the mother begins making antibodies that attack her own child’s blood.

The first fetus generally comes out fine, but a second Rh+ fetus is likely to miscarry. As a result, Female Rh- with Male Rh+ pairings tend not to have a lot of children. This seems really disadvantageous, so I’ve been trying to work out if Rh- bloodtype ought to disappear out over time.

Starting with a few simplifying assumptions and doing some quick back of the envelope calculations:

1. We’re in an optimal environment where everyone has 10 children unless Rh incompatibility gets in the way.
2. Blood type is inherited via a simple Mendelian model. People who are ++, +-, and -+ all have Rh+ blood. People with — are Rh-.
3. We start with a population that is 25% ++, +-, -+, and –, respectively.

So our 1st generation pairings are:

F++/M++   F++/M+-   F++/M-+   F++/M–

F+-/M++    F+-/M+-    F+-/M-+    F+-/M–

F-+/M++    F-+/M+-    F-+/M-+    F-+/M–

F–/M++     F–/M+-      F–/M-+     F–/M–

Which gives us:

10++,           5++, 5+-       5+-, 5++     10+-

5++, 5-+      2.5++, 2.5+-, 2.5-+, 2.5–   2.5+-, 2.5++, 2.5–, 2.5 -+      5+-, 5–

5-+, 5++      2.5-+, 2.5–, 2.5++, 2.5+-    2.5–, 2.5-+, 2.5+-, 2.5++      5–, 5+-

1-+,         It’s complicated   It’s complicated   10–

or

50++,   40+-,   21-+,   30–,   and some quantity of “It’s complicated.”

For the F–/M+- pairings, any — children will live and most -+ children will die. Since we’re assuming 10 children, we’re going to calculate the odds for ten kids. Dead kids in bold; live kids plain.

Kid 1: 50% -+,                     50% —

Kid 2: 25% -+, 25% —       25% -+, 25% —

Kid 3: 25% -+, 25% —       12.5% -+, 12.5% —    12.5% -+, 12.5% —

Kid 4: 25% -+, 25% —       12.5% -+, 12.5% —     6.3% -+, 6.3% —      6.3% -+, 6.3% ––

Kid 5: 25% -+, 25% —        12.5% -+, 12.5% —    6.3% -+, 6.3% —       3.1% -+, 3.3% —    3.1% -+, 3.1% —

Obvious pattern is obvious: F–/M+- pairings lose 25% of their second kids, 37.5% of their third kids, 43.3% of their fourth kids, 46.4% of their fifth kids, etc, on to about 50% of their 10th kids.

Which I believe works out to an average of 5–, 1+-

The outcomes for F–/M-+ pairings are the same, of course: 5–, 1+-

So this gives us a total of:

50++, 41+-, 22-+, 40–,  or  33% ++, 27% +-, 14% -+, 26% —  (or, 54% of the alleles are + and 46% are -).

(This assumes, of course, that people cannot increase their number of pregnancies.)

Running the numbers through again (I will spare you my arithmetic), we get:

35% ++, 32% +-, 11.8%-+, 21.4% —  (or, 57% of alleles are + and 43% are – ).

I’m going to be lazy and say that if this keeps up, it looks like the –s should become fewer and fewer over time.

But I’ve made a lot of simplifying assumptions to get here that might be affecting my outcome. For example, if people only have one kid, there’s no effect at all, because only second children on down get hit by the antibodies. Also, people can have additional pregnancies to make up for miscarriages. 20 pregnancies is obviously pushing the limits of what humans can actually get done, but let’s run with it.

So in the first generation, F–/M+- => 9–, 1+-  ; F–/M-+ => 9–, 1-+ (that is, the extra pregnancies result in 8 extra — children.) The F–/M++ pairing still results in only one -+ child.

This gives us 50++, 41+-, 22-+, 48– children, or 31%++, 25%+-, 13.7%, 30%– (or 51% + vs 49% – alleles.)

At this point, the effect is tiny. However, as I noted before, having 20 pregnancies is a bit of a stretch for most people; I suspect the effect would still be generally felt under normal conditions. For example, I know an older couple who suffered Rh incompatibility; they wanted 4 children, but after many miscarriages, only had 3.

Which leads to the question of why Rh-s exist at all, which we’ll discuss tomorrow.

 

*Lest I worry anyone, take heart: modern medicine has a method to prevent the miscarriage of Rh+ fetuses of Rh- mothers. Unfortunately, it requires an injection of human blood serum, which I obviously find icky.

 

 

 

 

Southern Election Data

(I divided the spreadsheet so it would fit comfortably on your screen.)

So I got curious about trends in the Southern election data, (see yesterday’s post on Northern election data and last week’s post about my migration/Civil War theory,) thinking to myself that perhaps an opposite trend happened in the South–maybe poor sods who couldn’t catch a break in slavery-dominated states decided to go test their luck on the frontier, leaving behind a remnant population of pro-slavery voters.

Methodology/discussion:

I took as the “South” all of the states south of the Mason-Dixon. This turned out to be incorrect for Delaware and Maryland, which both tended to vote against the Southern states; Delaware, IIRC, voted with Massachusetts more often than “Northern” New Jersey.

The practice of having the legislators rather than citizens vote for president persisted for longer in the South than in the North, especially in SC, which did not have popular voting until after the Civil War; all of SC’s votes here, therefore, come from the legislature.

A “yes” vote means the state voted with the Southern Block during the age before individual vote counts were recorded or the state did not allow individual voting. A “no” vote means the state voted against the Southern Block under the same circumstances.

Originally I had planned on using VA as my touchstone for determining the “Southern” candidates, but VA did not always vote with the rest of the South. So I decided which candidates were the “Southern” ones based primarily on how badly they polled in MA.

A few of the elections had some weird anomalies.

Four candidates ran in the 1824 election. Only one of them was popular in NE, so that was easy, but the other three each won electors in the South, which resulted in the election being decided by the House of Representatives. In this case, Jackson carried most of the Southern states, but not VA or KY, so I decided to count only votes for Jackson.

In 1832, SC decided to cast all of its votes for the “Nullification” (State’s Rights) party. Since “States Rights” is the more polite form of Civil War grievances, I decided to count this as SC voting in line with pro-slavery interests, even though it was not in line with the other Southern states.

In 28 and 32, the states of Georgia, Tennessee, Mississippi, and Alabama seem unsure how this “voting” thing works, and returned unanimous or near votes for their chosen candidates. Many Northern states also had anomalously high percents in those yeas, IIRC, so this may not be voter fraud so much as everyone just feeling like they ought to vote for the same guy.

In 1836, the Whigs ran four candidates in hopes of throwing the election to the House again, resulting in a fragmented Southern block. I counted all Whig candidates as part of the MA/Puritan side, and so give here the vote percents for Van Buren, the Democratic candidate.

In 1856, the Whig party had disintegrated, and two parties took its place. The Republicans, soon to be very famously anti-slavery, emerged in the North but do not appear to have run at all in the South; I don’t think they were even on the Southern ballots. In the South, an anti-immigrant/nativist party sprang up to balance the Democrats. It won few states, but performed well overall. I couldn’t decide whether to count the Democrats or the nativists as the more pro-South / pro-slavery party, so I wrote down both %s, Dems first and then nativists.

This oddity persists in 1860, when again the Republicans do not appear to have even been on the Southern ballots. The Democrats split in two, with one candidate running in the North against Lincoln, and another candidate running in the South on an explicitly pro-slavery platform, against the the “pro-union” party whose main platform was opposing the civil war. The Union party polled decently throughout the South–taking VA, KY, and Tenn.–but received very low %s in the North. The North, it appears, was not as concerned with trying to stop the Civil War as Virginia was.

Conclusions:

The data does not support my suspicion that less-slavery-minded people moved out of the Southern states. In fact, the most ardently pro-slavery, pro-secession states were Mississippi, Alabama, Arkansas, Florida, and Texas, who also happen to be the last 5 Southern states admitted to the Union, with last but not least Texas outstripping them all at 75%. In that same election, Virginia, the first Southern state, voted for the pro-union party.

So it looks like the same pattern appears here as in the Northern data: more conservative people have moved Westward.

However, the %s voting for the Southern candidates held fairly steady once the era of unanimous voting ended. Georgia, for example, went from 48% 1836 in to 49% in 1860. Mississippi went from 59% to 59%. VA hovered around 55%-50% until the last election. So I don’t see any clear trend of coastal states becoming more liberal over time, aside from maybe VA.

Theory: the inverse relationship between warfare and homicide

That whole myth about hunter-gatherers being peaceful and non-violent probably got its start because hunter-gatherers tend not to be as good at organized warfare as the Germans.

Homicide is an act of disorganized impulsive passion; warfare is an act of organized dispassion; the two are inverse of each other. Thus we see the highest homicide rates in the world’s least developed countries, and the lowest rates in its most developed countries.

 

Note that it is not an absolutely perfect correlation; many Latin American or Caribbean countries have higher homicide rates than even less-developed countries in Africa, but broadly speaking, the pinks and reds are poorer than the blues. (Russia excepted, ‘cuz Russia.)

Also, as you may recall:

Share of violent deaths, non-state societies vs. state societies

Countries involved in the world’s biggest wars:

WWI:

Nuclear stockpiles or programs by country:

(South Africa used to have nukes, but they got rid of them before the end of Apartheid.)

Here’s another graph that makes the size of the arsenals clear:

Source: SMTKS

And here’s another graph that says about the same thing, but is a wonderful example of how to display data:

Source: SMNTKS

I’m pretty sure this graph means we’re all going to die.

And likewise, space programs by country:

Also a nicely done graphic.

You might have heard about India’s space program:

Test launch of India’s GSLV Mk III

But have you heard of the Congolese space program?

To be fair, it’s more “One guy with a rocket-building hobby” than a real space program, but I understand where he’s coming from. Rockets are cool.

The point of all of these maps and graphs is that homicide rates tend to be highest, both today and throughout history, in the places with the lowest levels of social organization/complexity.

Even in our own society, convicted criminals are overwhelmingly lacking in the ability to handle complexity. It looks like they aren’t really all that much more retarded (note: PDF) than the general population (the truly intellectually impaired are often pretty highly supervised and lack the ability to execute many crimes, but are often victims of violence,) but they are drawn disproportionately from the dumber half.

According to respondents in the AR15.com forum thread “Cops and Lawyers – What percent of criminals/clients are retarded?” (Note: I know nothing about this forum or its reliability)

“Not retarded per se. My personal experience is most criminals stopped developing emotionally at about 3 or 4. They live life for the moment, think only of themselves, have no impulse control, can’t control their emotions, throw temper tantrums when they don’t get their way, can’t think past the next 10 minutes, don’t understand consequence, etc……. They are basically little children in adult bodies. Of course, most 3 or 4 year olds are better behaved than the average criminal, but you get the point.”

“I found that better than 90% of them were functionally illiterate, so when they say reading is fundamental, they aren’t kidding!”

“With the advent of welfare, it became profitable to squirt out children.It relegated men to the status of semen injectors. No men, no fathering. down the spiral 40 odd years and we’ve got multiple generations of female children “raising” children.
The results aren’t retarded, they are more like Comanches or Lakota, they have regressed several thousand years.
It is painful to watch good officers try to “reach” these kids. Watching with an unsympathetic eye, it is plain that most of these kids don’t even understand what the officers are talking about.
That’s the brutal truth that no one wants to face. These kids aren’t just lost-they are damned in our society.”

Recall our discussion back in Two Kinds of Dumb–just because someone has a low IQ, doesn’t mean they are retarded. But anyone who is illiterate (in our society) with the emotional maturity of a 3 or 4 your old is not very bright or capable of thinking through the results of their own actions.

The art of killing large numbers of people, by contrast, requires organization. One guy with a pointy stick might kill a few dozen guys who don’t have pointy sticks, but one guy who convince a thousand other guys to stand next to you with their pointy sticks, and you get this:

Source: Chapleton

The Romans didn’t conquer an empire by poking barbarians with pointy sticks; they did it by organizing themselves into an unstoppable war machine.

Armies do not generally fund themselves; they depend upon a vast support structure producing weapons, food, transportation, shelter, technology, etc. The bigger the army and more advanced the weaponry, the bigger the support structure has to be. Nukes take far more people to produce than pointy sticks, from the farmers making the food to feed the scientists working out the details to the structural engineers building the research labs to the guys building the rockets or planes to drop the bombs.

Complex organization requires large numbers of people working in close proximity without punching each other; it requires that people be able to suppress their own personal desires in pursuit of the group goal. All of this requires being less violent, less impulsive, and less inclined toward murdering each other.

Is gender dimorphism a luxury good?

So I was watching this documentary the other day, set in Norway, about whether or not gender dimorphism among humans is real.

Of course it’s real, but that’s not the point.

The documentary happened to interview a number of Norwegian women about why they chose to work in stereotypically “female” professions (the “paradox” here is that in one of the most “gender egalitarian” countries in the world, women are choosing to go disproportionately into stereotypically female professions instead of into STEM.) Then they interviewed female students somewhere in Africa, IIRC, who professed a desire to go into STEM and related fields.

African countries are not generally thought of as bastions of female equality and empowerment, though perhaps they should be.

Anyway, the Norwegian women wanted to go into feminine careers because they found those careers more “interesting”–they just wanted to do things that involved people, say, instead of boring old numbers. The African students, by contrast, wanted to go into technical or medical fields because they perceived these as high-pay and useful.

To make a Mazlow’s hierarchy of professions, we might say that doctors and civil engineers are necessities for a functioning society, while doing things you find fun and interesting is a luxury.

Back in the day–that is, back more or less in my childhood and nearby years–the gender split in the children’s aisles wasn’t so extreme. We didn’t have purple “girl Legos” and black “boy Legos;” they were just Legos:

1960 Legos Ad

The clothes have changed, too–these days, it is perfectly normal to send a girl to school wearing layers of sparkly fluff that would previously have been reserved for ballet recitals or Halloween. In my day, we just wore pants.

(There’s an obvious irony here, that the people who proclaim the loudest that male and female children [and adults] are neurologically the same and have the same preferences in jobs, toys, hobbies, etc., tend not to be the people who actually have children and have the most first-hand experience with their preferences.)

I was speaking with a friend recently, the youngest of five from a large extended family. They mentioned that as a kid, they always wore hand-me-downs; they received their first new piece of clothing (underwear excluded) at the age of ten. Which made me speculate that for poor people with several kids to clothe, clothing that could be worn by either gender might be seen as more useful than clothing that was obviously “for girls” or “for boys;” the same is true of toys, which are more useful if all of the kids are interested in them than if only one kid is interested in them. By contrast, rich people or people with only one kid may just get a specific item aimed at that kid.

 

Thus wealthier countries, despite their claims of egalitarianism, may use their wealth to indulge in more gendered behavior, goods, hobbies, jobs, etc., while less wealthy countries may focus their resources on high-utility, multi-use behaviors, goods, hobbies, jobs, etc.

Now, yes, I know that traditional societies did/do not dress their children in identical clothes; if you have 8 children, it is quite easy to have a set of dresses for four of them and a set of pants for the other four. But this is not necessarily any more gendered than our current clothing, and still leaves aside toys, jobs, etc.

Obviously I am just speculating; I’d be interested if anyone knows of any relevant data.

Adulterations in the Feed

It’s no secret that sperm counts have been dropping like rocks over the past 70 years or so (though the trend may have recently leveled out.)

” Sperm counts in the 1940s were typically well above 100m sperm cells per millilitre, but Professor Skakkebaek found they have dropped to an average of about 60m per ml. Other studies found that between 15 and 20 per cent of young men now find themselves with sperm counts of less than 20m per ml, which is technically defined as abnormal.” — from The Independent, “Out for the count: Why levels of sperm in men are falling“

While environmental effects (like smoking,) have effects on sperm counts in adults, these appear to be basically small or short-lasting. The biggest, longest-lasting effects on sperm counts appears to be the unterine environment where the future-low-sperm-count-male’s testicles were developing. Improper fetal testicle development => low sperm count for life. Eg,

“A man who smokes typically reduces his sperm count by a modest 15 per cent or so, which is probably reversible if he quits. However, a man whose mother smoked during pregnancy has a fairly dramatic decrease in sperm counts of up to 40 per cent – which also tends to be irreversible.”

What elsecould make a uterine environment hostile to testicular development?

How about too much estrogen?

I’ve posted before about Diethylstilbestrol, (or DES,)  is a synthetic nonsteroidal estrogen. Between 1940 and 1971, DES was given in large quantities to pregnant women to prevent miscarriages. Unfortunately, it turns out that pumping babies full of unnaturally high levels of estrogen might be bad for them–DES was discontinued as a medication for pregnant women because it gave their daughters cancer, (an actual epigenetic effect) and the sons appear to have high rates of transgender, transexual and intersex conditions.

Quoting the Wikipedia:

“In the 1970s and early 1980s, studies published on prenatally DES-exposed males investigated increased risk of testicular cancer, infertility and urogenital abnormalities in development, such as cryptorchidism and hypospadias.^[38][39]

“… The American Association of Clinical Endocrinologists (AACE) has documented that prenatal DES exposure in males is positively linked to a condition known as hypogonadism (low testosterone levels) that may require treatment with testosterone replacement therapy.^[43]

“… Research on DES sons has explored the long-standing question of whether prenatal exposure to DES in males may include sexual and gender-related behavioral effects and also intersex conditions. Dr. Scott Kerlin, a major DES researcher and founder of the DES Sons International Research Network in 1999, has documented for the past 16 years a high prevalence of individuals with confirmed prenatal DES exposure who self-identify as male-to-female transsexual, transgender, or have intersex conditions, and many individuals who report a history of experiencing difficulties with gender dysphoria.^{[45][46][47][48]}

“… Various neurological changes occur after prenatal exposure of embryonic males to DES and other estrogenic endocrine disrupters. Animals that exhibited these structural neurological changes were also shown to demonstrate various gender-related behavioral changes (so-called “feminization of males”). Several published studies in the medical literature on psychoneuroendocrinology have examined the hypothesis that prenatal exposure to estrogens (including DES) may cause significant developmental impact on sexual differentiation of the brain, and on subsequent behavioral and gender identity development in exposed males and females.”

Here is an excerpt from a paper, published in, I think, the early 40s.

Since the image quality is low, I’ve done my best to type it up for you:

“Experimental Intersexuality: The Effects of Combined Estrogens and Androgens on the Emryonic Sexual Development of the Rat

“RR. Greene, M. W. Rurrill and A. C. Ivy

“Department of Physiology and Pharmacology, Northwestern University Medical School, Chicago, Illinois

“In previous publications the authors have reported and described in detail the effects of large doses of sex hormones on the embryonic sexual development of the rat. Androgens, when administered to the pregnant female, cause a masculinization of the female embryos (Greene, Burrill and Ivy, ’38, ’39 a). The female type of differentiation of most sexual structures is inhibited and a male type of differentiation of those structures is stimulated. Administered estrogens cause a femininization of the male embryos (Greene, Burrill and Ivy, ’38, ’40) in that they inhibit the masculine type of differentiation of some sexual structures and, instead, cause a female type of differentiation.

“…The experimental demonstration that estrogens do have a profound effect…”

What are external sources of estrogens in modern life?

Birth control pills. I know FTM trans folks birth control pills for the hormones in them. (They are often cheaper and easier to get than hormones specifically prescribed for trans folks, especially if you have a female friend.)

Can those hormones stick around in a mother’s body even after she discontinues taking the pills?

Fat and estrogen appear to be correlated:

“Other conditions that cause low estrogen levels in younger women include excessive exercise, eating disorders and too little body fat.” (source)

“Excess estrogen in the body causes weight gain around the abdomen and upper thighs. … Weight gain caused by estrogen starts a vicious cycle. Excessive body fat produces the aromatase enzyme that synthesizes estrogen, thus creating more estrogen in the body, which then promotes additional weight gain, and so on, says Hofmekler.” (source)

“Researchers have found a correlation between estrogen and weight, particularly during menopause, when estrogen levels drop, but weight tends to rise. But since fat cells can produce estrogen, the issue facing researchers is how to target the estrogen receptors that will boost energy and manage hunger and not contribute to menopause-related weight gain.” (source)

“For postmenopausal women, estrogen levels increase with increasing BMI, presumably because conversion of androgens to estrogen in adipose tissue is a primary source of estrogen…” (source)

Since Americans have been getting fatter over the past century, I’d expect estrogen levels to be up, but I’ve found no studies on the subject so far. (Also, the Wikipedia claims there’s no evidence that birth control pills make people fat.)

However, I have found quite a bit of evidence that giving synthetic estrogen to animals makes them fatter:

(Stilbosol is another name for DES, as you may note in the ad’s upper right hand corner.)

Since the picture quality is bad, I’ll try to type it up for you:

Ralph:

“Ralph has been feeding cattle in New York state for 20 years. He runs 300 head a year through his feed lot, buying mountain (?) calves at 400 pounds and finishing them to about 1,000 pounds.  …

“”I lean very heavily on college tests and they’re in favor of Stilbosol. The first time we tried it, back in 1955, I noticed a very definite improvement in appetite.

“”Stilbosol is a ‘must’ in our feeding operations. It has added to our profit. If it didn’t, we wouldn’t be using it.””

Dan:

“We bring our cattle into the lots around 600 pounds. Feed for about 150 days. … We feed to all weights (950 to 1150 pounds) and take a little chance from time to time and feed t heavier weights,” Dan stated.

“We get about 2.75 lbs. daily gain. And I figure Stilbosol accounts for (unreadable) to 1/2 lb. of that daily gain. …

“Does Stilbosol make us money? There’s no doubt about it! Stilbosol has revolutionized the cattle business. I guess it’s the only good break through in the last ten years.”

Bill:

“”I tested Stilbosol. Took a bunch of 315 Montana yearlings and split them up. One group was actually lighter than the other. The only change I made in their rations was the addition of Stilbosol. The lighter group received Stilbosol. I figured that the lot fed Stilbosol gained over 1 1/2 lb. per day more than the lot which had no Stilbosol.

“”With all the competition, a man can’t afford to pas up anything that will lower his cost of grain. Stilbosol is one of them.””

John:

“We were trying to find the cheapest, most efficient ration. One group of calves received a ration containing Stilbosol. Another received a similar ration without Stilbosol. The group receiving Stilbosol had a feed conversion of (I can’t tell the number, but it’s clearly a single digit followed by .4). The group receiving no Stilbosol had a feed conversion of 10.35. The Stilbosol group gained 2.49 pounds per day. The group that did not receive Stilbosol gained 2.13 pounds per day.

“With Stilbosol, we figure our cost of grain to be substantially lower than similar rations without Stilbosol.) “

Four farmers wouldn’t lie to us, would they?

Interestingly, eating large quantities of beef while pregnant was one of the things that The Independent article (linked at the top) noted was correlated with low sperm counts years down the road in the all-grown-up-fetuses.

Of course, people who eat more beef may just weigh more, or have some other factors besides adulterations in the cattle feed.

DES was also put in chicken feed, for the exact same reasons as cattle feed, until it came out that DES causes cancer in humans. It was discontinued as a feed additive in the late 70s.

These days, I don’t know what–if anything–they’re using to finish cattle, but we may note that the vast majority of cattle are still finished in feedlots where they get much fatter than they would naturally. (That is, by wandering around eating grass like they normally do.) Feedlot cattle are, to put it bluntly, unnaturally fat.

Now I’m going to do a little math. The Independent article was published in 2010, and states that the article on falling sperm rates was published 19 years prior, or in 1991. The study therefore compared men in the 1940s to men in the 1980s and 1990. Men in the 1940s were fetuses before the age of feedlots, birth control pills, DES, or DES-fed cattle and chicken. Young(ish) men in 1990, by contrast, were born between 1950 and 1970–all within the era of feedlots, BCPs, DES, and DES-fed cattle and chicken.

If it is true that sperm counts have stabilized since the 90s, that is a point potentially in favor of my theory, since after the 70s, DES was basically gone.

This is all me speculating out loud, of course.

 

 

 

Further implications of hippocampal theory

So while on my walk today, I got to thinking about various potential implications of the hippocampal theory of time preference.

The short version if you don’t want to read yesterday’s post is that one’s degree of impulsivity/ability to plan / high or low time preference seems to be mediated by an interaction between the nucleus accumbens, which seems to a desire center, and the hippocampus, which does a lot of IQ-related tasks like learn new things and track objects through space. Humans with hippocampal damage become amnesiacs; rats with the connection between their nucleus accumbens and hipocampus severed lose their ability to delay gratification even for superior rewards, becoming slaves to instant gratification.

So, my suspicion:

Relatively strong hippocampus => inhibition of the nucleus accumbens => low time preference.

Relatively weak hippocamus => uninhibited nucleus accumbens => high time preference (aka impulsivity.)

Also, Strong hippocampus = skill at high IQ tasks.

Incentivise traits accordingly.

Anyway, so I was thinking about this, and it occurred to me that it could explain a number of phenomena, like the negative correlation between weight and IQ, eg:

Shamelessly stolen from Jayman’s post. As usual, I recommend it.

(Other theories on the subject: Intelligent people make lots of money and so marry attractive people, resulting in a general correlation between IQ and attractiveness; there is something about eating too much or the particular foods being eaten that causes brain degeneration.)

People generally claim that overweight people lack “willpower.” Note that I am not arguing about willpower; willpower is only a tiny part of the equation.

The skinny people I know do not have willpower. They just do not have big appetites. They are not sitting there saying, “OMG, I am so hungry, but I am going to force myself not to eat right now;” they just don’t actually feel that much hunger.

The fat people I know have big appetites. They’ve always had big appetites. Some of them have documented large appetites going back to infancy. Sure, their ability to stay on a diet may be directly affected by willpower, but they’re starting from a fundamentally different hunger setpoint.

So what might be going on is just a matter of whether the hippocampus or nucleus accumbens happens to be dominant. Where the NE is dominant, the person feels hunger (and all desires) quite strongly. Where the hippocampus is dominant, the person simply doesn’t feel as much hunger (or other desires.)

That a strong hippocampus also leads to high IQ may just be, essentially, a side effect of this trade-off between the two regions.

We might expect, therefore, to see higher inhibition in smart people across a range of behaviors–take socializing, sex, and drug use. *Wanders off to Google*

So, first of all, it looks like there’s a study that claims that higher IQ people do more drugs than lower IQ people. Since the study only looks at self-reported drug use, and most people lie about their illegal drug use, I consider this study probably not very useful; also, drug use is not the same as drug addiction, and there’s a big difference between trying something once and doing it compulsively.

Heroin and cocaine abusers have higher discount rates for delayed rewards than alcoholics or non-drug-using controls

IQ and personality traits assessed in childhood as predictors of drinking and smoking behaviour in middle-aged adults: a 24-year follow-up study (they found that lower IQ people smoke more)

Severity of neuropsychological impairment in cocaine and alcohol addiction: association with metabolism in the prefrontal cortex (Cocaine users are dumb)

HighAbility: The Gifted Introvert claims that 75% of people over 160 IQ are introverts.

Research Links High Sex Drive To High IQ, But Brainiacs Still Have Less Sex Than Everyone Else (Spoiler alert: research does not link high sex drive to IQ. Also, NSFW picture alert.)

I am reminded here of a story about P. A. M. Dirac, one of my favorite scientists:

“An anecdote recounted in a review of the 2009 biography tells of Werner Heisenberg and Dirac sailing on an ocean liner to a conference in Japan in August 1929. “Both still in their twenties, and unmarried, they made an odd couple. Heisenberg was a ladies’ man who constantly flirted and danced, while Dirac—’an Edwardian geek’, as biographer Graham Farmelo puts it—suffered agonies if forced into any kind of socialising or small talk. ‘Why do you dance?’ Dirac asked his companion. ‘When there are nice girls, it is a pleasure,’ Heisenberg replied. Dirac pondered this notion, then blurted out: ‘But, Heisenberg, how do you know beforehand that the girls are nice?'”^[30]” (from the Wikipedia.)

Folks speculate that Dirac was autistic; obviously folks don’t speculate such things about Heisenberg.

Autism I have previously speculated may be a side effect of the recent evolution of high math IQ, and the current theory implies a potential correlation between various ASDs and inhibition.

Looks like I’m not the first person to think of that: Atypical excitation–inhibition balance in autism captured by the gamma response to contextual modulation:

The atypical gamma response to contextual modulation that we identified can be seen as the link between the behavioral output (atypical visual perception) and the underlying brain mechanism (an imbalance in excitatory and inhibitory neuronal processing). The impaired inhibition–excitation balance is suggested to be part of the core etiological pathway of ASD (Ecker et al., 2013). Gamma oscillations emerge from interactions between neuronal excitation and inhibition (Buzsaki and Wang, 2012), are important for neuronal communication (Fries, 2009), and have been associated with e.g., perceptual grouping mechanisms (Singer, 1999).

Also, Response inhibition and serotonin in autism: a functional MRI study using acute tryptophan depletion:

“It has been suggested that the restricted, stereotyped and repetitive behaviours typically found in autism are underpinned by deficits of inhibitory control. … Following sham, adults with autism relative to controls had reduced activation in key inhibitory regions of inferior frontal cortex and thalamus, but increased activation of caudate and cerebellum. However, brain activation was modulated in opposite ways by depletion in each group. Within autistic individuals depletion upregulated fronto-thalamic activations and downregulated striato-cerebellar activations toward control sham levels, completely ‘normalizing’ the fronto-cerebellar dysfunctions. The opposite pattern occurred in controls. Moreover, the severity of autism was related to the degree of differential modulation by depletion within frontal, striatal and thalamic regions. Our findings demonstrate that individuals with autism have abnormal inhibitory networks, and that serotonin has a differential, opposite, effect on them in adults with and without autism. Together these factors may partially explain the severity of autistic behaviours and/or provide a novel (tractable) treatment target.”

This may not have anything at all to do with the hippocampus-NA system, of course.

Schizophrenic patients, on the other hand, appear to have the opposite problem: Hyper Hippocampus Fuels Schizophrenia?:

““What we found in animal models and others have found postmortem in schizophrenic patients is that the hippocampus is lacking a certain type of GABA-ergic [GABA-producing] neuron that puts the brakes on the system,” says Grace. “What we’re trying to do is fix the GABA system that’s broken and, by doing that, stabilize the system so the dopamine system responses are back to normal, so that we can actually fix what’s wrong rather than trying to patch it several steps downstream.””

Wow, I made it through two whole posts on the brain without mentioning the amygdala even once.

African Americans, Hispanics, and longevity

I’ve known for a while that women live longer than men, Hispanic Americans live longer than Euro Americans, and the oldest people in the US are disproportionately black:

At 116 years old, Susannah Mushatt Jones is not only the oldest woman in the US, but also the entire world.

One theory I considered was that higher infant mortality rates in Mexico (I don’t actually know the infant mortality rates in Mexico, this is just an idea,) results in the deaths of premature infants and others with severe health problems, whereas in the US these infants survive for several years–maybe even decades–before dying. The population of Hispanic immigrants, therefore, does not include these children–they’re already gone–but the US population does. This could result in a higher life expectancy among the immigrants than among non-immigrants.

But what about women and blacks? Their infant mortality would be included in the native rates, and even if worse medical care resulted in higher infant mortality among them, this still wouldn’t explain why so many supercentenarians are black.

While researching hippocampi yesterday, I ran across an article about hippocampal volumes in the elderly: Brain Morphology in Older African Americans, Caribbean Hispanics, and Whites From Northern Manhattan

We already know that different people age at different rates, but it appears as well that different races age at different rates, with black brains aging the slowest:

“Results of the regression analysis revealed significant effects of age, sex, vascular disease history, and race/ethnicity on relative brain volume (F_5,685 = 38.290, P < .001). For each additional year in age, there was an associated 0.3% decrease in relative brain volume (β = −0.003, t = 10.34, P < .001) (Figure 2). Relative brain volume among women was 2% larger than that among men (β = 0.02, t = 5.93, P < .001). Hispanic (β = 0.03, t = 7.20, P < .001) and African American (β = 0.02, t = 4.09, P < .001) participants had 2.8% and 1.6% larger relative brain volumes than white subjects, respectively. Finally, for each additional vascular disease, there was a 0.5% associated reduction in relative brain volume (β = −0.005, t = −2.70, P < .001). When interaction terms were entered into the model, none were significant, demonstrating that the association of vascular disease history and age with relative brain volume did not differ across race/ethnicity or sex. Analysis of variance controlling for age and vascular disease history confirmed main effects of sex (F_1,685 = 34.906, P < .001) and race/ethnicity (F_2,685 = 23.528, P < .001) but no sex × race/ethnicity interaction (F_2,685 = 0.167, P =.85) (Figure 3).“

Relative brain volume across racial/ethnic groups and by sex.

relationship among chronologic age, race/ethnicity, and relative brain volume.

 

So why are white males (at least in these samples) aging so quickly?