Below is why I haven’t been blogging much….

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Today we debuted the Denialism Blog, while David Dobbs of Smooth Pebbles bids farewell to ScienceBlogs. David offers cogent rationales for why he decided to leave ScienceBlogs (the proximate reason is that he just isn’t posting much as far as bloggers go). One thing to note that is I don’t think a blog is really worthwhile for most people without an intelligent commentariat. I’ve learned a lot from critiques, suggestions and recommendations from comments on my blogs over the past 5 years. Of course, the key is intelligent. Most humans aren’t very smart, so they’re basically just expending the minutes in your life.

Just a heads up, Genetics & Health has morphed into Eye on DNA. Update your RSS feeds!

Ape gestures and language evolution:

The natural communication of apes may hold clues about language origins, especially because apes frequently gesture with limbs and hands, a mode of communication thought to have been the starting point of human language evolution. The present study aimed to contrast brachiomanual gestures with orofacial movements and vocalizations in the natural communication of our closest primate relatives, bonobos (Pan paniscus) and chimpanzees (Pan troglodytes)…It was found that homologous facial/vocal displays were used very similarly by both ape species, yet the same did not apply to gestures. Both within and between species gesture usage varied enormously. Moreover, bonobos showed greater flexibility in this regard than chimpanzees and were also the only species in which multimodal communication (i.e., combinations of gestures and facial/vocal signals) added to behavioral impact on the recipient.

It is important to remember that phylogeny does not always track morphology or ethology. After all, superficially dolphins and fish exhibit gross morphological similarities, and domestic dogs are the non-human species most sensitive to the cues and messages we send via facial expressions. The power of natural selection can utilize the extant genetic variation within disparate lineages and drive them toward cognate phenotypic conformations. So, I think we should be cautious about the insights that we can glean from studies of our nearest genetic relatives in regards to our own species’ evolutionary history. In any case this work might be read with provisional paper on chimpanzee population substructure in mind.

Update: ScienceNow has a good summary.

A few years ago I pointed out to M. Yglesias that Turkey was more religious than the United States (he emailed me immediately and agreed that that characterization was about right). Less than a year ago I offered that Turkey was a nation with a greater percentage of Creationists than the United States, and so it was not culturally suitable for EU admission. Today M. Yglesias has a post where he suggests that the AKP, the current moderate Islamist party in power in Turkey is basically an analog to the Republican party. There are obviously differences (see Daniel Larison for more exposition), while the AKP has been from its inception (through itself proper or its predecessors) the vehicle for upwardly mobile religious conservatives, the Republican party has been transformed within the past few generations from a party dominated by elite affluent mainline WASPs to one where evangelicals call the shots (notionally at least). Nevertheless, along with Yglesias I tend to think that the rise of groups like the AKP is a good thing, even if they are regressive they accept the democratic principle and so are agents for long term (I mean generations, not years) cultural evolution. The EU agrees. But here is a paradox: I believe that genuine cultural democraticization makes it less plausible that Turkey could be an EU member because at the grassroots it is a far less European nation than its secular elite wants to project.1 And yet the same people who would wink at the idea of dividing North American between Jesusland and the United States of Canada tend to favor admission into the EU of a nation which is still mostly Allahland!

1 – Of course overall the EU been an elite pushed project, and democratic sentiment has tended to give a rubber stamp to something which was already fait accompli. With Turkey though I think this is problematic because the chasm between the alcohol drinking secular elite and Christian missionary throat cutting non-elites is pretty wide.

The New Yorker has an excellent article on geriatrics and the physiology of aging, including a mention of the classic studies in C. elegans. Now I know we have some older readers, and I hope they take no offense, but I have to say, shit, getting old must really suck. And for the younger readers, we are reminded that time is short– for the love of God, eat, drink, and screw while you still can.

Heiner Rindermann, Relevance of education and intelligence at the national level for the economic welfare of people, Intelligence, In Press

Cognitive abilities are important for the economic and non-economic success of individuals and societies. For international analyses, the collection of IQ-measures from Lynn and Vanhanen was supplemented and meliorated by data from international student assessment studies (IEA-Reading, TIMSS, PISA, PIRLS). The cognitive level of a nation is highly correlated with its educational level (r = .78, N = 173). In international comparisons, it also shows a high correlation with gross national product (GNP, r = .63, N = 185). However, in cross-sectional studies, the causal relationship between intelligence and national wealth is difficult to determine. In longitudinal analyses with various samples of nations, education and cognitive abilities appear to be more important as developmental factors for GNP than economic freedom. Education and intelligence are also more relevant to economic welfare than vice versa, but at the national level the influence of economic wealth on cognitive development is still substantial.

Combining IQ scores with a variety of other assessments of average cognitive ability at the national level has a lot to recommend it, and I’m glad others have caught on. The conclusions are quite interesting:

The results reported here show that during the last third of the 20th century, education and cognitive abilities were more important for economic wealth than economic wealth was for education and cognitive abilities. This result is stable across the different national samples of education and ability and remains after adding additional factors like economic freedom. Intelligence is even more important for wealth than economic freedom (see also Weede, 2006)! Whereas the importance of intelligence for many personal life outcomes has been recognized for some time (Gottfredson, 2003 and Herrnstein and Murray, 1994), we should realize that intelligence is also an important determinant for the economic and social development of nations (for example the functioning of institutions in the systems of law, economics and politics). The present study shows that a high level of cognitive development can be an antecedent and likely cause for economic growth, but other macro-social outcomes (e.g., democracy, rule of law, national power or health) are likely to be influenced by education and intelligence as well (Rindermann, submitted for publication and Rindermann, submitted for publication). Certainly the positive influence of young people’s schooling and intelligence on the level of economic freedom 30 years later (Fig. 4 and Fig. 5) deserves further investigation. Future theoretical and empirical research has to analyze the causal mechanism underlying the effects of ability on development of societies in a more detailed manner. For example, there is a positive relationship with low government spending ratio (r = .47 and rp = .24). Abilities seem to enable a more liberal economic constitution and thriftiness of state interventions. Conversely, a population with low education and intelligence seems to necessitate more state intervention, which tends to widen the influence of powerful special-interest groups.

So higher IQ populations tend to be more libertarian?

A re-colored version of Figure 1 — a world map — is below the fold.

Reading The Fall of Rome: And the End of Civilization made me a bit more curious about ‘the Dark Ages.’ So with that in mind I picked up Europe after Rome: A New Cultural History 500-1000. One page 29:

If we take a long-term perspective, however, it is clear that inherited Roman bureaucracy did not endure. To assert that it decayed would be to adapt and inappropriate narrative of ‘decline and fall.’ Rather, its constituent elements-documentary forms, legal norms, tax accounting, judicial and archival procedures, and so on-disaggregated and thinned out. In places-but only in some places-fragments of the once-coherent bureaucratic regime then perished. Other fragments took on a new life. Men of property freed slaves, negotiated marriage contracts, endowed churches, and arranged their testamentary bequests in formal documents….

What does decline and fall mean if not the collapse of the social order? Well, it means many things. As Daniel Larison contended in response to my previous post there was a problematic attitude amongst the older generation of classicists to idealize and world of Greece and Rome, as if nothing of greatness occurred between 476 and the Renaissance (an attitude that came to the fore, not surprisingly, during the Renaissance). So you have peculiar situations where authors can report an unending sequence of facts which suggest an epoch of relative material scarcity and decreased social complexity who just won’t admit that judged by these metrics there was a downsizing.

Update: Daniel Larison has a response. Let me be clear about one thing: I do not prefer diplomatic or institutional history. Nor do I shun it. But, I am curious as someone who wants to get the richest, most multi-dimensional, perception of the past, how the “small folk” lived.

How often should we expect to observe events that are 40 standard deviations above the mean? Probably not ever. If we do observe such events more frequently than never, that may be because our initial guess was based on an incorrect model. To pick an example relevant to current events, how many people do you think you’re capable of killing in cold blood — that is, elaborately planning the set-up, commission, and aftermath? Let’s assume you’re a civilian, not a soldier. I’m guessing most people would say zero, with maybe 1-5% capable of killing in the low single digits, and they’d probably know the victims (e.g., jilted lover). Committing a spree shooting seems so out-there that we ought to observe such things only incredibly rarely (maybe once every 100 years?).

Yet just recently a student at Virginia Tech killed 32, and not long ago the Columbine High School shooters killed 12, and in 1966 the UT-Austin shooter killed 13 (not to mention the dozens of wounded per incident). Such shootings are of course rare, but there’s “rare” and then there’s rare. This means that we’re probably looking at the problem the wrong way: it’s likely not true that number of people you could kill is normally distributed about a very low mean (say less than 1). What could make such extreme events so common? The math here will bore those who have studied probability, but hopefully the examples will be worth reading about. And for those who haven’t studied probability, this will make a nice “math for the people” intro to different types of distributions.

One easy way for extreme events to be more common than seems plausible is if several variables are involved which interact multiplicatively with each other. To understand some key differences between an additive vs. multiplicative scenario, consider rolling three 6-sided dice, each numbered 0 – 5, with each face equally likely and each die independent of the others. Suppose in Game A we record the “score” as the sum of the numbers showing, while in Game B we record their product.* The probability distributions of scores in the two Games are shown in the pictures linked to below:

We immediately see that the range of possible scores in A is far narrower than in B: 0 – 15 vs. 0 – 125, respectively. Thus, the “extreme score” is much more extreme in the multiplicative case. Importantly, the highest score in A is as likely to happen as its counterpart in B, since there is only 1 outcome out of 216 that will result in a highest score for either Game (all 5s). However, the lowest score in A is less likely to happen than its counterpart in B, since only 1 of 216 outcomes will give it in A (all 0s), but in Game B there are (1 – (5/6)^3) or about 42% of the 216 outcomes — so, 91 outcomes (in B, just one 0 is needed for the entire score to be 0). We can thus tell that the probability distribution is symmetrical in A (like a bell curve), but highly skewed in B — in the latter, most of the mass of the curve is concentrated at the lower values (the median is ~5). Still, the expected value is higher in B than in A (15.6 vs 7.5), due to the greater extreme values skewing the distribution in B.

To see how far off-base our thinking can go if we misjudge the way that the variables are related to the score, let’s say we observed the equivalent of a score of 125. Had we assumed that the variables were contributing to the score additively, we would then say that this data-point was (125 – 7.5) / 2.96 = 40 SD above the mean! For comparison, this would be like measuring the height of a human being who was 16 feet tall. That’s so rare you’d think it was a typo. However, if we paused and thought “y’know, maybe the variables interact with each other,” then we might settle on the more realistic idea that such an event had a probability of 1/216 = .005. Converting this into a z-score gives 2.6 SD above the mean, which is still rare in a sense but far more modest and realistic than the erroneous estimate.

Returning to reality, in summarizing the findings of much of the creativity research, I noted that creativity or genius appears to result from the multiplicative interaction between, for instance, high intelligence, together with various personality traits like Psychoticism. This creates a skewed, log-normal distribution whereby most people don’t produce anything creative enough to earn the esteem of those who matter, and a tiny handful dominate entire fields. That’s why the dice range from 0 to 5 instead of from 1 to 6 in the example above: 0 is special since it indicates lack of some key trait (intelligence, curiosity, persistence, etc.).

It’s conceivable that “cold-blooded homicidal output” is also log-normally distributed, with most people not killing anyone in cold blood (or at all), and a tiny few killing lots (e.g., spree shooters, serial killers, etc.). We know from the work on the MAO gene that those with “warrior genes” typically commit violence only when they treated violently during childhood, so that’s at least one interaction effect, as well as the sex-by-genotype interaction; presumably there are others (perhaps being taunted frequently at school). Also, researchers in finance talk about how frequently disasterous events occur.

And on the less gruesome side, personal “allure” is probably log-normally distributed, not just because physical attractiveness is probably so distributed, but a person’s demeanor animates (interacts with) their plastic form. To make this intuitively clear, model your attraction to someone according to the metaphor “to have a crush” — consider the average person who embodies your preferences for physical attractiveness (who has traits P), and for intelligence & personality (who has traits I). Let’s say that those with traits P make you weak enough to feel like you were burdened by a 10-pound weight, and that those with traits I also made you feel burdened by a 10-pound weight. Now, how much of a sinking feeling would you get if the person had it all in one package? I’d guess that you’d feel weighed down by 100 pounds, not just 20. Ah, romantic love — one local sickness compounded by many others!

In this way, “freak” occurrences may not be so freaky after all, if the proper relationship holds between the factors that contribute to the event.

* This is not exactly the difference between normal and log-normal distibutions, since each random variable (the outcome of a particular die) isn’t normally distributed but uniformly distributed. However, rather than concoct some bizarre game where the outcomes for a die are normal, I’ve altered some irrelevant details to convey the gist of the difference while keeping the analogy easily accessible.

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