Learning – theory and practice

This is a rewritten version of this old post.

Motive

Most people want to learn stuff. Some people prioritize learning higher than other people. But what is true for all people, is that they should learn as much as possible in the time they spend on it. This essay is about just that, optimizing learning speed.

The concept of information concentration

Think of a given chemical that is soluble in water. The water can contain more or less of this chemical. We call this the concentration. It is the same way with language and information. Think of language as a way of communicating info. Expresses vary in how much information ‘contain’ (communicate). From this, we can think of the amount of information per unit of language as the information concentration.

Relevant information

There is a lot of information communicated by a stream of language, not all of which we care about. There are two conditions for being relevant information: 1) It must concern the topic in which we are interested. 2) It must be information we do not already possses. In some cases, one is just generally curious, so the first condition may be easily satisfied. In other cases, one seeks information about a very particular topic. One sometimes does read a nonfiction book twice, but usually it is because one did get all of the information from it the first time because one read it too fast or without paying sufficient attention. Let R be the fraction of relevant information to the total amount of information.

Speed of the language stream

The speed of which we are exposed to the language stream varies. In reading, this various with reading speed, which is adjustable to some degree. In listening, it depends if it is live or not. If it isn’t live, then one can perhaps speed up the language stream. For instance, if one is watching a lecture in VLC, one can speed up the playback (this feature is also found in some Youtube videos now, e.g. for Khan Academy). If the speech stream is live, one can perhaps ask the speaker to speak faster. But in many cases this is not possible, such as lectures, or perhaps the person is already at his maximum speed (maximum speed without starting to be incomprehensible to the listener).

There are physical limitations as to how fast things can go. Even given a very low information concentration, sooner or later it does not work to increase the speed of the speech stream. There are limits on how fast speech can be while one still is able to recognize the words. For reading there is a similar limit. It is possible to increase one’s reading speed, especially with training. But there are limits, sooner or later it is simply not possible to recognize the words any faster due to physical limitations of eye movement.

Speed of the information stream

If one combines the two concepts introduced above, speed of language stream and information concentration, one gets the new concept of speed of the information stream. It is the speed by which information is communicated by the language stream. This is generally faster for written language streams, since reading a word is faster than listening to it.

Generally, the higher the speed of the information stream, the smaller a fraction of the information will one acquire. If one is reading very fast, one will miss out on a lot of details. Just how high a fraction of the information is acquired depends on many factors such as intelligence, mastery of the language, tiredness, interest in the topic and so on.

Skipping irrelevant information

Usually, some parts of the information stream will be irrelevant language as defined above. When this happens, we want to skip ahead in the language stream to the next part where it contains relevant information. If the language stream is non-live speech or written, one can generally skip past the irrelevant parts. It depends on whether the irrelevant parts are grouped together. If the speaker/writer just spreads irrelevant info throughout the stream, then it is more difficult to skip the irrelvant parts.

If stream is live (spoken or written), one can generally not skip. Although in some cases e.g. live streaming over the internet/TV, one can stop paying attention to the stream and get back to it when it begins containing relevant information again. If it is a one-to-one conversation, then one can perhaps ask the speaker to skip ahead. This may be considered rude. If it is a one-to-many live speech situation, one can generally not skip past. This is the case with live lectures.

Conceptually, the idea of skipping information is to keep R (the fraction of relevant information) as close to 1 as possible.

Attention span, mental energy, and available time

Some people have a hard time staying focused, that is, how long they can concentrate at a time before needing a break. Let’s call this concentration time.

Some people have inexhaustible amounts of mental energy. They can spend their entire day learning without getting mentally tired. Other people cannot. Let’s call this mental energy.

I imagine that concentration time and mental energy correlate positively, but probably not perfectly. Thus, there are going to be people who can spend practically all day learning as long as it is not in intervals of more than 1 hour. On the other hand, there are going to be people who only have the mental energy to learn for 4 hours a day, but can keep concentration up for 4 hours straight.

Music seems to have some effects on levels of mental energy. Music perhaps works only for when one is reading, although it seems possible that some people could get something out of listening to music while listening to speech as well. I find that I can go on for hours if I listen to the right music. Generally one wants to avoid distracting music. Often this happens when one starts paying attention to the lyrics instead of the language stream. For this reason, I generally prefer music with either no lyrics, or inaudible lyrics. In that way I don’t get distracted.

The relationships between time spent, speed of the language stream, information concentration and mental energy are probably not so straightforward. Perhaps when one is near max speed of acquiring information, the consumption rate of mental energy is higher than if one was learning at 80% speed. Depending on one’s levels of mental energy then, it might be an idea to not work as full speed, but slow down so as to not run out of mental energy during the day.

Time constraints are another issue. If one has close to every hour one is awake, then one should spread out one’s mental energy over the entire day to optimize the information acquired per day. If one only has a few hours a day to learn, learning at max speed will be more important.

Information stream speed and concentration

My thoughts tend to wander. This happens especially when my brain is not working at maximum or close to maximum capacity. When I do, I stop paying attention to the stream of language before me and think of other stuff. In that way, I’m not learning very much from the stream.

For my focus to work efficiently, I need to avoid language streams with too low information density. For this reason, I almost never use spoken language because I cannot effectively vary the speed so as to match the information stream speed that I want.

Self-control and distractions

People differ in their levels of self-control. In regards to learning, a relevant aspect is the ability to avoid getting distracted by other things. Most relevant today is perhaps the ability to avoid spending many hours talking about irrelevant matters on instant messengers, and not spending a lot of time on social media sites (Facebook, Google+, Twitter) or other similar sites (reddit, 4chan, 9gag).

People who have poor self-control may need to take measures to avoid falling prey to such temptations. Perhaps a good idea is to not read on a computer where there is a browser ready nearby that can take one to one of the sites mentioned before. I do most of my serious reading on a tablet in my bed, where there are fewer distractions. You may need to log off instant messengers, turn off the phone etc. to avoid distractions.

If you are not getting things done, you may need to sit down and think about how you want to structure your learning. Many people have trouble concentrating at home. In that case, it may help to go to a local library. If learning is important to you, you should find ways to optimize it. Perhaps this involves turning off the computer.

If you own a TV, you should sell it immediately. TVs are a waste of time.

Lectures and study groups

To put things together. We want to avoid irrelevant information. This is important when choosing which source to learn from. Lectures are generally not a good way of learning, so avoid them if possible. If not possible, then spend time at lectures reading. This may involve drowning out the noise of the lecture with music in a headset. I have done this extensively for lectures at high school and university, with and without music.

If one still wants to watch lectures, even socially, then it might still be an idea to stay at home. This is because the teachers vary in their teaching abilities as well. So, perhaps one can find better lectures on the same subject on the internet and watch those instead. If one can, then one can perhaps arrange a study group at home where one watches the lectures. This way one can also skip stuff that isn’t relevant if that is true for everybody in the group.

Since people vary so much in intelligence, learning speed, mental energy etc., it may be a good idea to learn by yourself instead of using study groups. If you have to use a study group, make sure to end up in one with people with a similar desire to learn as you have.

Handbooks of intelligence

Sometimes, one sees references to this or that handbook of intelligence. I have not previously read any of these, not even in part, because I could not find any anywhere online. However, today I took a new look and found to my surprise three such books:

Sternberg, R. J. (Ed.). (2000). Handbook of intelligence. Cambridge University Press.

Sternberg, R. J. (Ed.). (2004). International handbook of intelligence. Cambridge University Press.

Sternberg, R. J., & Kaufman, S. B. (Eds.). (2011). The Cambridge handbook of intelligence. Cambridge University Press.

I was actually looking for one chapter in the first book:

Loehlin, John (2000). “Group Differences in Intelligence”. In Robert J. Sternberg. The Handbook of Intelligence. Cambridge University Press

which I saw on Wikipedia when I was re-reading the Minnesota Transracial Adoption Study study article. Skimming the contents of the books, leaves one not surprised given that the editor is Sternberg who does little quality research himself, endlessly promotes his triarchic theory even tho every study I’ve seen of it shows that it does not fit the data better than traditional g models, and despite it lacking support from mainstream scholars in the field.

One might thus wonder why Sternberg edits all these books, if his opinions are not mainstream. One can only speculate, but presumably because he’s at a rich university and has politically respectable opinions. Surely, printing these behemoth books costs a fortune.

Still, some of the chapters are by respectable authors (looking at the newest handbook, I see: Mackintosh, Fagan, Haier, Nettelbeck, Rindermann, Deary, and Hunt), and will surely be useful to have access to. :)

Review: The Roma: A Balkan Underclass (Jelena Cvorovic)

www.goodreads.com/book/show/23621169-the-roma

Richard Lynn is so nice to periodically send me books for free. He is working on establishing his publisher, of course, and so needs media coverage.

In this case, he sent me a new book on the Roma by Jelena Cvorovic who was also present at the London conference on intelligence in the spring 2014. She has previously published a number of papers on the Roma from her field studies. Of most interest to differential psychologists (such as me), is that they obtain very low scores on g tests not generally seen outside SS Africa. In the book, she reviews much of the literature on the Roma, covering their history, migration in Europe, religious beliefs and other strange cultural beliefs. For instance, did you know that many Roma consider themselves ‘Egyptians’? Very odd! Her review also covers the more traditional stuff like medical problems, sociological conditions, crime rates and the like. Generally, they do very poorly, probably only on par with the very worst performing immigrant groups in Scandinavia (Somalia, Lebanese, Syrians and similar). Perhaps they are part of the reason why people from Serbia do so poorly in Denmark. Perhaps they are mostly Roma? There are no records of more specific ethnicities in Denmark for immigrant groups to my knowledge. Similar puzzles concern immigrants coded as “stateless” which are presumably mostly from Palestine, immigrants from Israel (perhaps mostly Muslims?) and reversely immigrants from South Africa (perhaps mostly Europeans?).

Another interesting part of the book concerns the next last chapter covering the Roma kings. I had never heard of these, but apparently there are or were a few very rich Romas. They built elaborate castles for their money which one can now see in various places in Eastern Europe. After they lost their income (which was due to black market trading during communism and similar activities), they seem to have reverted to the normal Roma pattern of unemployment, fast life style, crime and state benefits. This provides another illustration of the idea that if a group of persons for some reason acquire wealth, it will not generally boost their g or other capabilities, and their wealth will go away again once the particular circumstance that gave rise to it disappears. Other examples of this pattern are the story of Nauru and people who get rich from sports but are not very clever (e.g. African American athletes such as Mike Tyson). Oil States have also not seen any massive increase in g due to their oil riches nor are people who win lotteries known to suddenly acquire higher g. Clearly, there cannot be a strong causal link from income to g.

In general, this book was better than expected and definitely worth a read for those interesting in psychologically informed history.

Admixture in the Americas: Introduction, partial correlations and IQ predictions based on ancestry

For those who have been living under a rock (i.e. not following my on Twitter), John Fuerst have been very good at compiling data from published research. Have a look at Human Varieties with the tag Admixture Mapping. He asked me to help him analyze it and write it up. I gladly obliged, you can read the draft here. John thinks we should write it all into one huge paper instead of splitting it up as is standard practice. The standard practice is perhaps not entirely just for gaming the reputation system, but also because writing huge papers like that can seem overwhelming and may take a long time to get thru review.

So the project summarized so far is this:

  • Genetic models of trait admixture predict that mixed groups will be in-between the two source population in the trait in proportion to their admixture.
  • For psychological traits such as general intelligence (g), this has previously primarily been studied unsystematically in African Americans, but this line of research seems to have dried up, perhaps because it became too politically sensitive over there.
  • However, there have been some studies using the same method, just examining illness-related traits (e.g. diabetes). These studies usually include socioeconomic variables as controls. In doing so, they have found robust correlations between admixture at the individual level and socioeconomic outcomes: income, occupation, education and the like.
  • John has found quite a lot of these and compiled the results into a table that can be found here.
  • The results clearly show the expected results, namely that more European ancestry is associated with more favorable outcomes, more African or American less favorable outcomes. A few of them are non-significant, but none contradicts. A meta-analysis of this would find a very small p value indeed.
  • One study actually included cognitive measures as co-variates and found results in the generally expected direction. See material under the headline “Cognitive differences in the Americans” in the draft file.
  • There is no necessity that one has to look at the individual level. One can look at the group level too. For this reason John has compiled data about the ancestry proportions of American countries and Mexican regions.
  • For the countries, he has tested this against self-identified proportions, CIA World Factbook estimates, skin reflection data and stuff like that, see: humanvarieties.org/2014/10/19/racial-ancestry-in-the-americas-part-1-genomic-continental-racial-admixture-estimate-and-validation/ The results are pretty solid. The estimates are clearly in the right ballpark.
  • Now, genetic models of the world distribution of general intelligence clearly predict that these estimates will be strongly related to the countries’ estimated mean levels of general intelligence. To test this John has carried out a number of multiple regressions with various controls such as parasite prevalence or cold weather along with European ancestry with the dependent variable being skin color and national achievement scores (PISA tests and the like). Results are in the expected directions even with controls.
  • Using the Mexican regional data, John has compared the Amerindian estimates with PISA scores, Raven’s scores, and Human Development Index (a proxy for S factor (see here and here)). Post is here: humanvarieties.org/2014/10/15/district-level-variation-in-continental-racial-admixture-predicts-outcomes-in-mexico/

This is where we are. Basically, the data is all there, ready to be analyzed. Someone needs to do the other part of the grunt work, namely running all the obvious tests and writing everything up for a big paper. This is where I come in.

The first I did was to create an OSF repository for the data and code since John had been manually keeping track of versions on HV. Not too good. I also converted his SPSS datafile to one that works on all platforms (CSV with semi-colons).

Then I started writing code in R. First I wanted to look at the more obvious relationships, such as that between IQ and ancestry estimates (ratios). Here I discovered that John had used a newer dataset of IQ estimates Meisenberg had sent him. However, it seems to have wrong data (Guatemala) and covers fewer relevant countries (25 vs. 35) vs. than the standard dataset from Lynn and Vanhanen 2012 (+Malloyian fixes) that I have been using. So for this reason I merged up John’s already enormous dataset (126 variables) with the latest Megadataset (365 variables), to create the cleverly named supermegadataset to be used for this study.

IQ x Ancestry zero-order correlations

Here’s the three scatterplots:

Americas_Euro_Ancestry_IQ12data

IQ_amer

IQ_Afro

So the reader might wonder, what is wrong with the Amerindian data? Why is about nill? Simply inspecting it reveals the problem. The countries with low Amerindian ancestry have very mixed European vs. African which keeps the mean around 80-85 thus creating no correlation.

Partial correlations

So my idea was this, as I wrote it in my email to John:

Hey John,I wrote my bachelor in 4 days (5 pages per day), so now I’m back to working on more interesting things. I use the LV12 data because it seems better and is larger.

One thing that had been annoying me that was correlations between ancestry and IQ do not take into account that there are three variables that vary, not just two. Remember that odd low correlation Amer x IQ r=.14 compared with Euro x IQ = .68 and Afr x IQ = -.66. The reason for this, it seems to me, is that the countries with low Amer% are a mix of high and low Afr countries. That’s why you get a flat scatterplot. See attached.

Unfortunately, one cannot just use MR with these three variables, since the following equation is true of them 1 = Euro+Afr+Amer. They are structurally dependent. Remember that MR attempts to hold the other variables constant while changing one. This is impossible.
The solution is seems to me is to use partial correlations. In this way, one can partial out one of them and look at the remaining two. There are six possible ways to do this:Amer x IQ, partial out Afr = -.51
Amer x IQ, partial out Euro = .29
Euro x IQ, partial out Afr = .41
Euro x IQ, partial out Amer = .70
Afr x IQ, partial out Euro = -.37
Afr x IQ, partial out Amer = -.76
Assuming that genotypically, Amer=85, Afr=80, Euro=97 (or so), then these results are completed as expected direction wise. In the first case, we remove Afr, so we are comparing Amer vs. Euro. We expect negative since Amer<Euro
In two, we expect positive because Amer>Afr
In three, we expect positive because Euro>Amer
In four, we expect positive because Euro>Afr
In five, we expect negative because Afr<Amer
In six, we expect negative because Afr<Euro
All six predictions were as expected. The sample size is quite small at N=34 and LV12 isn’t perfect, certainly not for these countries. The overall results are quite reasonable in my review.
Estimates of IQ directly from ancestry
But instead merely looking at it via correlations or regressions, one can try to predict the IQs directly from the ancestry. Simple create a predicted IQ based on the proportions and these populations estimated IQs. I tried a number of variations, but they were all close to this: Euro*95+Amer*85+Afro*70. The reason to use Euro 95 and not, say, 100 is that 100 is the IQ of Northern Europeans, in particular the British (‘Greenwich Mean IQ’). The European genes found in the Americans are mostly from Spain and Portugal, which have estimated IQs of 96.6 and 94.4 (mean = 95.5). This creates a problem since the US and Canada are not mostly from these somewhat lower IQ Europeans, but the error source is small (one can always just try excluding them).

So, does the predictions work? Yes.

Now, there is another kind of error with such estimates, called elevation. It refers to getting the intervals between countries right, but generally either over or underestimating them. This kind of error is undetectable in correlation analysis. But one can calculate it by taking the predicted IQs and subtracting the measured IQs, and then taking the mean of these values. Positive values mean that one is overestimating, negative means underestimation. The value for the above is: 1.9, so we’re overestimating a little bit, but it’s fairly close. A bit of this is due to USA and CAN, but then again, LCA (St. Lucia) and DMA (Dominica) are strong negative outliers, perhaps just wrong estimates by Lynn and Vanhanen (the only study for St. Lucia is this, but I don’t have the norms so I can’t calculate the IQ).

I told Davide Piffer about these results and he suggested that I use his PCA factor scores instead. Now, these are not themselves meaningful, but they have the intervals directly estimated from the genetics. His numbers are: Africa: -1.71; Native American: -0.9; Spanish: -0.3. Ok, let’s try:

PCA_predicted_IQs

Astonishingly, the correlation is almost the same. .01 from. However, this fact is less overwhelming than it seems at first because it arises simply because the correlations between the three racial estimates is .999 (95.5

New paper out: The personal Jensen coefficient does not predict grades beyond its association with g

Found null results for a proposed metric (actually two). In the spirit of publishing failed ideas, I wrote this up.

Abstract

General intelligence (g) is known to predict grades at all educational levels. A Jensen coefficient is the correlation of subtests’ g-loadings with a vector of interest. I hypothesized that the personal Jensen coefficient from the subjects’ subtest scores might predict grade point average beyond g. I used an open dataset to test this. The results showed that it does not seem to have predictive power beyond g (partial correlation = -.02). I found the same result when using a similar metric suggested by Davide Piffer.

openpsych.net/ODP/2014/10/the-personal-jensen-coefficient-does-not-predict-grades-beyond-its-association-with-g/

Meisenberg’s new book chapter on intelligence, economics and other stuff

G.M. IQ & Economic growth

I noted down some comments while reading it.

In Table 1, Dominican birth cohort is reversed.

 

“0.70 and 0.80 in world-wide country samples. Figure 1 gives an impression of

this relationship.”

 

Figure 1 shows regional IQs, not GDP relationships.

“We still depend on these descriptive methods of quantitative genetics because

only a small proportion of individual variation in general intelligence and

school achievement can be explained by known genetic polymorphisms (e.g.,

Piffer, 2013a,b; Rietveld et al, 2013).”

 

We don’t. Modern BG studies can confirm A^2 estimates directly from the genes.

E.g.:

Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., … & Deary, I. J. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular psychiatry, 16(10), 996-1005.

Marioni, R. E., Davies, G., Hayward, C., Liewald, D., Kerr, S. M., Campbell, A., … & Deary, I. J. (2014). Molecular genetic contributions to socioeconomic status and intelligence. Intelligence, 44, 26-32.

Results are fairly low tho, in the 20’s, presumably due to non-additive heritability and rarer genes.

 

“Even in modern societies, the heritability of

intelligence tends to be higher for children from higher socioeconomic status

(SES) families (Turkheimer et al, 2003; cf. Nagoshi and Johnson, 2005; van

der Sluis et al, 2008). Where this is observed, most likely environmental

conditions are of similar high quality for most high-SES children but are more

variable for low-SES children. “

 

Or maybe not. There are also big studies that don’t find this interaction effect. en.wikipedia.org/wiki/Heritability_of_IQ#Heritability_and_socioeconomic_status

 

“Schooling has

only a marginal effect on growth when intelligence is included, consistent with

earlier results by Weede & Kämpf (2002) and Ram (2007).”

In the regression model of all countries, schooling has a larger beta than IQ does (.158 and .125). But these appear to be unstandardized values, so they are not readily comparable.

“Also, earlier studies that took account of

earnings and cognitive test scores of migrants in the host country or IQs in

wealthy oil countries have concluded that there is a substantial causal effect of

IQ on earnings and productivity (Christainsen, 2013; Jones & Schneider,

2010)”

 

National IQs were also found to predict migrant income, as well as most other socioeconomic traits, in Denmark and Norway (and Finland and the Netherland).

Kirkegaard, E. O. W. (2014). Crime, income, educational attainment and employment among immigrant groups in Norway and Finland. Open Differential Psychology.

Kirkegaard, E. O. W., & Fuerst, J. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark. Open Differential Psychology.

 

 

Figures 3 A-C are of too low quality.

 

 

“Allocation of capital resources has been an

element of classical growth theory (Solow, 1956). Human capital theory

emphasizes that individuals with higher intelligence tend to have lower

impulsivity and lower time preference (Shamosh & Gray, 2008). This is

predicted to lead to higher savings rates and greater resource allocation to

investment relative to consumption in countries with higher average

intelligence.”

 

Time preference data for 45 countries are given by:

Wang, M., Rieger, M. O., & Hens, T. (2011). How time preferences differ: evidence from 45 countries.

They are in the megadataset from version 1.7f

Correlations among some variables of interest:

r
             SlowTimePref Income.in.DK Income.in.NO   IQ lgGDP
SlowTimePref         1.00         0.45         0.48 0.57  0.64
Income.in.DK         0.45         1.00         0.89 0.55  0.59
Income.in.NO         0.48         0.89         1.00 0.65  0.66
IQ                   0.57         0.55         0.65 1.00  0.72
lgGDP                0.64         0.59         0.66 0.72  1.00

n
             SlowTimePref Income.in.DK Income.in.NO  IQ lgGDP
SlowTimePref          273           32           12  45    40
Income.in.DK           32          273           20  68    58
Income.in.NO           12           20          273  23    20
IQ                     45           68           23 273   169
lgGDP                  40           58           20 169   273

So time prefs predict income in DK and NO only slightly worse than national IQs or lgGDP.

 

 

“Another possible mediator of intelligence effects that is difficult to

measure at the country level is the willingness and ability to cooperate. A

review by Jones (2008) shows that cooperativeness, measured in the Prisoner‟s

dilemma game, is positively related to intelligence. This correlate of

intelligence may explain some of the relationship of intelligence with

governance. Other likely mediators of the intelligence effect include less red

tape and restrictions on economic activities (“economic freedom”), higher

savings and/or investment, and technology adoption in developing countries.”

 

There are data for IQ and trust too. Presumably trust is closely related to willingness to cooperate.

Carl, N. (2014). Does intelligence explain the association between generalized trust and economic development? Intelligence, 47, 83–92. doi:10.1016/j.intell.2014.08.008

 

 

“There is no psychometric evidence for rising intelligence before that time

because IQ tests were introduced only during the first decade of the 20th

century, but literacy rates were rising steadily after the end of the Middle Age

in all European countries for which we have evidence (Mitch, 1992; Stone,

1969), and the number of books printed per capita kept rising (Baten & van

Zanden, 2008).”

 

There’s also age heaping scores which are a crude measure of numeracy. AH scores for 1800 to 1970 are in the megadataset. They have been going up for centuries too just like literacy scores. See:

A’Hearn, B., Baten, J., & Crayen, D. (2009). Quantifying quantitative literacy: Age heaping and the history of human capital. The Journal of Economic History, 69(03), 783–808.

 

 

“Why did this spiral of economic and cognitive growth take off in Europe

rather than somewhere else, and why did it not happen earlier, for example in

classical Athens or the Roman Empire? One part of the answer is that this

process can start only when technologies are already in place to translate rising

economic output into rising intelligence. The minimal requirements are a

writing system that is simple enough to be learned by everyone without undue

effort, and a means to produce and disseminate written materials: paper, and

the printing press. The first requirement had been present in Europe and the

Middle East (but not China) since antiquity, and the second was in place in

Europe from the 15thcentury. The Arabs had learned both paper-making and

printing from the Chinese in the 13thcentury (Carter, 1955), but showed little

interest in books. Their civilization was entering into terminal decline at about

that time (Huff, 1993). “

 

Are there no FLynn effects in China? They still have a difficult writing system.

 

“Most important is that Flynn effect gains have been decelerating in recent

years. Recent losses (anti-Flynn effects) were noted in Britain, Denmark,

Norway and Finland. Results for the Scandinavian countries are based on

comprehensive IQ testing of military conscripts aged 18-19. Evidence for

losses among British teenagers is derived from the Raven test (Flynn, 2009)

and Piagetian tests (Shayer & Ginsburg, 2009). These observations suggest

that for cohorts born after about 1980, the Flynn effect is ending or has ended

in many and perhaps most of the economically most advanced countries.

Messages from the United States are mixed, with some studies reporting

continuing gains (Flynn, 2012) and others no change (Beaujean & Osterlind,

2008).”

 

These are confounded with immigration of low-g migrants however. Maybe the FLynn effect is still there, just being masked by dysgenics + low-g immigration.

 

 

“The unsustainability of this situation is obvious. Estimating that one third

of the present IQ differences between countries can be attributed to genetics,

and adding this to the consequences of dysgenic fertility within countries,

leaves us with a genetic decline of between 1 and 2 IQ points per generation

for the entire world population. This decline is still more than offset by Flynn

effects in less developed countries, and the average IQ of the world‟s

population is still rising. This phase of history will end when today‟s

developing countries reach the end of the Flynn effect. “Peak IQ” can

reasonably be expected in cohorts born around the mid-21stcentury. The

assumptions of the peak IQ prediction are that (1) Flynn effects are limited by

genetic endowments, (2) some countries are approaching their genetic limits

already, and others will fiollow, and (3) today‟s patterns of differential fertility

favoring the less intelligent will persist into the foreseeable future. “

 

It is possible that embryo selection for higher g will kick in and change this.

Shulman, C., & Bostrom, N. (2014). Embryo Selection for Cognitive Enhancement: Curiosity or Game-changer? Global Policy, 5(1), 85–92. doi:10.1111/1758-5899.12123

 

 

“Fertility differentials between countries lead to replacement migration: the

movement of people from high-fertility countries to low-fertility countries,

with gradual replacement of the native populations in the low-fertility

countries (Coleman, 2002). The economic consequences depend on the

quality of the migrants and their descendants. Educational, cognitive and

economic outcomes of migrants are influenced heavily by prevailing

educational, cognitive and economic levels in the country of origin (Carabaña,

2011; Kirkegaard, 2013; Levels & Dronkers, 2008), and by the selectivity of

migration. Brain drain from poor to prosperous countries is extensive already,

for example among scientists (Franzoni, Scellato & Stephan, 2012; Hunter,

Oswald & Charlton, 2009). “

 

There are quite a few more papers on the spatial transferability hypothesis. I have 5 papers on this alone in ODP: openpsych.net/ODP/tag/country-of-origin/

But there’s also yet unpublished data for crime in Netherlands and more crime data for Norway. Papers based off these data are on their way.

 

Does conscientiousness predict PISA scores at the national level? A cautious meta-analysis

Just a quick write-up before I write up a paper with this for ODP.

Introduction

Altho general cognitive ability (g) has received the most attention by differential psychologists, personality receives a fair share now a days. And just as g has been shown to have great predictive power in large meta-analyses in a variety of contexts (e.g. Gottfredson 1997 is still the best summary IMO), so has the personality trait of conscientiousness (C) (e.g. The Validity of Conscientiousness for Predicting Job Performance A meta-analytic test of two hypotheses A Meta-Analytic Investigation of Conscientiousness in the Prediction of Job Performance Examining the Intercorrelations and the Incremental Validity of Narrow Traits..asp(1) The Case for Conscientiousness Evidence and Implications for a Personality Trait Marker of Health and Longevity).

The ‘new’ thing in differential psych is to study national g estimates and how they correlate. This is the field ive been working mostly in with the spatial transferability hypothesis. The question then is, does C have predictive ability at the national level too? Well, maybe. There are some national estimates of the big five/OCEAN traits in Schmitt et al 2007. I added them to the Megdataset.

 

Partial correlations

The PISA x measured IQ (not the ones where scholastic ability have been factored in!) correlations were also of interest since no one apparently had calculated the mean PISA x measured IQ correlation. Well, it is .92. So, does C explain some of the remaining variance? One idea is to calculate the partial correlations of C and PISA with mIQ partialed out. However, this method seems to be wrong since some of the correlations are above 1! Ive never seen partial correlations above 1 before.

ConscientiousnessMeanSchmittEtAl2007
Math00Mean 1.4828725419
Read00Mean 1.1065080555
Sci00Mean 1.0012991174
Math03Mean 1.0742429148
Read03Mean 1.1147063889
Sci03Mean 1.2609157051
Sci06Mean 0.9137135525
Read06Mean 0.6593605051
Math06Mean 0.3923821506
Read09Mean 0.8607255528
Math09Mean 0.6409903363
Sci09Mean 0.843892485
Finance12Mean 0.3834897092
Math12Mean 0.3682415819
Read12Mean 0.5272534233
Sci12Mean 0.5563931581
CPS12Mean 0.1497008328

 

Multiple regression

So maybe another method is called for. I used multiple regression on all 17 PISA variables. One may be tempted to simply average them, but as Joost de Winter pointed out to me in an email, the PISA for the same year are not independent. So one cannot just count them as independent. One can get around this problem by doing the meta-analysis within test type, i.e. reading, math and science. Results:

Reading:
> IQ.betas.weighted.mean
[1] 0.9631086
> C.betas.weighted.mean
[1] 0.1673834
> sum(samples.sizes)
[1] 166Math:
> IQ.betas.weighted.mean
[1] 0.9621924
> C.betas.weighted.mean
[1] 0.02653771
> sum(samples.sizes)
[1] 167Science:
> IQ.betas.weighted.mean
[1] 0.9826468
> C.betas.weighted.mean
[1] 0.1080092
> sum(samples.sizes)
[1] 167

The results from reading have p=.03, so maybe. In 1-2 years, we will have more data from PISA15 to test with. There are plenty of reasons to be cautious: 1) The measured IQs are not perfectly reliably measured. This means that the true correlation between g and PISA scores is higher, leaving less variance to be explained by non-g factors. Maybe nothing? 2) The quality of the personality data is quite poor. Altho one may counter-argue that this is a reason to be more optimistic since the results (well, reading results) are still plausible.

The R sourcecode for the paper is here. The dataset is here.

What about measured IQ and PISA scores?

#the mean PISA x IQ correlation
DF.C.PISA.IQ.rcorr = rcorr(as.matrix(DF.C.PISA.IQ))
IQ.PISA.cors = DF.C.PISA.IQ.rcorr$r[19,] #get IQ row
IQ.PISA.cors = IQ.PISA.cors[2:18] #remove C and IQ-IQ
mean(IQ.PISA.cors) #the mean measured IQ x PISA correlation
round(IQ.PISA.cors,2)
#weighted mean
IQ.PISA.cors.n = DF.C.PISA.IQ.rcorr$n[19,] #get IQ row
IQ.PISA.cors.n = IQ.PISA.cors.n[2:18] #remove C and IQ-IQ
IQ.PISA.cors.weighted = IQ.PISA.cors*IQ.PISA.cors.n
IQ.PISA.cors.weighted.mean = sum(IQ.PISA.cors.weighted)/sum(IQ.PISA.cors.n)

The unweighted mean is 0.919, the weighted is 0.924.

 

Refs

Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martinez, V. (2007). The Geographic Distribution of Big Five Personality Traits: Patterns and Profiles of Human Self-Description Across 56 Nations. Journal of Cross-Cultural Psychology, 38(2), 173–212. doi:10.1177/0022022106297299

Appendix – full output from MR

PISA test IQ.betas C.betas samples.sizes
Math00Mean 0.9895461 0.096764646 22
Read00Mean 0.977835 0.297191736 22
Sci00Mean 0.9759363 0.099720868 22
Math03Mean 0.9812832 0.016108517 27
Read03Mean 1.0141552 0.27851122 27
Sci03Mean 1.008251 0.104575077 27
Sci06Mean 0.9796918 0.125369373 38
Read06Mean 0.9346129 0.118300942 37
Math06Mean 0.9455623 0.010964361 38
Read09Mean 0.9596431 0.140295939 39
Math09Mean 0.9628133 0.035653129 39
Sci09Mean 0.977768 0.102601624 39
Finance12Mean 0.5286025 -0.144810379 14
Math12Mean 0.9497653 0.001486034 41
Read12Mean 0.9506026 0.094608558 41
Sci12Mean 0.9767656 0.103772057 41
CPS12Mean 0.8830054 -0.025983714 29

International general factor of personality? yes, but…

I merged the dataset from Schmitt et al (2007)’s paper about OCEAN traits in 56 countries with the rest of the megadataset. Then i extracted the first factor of the OCEAN means and SDs. These two are nearly uncorrelated (.07). As for factor strength, they are not too bad:

> DF.OCEAN.mean.omega
Omega 
Call: omega(m = DF.OCEAN.mean)
Alpha:                 0.73 
G.6:                   0.74 
Omega Hierarchical:    0.54 
Omega H asymptotic:    0.64 
Omega Total            0.84 

Schmid Leiman Factor loadings greater than  0.2 
                                        g   F1*   F2*   F3*   h2   u2   p2
ExtraversionMeanSchmittEtAl2007      0.44        0.66       0.64 0.36 0.30
AgreeablenessMeanSchmittEtAl2007     0.58  0.56             0.66 0.34 0.51
ConscientiousnessMeanSchmittEtAl2007 0.62  0.52             0.66 0.34 0.58
NeuroticismMeanSchmittEtAl2007      -0.66  0.28  0.36 -0.36 0.76 0.24 0.56
OpennessMeanSchmittEtAl2007          0.23        0.21  0.51 0.38 0.62 0.14

With eigenvalues of:
   g  F1*  F2*  F3* 
1.40 0.69 0.62 0.40 

general/max  2.04   max/min =   1.7
mean percent general =  0.42    with sd =  0.19 and cv of  0.46 
Explained Common Variance of the general factor =  0.45

 

and

> DF.OCEAN.SD.omega
Omega 
Call: omega(m = DF.OCEAN.SD)
Alpha:                 0.79 
G.6:                   0.78 
Omega Hierarchical:    0.72 
Omega H asymptotic:    0.86 
Omega Total            0.84 

Schmid Leiman Factor loadings greater than  0.2 
                                      g   F1*   F2*   F3*   h2   u2   p2
ExtraversionSDSchmittEtAl2007      0.80                   0.64 0.36 0.99
AgreeablenessSDSchmittEtAl2007     0.57        0.47       0.55 0.45 0.59
ConscientiousnessSDSchmittEtAl2007 0.57  0.35             0.48 0.52 0.68
NeuroticismSDSchmittEtAl2007       0.78  0.52             0.87 0.13 0.69
OpennessSDSchmittEtAl2007          0.43        0.24       0.25 0.75 0.74

With eigenvalues of:
   g  F1*  F2*  F3* 
2.08 0.41 0.31 0.00 

general/max  5.09   max/min =   136.11
mean percent general =  0.74    with sd =  0.15 and cv of  0.2 
Explained Common Variance of the general factor =  0.74

 

Compare with values in Table 5 in my just published paper. GFP-mean is clearly weaker than g factor at individual level, GFP-SD is about the same.

Dataset
Var% MR
Var% MR SL Omega h. Omega h. a. ECV R2
NO Complete cases 0.68 0.65 0.87 0.91 0.78 0.98
NO Impute 1 0.66 0.62 0.86 0.9 0.74 0.96
NO Impute 2 0.64 0.6 0.85 0.89 0.75 0.95
NO Impute 3 0.63 0.59 0.82 0.87 0.73 0.99
DK complete cases 0.57 0.51 0.83 0.85 0.68 0.99
DK impute 4 0.55 0.51 0.86 0.88 0.73 0.99
Int. S. Factor 0.43 0.35 0.76 0.77 0.51 0.81
Cognitive data 0.33 0.74 0.79 0.57 0.78
Personality data 0.16 0.37 0.48 0.34 0.41

Then i correlated these with national IQ, S factor and local S factors in Norway and Denmark.

> round(cor(DF.OCEAN.general.scores,use="pairwise.complete.obs"),2)
             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int    IQ
GFP.mean         1.00   0.07        0.09        -0.25  0.17 -0.21 -0.58
GFP.SD           0.07   1.00        0.39         0.26 -0.14  0.36  0.24
S.in.Norway      0.09   0.39        1.00         0.78 -0.72  0.73  0.60
S.in.Denmark    -0.25   0.26        0.78         1.00 -0.71  0.54  0.54
Islam            0.17  -0.14       -0.72        -0.71  1.00 -0.33 -0.27
S.Int           -0.21   0.36        0.73         0.54 -0.33  1.00  0.86
IQ              -0.58   0.24        0.60         0.54 -0.27  0.86  1.00

So strangely, the correlation of GFP-mean x national IQ is very negative. It correlates weakly with S factors. Let’s try partialing out national IQ:

> DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)
> DF.OCEAN.general.scores.no.IQ
partial correlations 
             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int
GFP.mean         1.00   0.26        0.68         0.09  0.02  0.72
GFP.SD           0.26   1.00        0.31         0.16 -0.08  0.32
S.in.Norway      0.68   0.31        1.00         0.67 -0.73  0.53
S.in.Denmark     0.09   0.16        0.67         1.00 -0.70  0.19
Islam            0.02  -0.08       -0.73        -0.70  1.00 -0.21
S.Int            0.72   0.32        0.53         0.19 -0.21  1.00

Even more strange. GFP-mean strongly correlates with 2 S factors, but not the one in Denmark. The Danish data are very good (25 variables) and so are the international data (42-54 variables). And all the S factors correlate strongly before partialing (.78, .73, .54) but mixed after removing IQ (.67, .53, .19). Again Denmark is odd. For GFP-SD, it is similar, but weaker (before: .39, .26, .36; after: .31, .16, .32).

What to make of this? So i emailed some colleagues:

Dear [NAMES]

Do you know if someone have looked at an international general factor of personality? Because I did it just now using a dataset of OCEAN trait scores (big five) from Schmitt et al 2007. There is indeed an international GFP in the data. It correlates negatively with national IQs (-.58). Strangely, partialing out national IQs, it correlates highly with general socioeconomic factors in Norway (.68) and internationally (.72), but not in Denmark (.09). Strange? Thoughts? I can send you the data+code if you like.

Regards,
Emil

One of them had insider info:

Emil,

There is a paper about to appear in Intelligence in which an international GFP has been computed and analyzed.

Best,

[NAME].
So i publish this here quickly so i establish priority and independence.

What about OCEAN traits themselves?

(sorry, tables apparently not easy to make smaller)
All correlations:
E mean E SD A mean A SD C mean C SD N mean N SD O mean O SD Mean SD S.NO S.DK Islam Int.S IQ
E mean 1 0.14 0.2 0.22 0.25 0.23 -0.49 0.17 0.27 0.09 0.23 0.06 -0.19 -0.02 0.09 -0.02
E sd 0.14 1 -0.08 0.47 -0.07 0.48 0.13 0.66 0.3 0.34 0.81 0.45 0.35 -0.35 0.53 0.39
A mean 0.2 -0.08 1 0.15 0.65 0.21 -0.48 0.21 0.26 -0.13 0.11 0.08 -0.26 0.26 -0.25 -0.53
A SD 0.22 0.47 0.15 1 0.23 0.43 0 0.45 0.22 0.35 0.71 0.18 0.23 -0.18 0.12 -0.04
C mean 0.25 -0.07 0.65 0.23 1 0.1 -0.57 0.07 0.2 -0.03 0.07 0.04 -0.19 0.14 -0.19 -0.6
C SD 0.23 0.48 0.21 0.43 0.1 1 0.11 0.62 0.41 0.25 0.78 0.34 -0.03 0.04 0.19 0.04
N mean -0.49 0.13 -0.48 0 -0.57 0.11 1 0.22 -0.09 0.25 0.19 -0.1 0.13 -0.06 0.12 0.38
N SD 0.17 0.66 0.21 0.45 0.07 0.62 0.22 1 0.41 0.28 0.83 0.23 0.19 0 0.24 0.18
O mean 0.27 0.3 0.26 0.22 0.2 0.41 -0.09 0.41 1 0.07 0.4 -0.01 -0.07 0.04 -0.02 -0.06
O sd 0.09 0.34 -0.13 0.35 -0.03 0.25 0.25 0.28 0.07 1 0.56 0.22 0.14 -0.07 0.25 0.37
Mean SD 0.23 0.81 0.11 0.71 0.07 0.78 0.19 0.83 0.4 0.56 1 0.41 0.25 -0.15 0.36 0.25
S.factor.in.Norway 0.06 0.45 0.08 0.18 0.04 0.34 -0.1 0.23 -0.01 0.22 0.41 1 0.78 -0.72 0.73 0.6
S.factor.in.Denmark -0.19 0.35 -0.26 0.23 -0.19 -0.03 0.13 0.19 -0.07 0.14 0.25 0.78 1 -0.71 0.54 0.54
IslamPewResearch2010 -0.02 -0.35 0.26 -0.18 0.14 0.04 -0.06 0 0.04 -0.07 -0.15 -0.72 -0.71 1 -0.33 -0.27
International.S.Factor 0.09 0.53 -0.25 0.12 -0.19 0.19 0.12 0.24 -0.02 0.25 0.36 0.73 0.54 -0.33 1 0.86
LV2012estimatedIQ -0.02 0.39 -0.53 -0.04 -0.6 0.04 0.38 0.18 -0.06 0.37 0.25 0.6 0.54 -0.27 0.86 1
With IQ partialed out:
E mean E sd A mean A SD C mean C SD N mean N SD O mean O SD Mean SD S.NO S.DK Islam Int.S
E mean 1 0.17 0.22 0.22 0.3 0.23 -0.52 0.18 0.27 0.1 0.24 0.09 -0.21 -0.02 0.21
E sd 0.17 1 0.16 0.53 0.22 0.51 -0.02 0.65 0.35 0.23 0.8 0.29 0.18 -0.28 0.42
A mean 0.22 0.16 1 0.15 0.49 0.28 -0.36 0.36 0.27 0.07 0.29 0.58 0.03 0.15 0.48
A SD 0.22 0.53 0.15 1 0.25 0.43 0.02 0.47 0.21 0.4 0.74 0.26 0.3 -0.2 0.3
C mean 0.3 0.22 0.49 0.25 1 0.15 -0.46 0.23 0.21 0.25 0.29 0.63 0.2 -0.02 0.82
C SD 0.23 0.51 0.28 0.43 0.15 1 0.1 0.62 0.41 0.26 0.79 0.39 -0.05 0.06 0.31
N mean -0.52 -0.02 -0.36 0.02 -0.46 0.1 1 0.17 -0.07 0.13 0.11 -0.45 -0.1 0.05 -0.44
N SD 0.18 0.65 0.36 0.47 0.23 0.62 0.17 1 0.43 0.23 0.83 0.16 0.11 0.05 0.18
O mean 0.27 0.35 0.27 0.21 0.21 0.41 -0.07 0.43 1 0.1 0.42 0.03 -0.04 0.03 0.06
O sd 0.1 0.23 0.07 0.4 0.25 0.26 0.13 0.23 0.1 1 0.52 0.01 -0.07 0.03 -0.14
Mean SD 0.24 0.8 0.29 0.74 0.29 0.79 0.11 0.83 0.42 0.52 1 0.33 0.15 -0.09 0.3
S.factor.in.Norway 0.09 0.29 0.58 0.26 0.63 0.39 -0.45 0.16 0.03 0.01 0.33 1 0.67 -0.73 0.53
S.factor.in.Denmark -0.21 0.18 0.03 0.3 0.2 -0.05 -0.1 0.11 -0.04 -0.07 0.15 0.67 1 -0.7 0.19
IslamPewResearch2010 -0.02 -0.28 0.15 -0.2 -0.02 0.06 0.05 0.05 0.03 0.03 -0.09 -0.73 -0.7 1 -0.21
International.S.Factor 0.21 0.42 0.48 0.3 0.82 0.31 -0.44 0.18 0.06 -0.14 0.3 0.53 0.19 -0.21 1
R code (load in the megadataset as DF.mega3 first):
DF.interest = cbind(DF.mega3[2:12],
                    DF.mega3[14],
                    DF.mega3[40],
                    DF.mega3[42],
                    DF.mega3[64],
                    DF.mega3[76])
DF.interest.cor = rcorr(as.matrix(DF.interest))
round(DF.interest.cor$r,2)
write.csv(round(DF.interest.cor$r,2),file="OCEANCors.csv")

#remove IQ
DF.interest.cor.without.IQ = partial.r(DF.interest, c(1:15),16)
write.csv(round(DF.interest.cor.without.IQ,2), file="OCEANCors_no_g.csv")

DF.OCEAN.full = cbind(DF.mega3[2:12])
DF.OCEAN.full.omega = omega(DF.OCEAN.full)
DF.OCEAN.full.mr = fa(DF.OCEAN.full)

DF.OCEAN.mean = cbind(DF.mega3[c(2,4,6,8,10)])
DF.OCEAN.mean.omega = omega(DF.OCEAN.mean)
DF.OCEAN.mean.mr = fa(DF.OCEAN.mean)

DF.OCEAN.SD = cbind(DF.mega3[c(3,5,7,9,11)])
DF.OCEAN.SD.omega = omega(DF.OCEAN.SD)
DF.OCEAN.SD.mr = fa(DF.OCEAN.SD)

DF.OCEAN.general.scores = cbind(DF.OCEAN.mean.mr$scores,DF.OCEAN.SD.mr$scores,
                                DF.mega3[14],DF.mega3[40],DF.mega3[42],DF.mega3[64],DF.mega3[76])
colnames(DF.OCEAN.general.scores) = c("GFP.mean","GFP.SD","S.in.Norway","S.in.Denmark","Islam","S.Int","IQ")
round(cor(DF.OCEAN.general.scores,use="pairwise.complete.obs"),2)
DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)

Megadataset is in the OSF repository, version 1.6b.

New paper out: Crime, income, educational attainment and employment among immigrant groups in Norway and Finland

openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-immigrant-groups-in-norway-and-finland/

Abstract

I present new predictive analyses for crime, income, educational attainment and employment among immigrant groups in Norway and crime in Finland. Furthermore I show that the Norwegian data contains a strong general socioeconomic factor (S) which is highly predictable from country-level variables (National IQ .59, Islam prevalence -.71, international general socioeconomic factor .72, GDP .55), and correlates highly (.78) with the analogous factor among immigrant groups in Denmark. Analyses of the prediction vectors show very high correlations (generally ±.9) between predictors which means that the same variables are relatively well or weakly predicted no matter which predictor is used. Using the method of correlated vectors shows that it is the underlying S factor that drives the associations between predictors and socioeconomic traits, not the remaining variance (all correlations near unity).

All data and source files are at the OSF repository: osf.io/emfag/

Causal effect of education on IQ scores using the discordant twin design?

Recently, the twin-control design has been used to test causal models (e.g. exercise→happiness, exercise→¬depression/anxiety symptoms, casual sex→depression/suicidal thoughts). The theory is simple. Suppose we do a standard cross-sectional design study and find that X and Y are correlated. Suppose we suspect that X causes Y. Then, if X causes Y, then one would expect to see a relationship within identical twin pairs for X and Y. If the correlation between X and Y is due to shared genetics, then it will not be correlated within identical twin pairs (baring any de novo mutation being responsible for it). If it is found to be correlated within identical twins, then the education model may be true but also some developmental models relying on non-education environmentally caused differences as well as de novo mutation genetic models.

Did anyone do a study like this? I haven’t seen it, but it is quite simple to do. The only thing needed is a dataset with identical twins, educational attainment/years in school and some g proxy. Maybe NLSY? If you know of a dataset, contact me and we will try.