Yes, I am referring to “IQ” not g because most people do not know what g is. (For other readers ,IQ is the measurement, g is the real thing.) I have looked into IQ research a lot and spoken to a few experts. While genetics likely doesn’t play much of a role in the Flynn effect, it plays a huge role in g and IQ. This is established beyond any reasonable doubt. IQ is a very politically sensitive topic and people are not always honest about it. Indeed, some experts admit to other experts that they lie about IQ when discussing IQ in public (Source: my friend and podcasting partner Greg Cochran. The podcast is Future Strategist.). We don’t know if the Flynn effect is real, it might just come from measurement errors arising from people becoming more familiar with IQ-like tests, although it also could reflect real gains in g that are being captured by higher IQ scores. There is no good evidence that education raises g. The literature on IQ is so massive, and so poisoned by political correctness (and some would claim racism) that it is not possible to resolve the issues you raise by citing literature. If you ask IQ experts why they disagree with other IQ experts they will say that the other experts are idiots/liars/racists/cowards. I interviewed a lot of IQ experts when writing my book Singularity Rising.
To be clear, I think it’s very obvious that genetics has a large effect on g. The key question that you seemed to dismiss above is whether education or really any form of training has an additional effect (or more likely, some complicated dynamic with genetics) on g.
And after looking into this question a lot over the past few years, I think the answer is “maybe, probably a bit”. The big problem is that for population-wide studies, we can’t really get nice data on the effects of education because the Flynn effect is adding a pretty clear positive trend and geographic variance in education levels doesn’t really capture what we would naively think as the likely contributors to the observed increase in g.
And you can’t do directed interventions because all IQ tests (even very heavily g-loaded ones) are extremely susceptible to training effects, with even just an hour of practicing on Raven’s progressive matrices seeming to result in large gains. As such, you can’t really use IQ tests as any kind of feedback loop, and almost any real gains will be drowned out by the local training effects.
(For other readers ,IQ is the measurement, g is the real thing.)
This seems like a misleading summary of what g is.
g is the shared principal component of various subsets of IQ tests. As such, it measures the shared variance between your performance on many different tasks, and so is the thing that we expect to generalize most between different tasks. But in most psychometric contexts I’ve seen, we split g into 3-5 different components, which tends to add significant additional predictive accuracy (at the cost of simplicity, obviously).
To describe it as “the real thing” requires defining what our goal with IQ testing is. Results on IQ tests have predictive power over income and life-outcomes even beyond the variance that is explained by g, and predictive power over outcomes on a large variety of different tasks beyond only g.
The goal of IQ tests is not to measure g, it isn’t even clear whether g is a single thing that can be “measured”. The goal of IQ tests historically has been to assess aptitude for various jobs and roles (such as whether you should be admitted to the military, which is where a large fraction of our IQ-score data comes from). For those purposes, we’ve often found that solely focusing on trying to measure aptitude that generalizes between tasks is a bad idea, since there is still significant task-specific variance that we care about, and would have to give up on measuring in the case of defining g as the ultimate goal of measurement.
We don’t know if the Flynn effect is real, it might just come from measurement errors arising from people becoming more familiar with IQ-like tests, although it also could reflect real gains in g that are being captured by higher IQ scores.
I think the Flynn effect has been pretty solidly established, as well as the fact that it has had a significant effect on g.
I do think the most likely explanation of a large fraction of the effect on g is explained via the other factors I cited above, namely better nutrition and more broadly better health-care, resulting in significantly fewer deficiencies.
Yes, I am referring to “IQ” not g because most people do not know what g is. (For other readers ,IQ is the measurement, g is the real thing.) I have looked into IQ research a lot and spoken to a few experts. While genetics likely doesn’t play much of a role in the Flynn effect, it plays a huge role in g and IQ. This is established beyond any reasonable doubt. IQ is a very politically sensitive topic and people are not always honest about it. Indeed, some experts admit to other experts that they lie about IQ when discussing IQ in public (Source: my friend and podcasting partner Greg Cochran. The podcast is Future Strategist.). We don’t know if the Flynn effect is real, it might just come from measurement errors arising from people becoming more familiar with IQ-like tests, although it also could reflect real gains in g that are being captured by higher IQ scores. There is no good evidence that education raises g. The literature on IQ is so massive, and so poisoned by political correctness (and some would claim racism) that it is not possible to resolve the issues you raise by citing literature. If you ask IQ experts why they disagree with other IQ experts they will say that the other experts are idiots/liars/racists/cowards. I interviewed a lot of IQ experts when writing my book Singularity Rising.
To be clear, I think it’s very obvious that genetics has a large effect on g. The key question that you seemed to dismiss above is whether education or really any form of training has an additional effect (or more likely, some complicated dynamic with genetics) on g.
And after looking into this question a lot over the past few years, I think the answer is “maybe, probably a bit”. The big problem is that for population-wide studies, we can’t really get nice data on the effects of education because the Flynn effect is adding a pretty clear positive trend and geographic variance in education levels doesn’t really capture what we would naively think as the likely contributors to the observed increase in g.
And you can’t do directed interventions because all IQ tests (even very heavily g-loaded ones) are extremely susceptible to training effects, with even just an hour of practicing on Raven’s progressive matrices seeming to result in large gains. As such, you can’t really use IQ tests as any kind of feedback loop, and almost any real gains will be drowned out by the local training effects.
This seems like a misleading summary of what g is.
g is the shared principal component of various subsets of IQ tests. As such, it measures the shared variance between your performance on many different tasks, and so is the thing that we expect to generalize most between different tasks. But in most psychometric contexts I’ve seen, we split g into 3-5 different components, which tends to add significant additional predictive accuracy (at the cost of simplicity, obviously).
To describe it as “the real thing” requires defining what our goal with IQ testing is. Results on IQ tests have predictive power over income and life-outcomes even beyond the variance that is explained by g, and predictive power over outcomes on a large variety of different tasks beyond only g.
The goal of IQ tests is not to measure g, it isn’t even clear whether g is a single thing that can be “measured”. The goal of IQ tests historically has been to assess aptitude for various jobs and roles (such as whether you should be admitted to the military, which is where a large fraction of our IQ-score data comes from). For those purposes, we’ve often found that solely focusing on trying to measure aptitude that generalizes between tasks is a bad idea, since there is still significant task-specific variance that we care about, and would have to give up on measuring in the case of defining g as the ultimate goal of measurement.
I think the Flynn effect has been pretty solidly established, as well as the fact that it has had a significant effect on g.
I do think the most likely explanation of a large fraction of the effect on g is explained via the other factors I cited above, namely better nutrition and more broadly better health-care, resulting in significantly fewer deficiencies.