As is my usual custom, I wrote to the authors whose work I had commented upon in my previous post:

I asked John Protzko how long the effects of intelligence boosting interventions lasted. He said that he thought this “fadeout” effect was likely to happen somewhere between 3 and 5 years after the intervention had finished.

Here is the paper he wrote on this issue, and his discussion considers various possible explanations for apparently real gains eventually fading away.

https://drive.google.com/file/d/1zYhV-HwJbDWfACDjNZaFgnrkBwGOo1AV/view?usp=sharing

I speculate that when a skill is acquired, but is slightly out of reach of one’s “real” intellectual levels, it cannot be fully internalized, and therefore fails for lack of integration into everyday skills, in the way that attending a conference provides a temporary boost to intellectual excitement and apparent understanding of complex problems, but soon fades to humdrum insensibility. Protzko describes a version of this in paragraph 4.1.5. Genetic set point, and argues against it.

Protzko’s preferred explanation involves the concept of “scaffolding”: an environmental effect of a cognitively demanding environment being required to sustain and develop the cognitive skills acquired by the special intervention.

Elliot Tucker-Drob comments on my remarks about the Ritchie and Tucker-Drob paper:

I, for one, am most convinced by the policy change approach, as it is as close to an experiment as one can get. I understand your concerns. I do not entirely agree with them, but they are soundly argued. I would say that the results of the policy change will not stare us in the face in raw data because the policy only affects the unobserved subgroup of individuals who would have otherwise not completed the new minimum schooling requirement.

So, for example, if raising minimum schooling by 1 years increases schooling by 1 year in 10 percent of the population, and 1 additional year of schooling raises IQ by 1.5 points, we would only expect to see an effect of roughly .15 IQ points in the population as a whole.

The instrumental variable methodology that economists tend to use reverse engineers this math. It rescales the small population IQ boost associated with a policy change relative to amount by which average education was increased in the population (e.g. a 1 year raise in years of minimum compulsory education that only affects 10% of people amounts to a .10 year increase in the population as a whole) to get at IQ points per year.

The logic, and math, behind this has been formally worked out, and it tends to be very robust. However, as we mention in our discussion, the treatment effect is what is termed a “Local Average Treatment Effect,” (known as a LATE by many economists) that may not generalize to people who would exceed the minimum schooling level even in the absence of the policy.

Those are just my thoughts about why my money is on the policy change design, even if it is isn’t particularly conspicuous in raw historical national intelligence data.

I replied that one might have expected a very visible and permanent rise in intelligence scores after the school reform had been introduced, and drew a crude sketch of the predicted results in a figure.
Elliot replied:

A reason you might not see the abrupt step function in national data (as in the figure you attached) is if there is variable role-out across school districts (as there was in Brinch & Galloway’s study). This would result in a smoothing over time- but the step function would become apparent when you center each school-district’s time-series data around the date of policy implementation.

Here is an example of how the smoothing happens when there is variation in change point (this comes from a paper on terminal decline).

I replied: I can see now that the change would be far less abrupt than I imagined, but it would have to be detectable. I look forward to anything which might come up later which can be studied to confirm the expected increase in ability.

Stuart Ritchie comments:

There really were a lot of unexpected results in the meta-analysis. For instance, I expected that the “fade-out” would really be quite substantial in the “Control Prior Intelligence” and “Policy Change” studies, but the long-range ones, such as the former design done in the Lothian Birth Cohorts, still appear to show effects (and for the Lothian Birth Cohort, it’s even more believable since, at least for the Moray House Test, it’s the exact same measure at the early and outcome tests).

Another unexpected result was the size of the effect in the “School Age Cutoff” design. I’m very sceptical that an effect as large as that will persist later into life. One possibility is that there is a substantial, but not total “fadeout” of the effect after the completion of school, but it’s obviously rather tricky to test for long-term effects using this particular research design, which compares adjacent years.

My thanks to these three authors for their additional remarks.