On the other hand, "natural language aptitude" as measured by Modern (modern = ca. 1960s) Language Aptitude Test, which they used in that study, doesn't actually predict success in language acquisition. The test scores correlate with ability to perform well at _early_ phase of language curricula – but this is exactly the phase when the learners are still using the "general processing mechanisms" to process language, and not the mechanisms specialized for language that get engaged when they gain fluency years later.

Sadly, this is underappreciated in the general psychology and linguistics community. Some researchers in Second Language Acquisition research community are more concerned about this kind of mis-interpretation and confusion of different meanings of "language skills/aptitude".

>And yet natural language aptitude highly predicts programming ability

Its all correlated with g.

>[0]

They contrast "language learning ability", ie intrinsic intelligence, with "math knowledge", ie amount of schooling. For reference, you can take (a version of) the math knowledge test used here: https://www.idrlabs.com/numeracy/test.php

If the article wanted to give a more honest interpretation of the paper, they would have focused on the leading predictor of programming ability, "fluid reasoning and working-memory capacity". One supposes this to be quite useful in math and engineering.

It's important to note the methodology employed in the original paper used "did not have meaningful structure or variable names" [1]. If the code's like that, then yes it's less like reading a language and more like cryptic puzzles.

But limitedness of the methodology means the conclusion can't apply to software development at large, something that the title misleads us about.

[1]: https://twitter.com/wcrichton/status/1339235494102753280

I'm not intending to make some grandiose point here, only that I suspect natural language aptitude is highly correlated with success in various different domains, not just programming.