# Natural Language Aptitude and Learning Programming Languages

**URL:** <https://forum.codeselfstudy.com/t/natural-language-aptitude-and-learning-programming-languages/2147>\
**Category:** General Discussion\
**Created:** [March 3, 2020, 2:53am UTC](https://forum.codeselfstudy.com/t/natural-language-aptitude-and-learning-programming-languages/2147 "2020-03-03T02:53:43Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Josh](https://forum.codeselfstudy.com/user_avatar/forum.codeselfstudy.com/josh/32/4_2.png) [@Josh](https://forum.codeselfstudy.com/u/Josh)\
**Post date:** [March 3, 2020, 2:53am UTC](https://forum.codeselfstudy.com/t/natural-language-aptitude-and-learning-programming-languages/2147/1 "2020-03-03T02:53:43Z")

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This looks interesting. There is a [Reddit discussion](https://old.reddit.com/r/science/comments/fcdf8x/language_skills_are_a_stronger_predictor_of/) with more comments.

[https://www.nature.com/articles/s41598-020-60661-8](https://www.nature.com/articles/s41598-020-60661-8)

> This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second “natural” language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors.
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> Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions.
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> The resulting models explained 50–72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%).
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> These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments.