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Comment by solresol | Hacker News Reader

Actually, the article content doesn't seem to match the title.

The best predictor according to the article (50% of variance) is fluid intelligence. Language aptitude was a distant second place at 8% of variance explained, narrowly edging out memory for third place.

I don't think anyone is at all surprised by a result that says "being able to think logically in novel situations is a better predictor of whether you will be able to pick up programming quickly than all other predictors combined".

Nor am I particularly surprised to learn that the component of mathematical expertise that is not derived from fluid intelligence (e.g. ability to perform mental arithmetic, for example) isn't predictive of the ability to program.

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There are several different results. Language aptitude was the best predictor for learning rate. Fluid intelligence was the best predictor for programming accuracy (the numbers you quote), and for declarative knowledge.

But yeah the actual title of this 2020 article is "Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages", the current HN title "Predictor of learning to code is language aptitude - not math/cognitive ability" doesn't appear in the article, and I don't see at all how it is supported (besides numeracy specifically not showing up strongly) when they found general cognitive ability to be a significant predictor.

> Actually, article content doesn’t match the title … best predictor is fluid intelligence

On the contrary, the title, for both this submission and the article, emphasizes the “learning” phase, while you’re citing the success outcomes bit. So not actually.

On learning, the article says:

Learning: When the six predictors of Python learning rate (language aptitude, numeracy, fluid reasoning, working memory span, working memory updating, and right fronto-temporal beta power) competed to explain variance, the best fitting model included four predictors: language aptitude, fluid reasoning (RAPM), right fronto-temporal beta power, and numeracy. This model was highly significant [F(4,28) = 15.44, p < 0.001], and explained 72% of the total variance in Python learning rate. Language aptitude was the strongest predictor, explaining 43.1% of the variance, followed by fluid reasoning, which contributed an additional 12.8% of the variance, right fronto-temporal beta power, which explained 10%, and numeracy scores, which explained 6.1% of the variance.

TL;DR: For “learning”, language aptitude was the strongest predictor, explaining 43.1% of the variance, followed by fluid reasoning, which contributed an additional 12.8% of the variance…

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