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    Here’s my best summary (not a substitute for reading the paper; they studied many many cognitive factors which I admittedly don’t understand):

    The goal of our experiment was to investigate whether factors that predict natural language learning also predict learning to program in Python.

    Using the combination of neural and behavioral measures that have previously been associated with natural language learning, we were able to explain up to 70% of the variability in Python learning outcomes… In comparison, numeracy only explained unique variance in Python learning rate, and accounted for an average of 2% of the variance across outcome variables… The regression analyses also showed that general cognitive abilities, including fluid reasoning ability and working memory factors (dark red), were the best average predictors of programming outcomes, explaining nearly 34% of the variance across outcome measures…. rsEEG measures (Fig. 3: beige) explained an average of 10% of the variance in Python programming outcomes.

    Contrary to widely held stereotypes, the “computer whisperers” investigated herein were facile problem solvers with a high aptitude for natural languages. Although numeracy was a reliable predictor of programming aptitude, it was far from the most significant predictor… the research reported herein begins to paint a picture of what a good programmer actually looks like, and that picture is different in important ways from many previously held beliefs.

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      If I understand the abstract correctly, then my aptitude for programming must be horrible based upon my Spanish and German report cards.

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        They were essentially measuring ability to learn enough to pass tests.