> 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

> 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…