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. 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. 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%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments. Computer programming has moved from being a niche skill to one that is increasingly central for functioning in modern society. Despite this shift, remarkably little research has investigated the cognitive basis of what it takes to learn programming languages. The implications of such knowledge are wide reaching, both in terms of cultural barriers to pursuing computer sciences 1 and for educational practices 2. Central to both are commonly held ideas about what it takes to be a "good" programmer, many of which have not been empirically instantiated. In fact, remarkably little research has investigated the cognitive bases of "programming aptitude" 3-5 , and, to the best of our knowledge, no research to date has investigated its neural correlates. Critically, the existing research provides inconsistent evidence about the relevance of mathematical skills for learning to program 6-8. Despite this, programming classes in college environments regularly require advanced mathematical courses as prerequisites. This gap between what we know about learning to program and the environments in which programming is taught was described by Jenkins, who argued that "If computing educators are ever to truly develop a learning environment where all the students learn to program quickly and well, it is vital that an understanding of the difficulties and complexities faced by the students is developed. At the moment, the way in which programming is taught and learned is fundamentally broken" 2. Unfortunately, little progress has been made since this call to action 15 years ago. Across the same time period, the nature of programming languages has also changed, reducing the likelihood that the original research on learning to program in Pascal 5 or COBOL 3 , for instance, will generalize to contemporary programming languages. The research described herein is motivated by a...
