Abstract
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.
Highlights
Computer programming has moved from being a niche skill to one that is increasingly central for functioning in modern society
We argue that research on the neurocognitive bases of programming aptitude has largely missed the fact that computer programming languages are designed to resemble the communication structure of the programmer, an idea that was first formalized by Chomsky over 50 years ago[9]
These studies found that natural language ability either predicted unique variance in programming outcomes after mathematical skills were accounted for[3], or that language was a stronger predictor of programming outcomes than was math[10,11]
Summary
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. We argue that research on the neurocognitive bases of programming aptitude has largely missed the fact that computer programming languages are designed to resemble the communication structure of the programmer (human languages), an idea that was first formalized by Chomsky over 50 years ago[9] This idea www.nature.com/scientificreports has been revisited in recent reviews[10,11], only a small number of studies have investigated the predictive utility of linguistic skill for learning programming languages[3,12,13]. Python uses indentation patterns that mimic “paragraph” style hierarchies present in English writing systems instead of curly brackets (used in many languages to delimit functional blocks of code), and uses words (e.g., “not” and “is”) to denote operations commonly indicated with symbols (e.g., “!” and “==”)
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