ArticleScientific reports2020
Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Learning to Program "Recycles" Preexisting Frontoparietal Population Codes of Logical Algorithms.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Ten simple rules to complete successfully a computational MSc thesis project.PLoS computational biology · 2025Article
- Computer programmers show distinct, expertise-dependent brain responses to violations in form and meaning when reading code.Scientific reports · 2024Article
- Global and localized network characteristics of the resting brain predict and adapt to foreign language learning in older adults.Scientific reports · 2022Article
- Software Bug Detection Causes a Shift From Bottom-Up to Top-Down Effective Connectivity Involving the Insula Within the Error-Monitoring Network.Frontiers in human neuroscience · 2022Article
- EEG Resting-State and Event-Related Potentials as Markers of Learning Success in Older Adults Following Second Language Training: A Pilot Study.Brain plasticity (Amsterdam, Netherlands) · 2021Article
- Reading and Calculation Neural Systems and Their Weighted Adaptive Use for Programming Skills.Neural plasticity · 2021Review
- Comprehension of computer code relies primarily on domain-general executive brain regions.eLife · 2020Article
- Article
- Programming experience associated with neural efficiency during figural reasoning.Scientific reports · 2020Article
- Bilingual Language Experience Shapes Resting-State Brain Rhythms.Neurobiology of language (Cambridge, Mass.) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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