Karas, Zachary, Gold, Benjamin, Zhou, Violet, Reardon, Noah, Polk, Thad, Chang, Catie, & Huang, Yu. (2025). Studying programmers without programming: Investigating expertise using resting state fMRI. In *Proceedings of the International Conference on Software Engineering*, pp. 2380-2392. https://doi.org/10.1109/ICSE55347.2025.00164
Expert programmers tend to be better at coding, but it’s still unclear exactly why. Some researchers have used brain scans (like fMRI) to study how programmers think while doing specific coding tasks, such as understanding code. However, those studies haven’t found consistent brain differences based on experience. One possible reason is that focusing only on tasks may limit the brain areas that get activated during the scans.
In neuroscience, another approach is to study the brain while it’s at rest—that is, when a person is just lying still in the scanner. This “resting-state” brain activity reflects how the brain is naturally organized and can reveal long-term effects of experience. In this study, researchers analyzed resting brain scans from 150 people, including 96 programmers, to see how programming experience might shape brain networks.
They found that programmers showed stronger connections between brain regions related to language, math, and attention over time. In contrast, non-programmers showed more connections in areas linked to social and emotional thinking. The study also found that with more years of programming experience, there was less connection between two specific brain areas involved in reading visuals and speaking.
These findings suggest that the brain may reorganize itself with programming experience, particularly in ways that support logic, language, and focus.
Fig. 1:
Group-level functional connectivity measures for (a) programmers and (b) non-programmers. Values in these matrices represent the functional connectivity (i.e., Pearson correlation) from every cortical brain region to every other cortical brain region. Correlation coefficients in these figures were Fisher’s z-transformed to normalize their distribution. These values now denote scaled correlation values following a normal distribution between -1 and 1.
