Studying Programmers Without Programming: Investigating Expertise Using Resting State fMRI
Zachary Karas, Benjamin Gold, Violet Zhou, Noah Reardon, Thad A. Polk, Catie Chang, Yu Huang
摘要
Expert programmers are more effective at coding activities, but the reasons for this remain elusive. Accordingly, recent research has used neuroimaging such as fMRI to analyze how expert programmers might think as they perform coding activities. Those experiments have all involved specific program-ming tasks (i.e., comprehension), but have been unable to detect systematic differences based on coding experience. By using tasks, however, those studies may limit the number and type of brain networks involved. In Cognitive Neuroscience, researchers commonly analyze resting-state data, in which participants' brain activity is recorded as they lay idle in the scanner. The brain's functional organization is plastic, and can change with experience. These changes can be measured at rest, making this a suitable data type for studying how programming activities affect neural organization over time. In this paper, we analyzed the resting state scans from 150 participants, 96 of whom were programmers. We found increased connectivity in programmers between brain regions involved in language, math, and the tempo-ral attention. Non-programmers demonstrated more connectivity with regions involved in social and emotional cognition. We found that as years of programming experience increases, connectivity decreases between two regions associated with visual processing during reading and articulation, respectively.
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