Expert programmers have fine-tuned cortical representations of source code

Abstract

Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

Comprehension of computer code relies primarily on domain-general executive brain regions

“…Our findings suggest that earlier reports of left-lateralized code-evoked activity ( Siegmund et al, 2014 ) may reflect processing program content rather than code comprehension per se. This distinction should also be considered when interpreting results of other studies of programming effects on brain activity, such as debugging ( Castelhano et al, 2019 ), variable tracking ( Ikutani and Uwano, 2014 ; Nakagawa et al, 2014 ), use of semantic cues or program layout ( Fakhoury et al, 2018 ; Siegmund et al, 2017 ), program generation ( Krueger et al, 2020 ), and programming expertise ( Ikutani et al, 2020 ).…”

Comprehension of computer code relies primarily on domain-general executive brain regions

“…Our findings suggest that earlier reports of left-lateralized code-evoked activity (Siegmund et al, 2014) may reflect processing program content rather than code comprehension per se. This distinction should also be considered when interpreting results of other studies of programming effects on brain activity, such as debugging (Castelhano et al, 2019), variable tracking (Ikutani & Uwano, 2014;Nakagawa et al, 2014), use of semantic cues or program layout (Fakhoury et al, 2018;Siegmund et al, 2017), program generation (Krueger et al, 2020), and programming expertise (Ikutani et al, 2020).…”

The neural bases of program comprehension: a coordinate-based fMRI meta-analysis

“…Among these nine contrasts, six contrasts investigated program out-put estimation against control tasks, and two examined BOLD activations during source code inspections. Peak voxel coordinates in Ikutani et al (2020) showed significant correlations between decoding accuracy of source code categories and behavioral performance of program categorization tasks. From the the pro-gramming language perspective, four studies used Java, two used Python, and one study each used C and ScratchJr in the experiments.…”

“…We report the thresholded results as an MKDA statistic map with a voxel-level threshold of p ¡ 0.001 (uncorrected) and a cluster threshold of p ¡ 0.05 (family-wise error [FWE]-corrected). Note that our meta-analysis included the peak voxel coordinates reported in Ikutani et al (2020), derived from decoding analysis but not a BOLD contrast, to cover as many available neuroimaging results as possible in the current literature.…”

“…However, individual neuroimaging studies of program comprehension often provided inconsistent results. One of the central contradictions is that multiple studies argued cognitive similarity between program comprehension and natural language comprehension (Siegmund et al, 2014, 2017; Prat et al, 2020; Ikutani et al, 2020), while others emphasized the remarkable difference between them (Floyd et al, 2017; Krueger et al, 2020; Ivanova et al, 2020; Liu et al, 2020).…”