Figure 4. Follow-up analyses of responses to Python code problems.

(A) MD system responses to math problems vs. string manipulation problems. (B) MD system responses to code with different structure (sequential vs. for loops vs. if statements). (C) Language system responses to code problems with English identifiers (codeE) and code problems with Japanese identifiers (codeJ) in participants with no knowledge of Japanese (non-speakers) and some knowledge of Japanese (speakers) (see the ‘Language system responses...' section for details of this manipulation). (D) Spatial correlation analysis of voxel-wise responses within the language system during the main task (SP – sentence problems and CP – code problems) with the language localizer conditions (SR – sentence reading and NR – nonwords reading). Each cell shows a correlation between the activation patterns for each pair of conditions. Within-condition similarity is estimated by correlating activation patterns across independent runs.

Figure 4—figure supplement 1. Spatial correlation analysis of voxel responses within the MD system during the Python experiment (CP – code problems and SP – sentence problems) with the language localizer conditions for the same participants (SR – sentence reading and NR – nonword reading).

Each cell shows a correlation between voxel-level activation patterns for each condition. Within-condition similarity is estimated by correlating activation patterns across independent runs. Code problems correlate with sentence problems much more strongly than with sentence reading (β = −0.59, p<0.001) and with nonword reading (β = −0.55, p<0.001), but substantially weaker than with other code problems (β = 0.11, p<0.001). There was no main effect of hemisphere, but there was an interaction between some of the conditions and hemisphere (sentence reading: β = 0.17, p<0.001, nonword reading: β = 0.13, p=0.002), indicating that the correlation patterns of code/sentence problems were somewhat less robust in the right hemisphere.

Figure 4—figure supplement 2. The effect of programming expertise on code-specific response strength within the MD and language system in Experiment 1, Python (A, B) and Experiment 2, ScratchJr (C, D).

Python expertise was evaluated with a separate 1-hr-long Python assessment (see the paper’s website, https://github.com/ALFA-group/neural-program-comprehension); ScratchJr expertise was estimated with in-scanner response accuracies. No correlations were significant.