Rapid "recycling" of logical algorithm representations in fronto-parietal reasoning systems following computer programming instructions
Liu, Y.-F.; Bedny, M.
Show abstract
Programming is a cornerstone of modern society, yet its cognitive and neural basis remains poorly understood. In this study, we test the hypothesis that programming "recycles" pre-existing neural mechanisms and representations in fronto-parietal reasoning networks. Using fMRI, we scanned programming-naive undergraduates (n=22) before (PRE) and after (POST) an introductory Python course. During the PRE scan, participants viewed pseudocode (plain English descriptions of algorithms), and during the POST scan, they read Python code. We found that a left-lateralized fronto-parietal network, previously implicated in programming experts, distinguished between "for" loops and "if" conditionals across both pseudocode and Python code. Representational similarity analysis revealed consistent representations of algorithms across formats (code/pseudocode) and learning stages. Furthermore, such representations encode abstract meanings rather than superficial features. Our findings demonstrate that programming not only recycles pre-existing neural resources evolved for logical reasoning, but the recycling takes place rapidly with only a single semester of training.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Encoding neural representations of time-continuous stimulus-response transformations in the human brain with advanced deep neural networks 96%
- Disentangling reference frames in the neural compass. 96%
- fMRI signals of pattern separation in the neocortex and hippocampus to non-meaningful objects and their spatial location 96%
Similar papers in this journal
Similar papers in this journal
- Abstract neural representations of category membership beyond information coding stimulus or response 97%
- Individual-subject functional localization increases univariate activation but not multivariate pattern discriminability in the 'multiple-demand' frontoparietal network 96%
- Representing context and priority in working memory 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.