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

Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: The multiple demand (MD) system, typically recruited during mat...

Full description

Bibliographic Details
Main Authors: Ivanova, Anna A (Author), Srikant, Shashank (Author), Sueoka, Yotaro (Author), Kean, Hope (Author), Dhamala, Riva (Author), O'Reilly, Una-May (Author), Bers, Marina U (Author), Fedorenko, Evelina G (Author)
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor), McGovern Institute for Brain Research at MIT (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
Format: Article
Language:English
Published: eLife Sciences Publications, Ltd, 2021-02-22T16:56:16Z.
Subjects:
Online Access:Get fulltext
Description
Summary:Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: The multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.
National Science Foundation (Grant 1744809)