Graduate School of Science and Technology, Division of Information Science, Nara Institute of Science and Technology (NAIST), Ikoma, Nara 630-0192, Japan.
Graduate School of Science and Technology, Division of Information Science, Nara Institute of Science and Technology (NAIST), Ikoma, Nara 630-0192, Japan
eNeuro. 2021 Jan 28;8(1). doi: 10.1523/ENEURO.0405-20.2020. Print 2021 Jan-Feb.
Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many studies 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 of programmers 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 a source-code categorization task. Our results suggest that programming expertise is built on fine-tuned cortical representations specialized for the domain of programming.
专业知识使人类能够在特定领域的任务上取得卓越的表现,编程也不例外。许多研究表明,专家级程序员在行为表现、知识结构和选择性注意等方面与新手有显著的差异。然而,程序员大脑中的潜在差异仍不清楚。我们通过使用数据驱动的解码方法,将源代码的皮质代表与个体编程专业知识联系起来,解决了这个问题。这种方法使我们能够识别出七个广泛分布在前额、顶叶和颞叶皮质中的脑区,这些脑区与编程专业知识有着紧密的关系。在这些脑区中,可以从大脑活动中解码出源代码的功能类别,并且解码的准确性与个体在源代码分类任务上的行为表现显著相关。我们的研究结果表明,编程专业知识是建立在针对编程领域的精细调整的皮质代表之上的。