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Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise

机译:解码大脑中的代码表示形式:对代码审查和专业知识的功能磁共振成像研究

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Subjective judgments in software engineering tasks are of critical importance but can be difficult to study with conventional means. Medical imaging techniques hold the promise of relating cognition to physical activities and brain structures. In a controlled experiment involving 29 participants, we examine code comprehension, code review and prose review using functional magnetic resonance imaging. We find that the neural representations of programming languages vs. natural languages are distinct. We can classify which task a participant is undertaking based solely on brain activity (balanced accuracy 79%, p <; 0.001). Further, we find that the same set of brain regions distinguish between code and prose (near-perfect correlation, r = 0.99, p <; 0.001). Finally, we find that task distinctions are modulated by expertise, such that greater skill predicts a less differentiated neural representation (r = -0.44, p = 0.016) indicating that more skilled participants treat code and prose more similarly at a neural activation level.
机译:软件工程任务中的主观判断至关重要,但使用常规方法可能很难研究。医学成像技术有望将认知与身体活动和大脑结构相关联。在一个由29名参与者组成的对照实验中,我们使用功能磁共振成像技术检查了代码理解,代码审查和散文审查。我们发现,编程语言与自然语言的神经表示形式是截然不同的。我们可以仅根据大脑活动对参与者执行的任务进行分类(准确度为79%,p <; 0.001)。此外,我们发现同一组大脑区域可以区分代码和散文(近乎完美的相关性,r = 0.99,p <; 0.001)。最后,我们发现任务的区分受专业技能的调节,因此技能越高,预测的神经表示差异就越小(r = -0.44,p = 0.016),这表明技能更高的参与者在神经激活水平上对待代码并提出相似的要求更高。

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