Neural recycling and plasticity in computer programming expertise

计算机编程专业知识中的神经回收和可塑性

• 批准号: 2318685
• 负责人: Marina Bedny
• 金额: $ 98.01万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318685&HistoricalAwards=false
• 关键词: Neural; recycling; plasticity; computer; programming

Computer programming skills are increasingly fundamental to many jobs across diverse fields, including in healthcare, science, communication, finance, and transport. Programming instruction is being incorporated into standard K-12 and post-secondary educational curricula. Programming is a key part of STEM education and a gateway to success in the STEM workforce. However, compared to other skills, like math and reading, we know little about the cognitive and neural underpinnings of programming. Many people become highly proficient coders and program professionally, as well as for pleasure. But there are wide individual differences in how quickly programming is learned and the ultimate programming ability that is achieved. The causes of these differences are not well understood. This proposal uses cutting edge neuroscience and cognitive science approaches to study the neurocognitive systems that support programming skills. We investigate which neural and cognitive systems support programming and how the human brain changes itself to make learning to program possible. This research is a first step to harnessing the adaptive capability of the human brain to optimize the training of programming skills. The project aims to directly engage students with disabilities and from minoritized groups to provide an opportunity to participate in cutting edge research on this critical topic.In this proposal the researchers test hypotheses about which neural systems support programming and how these systems change during learning. One hypothesis is that learning programming ‘languages’ like Python engages parts of the brain that evolved for processing natural languages, like English and Spanish. There is also evidence that programming engages logical reasoning systems in prefrontal and parietal cortices that support solving logic puzzles. This proposal uses cutting edge neuroimaging techniques to study the different contributions of these systems and their connectivity to programming skills. First, the researchers aim to measure brain function, anatomy, and behavior in the same students before and after they take their first programming class. This approach tests what pre-existing mechanisms are repurposed by programming education. Machine learning analyses can then be used to study detailed neural patterns in the brains of people before and after they learn to program and the locations and extent of changes quantified. Further, changes in the anatomical communication pathways between language and logical reasoning systems can also be quantified before and after learning. A second study compares brain function and behavior across people with widely different programming expertise, from people who are programming naïve to people who are programming experts and code every day as part of their jobs. Together these approaches can yield a better understanding of the neural and cognitive basis of programming and which cognitive abilities (e.g., language, reasoning, math) and neural measures predict programming ability. This research aims to serve as a foundation for education research and the design of interventions to optimize programming instruction. The study of programming also provides insight into mechanisms of plasticity in higher-order cognition.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机编程技能对于不同领域的许多工作越来越重要，包括医疗保健、科学、通信、金融和运输。编程教学正在被纳入标准 K-12 和高等教育课程。编程是 STEM 教育的关键部分，也是 STEM 劳动力取得成功的途径。然而，与数学和阅读等其他技能相比，我们对编程的认知和神经基础知之甚少。许多人成为了非常熟练的程序员，并以专业的方式进行编程，同时也为了乐趣。但学习编程的速度和最终达到的编程能力存在很大的个体差异。这些差异的原因尚不清楚。该提案使用最先进的神经科学和认知科学方法来研究支持编程技能的神经认知系统。我们研究哪些神经和认知系统支持编程，以及人脑如何改变自身以使学习编程成为可能。这项研究是利用人脑的适应能力来优化编程技能训练的第一步。该项目旨在直接吸引残疾学生和少数群体的学生，提供参与这一关键主题的前沿研究的机会。在这项提案中，研究人员测试了关于哪些神经系统支持编程以及这些系统在学习过程中如何变化的假设。一种假设是，学习 Python 这样的编程“语言”会调动大脑中为处理自然语言（例如英语和西班牙语）而进化的部分。还有证据表明，编程涉及前额叶和顶叶皮层的逻辑推理系统，支持解决逻辑难题。该提案使用尖端的神经影像技术来研究这些系统的不同贡献及其与编程技能的连接。首先，研究人员的目标是测量同一学生在上第一堂编程课之前和之后的大脑功能、解剖结构和行为。这种方法测试了编程教育重新利用了哪些预先存在的机制。然后，机器学习分析可用于研究人们学习编程前后大脑中的详细神经模式，并量化变化的位置和程度。此外，语言和逻辑推理系统之间的解剖学交流路径的变化也可以在学习前后进行量化。第二项研究比较了具有不同编程专业知识的人的大脑功能和行为，从编程新手到作为工作一部分每天编码的编程专家。这些方法一起可以更好地理解编程的神经和认知基础，以及哪些认知能力（例如语言、推理、数学）和神经测量可以预测编程能力。这项研究旨在作为教育研究和优化编程教学干预措施设计的基础。编程研究还提供了对高阶认知可塑性机制的见解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。