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的K-12和中学后教育课程。编程是STEM教育的关键部分，也是STEM劳动力成功的门户。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。许多人成为高度熟练的程序员和专业程序员，以及娱乐。但是在学习编程的速度和最终达到的编程能力方面存在很大的个体差异。这些差异的原因还不清楚。该提案使用尖端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，以及人类大脑如何改变自己，使学习编程成为可能。这项研究是利用人类大脑的适应能力来优化编程技能培训的第一步。该项目旨在直接吸引残疾学生和少数群体的学生，为他们提供参与这一关键主题前沿研究的机会。在该项目中，研究人员测试了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等编程“语言”会激活大脑中为处理英语和西班牙语等自然语言而进化的部分。也有证据表明，编程涉及前额叶和顶叶皮质中支持解决逻辑难题的逻辑推理系统。该提案使用尖端的神经成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批学生在第一堂编程课之前和之后的大脑功能、解剖学和行为。这种方法测试了编程教育重新利用了哪些现有机制。然后，机器学习分析可以用来研究人们在学习编程之前和之后大脑中的详细神经模式，以及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沟通途径的变化也可以在学习前后量化。第二项研究比较了具有广泛不同编程专业知识的人的大脑功能和行为，从编程天真的人到编程专家和每天将代码作为工作一部分的人。这些方法可以更好地了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）和神经测量预测编程能力。本研究旨在为教育研究和干预措施的设计提供基础，以优化编程教学。编程的研究也提供了深入了解机制的可塑性在高阶cognition.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。