学习编程"回收"先前存在的前端对象群代码的逻辑算法.
Yun-Fei Liu 劉耘非1, Marina Bedny1
1Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland 21211.
概括
计算机编程将现有的大脑网络重新用于逻辑算法,支持用于技能获取的神经循环假设. 这表明我们的大脑适应了先前存在的结构,以获得新的文化技能,如编码.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算机科学 计算机科学
背景情况:
- 像编程这样的文化技能重新利用现有的神经网络 (神经循环假说).
- 另外,技能的神经图可能会在学习过程中出现 (de novo).
- 了解编程的神经基础,这是最近的文化技能,至关重要.
研究的目的:
- 调查逻辑算法的表示 (例如",for"循环",if"条件) 是否在编程指令中获得或回收.
- 在学习计算机编程的背景下测试神经循环假说.
主要方法:
- 功能磁共振成像 (fMRI) 用于研究大学生 (n=22) 在一个学期的Python课程之前和之后.
- 参与者完成行为任务和fMRI扫描,同时查看Python函数和伪代码.
- 使用多变量种群编码和代表性相似性分析.
主要成果:
- 学习Python激活了一个左侧化的前面对面推理网络.
- 这个网络在编程指令之前就被伪代码参与了.
- 这个网络中的神经表示区分了"for"循环和"if"条件在课程前后,在两个时间点共享信息.
结论:
- 编程指令在前端对象网络中招募和完善先前存在的神经表征.
- 这支持神经回收框架,表明大脑回收现有的逻辑算法表示用于编程.
- 这些发现突显了大脑适应已建立的神经回路以获得新的文化技能的能力.
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