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通过机器学习从大脑到教育:从神经成像数据预测识字和算术技能

Tomoya Nakai^1,2, Coumarane Tirou¹, Jérôme Prado¹

¹Lyon Neuroscience Research Center (CRNL), INSERM U1028 - CNRS UMR5292, University of Lyon, Bron, France.

Imaging neuroscience (Cambridge, Mass.)

|August 13, 2025

概括

应用于神经数据的机器学习显示出预测识字和算法的学术成果的前景. 进一步的研究需要标准化的方法和可访问的神经成像,以便更广泛地应用.

科学领域:

教育神经科学教育神经科学
神经成像是一种神经成像.
机器学习是机器学习.

背景情况:

教育神经科学探索使用神经数据来预测学术成功.
机器学习 (ML) 和神经成像技术的进步在这个领域提供了新的潜力.
之前的研究已经探索了预测识字和数学的结果.

研究的目的:

审查使用ML来预测识字和数学的神经成像研究.
分析方法,包括横截面和纵向设计,以及ML方法.
确定该领域的挑战和未来方向.

主要方法:

使用机器学习进行的神经成像研究的系统审查.
对预测不同人群 (儿童,成年人,学习障碍,典型表现) 识字和计算能力的研究进行分析.
检查横截面和纵横设计,回归和分类技术.

主要成果:

神经成像中的ML显示了预测识字和数学的潜力.
在研究中的算法,大脑电路和方法学中存在显著的变化.
在对幼儿在正规教育之前进行的纵向预测研究中存在一个显著的差距.

结论:

用ML增强的神经成像对教育神经科学应用具有前景.
方法的标准化和便携式神经成像的使用增加对于进步至关重要.
未来的研究应该专注于幼儿时期的纵向预测和可访问的神经成像.

关键词:

发展发展发展发展发展.教育教育教育教育的教育.学习障碍学习障碍是什么机器学习是机器学习.神经科学神经科学

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Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.