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, Coumarane Tirou, Jérôme Prado
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摘要

摘要 利用神经数据预测学习成绩的潜力一直是教育神经科学的核心,而教育神经科学是心理学、神经科学和教育科学交叉领域的新兴学科。虽然这一前景长期以来一直难以实现,但神经影像学中机器学习先进技术的指数级应用可能会改变这一现状。在此,我们回顾了利用机器学习预测成人和儿童识字和算术结果的神经成像研究,包括学习障碍和典型表现两个方面。我们特别回顾了此类研究中使用的横断面和纵向设计,并介绍了如何将它们与回归和分类方法相结合。我们的综述强调了这些方法在预测识字和算术结果方面的前景及其困难。然而,我们也发现,不同的研究在算法和基础脑回路方面存在很大的差异,而且相对缺乏对正规教育开始前的幼儿学习结果进行纵向预测的研究。我们认为,这一领域需要方法的标准化,以及更多地使用比实验室神经成像技术更有应用潜力的便携式神经成像方法。
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From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data
Abstract The potential of using neural data to predict academic outcomes has always been at the heart of educational neuroscience, an emerging field at the crossroad of psychology, neuroscience, and education sciences. Although this prospect has long been elusive, the exponential use of advanced techniques in machine learning in neuroimaging may change this state of affairs. Here we provide a review of neuroimaging studies that have used machine learning to predict literacy and numeracy outcomes in adults and children, in both the context of learning disability and typical performance. We notably review the cross-sectional and longitudinal designs used in such studies, and describe how they can be coupled with regression and classification approaches. Our review highlights the promise of these methods for predicting literacy and numeracy outcomes, as well as their difficulties. However, we also found a large variability in terms of algorithms and underlying brain circuits across studies, and a relative lack of studies investigating longitudinal prediction of outcomes in young children before the onset of formal education. We argue that the field needs a standardization of methods, as well as a greater use of accessible and portable neuroimaging methods that have more applicability potential than lab-based neuroimaging techniques.
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