Using machine learning to reveal neural mechanisms of word-learning across development

出生后，婴儿立即沉浸在一个交响乐的声音，他们将需要识别他们成熟。人类的语言是这个早期音景的主要部分，在出生后的最初几个月，婴儿已经学会了在特定的语言类别中解析出其中的一些声音。婴儿学习者如何在生命的早期就做到这一点仍然没有解决。我的研究的一个长期目标是描述早期基于声音的表征的性质以及形成和改变这些表征的学习机制。神经影像学研究的最新进展已经彻底改变了我们表征个体语言表征的神经实例化的能力。因此，在接下来的五年里，我和我的团队追求四个目标：目标1：招募机器学习分析工具来描述成年人已知和未知单词的脑电图（EEG）签名目标2：使这种分析和范式工具箱适应发育人群目标3：检查通过统计学习获得的单词表征的转换，这是一种被认为有助于早期单词学习的机制。目标4：将EEG特征与个体差异的行为测量相关联。三组实验都是为了实现这些目标。实验集1（成人EEG的机器学习分析）通过使用机器学习分类器识别和比较与单词和非单词相关的大脑活动的时空EEG特征来实现目标1。它还通过将行为措施（例如，对单词与非单词的响应时间）到机器学习分类器性能。这组实验将增加我们对成熟单词和非单词神经表征的了解，并将在机器学习分析工具包应用于婴儿和儿童大脑记录之前提供其原理证明。实验组2将这些范例和分析工具适用于5-7岁的儿童和12- 18个月大的婴儿（目标2）。这将揭示在时空EEG签名有稳定性，并在整个发展的单词和非单词表示的组织有分歧。实验组3通过教授婴儿、儿童和成人新单词来实现目标3，这将使我们能够研究学习者的表征如何在特定类型的学习经验中发生变化。机器学习分析技术已经彻底改变了神经成像研究，但直到最近才被应用于发育人群。我们希望我们的研究计划将改善现有的机器学习神经成像分析工具，特别是在发育人群中的应用。至关重要的是，这项工作将提高我们对人类大脑如何发展处理和分类复杂声音流为有意义的单词的基本能力的基本理解。

Immediately after birth, infants are immersed in a symphony of sounds that they will need to identify as they mature. Human language is a major part of this early soundscape, and within the first few months of life, infants have learned to parse out some of these sounds in language-specific categories. How infant learners can do this so early in life remains unsolved. A long-term goal of my research is to characterize the nature of early sound-based representations and the learning mechanisms that form and change these representations. Recent advances in neuroimaging research have revolutionized our ability to characterize the neural instantiation of individuals' linguistic representations. In the next five years, my team and I therefore pursue four objectives: Objective 1: Recruit machine-learning analysis tools to characterize the electro-encephalographic (EEG) signature of known and unknown words in adults Objective 2: Adapt this analysis and paradigm toolkit to developmental populations Objective 3: Examine the transformation of word representations as they are acquired via statistical learning, a mechanism believed to subserve early word-learning. Objective 4: Link the EEG signatures to behavioural measures of individual differences. Three sets of experiments address these goals. Experiment Set 1 (machine-learning analysis of adult EEG) addresses Objective 1 by using machine-learning classifiers to identify and compare the spatio-temporal EEG signatures of brain activity associated with words and non-words. It also addresses Objective 4 by correlating behavioural measures (e.g., response times to words vs nonwords) to machine-learning classifier performance. This set of experiments will increase our knowledge of mature word and non-word neural representations and will provide a proof-of-principle of the machine-learning analysis toolkit before it is applied to infant and child brain recordings. Experiment Set 2 adapts these paradigms and analysis tools to 5-7 year old children and 12- to 18-month-old infants (Objective 2). This will reveal where in the spatio-temporal EEG signature there is stability and where there is divergence in the organization of word and non-word representations across development. Experiment Set 3 addresses Objective 3 by teaching infants, children, and adults new words, which will allow us to examine how learners' representations transform given specific types of learning experience. Machine-learning analysis techniques have revolutionized neuroimaging research, but have only very recently been applied to developmental populations. We expect that our program of research will improve existing machine-learning neuroimaging analysis tools, particularly with respect to their application in developmental populations. Crucially, this work will improve our basic understanding of how human brains develop the fundamental ability to process and classify complex streams of sounds into meaningful words.