Department of Computer Science, University of Victoria, Victoria, Canada.
Centre for Biomedical Research, University of Victoria, Victoria, Canada.
Brain Behav. 2021 Aug;11(8):e2234. doi: 10.1002/brb3.2234. Epub 2021 Jun 15.
As we learn a new nonnative language (L2), we begin to build a new map of concepts onto orthographic representations. Eventually, L2 can conjure as rich a semantic representation as our native language (L1). However, the neural processes for mapping a new orthographic representation to a familiar meaning are not well understood or characterized.
Using electroencephalography and an artificial language that maps symbols to English words, we show that it is possible to use machine learning models to detect a newly formed semantic mapping as it is acquired.
Through a trial-by-trial analysis, we show that we can detect when a new semantic mapping is formed. Our results show that, like word meaning representations evoked by a L1, the localization of the newly formed neural representations is highly distributed, but the representation may emerge more slowly after the onset of the symbol. Furthermore, our mapping of word meanings to symbols removes the confound of the semantics to the visual characteristics of the stimulus, a confound that has been difficult to disentangle previously.
We have shown that the L1 semantic representation conjured by a newly acquired L2 word can be detected using decoding techniques, and we give the first characterization of the emergence of that mapping. Our work opens up new possibilities for the study of semantic representations during L2 learning.
当我们学习一门新的外语（L2）时，我们开始在拼写上建立一个新的概念图。最终，L2 可以像我们的母语（L1）一样唤起丰富的语义表达。然而，将新的拼写表示映射到熟悉的含义的神经过程还没有得到很好的理解或描述。
我们使用脑电图和一种将符号映射到英语单词的人工语言，表明可以使用机器学习模型来检测新形成的语义映射的习得情况。
通过逐次分析，我们表明我们可以检测到新的语义映射何时形成。我们的结果表明，就像由 L1 唤起的单词意义表示一样，新形成的神经表示的定位是高度分布式的，但在符号出现后可能会出现得更慢。此外，我们将单词的意义映射到符号上，消除了刺激的视觉特征与语义之间的混淆，这是以前很难解决的一个混淆。
我们已经表明，使用解码技术可以检测到新习得的 L2 单词唤起的 L1 语义表示，并且我们给出了该映射出现的第一个特征描述。我们的工作为研究 L2 学习期间的语义表示开辟了新的可能性。
