第二语言言语学习的神经基础——过去与未来——评《第二语言加工的神经认知基础:过去与未来展望的知识收获》

IF 3.5 1区 文学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Patrick C. M. Wong
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The issues raised by van Hell such as the critical period hypothesis, age of acquisition, proficiency, and individual differences have also been studied in the context of these components (e.g., Golestani &amp; Zatorre, <span>2004</span>). This line of research often focused on individual differences and demonstrated that pretraining neural differences may forecast learning success at the group level (e.g., Sheppard et al., <span>2012</span>). Future studies can explore how individual differences in neural speech tracking of different chunk sizes (e.g., Ding et al., <span>2015</span>) may lead to differences in L2 learning outcomes.</p><p>To investigate individual differences, research must augment analytics that are designed for observing group-level performance by also using methods that are precise enough for making individual-level predictions. In research on first language acquisition (Wong et al., <span>2021</span>), machine learning techniques have been adopted to make predictions about individual learners’ learning outcomes with very promising prediction performance. The use of such techniques has begun in L2 learning as well (Feng et al., 2021). In addition to forecasting learning success, future research can also predict differences in response to different types of interventions, so that training can be altered before it even begins in order to optimize learning for every learner.</p><p>In addition to investigating learner-internal individual difference variables, as reviewed by van Hell (see Wong et al., <span>2022</span>, for potential genetic variables), L2 research has also examined how different learner-external variables (e.g., training methods such as explicit training) lead to better or worse outcomes as discussed in the target article. To inform pedagogical practice, research must also consider how subject-internal and subject-external variables interact. Some of this learner-by-training research has been conducted in phonetic and phonological learning as well. Different training methods can lead to different brain activities in foreign speech learning (Deng et al., <span>2018</span>). Methods that allow for more precise individual-level prediction such as machine learning, coupled with studies that investigate how different types of training should be prescribed to different learners, would have the best chance of enabling personalized learning (Wong et al., <span>2017</span>).</p><p>In her article, van Hell also discussed the importance of conducting ecologically valid research in L2 learning. From the neurocognitive perspective, one new avenue of research could include an understanding of how brains of learners and teachers interact in natural interactions, including studies of brain synchronies in a conversation involving a L2 as well as L2 learning in a classroom. There is a small but growing literature on using hyperscanning techniques to study first language acquisition. Those studies have typically investigated parent–child dyads communicating, with parents speaking in child-directed speech. Independently, research has also begun to examine student-to-student and student-to-teacher brain correlations in a classroom, with as many as 12 brains being examined at the same time (Dikker et al., <span>2017</span>). Lessons from these studies in terms of both technical and neural theoretical contributions can propel L2 research to be more ecologically valid in the learning of sound and other components of language.</p><p>Beside its translational impact, research concerning phonetics and phonology in L2 learning can shed light on the basic mechanisms of learning and processing. An important property of phonetic and phonological features in spoken language is that they are acoustic in nature, which necessitates processing by the neural auditory system. Therefore, the study of the neural basis of speech can inform researchers about the fundamentals of how acoustically and functionally complex sounds are processed in the central nervous system. In fact, studies have investigated the interaction of music and speech in L2, both of which are acoustically and functionally complex. While much research on L2 has focused on cortical structures as reviewed by van Hell's target article, studies of music and speech additionally can allow researchers to investigate functions of subcortical neural centers such as the inferior colliculus that may not be engaged during the processing of other language components.</p><p>The field of L2 learning has expanded in unprecedented scope in the past decades to include not only behavioral research across different disciplines but also neuroscience fields. Researchers now know much more about how the nervous system processes and learns two or more languages. The goal of this commentary was not to provide a comprehensive review but to highlight just some of the research studies that have been conducted in phonetics and phonology and to discuss areas of potential future direction. 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The issues raised by van Hell such as the critical period hypothesis, age of acquisition, proficiency, and individual differences have also been studied in the context of these components (e.g., Golestani &amp; Zatorre, <span>2004</span>). This line of research often focused on individual differences and demonstrated that pretraining neural differences may forecast learning success at the group level (e.g., Sheppard et al., <span>2012</span>). Future studies can explore how individual differences in neural speech tracking of different chunk sizes (e.g., Ding et al., <span>2015</span>) may lead to differences in L2 learning outcomes.</p><p>To investigate individual differences, research must augment analytics that are designed for observing group-level performance by also using methods that are precise enough for making individual-level predictions. 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To inform pedagogical practice, research must also consider how subject-internal and subject-external variables interact. Some of this learner-by-training research has been conducted in phonetic and phonological learning as well. Different training methods can lead to different brain activities in foreign speech learning (Deng et al., <span>2018</span>). Methods that allow for more precise individual-level prediction such as machine learning, coupled with studies that investigate how different types of training should be prescribed to different learners, would have the best chance of enabling personalized learning (Wong et al., <span>2017</span>).</p><p>In her article, van Hell also discussed the importance of conducting ecologically valid research in L2 learning. 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引用次数: 1

摘要

van Hell的这篇最先进的文章对第二语言(L2)处理和学习的神经和神经认知基础的科学现状进行了极好的概述。虽然目标文章花了很多精力来回顾与语言的句法和语义成分有关的研究,而较少涉及词汇,但在二语研究中考虑语言的语音和语音成分也很重要。我强调了这一研究领域的一些发现,并讨论了一些潜在的新方向。成功的(口语)第二语言学习包括从语音流中提取语音和语音信息。van Hell提出的关键时期假说、习得年龄、熟练程度和个体差异等问题也在这些组成部分的背景下进行了研究(例如,Golestani &Zatorre, 2004)。这方面的研究通常侧重于个体差异,并证明预训练神经差异可以预测群体层面的学习成功(例如,Sheppard等人,2012)。未来的研究可以探索不同块大小的神经语音跟踪的个体差异(例如,Ding et al., 2015)如何导致第二语言学习结果的差异。为了调查个体差异,研究必须通过使用足够精确的方法来进行个人层面的预测,从而增强为观察群体层面表现而设计的分析。在第一语言习得的研究中(Wong et al., 2021),已经采用机器学习技术对个体学习者的学习结果进行预测,预测效果非常好。在第二语言学习中也开始使用这些技术(Feng et al., 2021)。除了预测学习成功之外,未来的研究还可以预测对不同类型干预的反应差异,以便可以在训练开始之前进行改变,以优化每个学习者的学习。除了调查学习者内部的个体差异变量,如van Hell所述(见Wong等人,2022,潜在的遗传变量),L2研究还研究了不同的学习者外部变量(例如,训练方法,如显性训练)如何导致目标文章中讨论的更好或更差的结果。为了为教学实践提供信息,研究还必须考虑主体内部变量和主体外部变量如何相互作用。这种“通过训练学习”的研究也在语音和语音学习中进行。不同的训练方法会导致外语学习中不同的大脑活动(Deng et al., 2018)。允许更精确的个人层面预测(如机器学习)的方法,加上研究如何为不同的学习者规定不同类型的训练的研究,将最有可能实现个性化学习(Wong et al., 2017)。在她的文章中,van Hell还讨论了在第二语言学习中进行生态有效研究的重要性。从神经认知的角度来看,一个新的研究途径可能包括理解学习者和教师的大脑如何在自然互动中互动,包括研究涉及第二语言的对话中的大脑同步以及课堂上的第二语言学习。关于使用超扫描技术研究第一语言习得的文献不多,但数量在不断增长。这些研究通常调查父母与孩子的交流,父母以孩子为导向的语言说话。独立地,研究也开始检查教室中学生对学生和学生对教师的大脑相关性,同时检查多达12个大脑(Dikker et al., 2017)。这些研究在技术和神经理论方面的贡献可以推动第二语言研究在声音和语言其他组成部分的学习中更加生态有效。除了翻译方面的影响外,语音学和音系学在二语学习中的研究还可以揭示二语学习和加工的基本机制。口语语音和语音特征的一个重要特性是它们本质上是声学的,这就需要神经听觉系统对其进行处理。因此,对言语的神经基础的研究可以让研究人员了解中枢神经系统在声学和功能上如何处理复杂声音的基本原理。事实上,研究已经调查了二语中音乐和言语的相互作用,两者在声学和功能上都是复杂的。 虽然很多关于第二语言的研究都集中在皮层结构上,正如van Hell的目标文章所回顾的那样,对音乐和语言的研究还可以让研究人员研究皮层下神经中心的功能,比如下丘,这些神经中心在处理其他语言成分时可能不会参与。在过去的几十年里,第二语言学习领域以前所未有的规模扩张,不仅包括跨学科的行为研究,还包括神经科学领域的研究。研究人员现在对神经系统如何处理和学习两种或两种以上语言有了更多的了解。这篇评论的目的不是提供一个全面的回顾,而是强调一些已经在语音学和音韵学方面进行的研究,并讨论潜在的未来方向。通过开展针对个人水平预测、学习者与训练之间的互动、大脑与大脑之间的相关性的研究,以及对语言系统的整体研究,研究人员将在理解学习背后的基本机制方面达到新的高度。此外,这项研究将使研究人员在制定教学策略方面处于更有利的地位,以使所有学习者的学习最有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Basis of Second Language Speech Learning – Past and Future: A Commentary on “The Neurocognitive Underpinnings of Second Language Processing: Knowledge Gains From the Past and Future Outlook”

The state-of-the-art article by van Hell provided an excellent overview of the current state of the science in the neural and neurocognitive basis of second language (L2) processing and learning. While the target article devoted much effort to reviewing studies related to the syntactic and semantic components of language and to a lesser extent to the lexicon, it is important to also consider the phonetic and phonological components of language in L2 research. I have highlighted some of the findings in this area of research and discussed some potential new directions.

Successful (spoken) L2 learning includes extracting phonetic and phonological information from the speech stream. The issues raised by van Hell such as the critical period hypothesis, age of acquisition, proficiency, and individual differences have also been studied in the context of these components (e.g., Golestani & Zatorre, 2004). This line of research often focused on individual differences and demonstrated that pretraining neural differences may forecast learning success at the group level (e.g., Sheppard et al., 2012). Future studies can explore how individual differences in neural speech tracking of different chunk sizes (e.g., Ding et al., 2015) may lead to differences in L2 learning outcomes.

To investigate individual differences, research must augment analytics that are designed for observing group-level performance by also using methods that are precise enough for making individual-level predictions. In research on first language acquisition (Wong et al., 2021), machine learning techniques have been adopted to make predictions about individual learners’ learning outcomes with very promising prediction performance. The use of such techniques has begun in L2 learning as well (Feng et al., 2021). In addition to forecasting learning success, future research can also predict differences in response to different types of interventions, so that training can be altered before it even begins in order to optimize learning for every learner.

In addition to investigating learner-internal individual difference variables, as reviewed by van Hell (see Wong et al., 2022, for potential genetic variables), L2 research has also examined how different learner-external variables (e.g., training methods such as explicit training) lead to better or worse outcomes as discussed in the target article. To inform pedagogical practice, research must also consider how subject-internal and subject-external variables interact. Some of this learner-by-training research has been conducted in phonetic and phonological learning as well. Different training methods can lead to different brain activities in foreign speech learning (Deng et al., 2018). Methods that allow for more precise individual-level prediction such as machine learning, coupled with studies that investigate how different types of training should be prescribed to different learners, would have the best chance of enabling personalized learning (Wong et al., 2017).

In her article, van Hell also discussed the importance of conducting ecologically valid research in L2 learning. From the neurocognitive perspective, one new avenue of research could include an understanding of how brains of learners and teachers interact in natural interactions, including studies of brain synchronies in a conversation involving a L2 as well as L2 learning in a classroom. There is a small but growing literature on using hyperscanning techniques to study first language acquisition. Those studies have typically investigated parent–child dyads communicating, with parents speaking in child-directed speech. Independently, research has also begun to examine student-to-student and student-to-teacher brain correlations in a classroom, with as many as 12 brains being examined at the same time (Dikker et al., 2017). Lessons from these studies in terms of both technical and neural theoretical contributions can propel L2 research to be more ecologically valid in the learning of sound and other components of language.

Beside its translational impact, research concerning phonetics and phonology in L2 learning can shed light on the basic mechanisms of learning and processing. An important property of phonetic and phonological features in spoken language is that they are acoustic in nature, which necessitates processing by the neural auditory system. Therefore, the study of the neural basis of speech can inform researchers about the fundamentals of how acoustically and functionally complex sounds are processed in the central nervous system. In fact, studies have investigated the interaction of music and speech in L2, both of which are acoustically and functionally complex. While much research on L2 has focused on cortical structures as reviewed by van Hell's target article, studies of music and speech additionally can allow researchers to investigate functions of subcortical neural centers such as the inferior colliculus that may not be engaged during the processing of other language components.

The field of L2 learning has expanded in unprecedented scope in the past decades to include not only behavioral research across different disciplines but also neuroscience fields. Researchers now know much more about how the nervous system processes and learns two or more languages. The goal of this commentary was not to provide a comprehensive review but to highlight just some of the research studies that have been conducted in phonetics and phonology and to discuss areas of potential future direction. By conducting research that targets individual-level prediction, learner-by-training interaction, and brain-to-brain correlations, and by studying the language system holistically, researchers will reach newer heights in understanding the basic mechanisms behind learning. Furthermore, this research will put researchers in a much stronger position for developing pedagogical strategies to make learning most effective for all learners.

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来源期刊
Language Learning
Language Learning Multiple-
CiteScore
9.10
自引率
15.90%
发文量
65
期刊介绍: Language Learning is a scientific journal dedicated to the understanding of language learning broadly defined. It publishes research articles that systematically apply methods of inquiry from disciplines including psychology, linguistics, cognitive science, educational inquiry, neuroscience, ethnography, sociolinguistics, sociology, and anthropology. It is concerned with fundamental theoretical issues in language learning such as child, second, and foreign language acquisition, language education, bilingualism, literacy, language representation in mind and brain, culture, cognition, pragmatics, and intergroup relations. A subscription includes one or two annual supplements, alternating among a volume from the Language Learning Cognitive Neuroscience Series, the Currents in Language Learning Series or the Language Learning Special Issue Series.
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