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.