第二语言处理的神经认知基础:过去的知识积累与未来展望:对同行公开评论的回应

IF 3.5 1区 文学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Janet G. van Hell
{"title":"第二语言处理的神经认知基础:过去的知识积累与未来展望:对同行公开评论的回应","authors":"Janet G. van Hell","doi":"10.1111/lang.12618","DOIUrl":null,"url":null,"abstract":"<p>Writing a review of the neural underpinnings of second language (L2) learning and processing, with a serious eye to future avenues for research, is among the most fun writing invitations that I have ever received. If not curtailed by <i>Language Learning</i>’s word limit, this article would have become a full issue, or even a book! I am thrilled that my passion for this field and enthusiasm for the future of neurocognitive inquiries into L2 learning and processing is shared by eminent and highly esteemed colleagues in the field who read and commented on this keynote article. These commentators lauded the field's amazing achievements, offered their praise and thoughtful insights on future promises and avenues outlined in my review, and extended several of these ideas in interesting and engaging directions.</p><p>In my review paper, I started with two lines of classical studies that set the research stage and sparked highly productive lines of research. I then illustrated the field's impressive achievements by selectively reviewing electrophysiological and neuroimaging research on L2 processing and bilingual brain organization and outlined major insights acquired over the past 25 years. I also discussed changing perspectives (including individual variability and experience-based perspectives, neural network approaches, neuroplasticity and L2-learning related neural changes) and identified challenges, promises and future directions in order to better understand the neurocognitive underpinnings of L2 learning and processing. Such future directions include revisiting the native-speaker benchmark for L2 attainment and related methodological implications, applying advanced electrophysiological and neuroimaging techniques to better capture newer perspectives in the field, increasing linguistic diversity in neurocognitive research on L2 processing, enhancing the ecological validity of neurocognitive experimentation, intensifying research on child L2 learners’ brain, and adopting a lifelong approach to L2 learning.</p><p>One theme that emerged from the commentaries is the overall agreement on the critical importance of incorporating individual differences perspectives and approaches in future research on L2 learning and processing to push knowledge forward (as explicitly voiced by Martin and Stoehr, Wong, Rossi and Nakamura, Birdsong, and Marian). As I had concluded in my article, future research should move beyond studying the roles of age of acquisition and L2 proficiency and embrace a wider focus on learner-internal and learner-external variables that shape L2 learning trajectories and L2 learners’ neurocognitive profiles. We need to better capture how L2 learners’ experiences (including age of acquisition but also current language uses and environmental context; see, e.g., DeLuca et al., <span>2019</span>; Gullifer et al., <span>2018</span>) and variability in cognitive functions (e.g., cognitive control, working memory, declarative and procedural memory abilities), language learning aptitude, and motivation impact the neural correlates of L2 learning and processing. Moreover, an experience-based perspective also encompasses the notion that, building on Grosjean's language modes (e.g., Grosjean, <span>2001</span>), bilingualism is not a static but a dynamic phenomenon that varies along a continuum of how bilinguals utilize their languages in various sociolinguistic contexts and that changes across the life-span for most bilingual speakers.</p><p>In their commentary, Clara Martin and Antje Stoehr elaborated on the critical importance of studying individual variability in neural correlates of L2 learning and processing by highlighting several variables that so far have received relatively little empirical attention. One of these variables is auditory processing precision (“having a good ear”), an individual's lower-order abilities in precisely perceiving domain-general acoustic information (i.e., pitch, formants, duration, and intensity). Auditory processing has been associated with L2 speech learning success (for review, see Saito, in press). Martin and Stoehr convincingly argued that, because auditory processing is critical in identifying word and phrase boundaries, morphosyntactic markers, and syntactic structures, the assessment of L2 learners’ auditory processing precision is important to better understand individual variability in L2 learning and processing (Martin and Stoehr also pointed at open-source tools [Mora-Plaza et al., <span>2022</span>]) to measure auditory processing precision). A related point was offered by Patrick Wong, from a neurocognitive perspective. Highlighting research on individual differences in neural speech tracking and research from his lab demonstrating that pretraining differences in learners’ cortical functional networks were associated with their future success in learning words of an artificial spoken language (Sheppard et al., <span>2012</span>), Wong proposed that future work may explore how individual variation in neural speech tracking of different chunk sizes (cf. Ding et al., <span>2015</span>) may result in variability in L2 learning outcomes. To further advance research on how individual differences impact L2 learning and processing, Wong made the valuable suggestion to adopt machine learning techniques to make predictions about individual learners’ learning outcomes as has been successfully done in research on neural speech encoding in native language acquisition (Wong et al., <span>2021</span>).</p><p>Martin and Stoehr also highlighted that variability in L2 processing may be partially explained by variability in first language (L1) processing, and I concur with the importance of measuring L2 learners’ variability in L1 processing. I add the caveat here that, as recently evidenced by Vermeiren and Brysbaert (2023), researchers should be cautious using vocabulary tests developed for native speakers of that language, even when testing advanced L2 speakers. The critical importance of studying bilinguals’ L1 processing was also highlighted in Jorge Valdés Kroff and Keng-Yu Lin's commentary, yet for a different but somewhat related reason: L1 processing can change because of L2 learning. Valdés Kroff and Lin postulated that, in fact, successful L2 learning and real-time processing require adaptive changes to the L1, and the recruitment of domain-general processes to regulate the language systems. A comprehensive understanding of L2 learning and processing should therefore also entail a close inspection of the learner's L1 processing and L2-learning-induced changes therein. Valdés Kroff and Lin exemplified this by the observation that Spanish–English bilinguals’ frequent exposure to specific patterns of codeswitched determiner–noun phrases induced changes in how L1 Spanish was processed in monolingual contexts and that the Spanish–English bilinguals showed adaptive changes that differed from monolingual speakers (Valdés Kroff &amp; Dussias, <span>2023</span>).</p><p>Variability in native language processing was also highlighted by David Birdsong. In addition to further historically contextualizing ongoing debates on the critical period hypothesis and the native-speaker benchmark for L2 learning outcomes, Birdsong advocated studying patterns of dispersion in native speaker data as well as in L2 learner data. He specifically encouraged researchers to conduct analyses of signal dispersion in their behavioral, electrophysiological, and neuroimaging data and to study patterns of signal dispersion (e.g., via the coefficient of variation [CV], to quantify the signal's variability) within and across participant groups, measures, tasks, and stimulus types. I concur that signal dispersion analyses add value to the researchers’ toolbox to further quantify how signal variability across languages shapes the cognitive and neural correlates of L1 and L2 processing, and L2-learning-induced changes in language processing, in L2 learners and bilinguals.</p><p>Two sources of (inter)individual variability highlighted by Martin and Stoehr, namely the speakers’ experience with target and nontarget languages and their exposure to native- and nonnative-accented input, align with the key point made by Eleonora Rossi and Megan Nakamura. Rossi and Nakamura expanded on the importance of better capturing variability in the L2 learner and bilingual experiences and ways to optimally model this variability in order to better understand how it shapes neural indices of L2 processing. While acknowledging the value of the language entropy measure (that estimates the social diversity of language use and has been used to characterize individual differences in bilingual/multilingual language experience related to the social diversity of language use [Gullifer et al., <span>2018</span>; Gullifer &amp; Titone, <span>2020</span>]), Rossi and Nakamura illustrated how personal social network (PSN) analysis can further advance our understanding of how bilingual experience may affect the neurocognitive correlates of L2 processing. Social network analysis identifies patterns of relationships, behaviors, or experiences among social actors, enabling researchers to explore how variability in individuals’ social environment predicts or affects particular outcomes. PSN analysis (or egocentric network analysis) is concerned with social networks around specific individuals (i.e., egos), the members of their networks (i.e., alters), and the relationships among alters. Cuartero, Rossi, and colleagues (<span>2023</span>) unpacked how PSN analysis can be used to better understand the complex language-related dynamics and heterogeneity that characterize heritage speaker bilingualism. In their thoughtful commentary, Rossi and Nakamura proposed to extend the use of PSN analysis to understand how variability in language use affects the behavioral and neural correlates of L2 learning and processing. Rossi and Nakamura highlighted a particularly valuable aspect of PSN analysis, namely that it captures variability in language use beyond the individual (i.e., ego). Specifically, PSN analysis not only measures variability in language at the level of the L2 learner (ego)—as do language experience questionnaires (such as Language Experience and Proficiency Questionnaire [LEAP-Q; Marian et al., <span>2007</span>] and the Language History Questionnaire [LHQ; Li et al., <span>2020</span>, the language entropy measure [Gullifer &amp; Titone, <span>2020</span>], and the bilingualism quotient [Marian &amp; Hayakawa, <span>2021</span>])—it also collects information on communicative behaviors of the L2 learner (ego) and members of their network (i.e., alters), as well as communication behaviors among the members of the network. I agree with Rossi and Nakamura that these unique indices of structural and compositional features of communication patterns in L2 speakers’ networks (such as codeswitching patters among network members; Navarro et al., <span>2022</span>) have strong potential to further advance our understanding of how complex language-related dynamics and sources of sociolinguistic variation can shape L2 learners’ language use and neurocognitive profiles. I will add that integrating PSN analysis into neurocognitive research on L2 learning and processing also aligns with recent calls to incorporate sociolinguistic and sociocultural approaches to better understand the cognitive and neural bases of L2 learning and processing (as also voiced in Titone and Tiv's [<span>2023</span>] “Systems Framework of Bilingualism”; Tiv et al. [<span>2022</span>]), as well as neural network science approaches that use a data-driven quantitative approach to model language structure.</p><p>Viorica Marian explicitly related language experience to neural networks and agreed that neural network approaches to L2 processing are a valuable newer research direction. In her commentary, she cited research from her lab and others evidencing that (bi/multi)language experience changes the neural signatures associated with a multitude of language processes (including speech, language learning, and competition within and across languages) and cognitive functions (such as attentional and executive control); language experience can even impact the subcortical encoding of sounds and otoacoustic emissions (sounds generated from within the inner ear). Marian further concurred that the neural network approach is a promising research avenue in the neuroscience of L2 learning and bilingualism, particularly in light of the fast developments in generative AI and large language models that utilize deep learning in natural language processing and natural language generation tasks. As these large language models are pretrained on vast amounts of data and potentially challenge long-held beliefs and established empirical knowledge in language science, Marian is exactly right that our field needs to make sure that questions and insights on the neurocognitive underpinnings of L2 learning, bi/multilingual experience, and linguistic diversity take a central stage in research and discussions in generative AI and large language models. In fact, with my colleagues at Penn State, I lead an NSF-funded research training program for graduate students in the language sciences, psychology, communication sciences and disorders, information sciences and technology, learning design and technology, and computer science and engineering (entitled “Linguistic diversity across the lifespan: Transforming training to advance human–technology interaction”) in which these discussions are integral to the students’ research training and research design projects. This also illustrates another parallel in the many ways Viorica Marian's and my professional and personal lives overlap—as she so elegantly portrayed in her commentary.</p><p>Taomei Guo, Cristina Sanz, Jorge Valdés Kroff and Keng-Yu Lin, and Patrick Wong also commemorated the strides that the field has made, reinforced and acknowledged the value of the directions of future research that I had outlined in my target article, and picked up on several themes and extended them in interesting and engaging directions. Guo highlighted the value of noninvasive brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, to examine the causal relations between specific brain regions and L2 learning and processing. These neurocognitive intervention techniques carry a strong promise to push the field forward, as they allow the field to leverage current insights largely based on observational neurocognitive methods (electroencephalography, functional magnetic resonance imaging) to make causal inferences about specific brain regions and language functions (for a detailed review of using noninvasive brain stimulation in L2 learning and bilingualism research, see Pandža, in press).</p><p>Patrick Wong reinforced my future research recommendation to make an effort to enhance the ecological validity of neurocognitive research on L2 learning and processing. I fully endorse his suggestion to examine how the brains of learners and teachers interact by studying brain synchronies during conversations involving L2 learners and interactions in the L2 classroom, using, for example, hyperscanning techniques. Indeed, interbrain coupling during face-to-face interactions and (electroencephalography-based) hyperscanning techniques have been successfully used in public spaces, such as museums and festivals (Dikker et al., <span>2021</span>), and in classrooms (e.g., Davidesco et al., <span>2023</span>; Dikker et al., <span>2017</span>); the technical know-how and insights obtained in “real-world neuroscience” (Matusz et al., <span>2019</span>) can be readily applied to L2 classroom contexts. Jorge Valdés Kroff and Keng-Yu Lin also voiced a belief that enhancing ecological validity is imperative for future research endeavors and highlighted the importance of understanding speakers’ more nuanced use of their L2 and their processing of discourse and pragmatic expressions beyond the level of morphosyntactic processing. I fully agree with their statement that much more work is needed to better understand the neural and cognitive mechanisms associated with L2 learners “high-end” L2 language use, such as figurative language (including idioms and metaphors), irony, politeness, humor, narrative and expository discourse, and emotional expressions (for a review on the neuropragmatics of L2 processing, see Citron, <span>2023</span>).</p><p>In her particularly creative commentary, Cristina Sanz took several topics that I had identified as issues that need to be resolved, gaps in current knowledge, and promising avenues of future research as the starting point for designing an empirical study (“thought experiment”) that overcomes these limitations and that models a key step forward in understanding the neurocognitive underpinnings of L2. This exemplary experiment elegantly incorporated many of my and others’ recommendations, including using rigorous research designs, moving beyond the native speaker benchmark and acknowledging that L2 learning trajectories are complex and multifaceted, incorporating individual variability and dynamic changes in both L1 and L2 processing, as well as enriching linguistic diversity and ecological validity and considering the translational implications of research outcomes. Sanz's thought experiment is an example of how we can optimally move the field forward. So let us do it! And let us leverage the insights and discussions on open science practices in Marsden and Morgan-Short's (in press) keynote article in <i>Language Learning</i>’s 75<sup>th</sup> Jubilee volume.</p><p>To conclude, the neurocognition of L2 learning and processing is a relatively young field that has yielded tremendously rich insights and has made significant strides forward in the past decades. The peer commentators to my keynote article have each made, and continue to make, foundational contributions to this field, and I thank all of the commentators for their thoughtful engagement with my ideas and their invaluable insights. As part of the celebration of <i>Language Learning</i>’s 75<sup>th</sup> Jubilee edition, I hope that my keynote article and the commentators’ insights will inspire readers, contribute to shaping and paving the way for new discoveries, and nudge knowledge forward to new levels of fully understanding the complexity and enchantment of learning and processing multiple languages.</p>","PeriodicalId":51371,"journal":{"name":"Language Learning","volume":"73 S2","pages":"172-181"},"PeriodicalIF":3.5000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/lang.12618","citationCount":"0","resultStr":"{\"title\":\"The Neurocognitive Underpinnings of Second Language Processing: Knowledge Gains From the Past and Future Outlook: A Response to Open Peer Commentaries\",\"authors\":\"Janet G. van Hell\",\"doi\":\"10.1111/lang.12618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Writing a review of the neural underpinnings of second language (L2) learning and processing, with a serious eye to future avenues for research, is among the most fun writing invitations that I have ever received. If not curtailed by <i>Language Learning</i>’s word limit, this article would have become a full issue, or even a book! I am thrilled that my passion for this field and enthusiasm for the future of neurocognitive inquiries into L2 learning and processing is shared by eminent and highly esteemed colleagues in the field who read and commented on this keynote article. These commentators lauded the field's amazing achievements, offered their praise and thoughtful insights on future promises and avenues outlined in my review, and extended several of these ideas in interesting and engaging directions.</p><p>In my review paper, I started with two lines of classical studies that set the research stage and sparked highly productive lines of research. I then illustrated the field's impressive achievements by selectively reviewing electrophysiological and neuroimaging research on L2 processing and bilingual brain organization and outlined major insights acquired over the past 25 years. I also discussed changing perspectives (including individual variability and experience-based perspectives, neural network approaches, neuroplasticity and L2-learning related neural changes) and identified challenges, promises and future directions in order to better understand the neurocognitive underpinnings of L2 learning and processing. Such future directions include revisiting the native-speaker benchmark for L2 attainment and related methodological implications, applying advanced electrophysiological and neuroimaging techniques to better capture newer perspectives in the field, increasing linguistic diversity in neurocognitive research on L2 processing, enhancing the ecological validity of neurocognitive experimentation, intensifying research on child L2 learners’ brain, and adopting a lifelong approach to L2 learning.</p><p>One theme that emerged from the commentaries is the overall agreement on the critical importance of incorporating individual differences perspectives and approaches in future research on L2 learning and processing to push knowledge forward (as explicitly voiced by Martin and Stoehr, Wong, Rossi and Nakamura, Birdsong, and Marian). As I had concluded in my article, future research should move beyond studying the roles of age of acquisition and L2 proficiency and embrace a wider focus on learner-internal and learner-external variables that shape L2 learning trajectories and L2 learners’ neurocognitive profiles. We need to better capture how L2 learners’ experiences (including age of acquisition but also current language uses and environmental context; see, e.g., DeLuca et al., <span>2019</span>; Gullifer et al., <span>2018</span>) and variability in cognitive functions (e.g., cognitive control, working memory, declarative and procedural memory abilities), language learning aptitude, and motivation impact the neural correlates of L2 learning and processing. Moreover, an experience-based perspective also encompasses the notion that, building on Grosjean's language modes (e.g., Grosjean, <span>2001</span>), bilingualism is not a static but a dynamic phenomenon that varies along a continuum of how bilinguals utilize their languages in various sociolinguistic contexts and that changes across the life-span for most bilingual speakers.</p><p>In their commentary, Clara Martin and Antje Stoehr elaborated on the critical importance of studying individual variability in neural correlates of L2 learning and processing by highlighting several variables that so far have received relatively little empirical attention. One of these variables is auditory processing precision (“having a good ear”), an individual's lower-order abilities in precisely perceiving domain-general acoustic information (i.e., pitch, formants, duration, and intensity). Auditory processing has been associated with L2 speech learning success (for review, see Saito, in press). Martin and Stoehr convincingly argued that, because auditory processing is critical in identifying word and phrase boundaries, morphosyntactic markers, and syntactic structures, the assessment of L2 learners’ auditory processing precision is important to better understand individual variability in L2 learning and processing (Martin and Stoehr also pointed at open-source tools [Mora-Plaza et al., <span>2022</span>]) to measure auditory processing precision). A related point was offered by Patrick Wong, from a neurocognitive perspective. Highlighting research on individual differences in neural speech tracking and research from his lab demonstrating that pretraining differences in learners’ cortical functional networks were associated with their future success in learning words of an artificial spoken language (Sheppard et al., <span>2012</span>), Wong proposed that future work may explore how individual variation in neural speech tracking of different chunk sizes (cf. Ding et al., <span>2015</span>) may result in variability in L2 learning outcomes. To further advance research on how individual differences impact L2 learning and processing, Wong made the valuable suggestion to adopt machine learning techniques to make predictions about individual learners’ learning outcomes as has been successfully done in research on neural speech encoding in native language acquisition (Wong et al., <span>2021</span>).</p><p>Martin and Stoehr also highlighted that variability in L2 processing may be partially explained by variability in first language (L1) processing, and I concur with the importance of measuring L2 learners’ variability in L1 processing. I add the caveat here that, as recently evidenced by Vermeiren and Brysbaert (2023), researchers should be cautious using vocabulary tests developed for native speakers of that language, even when testing advanced L2 speakers. The critical importance of studying bilinguals’ L1 processing was also highlighted in Jorge Valdés Kroff and Keng-Yu Lin's commentary, yet for a different but somewhat related reason: L1 processing can change because of L2 learning. Valdés Kroff and Lin postulated that, in fact, successful L2 learning and real-time processing require adaptive changes to the L1, and the recruitment of domain-general processes to regulate the language systems. A comprehensive understanding of L2 learning and processing should therefore also entail a close inspection of the learner's L1 processing and L2-learning-induced changes therein. Valdés Kroff and Lin exemplified this by the observation that Spanish–English bilinguals’ frequent exposure to specific patterns of codeswitched determiner–noun phrases induced changes in how L1 Spanish was processed in monolingual contexts and that the Spanish–English bilinguals showed adaptive changes that differed from monolingual speakers (Valdés Kroff &amp; Dussias, <span>2023</span>).</p><p>Variability in native language processing was also highlighted by David Birdsong. In addition to further historically contextualizing ongoing debates on the critical period hypothesis and the native-speaker benchmark for L2 learning outcomes, Birdsong advocated studying patterns of dispersion in native speaker data as well as in L2 learner data. He specifically encouraged researchers to conduct analyses of signal dispersion in their behavioral, electrophysiological, and neuroimaging data and to study patterns of signal dispersion (e.g., via the coefficient of variation [CV], to quantify the signal's variability) within and across participant groups, measures, tasks, and stimulus types. I concur that signal dispersion analyses add value to the researchers’ toolbox to further quantify how signal variability across languages shapes the cognitive and neural correlates of L1 and L2 processing, and L2-learning-induced changes in language processing, in L2 learners and bilinguals.</p><p>Two sources of (inter)individual variability highlighted by Martin and Stoehr, namely the speakers’ experience with target and nontarget languages and their exposure to native- and nonnative-accented input, align with the key point made by Eleonora Rossi and Megan Nakamura. Rossi and Nakamura expanded on the importance of better capturing variability in the L2 learner and bilingual experiences and ways to optimally model this variability in order to better understand how it shapes neural indices of L2 processing. While acknowledging the value of the language entropy measure (that estimates the social diversity of language use and has been used to characterize individual differences in bilingual/multilingual language experience related to the social diversity of language use [Gullifer et al., <span>2018</span>; Gullifer &amp; Titone, <span>2020</span>]), Rossi and Nakamura illustrated how personal social network (PSN) analysis can further advance our understanding of how bilingual experience may affect the neurocognitive correlates of L2 processing. Social network analysis identifies patterns of relationships, behaviors, or experiences among social actors, enabling researchers to explore how variability in individuals’ social environment predicts or affects particular outcomes. PSN analysis (or egocentric network analysis) is concerned with social networks around specific individuals (i.e., egos), the members of their networks (i.e., alters), and the relationships among alters. Cuartero, Rossi, and colleagues (<span>2023</span>) unpacked how PSN analysis can be used to better understand the complex language-related dynamics and heterogeneity that characterize heritage speaker bilingualism. In their thoughtful commentary, Rossi and Nakamura proposed to extend the use of PSN analysis to understand how variability in language use affects the behavioral and neural correlates of L2 learning and processing. Rossi and Nakamura highlighted a particularly valuable aspect of PSN analysis, namely that it captures variability in language use beyond the individual (i.e., ego). Specifically, PSN analysis not only measures variability in language at the level of the L2 learner (ego)—as do language experience questionnaires (such as Language Experience and Proficiency Questionnaire [LEAP-Q; Marian et al., <span>2007</span>] and the Language History Questionnaire [LHQ; Li et al., <span>2020</span>, the language entropy measure [Gullifer &amp; Titone, <span>2020</span>], and the bilingualism quotient [Marian &amp; Hayakawa, <span>2021</span>])—it also collects information on communicative behaviors of the L2 learner (ego) and members of their network (i.e., alters), as well as communication behaviors among the members of the network. I agree with Rossi and Nakamura that these unique indices of structural and compositional features of communication patterns in L2 speakers’ networks (such as codeswitching patters among network members; Navarro et al., <span>2022</span>) have strong potential to further advance our understanding of how complex language-related dynamics and sources of sociolinguistic variation can shape L2 learners’ language use and neurocognitive profiles. I will add that integrating PSN analysis into neurocognitive research on L2 learning and processing also aligns with recent calls to incorporate sociolinguistic and sociocultural approaches to better understand the cognitive and neural bases of L2 learning and processing (as also voiced in Titone and Tiv's [<span>2023</span>] “Systems Framework of Bilingualism”; Tiv et al. [<span>2022</span>]), as well as neural network science approaches that use a data-driven quantitative approach to model language structure.</p><p>Viorica Marian explicitly related language experience to neural networks and agreed that neural network approaches to L2 processing are a valuable newer research direction. In her commentary, she cited research from her lab and others evidencing that (bi/multi)language experience changes the neural signatures associated with a multitude of language processes (including speech, language learning, and competition within and across languages) and cognitive functions (such as attentional and executive control); language experience can even impact the subcortical encoding of sounds and otoacoustic emissions (sounds generated from within the inner ear). Marian further concurred that the neural network approach is a promising research avenue in the neuroscience of L2 learning and bilingualism, particularly in light of the fast developments in generative AI and large language models that utilize deep learning in natural language processing and natural language generation tasks. As these large language models are pretrained on vast amounts of data and potentially challenge long-held beliefs and established empirical knowledge in language science, Marian is exactly right that our field needs to make sure that questions and insights on the neurocognitive underpinnings of L2 learning, bi/multilingual experience, and linguistic diversity take a central stage in research and discussions in generative AI and large language models. In fact, with my colleagues at Penn State, I lead an NSF-funded research training program for graduate students in the language sciences, psychology, communication sciences and disorders, information sciences and technology, learning design and technology, and computer science and engineering (entitled “Linguistic diversity across the lifespan: Transforming training to advance human–technology interaction”) in which these discussions are integral to the students’ research training and research design projects. This also illustrates another parallel in the many ways Viorica Marian's and my professional and personal lives overlap—as she so elegantly portrayed in her commentary.</p><p>Taomei Guo, Cristina Sanz, Jorge Valdés Kroff and Keng-Yu Lin, and Patrick Wong also commemorated the strides that the field has made, reinforced and acknowledged the value of the directions of future research that I had outlined in my target article, and picked up on several themes and extended them in interesting and engaging directions. Guo highlighted the value of noninvasive brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, to examine the causal relations between specific brain regions and L2 learning and processing. These neurocognitive intervention techniques carry a strong promise to push the field forward, as they allow the field to leverage current insights largely based on observational neurocognitive methods (electroencephalography, functional magnetic resonance imaging) to make causal inferences about specific brain regions and language functions (for a detailed review of using noninvasive brain stimulation in L2 learning and bilingualism research, see Pandža, in press).</p><p>Patrick Wong reinforced my future research recommendation to make an effort to enhance the ecological validity of neurocognitive research on L2 learning and processing. I fully endorse his suggestion to examine how the brains of learners and teachers interact by studying brain synchronies during conversations involving L2 learners and interactions in the L2 classroom, using, for example, hyperscanning techniques. Indeed, interbrain coupling during face-to-face interactions and (electroencephalography-based) hyperscanning techniques have been successfully used in public spaces, such as museums and festivals (Dikker et al., <span>2021</span>), and in classrooms (e.g., Davidesco et al., <span>2023</span>; Dikker et al., <span>2017</span>); the technical know-how and insights obtained in “real-world neuroscience” (Matusz et al., <span>2019</span>) can be readily applied to L2 classroom contexts. Jorge Valdés Kroff and Keng-Yu Lin also voiced a belief that enhancing ecological validity is imperative for future research endeavors and highlighted the importance of understanding speakers’ more nuanced use of their L2 and their processing of discourse and pragmatic expressions beyond the level of morphosyntactic processing. I fully agree with their statement that much more work is needed to better understand the neural and cognitive mechanisms associated with L2 learners “high-end” L2 language use, such as figurative language (including idioms and metaphors), irony, politeness, humor, narrative and expository discourse, and emotional expressions (for a review on the neuropragmatics of L2 processing, see Citron, <span>2023</span>).</p><p>In her particularly creative commentary, Cristina Sanz took several topics that I had identified as issues that need to be resolved, gaps in current knowledge, and promising avenues of future research as the starting point for designing an empirical study (“thought experiment”) that overcomes these limitations and that models a key step forward in understanding the neurocognitive underpinnings of L2. This exemplary experiment elegantly incorporated many of my and others’ recommendations, including using rigorous research designs, moving beyond the native speaker benchmark and acknowledging that L2 learning trajectories are complex and multifaceted, incorporating individual variability and dynamic changes in both L1 and L2 processing, as well as enriching linguistic diversity and ecological validity and considering the translational implications of research outcomes. Sanz's thought experiment is an example of how we can optimally move the field forward. So let us do it! And let us leverage the insights and discussions on open science practices in Marsden and Morgan-Short's (in press) keynote article in <i>Language Learning</i>’s 75<sup>th</sup> Jubilee volume.</p><p>To conclude, the neurocognition of L2 learning and processing is a relatively young field that has yielded tremendously rich insights and has made significant strides forward in the past decades. The peer commentators to my keynote article have each made, and continue to make, foundational contributions to this field, and I thank all of the commentators for their thoughtful engagement with my ideas and their invaluable insights. 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引用次数: 0

摘要

写一篇关于第二语言(L2)学习和处理的神经基础的综述,并着眼于未来的研究途径,是我收到的最有趣的写作邀请之一。如果没有被《语言学习》的字数限制所限制,这篇文章可能会成为一个完整的问题,甚至是一本书!我很高兴我对这个领域的热情和对二语学习和加工的神经认知研究的未来的热情被该领域的杰出和备受尊敬的同事分享,他们阅读并评论了这篇主题文章。这些评论者赞扬了该领域的惊人成就,对我在评论中概述的未来前景和途径提出了他们的赞扬和深思熟虑的见解,并将其中一些想法扩展到有趣和引人入胜的方向。在我的综述论文中,我从两条经典研究开始,它们为研究奠定了基础,并引发了高生产率的研究方向。然后,我通过选择性地回顾关于第二语言处理和双语大脑组织的电生理和神经成像研究,阐述了该领域令人印象深刻的成就,并概述了过去25年来获得的主要见解。我还讨论了不断变化的观点(包括个体可变性和基于经验的观点,神经网络方法,神经可塑性和L2学习相关的神经变化),并确定了挑战,承诺和未来的方向,以便更好地理解L2学习和加工的神经认知基础。未来的研究方向包括:重新审视母语人士的二语学习基准和相关的方法论影响,应用先进的电生理学和神经成像技术来更好地捕捉该领域的新视角,增加二语加工神经认知研究中的语言多样性,增强神经认知实验的生态有效性,加强对儿童二语学习者大脑的研究,以及采用终身学习二语的方法。从评论中出现的一个主题是,在未来的第二语言学习和加工研究中,将个体差异的观点和方法纳入到推动知识进步的关键重要性上的总体共识(正如Martin和Stoehr, Wong, Rossi和Nakamura, Birdsong和Marian明确表达的那样)。正如我在文章中总结的那样,未来的研究应该超越学习年龄和二语熟练程度的作用,更广泛地关注塑造二语学习轨迹和二语学习者神经认知概况的学习者内部和学习者外部变量。我们需要更好地捕捉二语学习者的经历(包括习得年龄,但也包括当前的语言使用和环境背景;参见,例如,DeLuca等人,2019;Gullifer等人,2018)和认知功能(如认知控制、工作记忆、陈述性和程序性记忆能力)、语言学习能力和动机的可变性影响第二语言学习和加工的神经关联。此外,基于经验的视角还包含了这样一种观点,即在格罗斯让的语言模式(例如,格罗斯让,2001)的基础上,双语不是静态的,而是一种动态的现象,这种现象随着双语者在各种社会语言环境中如何使用他们的语言而不断变化,并且在大多数双语者的一生中都会发生变化。在他们的评论中,Clara Martin和Antje Stoehr通过强调几个迄今为止很少得到实证关注的变量,阐述了研究第二语言学习和加工的神经相关的个体差异的关键重要性。其中一个变量是听觉处理精度(“有一个好的耳朵”),一个人在精确感知领域一般声学信息(即音调、共振峰、持续时间和强度)方面的低阶能力。听觉处理与第二语言学习的成功有关(回顾,见Saito, in press)。Martin和Stoehr令人信服地认为,由于听觉处理对于识别单词和短语边界、形态句法标记和句法结构至关重要,因此评估二语学习者的听觉加工精度对于更好地理解二语学习和加工中的个体差异非常重要(Martin和Stoehr还指出了开源工具[Mora-Plaza et al., 2022])来衡量听觉加工精度)。Patrick Wong从神经认知的角度提出了一个相关的观点。Wong强调了神经语音跟踪的个体差异研究,以及他的实验室研究表明,学习者皮层功能网络的预训练差异与他们未来学习人工口语单词的成功相关(Sheppard et al., 2012),他提出,未来的工作可能会探索不同块大小的神经语音跟踪的个体差异(cf。 Ding et al., 2015)可能导致第二语言学习结果的可变性。为了进一步推进个体差异如何影响第二语言学习和加工的研究,Wong提出了一个有价值的建议,即采用机器学习技术来预测个体学习者的学习结果,这在母语习得中的神经语音编码研究中已经成功实现(Wong et al., 2021)。Martin和Stoehr还强调,第二语言加工的可变性可以部分地用第一语言加工的可变性来解释,我同意测量第二语言学习者在L1加工中的可变性的重要性。我在这里补充一句,正如Vermeiren和Brysbaert(2023)最近所证明的那样,研究人员应该谨慎使用为该语言的母语人士开发的词汇测试,即使是在测试高级第二语言使用者时。研究双语者的母语加工的重要性也在Jorge vald<s:1> Kroff和Keng-Yu Lin的评论中得到了强调,但原因不同,但在某种程度上是相关的:母语加工会因为第二语言学习而改变。vald<s:1>·克罗夫和林假设,事实上,成功的第二语言学习和实时处理需要对第一语言进行适应性的改变,并利用领域通用过程来调节语言系统。因此,对第二语言学习和加工的全面理解也需要仔细检查学习者的第一语言加工和由第二语言学习引起的变化。瓦尔德萨默斯·克罗夫和林通过观察西班牙-英语双语者频繁接触特定模式的代码转换限定词-名词短语来证明这一点,他们观察到,西班牙-英语双语者在单语环境下对母语西班牙语的处理方式发生了变化,西班牙-英语双语者表现出与单语者不同的适应性变化(瓦尔德萨默斯·克罗夫和林;Dussias, 2023)。大卫·伯德桑也强调了母语处理的可变性。除了进一步将关键时期假说和母语人士对二语学习结果的基准进行辩论的历史背景化外,Birdsong还主张研究母语人士数据和二语学习者数据中的分散模式。他特别鼓励研究人员在他们的行为、电生理和神经成像数据中进行信号分散的分析,并研究参与者群体、测量、任务和刺激类型内部和之间的信号分散模式(例如,通过变异系数[CV]来量化信号的可变性)。我同意信号分散分析为研究人员的工具箱增加了价值,以进一步量化跨语言的信号可变性如何塑造母语和第二语言处理的认知和神经相关性,以及第二语言学习者和双语者在语言处理中由L2学习引起的变化。Martin和Stoehr强调了个体间差异的两个来源,即说话者对目标语言和非目标语言的体验,以及他们对母语和非母语口音输入的接触,这与Eleonora Rossi和Megan Nakamura提出的关键点一致。Rossi和Nakamura进一步阐述了更好地捕捉第二语言学习者和双语体验的可变性的重要性,以及如何最佳地模拟这种可变性,以便更好地理解它如何塑造第二语言加工的神经指标。虽然承认语言熵度量(估计语言使用的社会多样性,并已被用于表征与语言使用的社会多样性相关的双语/多语言语言体验的个体差异)的价值[Gullifer等人,2018;Gullifer,Titone, 2020]), Rossi和Nakamura说明了个人社会网络(PSN)分析如何进一步促进我们对双语经验如何影响第二语言加工的神经认知相关的理解。社会网络分析识别社会参与者之间的关系、行为或经验模式,使研究人员能够探索个体社会环境的可变性如何预测或影响特定的结果。PSN分析(或自我中心网络分析)关注的是围绕特定个体(即自我)、其网络成员(即改变者)以及改变者之间的关系的社会网络。Cuartero、Rossi及其同事(2023)揭示了如何使用PSN分析来更好地理解传统说话者双语特征的复杂语言相关动态和异质性。在他们深思熟虑的评论中,Rossi和Nakamura建议扩展PSN分析的使用,以了解语言使用的可变性如何影响第二语言学习和加工的行为和神经相关。Rossi和Nakamura强调了PSN分析的一个特别有价值的方面,即它捕捉了超越个人(即自我)的语言使用的可变性。 具体来说,PSN分析不仅测量了第二语言学习者(自我)水平上的语言可变性,也测量了语言经验问卷(如语言经验和熟练程度问卷[LEAP-Q;Marian et al., 2007]和语言历史问卷[LHQ;Li et al., 2020,语言熵测度[Gullifer &Titone, 2020],以及双语商[Marian &Hayakawa, 2021]) -它还收集第二语言学习者(自我)及其网络成员(即改变者)的交流行为信息,以及网络成员之间的交流行为信息。我同意Rossi和Nakamura的观点,即第二语言说话者网络中沟通模式的结构和组成特征的这些独特指数(如网络成员之间的代码转换模式;Navarro et al., 2022)具有强大的潜力,可以进一步促进我们对复杂的语言相关动态和社会语言学变异来源如何影响第二语言学习者的语言使用和神经认知概况的理解。我将补充说,将PSN分析整合到第二语言学习和加工的神经认知研究中,也符合最近的呼吁,即纳入社会语言学和社会文化方法,以更好地理解第二语言学习和加工的认知和神经基础(正如Titone和Tiv的[2023]“双语系统框架”中所表达的那样);Tiv等人[2022]),以及使用数据驱动的定量方法对语言结构建模的神经网络科学方法。Viorica Marian明确地将语言经验与神经网络联系起来,并同意神经网络方法用于第二语言处理是一个有价值的新研究方向。在她的评论中,她引用了她的实验室和其他证据的研究,证明(双/多)语言经历改变了与大量语言过程(包括演讲、语言学习、语言内部和语言之间的竞争)和认知功能(如注意力和执行控制)相关的神经特征;语言经验甚至可以影响声音的皮层下编码和耳声发射(内耳产生的声音)。Marian进一步同意,神经网络方法在二语学习和双语的神经科学研究中是一个很有前途的研究途径,特别是考虑到生成式人工智能和大型语言模型的快速发展,这些模型在自然语言处理和自然语言生成任务中利用深度学习。由于这些大型语言模型是在大量数据上进行预训练的,可能会挑战语言科学中长期持有的信念和已建立的经验知识,Marian完全正确地认为,我们的领域需要确保在生成式人工智能和大型语言模型的研究和讨论中,关于第二语言学习、双/多语言体验和语言多样性的神经认知基础的问题和见解占据中心位置。事实上,我和我在宾夕法尼亚州立大学的同事们一起,领导着一个由美国国家科学基金会资助的研究培训项目,面向语言科学、心理学、交流科学与障碍、信息科学与技术、学习设计与技术以及计算机科学与工程领域的研究生(名为“一生中的语言多样性:将培训转变为促进人与技术的互动”),其中这些讨论是学生研究训练和研究设计项目的组成部分。这也说明了维奥丽卡·玛丽安和我的职业生活和个人生活在许多方面的相似之处——正如她在评论中优雅地描述的那样。Taomei Guo, Cristina Sanz, Jorge vald<s:1> Kroff, Keng-Yu Lin和Patrick Wong也纪念了该领域所取得的进步,强调并承认了我在目标文章中概述的未来研究方向的价值,并选择了几个主题并将其扩展到有趣且引人入胜的方向。郭强调了非侵入性脑刺激技术的价值,如经颅磁刺激和经颅直流电刺激,以检查特定大脑区域与第二语言学习和加工之间的因果关系。这些神经认知干预技术具有推动该领域向前发展的强大前景,因为它们允许该领域利用目前主要基于观察性神经认知方法(脑电图、功能性磁共振成像)的见解,对特定大脑区域和语言功能进行因果推断(有关在第二语言学习和双语研究中使用非侵入性脑刺激的详细回顾,请参阅Pandža,已出版)。Patrick Wong加强了我未来的研究建议,努力提高二语学习和加工的神经认知研究的生态效度。 我完全赞同他的建议,通过研究涉及第二语言学习者的对话和第二语言课堂互动中的大脑同步,例如使用超扫描技术,来研究学习者和教师的大脑是如何互动的。事实上,面对面互动期间的脑间耦合和(基于脑电图的)超扫描技术已经成功地应用于公共空间,如博物馆和节日(Dikker等人,2021),以及教室(例如Davidesco等人,2023;Dikker et al., 2017);在“现实世界的神经科学”中获得的技术知识和见解(Matusz等人,2019)可以很容易地应用于第二语言课堂环境。Jorge vald<s:1> Kroff和Keng-Yu Lin也认为,加强生态有效性对于未来的研究工作至关重要,并强调了理解说话者对第二语言的更细微的使用以及他们在形态句法处理层面之外对话语和语用表达的处理的重要性。我完全同意他们的观点,即需要做更多的工作来更好地理解与二语学习者“高端”二语语言使用相关的神经和认知机制,如比喻语言(包括成语和隐喻)、讽刺、礼貌、幽默、叙事和说述性话语以及情感表达(关于二语处理的神经语用学综述,见Citron, 2023)。Cristina Sanz在她特别有创意的评论中,将我认为需要解决的问题、现有知识的差距和未来研究的有希望的途径作为设计经验研究(“思维实验”)的起点,克服了这些限制,并为理解第二语言的神经认知基础迈出了关键一步。这个典型的实验优雅地结合了我和其他人的许多建议,包括使用严格的研究设计,超越母语者基准,承认第二语言学习轨迹是复杂和多方面的,结合了L1和L2处理中的个体差异和动态变化,丰富了语言多样性和生态有效性,并考虑了研究结果的翻译意义。桑兹的思想实验是我们如何以最佳方式推动这一领域向前发展的一个例子。所以让我们行动起来吧!让我们充分利用马斯登和摩根-肖特在《语言学习》第75期的主题文章中对开放科学实践的见解和讨论。总之,第二语言学习和加工的神经认知是一个相对年轻的领域,在过去的几十年里已经产生了非常丰富的见解,并取得了重大进展。我的主题文章的同行评论者都对这个领域做出了并将继续做出基础性的贡献,我感谢所有的评论者,感谢他们对我的想法和宝贵的见解的深思熟虑的参与。作为庆祝语言学习75周年的一部分,我希望我的主题文章和评论员的见解能够激励读者,为新发现的形成和铺平道路做出贡献,并将知识推向一个新的水平,以充分理解学习和处理多种语言的复杂性和魅力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Neurocognitive Underpinnings of Second Language Processing: Knowledge Gains From the Past and Future Outlook: A Response to Open Peer Commentaries

Writing a review of the neural underpinnings of second language (L2) learning and processing, with a serious eye to future avenues for research, is among the most fun writing invitations that I have ever received. If not curtailed by Language Learning’s word limit, this article would have become a full issue, or even a book! I am thrilled that my passion for this field and enthusiasm for the future of neurocognitive inquiries into L2 learning and processing is shared by eminent and highly esteemed colleagues in the field who read and commented on this keynote article. These commentators lauded the field's amazing achievements, offered their praise and thoughtful insights on future promises and avenues outlined in my review, and extended several of these ideas in interesting and engaging directions.

In my review paper, I started with two lines of classical studies that set the research stage and sparked highly productive lines of research. I then illustrated the field's impressive achievements by selectively reviewing electrophysiological and neuroimaging research on L2 processing and bilingual brain organization and outlined major insights acquired over the past 25 years. I also discussed changing perspectives (including individual variability and experience-based perspectives, neural network approaches, neuroplasticity and L2-learning related neural changes) and identified challenges, promises and future directions in order to better understand the neurocognitive underpinnings of L2 learning and processing. Such future directions include revisiting the native-speaker benchmark for L2 attainment and related methodological implications, applying advanced electrophysiological and neuroimaging techniques to better capture newer perspectives in the field, increasing linguistic diversity in neurocognitive research on L2 processing, enhancing the ecological validity of neurocognitive experimentation, intensifying research on child L2 learners’ brain, and adopting a lifelong approach to L2 learning.

One theme that emerged from the commentaries is the overall agreement on the critical importance of incorporating individual differences perspectives and approaches in future research on L2 learning and processing to push knowledge forward (as explicitly voiced by Martin and Stoehr, Wong, Rossi and Nakamura, Birdsong, and Marian). As I had concluded in my article, future research should move beyond studying the roles of age of acquisition and L2 proficiency and embrace a wider focus on learner-internal and learner-external variables that shape L2 learning trajectories and L2 learners’ neurocognitive profiles. We need to better capture how L2 learners’ experiences (including age of acquisition but also current language uses and environmental context; see, e.g., DeLuca et al., 2019; Gullifer et al., 2018) and variability in cognitive functions (e.g., cognitive control, working memory, declarative and procedural memory abilities), language learning aptitude, and motivation impact the neural correlates of L2 learning and processing. Moreover, an experience-based perspective also encompasses the notion that, building on Grosjean's language modes (e.g., Grosjean, 2001), bilingualism is not a static but a dynamic phenomenon that varies along a continuum of how bilinguals utilize their languages in various sociolinguistic contexts and that changes across the life-span for most bilingual speakers.

In their commentary, Clara Martin and Antje Stoehr elaborated on the critical importance of studying individual variability in neural correlates of L2 learning and processing by highlighting several variables that so far have received relatively little empirical attention. One of these variables is auditory processing precision (“having a good ear”), an individual's lower-order abilities in precisely perceiving domain-general acoustic information (i.e., pitch, formants, duration, and intensity). Auditory processing has been associated with L2 speech learning success (for review, see Saito, in press). Martin and Stoehr convincingly argued that, because auditory processing is critical in identifying word and phrase boundaries, morphosyntactic markers, and syntactic structures, the assessment of L2 learners’ auditory processing precision is important to better understand individual variability in L2 learning and processing (Martin and Stoehr also pointed at open-source tools [Mora-Plaza et al., 2022]) to measure auditory processing precision). A related point was offered by Patrick Wong, from a neurocognitive perspective. Highlighting research on individual differences in neural speech tracking and research from his lab demonstrating that pretraining differences in learners’ cortical functional networks were associated with their future success in learning words of an artificial spoken language (Sheppard et al., 2012), Wong proposed that future work may explore how individual variation in neural speech tracking of different chunk sizes (cf. Ding et al., 2015) may result in variability in L2 learning outcomes. To further advance research on how individual differences impact L2 learning and processing, Wong made the valuable suggestion to adopt machine learning techniques to make predictions about individual learners’ learning outcomes as has been successfully done in research on neural speech encoding in native language acquisition (Wong et al., 2021).

Martin and Stoehr also highlighted that variability in L2 processing may be partially explained by variability in first language (L1) processing, and I concur with the importance of measuring L2 learners’ variability in L1 processing. I add the caveat here that, as recently evidenced by Vermeiren and Brysbaert (2023), researchers should be cautious using vocabulary tests developed for native speakers of that language, even when testing advanced L2 speakers. The critical importance of studying bilinguals’ L1 processing was also highlighted in Jorge Valdés Kroff and Keng-Yu Lin's commentary, yet for a different but somewhat related reason: L1 processing can change because of L2 learning. Valdés Kroff and Lin postulated that, in fact, successful L2 learning and real-time processing require adaptive changes to the L1, and the recruitment of domain-general processes to regulate the language systems. A comprehensive understanding of L2 learning and processing should therefore also entail a close inspection of the learner's L1 processing and L2-learning-induced changes therein. Valdés Kroff and Lin exemplified this by the observation that Spanish–English bilinguals’ frequent exposure to specific patterns of codeswitched determiner–noun phrases induced changes in how L1 Spanish was processed in monolingual contexts and that the Spanish–English bilinguals showed adaptive changes that differed from monolingual speakers (Valdés Kroff & Dussias, 2023).

Variability in native language processing was also highlighted by David Birdsong. In addition to further historically contextualizing ongoing debates on the critical period hypothesis and the native-speaker benchmark for L2 learning outcomes, Birdsong advocated studying patterns of dispersion in native speaker data as well as in L2 learner data. He specifically encouraged researchers to conduct analyses of signal dispersion in their behavioral, electrophysiological, and neuroimaging data and to study patterns of signal dispersion (e.g., via the coefficient of variation [CV], to quantify the signal's variability) within and across participant groups, measures, tasks, and stimulus types. I concur that signal dispersion analyses add value to the researchers’ toolbox to further quantify how signal variability across languages shapes the cognitive and neural correlates of L1 and L2 processing, and L2-learning-induced changes in language processing, in L2 learners and bilinguals.

Two sources of (inter)individual variability highlighted by Martin and Stoehr, namely the speakers’ experience with target and nontarget languages and their exposure to native- and nonnative-accented input, align with the key point made by Eleonora Rossi and Megan Nakamura. Rossi and Nakamura expanded on the importance of better capturing variability in the L2 learner and bilingual experiences and ways to optimally model this variability in order to better understand how it shapes neural indices of L2 processing. While acknowledging the value of the language entropy measure (that estimates the social diversity of language use and has been used to characterize individual differences in bilingual/multilingual language experience related to the social diversity of language use [Gullifer et al., 2018; Gullifer & Titone, 2020]), Rossi and Nakamura illustrated how personal social network (PSN) analysis can further advance our understanding of how bilingual experience may affect the neurocognitive correlates of L2 processing. Social network analysis identifies patterns of relationships, behaviors, or experiences among social actors, enabling researchers to explore how variability in individuals’ social environment predicts or affects particular outcomes. PSN analysis (or egocentric network analysis) is concerned with social networks around specific individuals (i.e., egos), the members of their networks (i.e., alters), and the relationships among alters. Cuartero, Rossi, and colleagues (2023) unpacked how PSN analysis can be used to better understand the complex language-related dynamics and heterogeneity that characterize heritage speaker bilingualism. In their thoughtful commentary, Rossi and Nakamura proposed to extend the use of PSN analysis to understand how variability in language use affects the behavioral and neural correlates of L2 learning and processing. Rossi and Nakamura highlighted a particularly valuable aspect of PSN analysis, namely that it captures variability in language use beyond the individual (i.e., ego). Specifically, PSN analysis not only measures variability in language at the level of the L2 learner (ego)—as do language experience questionnaires (such as Language Experience and Proficiency Questionnaire [LEAP-Q; Marian et al., 2007] and the Language History Questionnaire [LHQ; Li et al., 2020, the language entropy measure [Gullifer & Titone, 2020], and the bilingualism quotient [Marian & Hayakawa, 2021])—it also collects information on communicative behaviors of the L2 learner (ego) and members of their network (i.e., alters), as well as communication behaviors among the members of the network. I agree with Rossi and Nakamura that these unique indices of structural and compositional features of communication patterns in L2 speakers’ networks (such as codeswitching patters among network members; Navarro et al., 2022) have strong potential to further advance our understanding of how complex language-related dynamics and sources of sociolinguistic variation can shape L2 learners’ language use and neurocognitive profiles. I will add that integrating PSN analysis into neurocognitive research on L2 learning and processing also aligns with recent calls to incorporate sociolinguistic and sociocultural approaches to better understand the cognitive and neural bases of L2 learning and processing (as also voiced in Titone and Tiv's [2023] “Systems Framework of Bilingualism”; Tiv et al. [2022]), as well as neural network science approaches that use a data-driven quantitative approach to model language structure.

Viorica Marian explicitly related language experience to neural networks and agreed that neural network approaches to L2 processing are a valuable newer research direction. In her commentary, she cited research from her lab and others evidencing that (bi/multi)language experience changes the neural signatures associated with a multitude of language processes (including speech, language learning, and competition within and across languages) and cognitive functions (such as attentional and executive control); language experience can even impact the subcortical encoding of sounds and otoacoustic emissions (sounds generated from within the inner ear). Marian further concurred that the neural network approach is a promising research avenue in the neuroscience of L2 learning and bilingualism, particularly in light of the fast developments in generative AI and large language models that utilize deep learning in natural language processing and natural language generation tasks. As these large language models are pretrained on vast amounts of data and potentially challenge long-held beliefs and established empirical knowledge in language science, Marian is exactly right that our field needs to make sure that questions and insights on the neurocognitive underpinnings of L2 learning, bi/multilingual experience, and linguistic diversity take a central stage in research and discussions in generative AI and large language models. In fact, with my colleagues at Penn State, I lead an NSF-funded research training program for graduate students in the language sciences, psychology, communication sciences and disorders, information sciences and technology, learning design and technology, and computer science and engineering (entitled “Linguistic diversity across the lifespan: Transforming training to advance human–technology interaction”) in which these discussions are integral to the students’ research training and research design projects. This also illustrates another parallel in the many ways Viorica Marian's and my professional and personal lives overlap—as she so elegantly portrayed in her commentary.

Taomei Guo, Cristina Sanz, Jorge Valdés Kroff and Keng-Yu Lin, and Patrick Wong also commemorated the strides that the field has made, reinforced and acknowledged the value of the directions of future research that I had outlined in my target article, and picked up on several themes and extended them in interesting and engaging directions. Guo highlighted the value of noninvasive brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, to examine the causal relations between specific brain regions and L2 learning and processing. These neurocognitive intervention techniques carry a strong promise to push the field forward, as they allow the field to leverage current insights largely based on observational neurocognitive methods (electroencephalography, functional magnetic resonance imaging) to make causal inferences about specific brain regions and language functions (for a detailed review of using noninvasive brain stimulation in L2 learning and bilingualism research, see Pandža, in press).

Patrick Wong reinforced my future research recommendation to make an effort to enhance the ecological validity of neurocognitive research on L2 learning and processing. I fully endorse his suggestion to examine how the brains of learners and teachers interact by studying brain synchronies during conversations involving L2 learners and interactions in the L2 classroom, using, for example, hyperscanning techniques. Indeed, interbrain coupling during face-to-face interactions and (electroencephalography-based) hyperscanning techniques have been successfully used in public spaces, such as museums and festivals (Dikker et al., 2021), and in classrooms (e.g., Davidesco et al., 2023; Dikker et al., 2017); the technical know-how and insights obtained in “real-world neuroscience” (Matusz et al., 2019) can be readily applied to L2 classroom contexts. Jorge Valdés Kroff and Keng-Yu Lin also voiced a belief that enhancing ecological validity is imperative for future research endeavors and highlighted the importance of understanding speakers’ more nuanced use of their L2 and their processing of discourse and pragmatic expressions beyond the level of morphosyntactic processing. I fully agree with their statement that much more work is needed to better understand the neural and cognitive mechanisms associated with L2 learners “high-end” L2 language use, such as figurative language (including idioms and metaphors), irony, politeness, humor, narrative and expository discourse, and emotional expressions (for a review on the neuropragmatics of L2 processing, see Citron, 2023).

In her particularly creative commentary, Cristina Sanz took several topics that I had identified as issues that need to be resolved, gaps in current knowledge, and promising avenues of future research as the starting point for designing an empirical study (“thought experiment”) that overcomes these limitations and that models a key step forward in understanding the neurocognitive underpinnings of L2. This exemplary experiment elegantly incorporated many of my and others’ recommendations, including using rigorous research designs, moving beyond the native speaker benchmark and acknowledging that L2 learning trajectories are complex and multifaceted, incorporating individual variability and dynamic changes in both L1 and L2 processing, as well as enriching linguistic diversity and ecological validity and considering the translational implications of research outcomes. Sanz's thought experiment is an example of how we can optimally move the field forward. So let us do it! And let us leverage the insights and discussions on open science practices in Marsden and Morgan-Short's (in press) keynote article in Language Learning’s 75th Jubilee volume.

To conclude, the neurocognition of L2 learning and processing is a relatively young field that has yielded tremendously rich insights and has made significant strides forward in the past decades. The peer commentators to my keynote article have each made, and continue to make, foundational contributions to this field, and I thank all of the commentators for their thoughtful engagement with my ideas and their invaluable insights. As part of the celebration of Language Learning’s 75th Jubilee edition, I hope that my keynote article and the commentators’ insights will inspire readers, contribute to shaping and paving the way for new discoveries, and nudge knowledge forward to new levels of fully understanding the complexity and enchantment of learning and processing multiple languages.

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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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