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 75^th 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 75^th 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.

写一篇关于第二语言（L2）学习和处理的神经基础的评论，并认真关注未来的研究途径，是我收到过的最有趣的写作邀请之一。如果不是《语言学习》字数限制，这篇文章早就成为整期，甚至成为一本书了！我很高兴看到我对这个领域的热情以及对二语学习和处理的神经认知研究的未来的热情得到了该领域的杰出和德高望重的同事的分享，他们阅读并评论了这篇主题文章。这些评论员赞扬了该领域的惊人成就，对我的评论中概述的未来承诺和途径提出了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。

在我的评论论文中，我从两条经典研究开始，它们奠定了研究阶段并激发了高产的研究。然后，我通过选择性回顾关于 L2 处理和双语大脑组织的电生理学和神经影像学研究来说明该领域取得的令人印象深刻的成就，并概述了过去 25 年中获得的主要见解。我还讨论了不断变化的观点（包括个体差异和基于经验的观点、神经网络方法、神经可塑性和第二语言学习相关的神经变化），并确定了挑战、承诺和未来方向，以便更好地理解第二语言学习和处理的神经认知基础。这些未来的方向包括重新审视第二语言水平的母语基准和相关的方法论意义、应用先进的电生理学和神经影像技术来更好地捕捉该领域的新观点、增加第二语言处理的神经认知研究的语言多样性、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，采取终身二语学习的方法。

评论中出现的一个主题是总体一致认为，在未来的二语学习和处理研究中纳入个体差异观点和方法以推动知识向前发展至关重要（正如 Martin 和 Stoehr、Wong、Rossi 和 Nakamura、Birdsong 明确指出的那样）和玛丽安）。正如我在文章中得出的结论，未来的研究应该超越学习习得年龄和第二语言熟练程度的作用，并更广泛地关注塑造第二语言学习轨迹和第二语言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第二语言学习者的经历（包括习得年龄，还包括当前的语言使用和环境背景；参见，例如，DeLuca 等人，2019 年；Gullifer 等人，2018 年）和认知功能的变异性（例如，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影响第二语言学习和处理的神经相关性。此外，基于经验的观点还包含这样一种观念，即基于格罗斯让的语言模式（例如，格罗斯让，2001），双语不是静态的，而是动态的现象，随着双语者如何在各种社会语言环境中使用其语言的连续体而变化。对于大多数双语使用者来说，这种情况会在一生中发生变化。

在他们的评论中，Clara Martin 和 Antje Stoehr 通过强调迄今为止受到相对较少的实证关注的几个变量，阐述了研究第二语言学习和处理的神经相关性中的个体变异性的至关重要性。这些变量之一是听觉处理精度（“拥有一双好耳朵”），即个体精确感知域一般声学信息（即音调、共振峰、持续时间和强度）的低阶能力。听觉处理与 L2 语音学习的成功相关（有关评论，请参阅 Saito，正在出版）。马丁和斯托尔令人信服地认为，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，因此评估第二语言学习者的听觉处理精度对于更好地理解第二语言学习和处理中的个体差异非常重要（马丁和斯托尔） Stoehr 还指出了用于测量听觉处理精度的开源工具 [Mora-Plaza 等人，2022 ]。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中个体差异的研究，以及他实验室的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 等，2012），Wong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（参见 Ding 等人，2015）如何导致 L2 学习结果的变化。为了进一步推进个体差异如何影响二语学习和处理的研究，Wong 提出了宝贵的建议，即采用机器学习技术来预测个体学习者的学习结果，就像在母语习得中的神经语音编码研究中取得的成功一样（Wong等人，2021）。

Martin 和 Stoehr 还强调，L2 处理的变异性可能部分是由第一语言 (L1) 处理的变异性来解释的，我同意衡量 L2 学习者在 L1 处理中的变异性的重要性。我在此补充一点，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 L2 使用者时也是如此。Jorge Valdés Kroff 和 Keng-Yu Lin 的评论也强调了研究双语者的 L1 处理的至关重要性，但出于不同但有些相关的原因：L1 处理可能会因为 L2 学习而改变。Valdés Kroff 和 Lin 假设，事实上，成功的 L2 学习和实时处理需要对 L1 进行适应性改变，并招募领域通用过程来调节语言系统。因此，对 L2 学习和处理的全面理解还应该需要仔细检查学习者的 L1 处理和 L2 学习引起的变化。Valdés Kroff 和 Lin 通过观察发现，西英双语者频繁接触语码转换限定词-名词短语的特定模式，导致了第一语西班牙语在单语环境中的处理方式发生了变化，并且西英双语者表现出了与仅讲一种语言的人（Valdés Kroff & Dussias，2023）。

David Birdsong 还强调了母语处理的可变性。除了进一步历史背景地分析关于关键期假说和第二语言学习成果的母语者基准的持续争论之外，Birdsong 还主张研究母语者数据以及第二语言学习者数据的分散模式。他特别鼓励研究人员对其行为、电生理和神经影像数据中的信号分散进行分析，并研究参与者组内和之间的信号分散模式（例如，通过变异系数 [CV] 来量化信号的变异性），措施、任务和刺激类型。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变异如何塑造 L1 和 L2 处理的认知和神经相关性，以及 L2 学习引起的 L2 学习者和双语者的语言处理变化。

马丁和斯托尔强调了个体（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，这与埃莱奥诺拉·罗西和梅根·中村提出的关键点是一致的。Rossi 和 Nakamura 阐述了更好地捕捉 L2 学习者和双语体验中的变异性的重要性，以及对这种变异性进行最佳建模的方法，以便更好地理解它如何塑造 L2 处理的神经指数。同时承认语言熵度量的价值（估计语言使用的社会多样性，并已用于表征与语言使用的社会多样性相关的双语/多语语言体验的个体差异[Gullifer et al., 2018 ; Gullifer & Titone，2020 ])、Rossi 和 Nakamura 阐述了个人社交网络 (PSN) 分析如何进一步加深我们对双语体验如何影响 L2 处理的神经认知相关性的理解。社交网络分析识别社会参与者之间的关系、行为或经历的模式，使研究人员能够探索个人社会环境的变化如何预测或影响特定的结果。PSN 分析（或以自我为中心的网络分析）涉及特定个体（即自我）周围的社交网络、其网络成员（即替代者）以及替代者之间的关系。Cuartero、Rossi 及其同事 ( 2023 ) 揭示了如何使用 PSN 分析来更好地理解复杂的语言相关动态和异质性，这些动态和异质性是传统说话者双语的特征。在他们深思熟虑的评论中，Rossi 和 Nakamura 提议扩展 PSN 分析的使用，以了解语言使用的可变性如何影响 L2 学习和处理的行为和神经相关性。Rossi 和 Nakamura 强调了 PSN 分析的一个特别有价值的方面，即它捕获了个人（即自我）之外的语言使用的可变性。具体来说，PSN 分析不仅测量 L2 学习者（自我）层面的语言变异性，语言体验问卷（例如语言体验和熟练程度问卷 [LEAP-Q；Marian 等，2007] 和语言历史）也是如此。问卷 [LHQ；Li et al., 2020]、语言熵度量 [Gullifer & Titone, 2020 ] 和双语商数 [Marian & Hayakawa, 2021 ]]）——它还收集有关 L2 学习者（自我）及其网络成员（即改变者）的交流行为以及网络成员之间的交流行为的信息。我同意 Rossi 和 Nakamura 的观点，即 L2 说话者网络中通信模式的结构和组成特征的这些独特指标（例如网络成员之间的语码转换模式；Navarro 等人，2022）具有强大的潜力，可以进一步推进我们对复杂性的理解语言相关的动态和社会语言变异的来源可以塑造第二语言学习者的语言使用和神经认知概况。我要补充的是，将 PSN 分析整合到 L2 学习和处理的神经认知研究中也符合最近的呼吁，即结合社会语言学和社会文化方法，以更好地理解 L2 学习和处理的认知和神经基础（Titone 和 Tiv 的 [2023] 中也提到了这一点） “双语系统框架”；Tiv 等人 [ 2022 ]），以及使用数据驱动的定量方法来建模语言结构的神经网络科学方法。

Viorica Marian 明确地将语言体验与神经网络联系起来，并同意 L2 处理的神经网络方法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人的研究，证明（双/多）语言体验改变了与多种语言过程（包括语音、语言学习以及语言内部和跨语言的竞争）和认知功能相关的神经特征。例如注意力和执行控制）；语言体验甚至可以影响声音和耳声发射（内耳内产生的声音）的皮层下编码。Marian 进一步同意，神经网络方法是二语学习和双语神经科学领域一个有前途的研究途径，特别是考虑到在自然语言处理和自然语言生成任务中利用深度学习的生成人工智能和大型语言模型的快速发展。由于这些大型语言模型是根据大量数据进行预训练的，并且可能会挑战语言科学中长期持有的信念和既定的经验知识，因此玛丽安完全正确，我们的领域需要确保关于第二语言学习的神经认知基础的问题和见解，双语/多语经验和语言多样性在生成人工智能和大型语言模型的研究和讨论中占据中心地位。事实上，我和宾夕法尼亚州立大学的同事一起领导了一个由国家科学基金会资助的研究培训项目，该项目针对语言科学、心理学、传播科学与疾病、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。题为“整个生命周期的语言多样性：转变培训以促进人与技术的互动”），其中这些讨论是学生研究培训和研究设计项目的组成部分。这也说明了维奥丽卡·玛丽安和我的职业和个人生活在许多方面重叠的另一个相似之处——正如她在评论中如此优雅地描绘的那样。

郭涛梅、克里斯蒂娜·桑斯、豪尔赫·巴尔德斯·克罗夫、林景宇以及帕特里克·黄也纪念了该领域所取得的进步，强化并承认了我在目标文章中概述的未来研究方向的价值，并选择了讨论了几个主题，并将它们延伸到有趣且引人入胜的方向。郭强调了非侵入性脑刺激技术（例如经颅磁刺激和经颅直流电刺激）在检查特定大脑区域与第二语言学习和处理之间的因果关系方面的价值。这些神经认知干预技术有望推动该领域向前发展，因为它们允许该领域利用主要基于观察性神经认知方法（脑电图、功能磁共振成像）的当前见解，对特定的大脑区域和语言功能进行因果推断（例如在第二语言学习和双语研究中使用无创脑刺激的详细回顾，参见 Pandža，正在出版）。

Patrick Wong 强调了我未来的研究建议，即努力提高二语学习和处理的神经认知研究的生态有效性。我完全赞同他的建议，即通过使用超扫描技术等研究涉及第二语言学习者的对话和第二语言课堂互动期间的大脑同步来检查学习者和教师的大脑如何互动。事实上，面对面互动期间的脑间耦合和（基于脑电图的）超扫描技术已成功应用于博物馆和节日等公共场所（Dikker 等人，2021）和教室（例如 Davidesco 等人） ., 2023 ; Dikker 等人, 2017 ); 在“现实世界神经科学”（Matusz 等人， 2019 ）中获得的技术知识和见解可以轻松应用于第二语言课堂环境。Jorge Valdés Kroff 和 Keng-Yu Lin 也表示，增强生态有效性对于未来的研究工作至关重要，并强调了解说话者更细致地使用 L2 以及超越形态句法处理水平的话语和语用表达处理的重要性。我完全同意他们的说法，即需要做更多的工作来更好地理解与二语学习者“高端”二语使用相关的神经和认知机制，例如比喻语言（包括习语和隐喻）、讽刺、礼貌、幽默、叙述性和说明性话语以及情感表达（有关 L2 处理的神经语用学的综述，请参阅 Citron，2023）。

在她特别有创意的评论中，克里斯蒂娜·桑兹（Cristina Sanz）将我认为需要解决的几个主题、当前知识的差距以及未来研究的有希望的途径作为设计实证研究（“思想实验”）的起点，克服了这些限制，并为理解 L2 的神经认知基础迈出了关键的一步。这个示范性实验巧妙地融入了我和其他人的许多建议，包括使用严格的研究设计、超越母语者基准并承认 L2 学习轨迹是复杂且多方面的，在 L1 和 L2 处理中纳入个体差异和动态变化，如以及丰富语言多样性和生态有效性并考虑研究成果的翻译意义。桑兹的思想实验是我们如何以最佳方式推动该领域向前发展的一个例子。那么让我们来做吧！让我们利用 Marsden 和 Morgan-Short 在《语言学习》第 75^周年纪念卷中发表的主题文章（正在出版）中对开放科学实践的见解和讨论。

总而言之，第二语言学习和处理的神经认知是一个相对年轻的领域，在过去几十年中已经产生了极其丰富的见解，并取得了重大进展。我的主题文章的同行评论员都已经并将继续为这一领域做出基础性贡献，我感谢所有评论员对我的想法的深思熟虑和宝贵见解。作为庆祝《语言学习》第 75^周年纪念版的一部分，我希望我的主题文章和评论者的见解能够启发读者，为新发现的形成和铺平道路做出贡献，并将知识推向全面发展的新水平。了解学习和处理多种语言的复杂性和魅力。