In their review article, Li and Xu offered an insightful overview of the contributions and limitations of computational models of bilingual language learning and processing to our current understanding of the bilingual mind. They further proposed joining cross-disciplinary efforts toward building a computational account that links cognitive theory and neurobiological accounts of bilingualism as part of their suggested future research agenda. We agree with Li and Xu's suggestions and further propose that (a) the scope of computational models of bilingual language learning and processing should be expanded to include other perspectives: language learning context, maintenance, and decay of linguistic competence and bilingual language breakdown and that (b) existing modeling efforts already work toward addressing these areas, answering the proposed desiderata for good computational models.

As reviewed by Li and Xu, developmental computational models have helped researchers understand how language representation emerges as a function of a speaker's bilingual experience. However, language learning context must be better accounted for. Specifically, bilingual language learning poses additional challenges for behavioral research when studies seek to address more naturalistic learning contexts such as second language (L2) acquisition via immersion in a foreign language context versus L2 acquisition in the classroom, and the involvement of implicit and explicit learning mechanisms constitutes an important axis of differentiation in this regard. Thus, while computational models include data-driven learning mechanisms to discover and organize linguistic representations as indicated by Li and Xu, future models should incorporate testable theory-driven implicit and explicit mechanisms for language learning (Peñaloza et al., 2022). Existing computational models of bilingual lexical access (Peñaloza et al., 2019) could incorporate such mechanisms to help test the contributions of these mechanisms to bilingual language learning.

In addition, in modeling bilingual learning, both maintenance of the acquired linguistic knowledge and the reverse decay process are equally important in the lifespan timeline. For example, the extant literature makes it clear that contextual changes may reduce bilingual exposure and use that affect young and older bilinguals, yet bilingual processing decay in older adults can be further confounded with age-related language and cognitive decline (Goral et al., 2008). In supporting Li and Xu's proposal for cross-disciplinary work, we argue that computational modeling could implement and test assumptions from closely related fields including memory theory (Mickan et al., 2019) to gain understanding on how speakers’ language processing abilities in their first language (L1) and their L2 change with contextual experience and over the lifespan.

Cognitive control is a domain-general ability worth incorporating in an expanded scope for computational models of bilingualism. Although it is well known that different brain regions contribute to language control in bilinguals, their unique contributions to helping bilinguals overcome cross-language interference across different learning environments and language processing contexts with high versus low cognitive control demands remain an open question. We propose that the study of bilingual language breakdown following damage to critical brain regions offers a unique window into the modeling of lexical access and cognitive control. At the broadest level, lesion studies and patient behavioral data can inform computational models of bilingual language learning and processing to achieve the cognitive and neurobiological plausibility proposed by Li and Xu. At the most specific level, the computational simulation of control, learning, and processing mechanisms in bilingual patients with brain damage could help specify causal links between specific brain regions and bilingual behavior while controlling for relevant variables difficult to manipulate in behavioral research. For instance, using the BiLex computational model (Peñaloza et al., 2019), we demonstrated that applying damage to the semantic and L1 and L2 phonetic components of individual prestroke models reproduced L1 and L2 lexical retrieval deficits in bilingual aphasia patients (Grasemann et al., 2021). In turn, applying semantic but not other lesion types could best reproduce the pattern of language decline in semantic dementia patients (Fidelman et al., 2022). These simulation findings align with theories of lexical access deficits versus semantic storage deficits put forth for these patient groups, respectively (Mirman & Britt, 2014). Notably, BiLex could offer prognostic value in predicting rates of language decline in bilingual dementia and in identifying the language to target in treatment to achieve optimal recovery across the two languages in bilingual aphasia, an approach currently being tested in a clinical trial (Peñaloza et al., 2020).

Models that expand the scope outlined above go a long way toward the desiderata for good computational models proposed by Li and Xu. One example is the BiLex model (Peñaloza et al., 2019): While based on the same principles of self-organizing maps and Hebbian learning reviewed by Li and Xu, it focuses on modeling individual proficiency, impairment, and treatment-induced recovery in stroke and dementia patients. The model is valid in that its initial training parameters map to actual L2 age of acquisition and language exposure of bilingual speakers, its performance is measured via clinical tests used with patients, and its rehabilitation training parameters map to actual treatment items, intensity, and duration. It makes good contact with real language in that the semantic representations encode the semantic dimensions identified through crowdsourcing (Mechanical Turk) experiments, and its phonetic representations encode the International Phonetic Alphabet principles of the languages modeled. BiLex is interpretable in that the maps and connectivity patterns explain the behavioral patterns, and specific damage leads to specific impairments; it is also predictive because it can be used to identify the treatment options leading to the best possible recovery.

In summary, we agree with Li and Xu in that an integrative computational neuroscience of bilingualism is needed, but its scope needs to be expanded to include language learning context, maintenance and decay of linguistic competence, and bilingual language breakdown to account for a larger range of phenomena in bilingual language learning and processing. Building upon work addressing the desiderata for good models proposed by Li and Xu, computational modeling can achieve cognitive and neurobiological plausibility by incorporating evidence from lesion studies and behavioral patient data to provide a vehicle for contrasting bilingualism theories and a bridge between computational science and real-world clinical needs.

在他们的评论文章中，Li 和 Xu 深刻地概述了双语语言学习和处理的计算模型对我们目前对双语思维的理解的贡献和局限性。他们进一步提议加入跨学科的努力，以建立一个计算账户，将认知理论和双语的神经生物学账户联系起来，作为他们建议的未来研究议程的一部分。我们同意 Li 和 Xu 的建议，并进一步提出 (a) 双语语言学习和处理的计算模型的范围应扩大到包括其他视角：语言学习上下文、语言能力的维持和衰退以及双语语言的崩溃，并且(b) 现有的建模工作已经致力于解决这些领域，

正如 Li 和 Xu 所评论的那样，发展计算模型帮助研究人员了解语言表征是如何随着说话者的双语体验而出现的。但是，必须更好地考虑语言学习环境。具体而言，当研究试图解决更自然的学习环境时，双语学习对行为研究提出了额外的挑战，例如通过沉浸在外语环境中的第二语言 (L2) 习得与课堂上的 L2 习得，以及内隐和外显学习的参与在这方面，机制构成了一个重要的分化轴。因此，虽然计算模型包括数据驱动的学习机制来发现和组织语言表示，如 Li 和 Xu 所指出的，2022 年）。现有的双语词汇访问计算模型（Peñaloza 等人，2019 年）可以结合此类机制来帮助测试这些机制对双语语言学习的贡献。

此外，在双语学习建模中，获得的语言知识的维护和反向衰减过程在生命周期时间轴中同样重要。例如，现存的文献清楚地表明，语境变化可能会减少影响年轻和年长双语者的双语接触和使用，但老年人的双语处理衰退可能会进一步与年龄相关的语言和认知衰退混淆（Goral 等人，2008 年）。为了支持 Li 和 Xu 关于跨学科工作的建议，我们认为计算建模可以实施和测试来自密切相关领域（包括记忆理论）的假设（Mickan 等人，2019 年）) 以了解说话者的第一语言 (L1) 和 L2 的语言处理能力如何随着上下文经验和整个生命周期而变化。

认知控制是一种领域通用能力，值得纳入双语计算模型的扩展范围。尽管众所周知，不同的大脑区域有助于双语者的语言控制，但它们在帮助双语者克服不同学习环境和语言处理环境中具有高认知控制需求和低认知控制需求的跨语言干扰方面的独特贡献仍然是一个悬而未决的问题。我们建议，对大脑关键区域受损后双语语言崩溃的研究为词汇访问和认知控制的建模提供了一个独特的窗口。在最广泛的层面上，病变研究和患者行为数据可以为双语语言学习和处理的计算模型提供信息，以实现 Li 和 Xu 提出的认知和神经生物学合理性。在最具体的层面上，双语脑损伤患者的控制、学习和处理机制的计算模拟可以帮助确定特定大脑区域与双语行为之间的因果关系，同时控制行为研究中难以操纵的相关变量。例如，使用 BiLex 计算模型（Peñaloza 等人，双语脑损伤患者的处理机制有助于确定特定大脑区域与双语行为之间的因果关系，同时控制行为研究中难以操纵的相关变量。例如，使用 BiLex 计算模型（Peñaloza 等人，双语脑损伤患者的处理机制有助于确定特定大脑区域与双语行为之间的因果关系，同时控制行为研究中难以操纵的相关变量。例如，使用 BiLex 计算模型（Peñaloza 等人，2019 年），我们证明对单个卒中前模型的语义和 L1 和 L2 语音成分应用损伤可重现双语失语症患者的 L1 和 L2 词汇检索缺陷（Grasemann 等人，2021 年）。反过来，应用语义而不是其他病变类型可以最好地重现语义痴呆患者的语言下降模式（Fidelman 等人，2022）。这些模拟结果与分别为这些患者群体提出的词汇访问缺陷与语义存储缺陷理论相一致（Mirman & Britt，2014 年）). 值得注意的是，BiLex 可以在预测双语痴呆症的语言下降率和确定治疗目标语言以实现双语失语症两种语言的最佳恢复方面提供预后价值，这种方法目前正在临床试验中进行测试（Peñaloza 等人., 2020 年）。

扩展上述范围的模型对 Li 和 Xu 提出的良好计算模型的需求大有帮助。一个例子是 BiLex 模型（Peñaloza 等人，2019)：虽然基于 Li 和 Xu 审查的自组织映射和赫布学​​习的相同原则，但它侧重于对中风和痴呆患者的个体熟练程度、损伤和治疗诱导的恢复进行建模。该模型的有效性在于其初始训练参数映射到双语者的实际 L2 习得年龄和语言接触，其性能通过对患者进行的临床测试进行测量，其康复训练参数映射到实际治疗项目、强度和持续时间. 它与真实语言有很好的联系，因为语义表示对通过众包（Mechanical Turk）实验确定的语义维度进行编码，其语音表示对所建模语言的国际音标原则进行编码。BiLex 是可解释的，因为地图和连接模式解释了行为模式，特定的损伤导致特定的损伤；它还具有预测性，因为它可用于确定导致最佳康复的治疗方案。

总之，我们同意 Li 和 Xu 的观点，双语的综合计算神经科学是必要的，但它的范围需要扩大到包括语言学习情境、语言能力的维持和衰退，以及双语语言分解，以解释更大的范围双语语言学习和处理中的现象。基于解决 Li 和 Xu 提出的对良好模型的需求的工作，计算模型可以通过结合病变研究和行为患者数据的证据来实现认知和神经生物学的合理性，从而提供对比双语理论的工具以及计算科学与现实之间的桥梁。世界临床需求。