Doctoral Dissertation Research: Disentangling neural indices of implicit vs. explicit second language processing

博士论文研究：解开隐式与显式第二语言处理的神经指标

• 批准号: 1941189
• 负责人: Kara Short
• 金额: $ 0.84万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941189&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Disentangling; neural

Given the increasing relevance of second language (L2) proficiency in a globalizing world as well as the benefits that multilingualism might confer for individuals' socioeconomic, cognitive, and academic outcomes, improving L2 learning is an area of growing importance. A critical part of this endeavor involves advancing our understanding of the relative contributions of learning that is explicit (i.e., involves conscious awareness) vs. implicit (i.e., does not involve conscious awareness). To investigate the relative contributions of and relationship between explicit and implicit L2 processing, this project combines an artificial language learning experiment with recently-developed machine learning analysis techniques for electroencephalography (EEG), a neurolinguistic method that can detect brain activity related to language processing in a non-invasive way. This will inform theoretical debates between models of language learning that posit that implicit/explicit processing are separated by a strong interface, a weak interface, or no interface. Such an improved understanding of implicit/explicit grammar processing might inform second language pedagogy by suggesting how implicit methods (e.g., naturalistic exposure) and explicit methods (e.g., grammar-oriented drills) should be balanced to maximize learning.Experiment participants will perform a language learning task involving four new words with a hidden grammatical rule (two words usually co-occur with living things and two with non-living things). Afterwards, participants will answer debriefing questions to determine if/when they became aware of the rule. Prior research indicates that even rule-unaware participants show signs of learning, in that rule-violating trials feature distinct neural activity and slower responses when compared to rule-following trials. This project extends such research with machine learning-based analysis techniques which can assess the degree to which different neurocognitive events are similar in the timing and location of brain activity. The first analysis tests whether neural markers of implicit (rule-unaware) grammar processing occur during periods of explicit (rule-aware) processing. The second analysis tests whether implicit and explicit processing differ in the occurrence of linguistic prediction (defined here as the occurrence of neural patterns related to the processing of living words as opposed to non-living words, when participants read the living/non-living-encoding artificial language word in isolation). The third analysis tests whether language task reaction times show a consistent relationship with the timing of neural markers of implicit and explicit processing, to determine whether participants’ external responses are driven by conscious vs. unconscious processing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

鉴于第二语言(L2)熟练程度在全球化世界中的重要性日益增强，以及多语言能力可能为个人的社会经济、认知和学术成果带来的好处，改善L2学习变得越来越重要。这一努力的一个关键部分涉及促进我们对学习的相对贡献的理解，即显性(即，涉及意识意识)与隐性(即，不涉及意识意识)。为了研究外显和内隐二语加工之间的相对贡献和关系，本项目将人工语言学习实验与最近发展起来的用于脑电的机器学习分析技术相结合，脑电是一种神经语言学方法，可以以非侵入性的方式检测与语言加工相关的大脑活动。这将为语言学习模型之间的理论辩论提供信息，这些模型假设内隐/外显加工被强接口、弱接口或无接口分开。这种对内隐/外显语法加工的更好的理解可能会通过建议如何平衡内隐方法(例如，自然暴露)和外显方法(例如，以语法为导向的练习)来指导第二语言教学。实验参与者将执行一项包括四个具有隐藏语法规则的生词的语言学习任务(两个词通常与生物共存，两个词与非生物共存)。之后，参与者将回答汇报问题，以确定他们是否/何时意识到这一规则。先前的研究表明，即使是不知道规则的参与者也显示出学习的迹象，因为与遵循规则的试验相比，违反规则的试验具有明显的神经活动和较慢的反应。这个项目用基于机器学习的分析技术扩展了这项研究，这种技术可以评估不同的神经认知事件在大脑活动的时间和位置上的相似程度。第一个分析测试隐含(规则不知道)语法处理的神经标记是否在显性(规则知道)处理期间发生。第二个分析测试了内隐加工和外显加工在语言预测的出现上是否存在差异(这里的定义是，当参与者孤立地阅读活的/非活的编码的人工语言单词时，与有生命的单词和非有生命的单词的加工有关的神经模式的出现)。第三个分析测试语言任务反应时是否与内隐和外显加工的神经标记的时间显示出一致的关系，以确定参与者的外部反应是否是由意识或无意识加工驱动的。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Differences in implicit vs. explicit grammar processing as revealed by drift-diffusion modeling of reaction times

• 发表时间: 2021
• 作者: David Abugaber;K. Morgan‐Short