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）的熟练程度在全球化世界中的相关性越来越高，以及多语言使用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学习是一个日益重要的领域。这一奋进的一个关键部分涉及推进我们对学习的相对贡献的理解，这是明确的（即，涉及有意识的意识）与内隐的（即，不涉及有意识的意识）。为了研究外显和内隐L2处理的相对贡献和关系，该项目将人工语言学习实验与最近开发的用于脑电图（EEG）的机器学习分析技术相结合，EEG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处理相关的大脑活动。这将为语言学习模型之间的理论辩论提供信息，这些模型认为内隐/外显处理被强界面、弱界面或无界面分开。这种对隐式/显式语法处理的改进的理解可以通过建议隐式方法（例如，自然的暴露）和明确的方法（例如，实验参与者将执行一项语言学习任务，该任务涉及四个具有隐藏语法规则的新单词（两个单词通常与生物共同出现，两个单词与非生物共同出现）。之后，参与者将回答汇报问题，以确定他们是否/何时知道该规则。先前的研究表明，即使是不知道规则的参与者也表现出学习的迹象，因为与遵守规则的试验相比，违反规则的试验具有独特的神经活动和较慢的反应。该项目通过基于机器学习的分析技术扩展了此类研究，该技术可以评估不同神经认知事件在大脑活动的时间和位置方面的相似程度。第一个分析测试是否隐式（规则不知道）语法处理的神经标记发生在显式（规则知道）处理期间。第二个分析测试是否内隐和外显处理不同的语言预测的发生（这里定义为神经模式的发生与生活的话，而不是非生活的话，当参与者阅读生活/非生活编码的人工语言字在隔离处理）。第三项分析测试语言任务反应时间是否与内隐和外显处理的神经标记的时间一致，以确定参与者的外部反应是否由有意识与无意识处理驱动。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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