Insights into statistical learning from neural entrainment

从神经夹带中洞察统计学习

• 批准号: RGPIN-2019-05132
• 负责人: Batterink, Laura
• 金额: $ 2.4万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715296
• 关键词: Insights; into; statistical; learning; neural

Learning a new language requires us to become sensitive to many different types of linguistic patterns. For example, English speakers must learn that -s denotes a plural noun and that certain sound combinations cannot occur at the beginnings of words (e.g., tl). The ability to extract these types of patterns from environmental input is known as statistical learning. Although statistical learning is a core human ability that contributes to language acquisition and many other aspects of cognition, the neural mechanisms underlying this important ability have rarely been investigated. Most previous studies have tested learners' knowledge of patterns only after learning has occurred, and have not examined the mechanisms engaged during the learning process. My long-term goal is to develop and inform theories of statistical language learning by characterizing the neural mechanisms that support the extraction of patterns from language. My short-term goal is to understand how neural entrainment to linguistic patterns may both reflect and guide statistical learning. Neural entrainment refers to the synchrony between brain rhythms and external stimuli. My recent work shows that as learners discover patterns in continuous speech, their brains gradually show greater entrainment to the underlying patterns, as measured through electroencephalography (EEG). This measure directly indexes learners' perceptions of linguistic regularities during exposure to language, and thus represents a major methodological step forward. Building on this work, my trainees and I will carry out four interrelated projects to understand how neural entrainment may contribute to statistical learning. In Project 1, we will test whether interindividual differences in neural entrainment, as assessed to random stimulus sequences, predict statistical learning ability. In Project 2, we will test whether neural entrainment to linguistic patterns can emerge at least partially automatically by assessing learning during sleep. In Project 3, we will assess effects of prior knowledge on neural entrainment. In Project 4, we will test whether neural entrainment to structure causally contributes to statistical learning by externally manipulating neural entrainment at different frequencies, using both sensory stimulation methods and noninvasive brain stimulation. The proposed research will significantly advance our understanding of the online, dynamic neural mechanisms that drive statistical learning. Because statistical learning plays a fundamental role in many aspects of cognition beyond language, this research program will impact multiple fields. This research also has exciting implications for improving language acquisition, particularly for adult second language learners. These findings will help identify the optimal conditions for statistical learning, uncover previously overlooked learning opportunities (e.g., during sleep) and produce novel methods of boosting statistical learning.

学习一门新语言需要我们对许多不同类型的语言模式变得敏感。例如，说英语的人必须知道-s表示复数名词，并且某些声音的组合不能出现在单词的开头（例如，tl）。从环境输入中提取这些类型模式的能力被称为统计学习。虽然统计学习是一项核心的人类能力，有助于语言习得和认知的许多其他方面，但这种重要能力背后的神经机制很少被研究。大多数以前的研究都是在学习发生后才测试学习者对模式的知识，而没有检查学习过程中参与的机制。