Insights into statistical learning from neural entrainment

学习一门新的语言需要我们对许多不同类型的语言模式变得敏感。例如，说英语的人必须学习-s表示复数名词，某些声音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的模式的能力被称为统计学习。虽然统计学习是人类的核心能力，有助于语言习得和认知的许多其他方面，但很少有人研究这种重要能力背后的神经机制。大多数以前的研究只在学习发生后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。我的长期目标是通过描述支持从语言中提取模式的神经机制来发展和告知统计语言学习理论。我的短期目标是了解神经诱导语言模式如何反映和指导统计学习。神经诱导是指大脑节律与外界刺激之间的同步。我最近的工作表明，随着学习者在连续的语音中发现模式，他们的大脑逐渐显示出对潜在模式的更大的夹带，通过脑电图（EEG）进行测量。这一测量直接反映了学习者在接触语言过程中对语言学习的感知，因此代表了方法上的一个重大进步。在这项工作的基础上，我和我的学员将开展四个相互关联的项目，以了解神经夹带如何有助于统计学习。在项目1中，我们将测试神经夹带的个体间差异，随机刺激序列评估，预测统计学习能力。在项目2中，我们将通过评估睡眠期间的学习来测试神经对语言模式的诱导是否至少部分地自动出现。在项目3中，我们将评估先验知识对神经夹带的影响。在项目4中，我们将测试结构的神经夹带是否因果地有助于统计学习，通过在不同频率下外部操纵神经夹带，使用感觉刺激方法和非侵入性脑刺激。这项研究将大大推进我们对驱动统计学习的在线动态神经机制的理解。由于统计学习在语言以外的认知的许多方面发挥着重要作用，因此这项研究计划将影响多个领域。这项研究也对提高语言习得，特别是对成年第二语言学习者有着令人兴奋的意义。这些发现将有助于确定统计学习的最佳条件，发现以前被忽视的学习机会（例如，在睡眠期间），并产生新的方法来促进统计学习。

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.