The neural mechanisms of predictive coding during language comprehension

语言理解过程中预测编码的神经机制

• 批准号: 1829527
• 负责人: Albert Kim
• 金额: $ 57.13万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829527&HistoricalAwards=false
• 关键词: neural; mechanisms; predictive; coding; during

During skilled reading, people recognize words from written character strings and combine words into a larger message-level interpretation. Reading is a pervasive part of modern human life, and impaired reading is highly detrimental to scholastic and professional success. Despite its importance, the neural processes that enable skilled readers to comprehend multi-word combinations remain poorly understood. In this research, Dr. Albert Kim and Dr. Phillip Gilley of the University of Colorado Boulder will use cutting edge neurophysiological methods to understand how the brain generates and uses predictions about the upcoming words within a text during reading, guided by prior linguistic context. Predictions are thought to support the fast pace of normal reading, which is typically 3-5 words per second, by allowing the brain to prepare in advance for anticipated text before it arrives in the retinal input. Predictions are also thought to be crucial to the common ability to perceive words accurately in the face of impoverished inputs, such as conditions of poor lighting, print quality, or visual acuity. The research in this project is expected to produce knowledge that guides the identification of reading disorders and distinctions between different sorts of disorders, which can in turn guide clinical and pedagogical approaches to reading disorders.The project will use scalp-recorded encephalography (EEG) to observe neural oscillatory activity -- reflecting the dynamic coupling and uncoupling of neural networks -- while healthy young adults read sentences. The sentences that participants read will be manipulated so that some sentence contexts render a specific word likely to occur, according to computational language models. Under these conditions, brain activity that occurs before and after the appearance of a word in the linguistic input will be examined for evidence of predictions. The project will investigate the role of neural oscillations in conveying predictions about upcoming words from high level brain regions to low level visual cortical areas and also in assessing the match between predictions and the bottom-up sensory input. The researchers will also study the types of linguistic contexts and processing demands that engage such predictive activity. Results of this research are expected to contribute significantly to cognitive neuroscience by combining three critical areas of research that have typically been studied separately: language processing, prediction in cognition, and neural oscillations. The project will develop new analytic methods for characterizing neural oscillations in EEG data, which will be developed into a toolbox to be shared with other researchers. The project will also produce a rich database of brain activity during the reading of naturalistic stories, which will be shared with the scientific community as a resource for further research.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.

在熟练的阅读过程中，人们从书面字符串中识别单词，并将单词组合成更大的消息级解释。阅读是现代人类生活的普遍组成部分，阅读障碍对学业和职业成功非常不利。尽管它很重要，但使熟练读者能够理解多词组合的神经过程仍然知之甚少。在这项研究中，科罗拉多大学博尔德分校的阿尔伯特·金博士和菲利普·吉利博士将使用最先进的神经生理学方法来了解大脑如何在阅读过程中在先前的语言背景的指导下生成和使用对文本中即将出现的单词的预测。人们认为，预测可以让大脑在预期文本到达视网膜输入之前提前做好准备，从而支持正常阅读的快节奏（通常为每秒 3-5 个单词）。预测也被认为对于在面对匮乏的输入（例如照明、打印质量或视力不佳的条件）时准确感知单词的普遍能力至关重要。该项目的研究预计将产生指导阅读障碍识别和不同类型障碍之间区别的知识，从而指导阅读障碍的临床和教学方法。该项目将使用头皮记录脑电图（EEG）来观察健康年轻人阅读句子时的神经振荡活动，反映神经网络的动态耦合和解耦合。根据计算语言模型，参与者阅读的句子将被操纵，以便某些句子上下文呈现可能出现的特定单词。 在这些条件下，将检查语言输入中单词出现之前和之后发生的大脑活动，以寻找预测的证据。该项目将研究神经振荡在将即将出现的单词的预测从高级大脑区域传递到低级视觉皮层区域以及评估预测与自下而上的感官输入之间的匹配方面的作用。研究人员还将研究参与此类预测活动的语言环境和处理需求的类型。这项研究的结果预计将通过结合通常单独研究的三个关键研究领域对认知神经科学做出重大贡献：语言处理、认知预测和神经振荡。该项目将开发新的分析方法来表征脑电图数据中的神经振荡，该方法将被开发成与其他研究人员共享的工具箱。该项目还将生成阅读自然故事期间大脑活动的丰富数据库，该数据库将作为进一步研究的资源与科学界共享。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。