Predicting Speech: Cortical Oscillations and Dynamics

预测语音：皮质振荡和动力学

• 批准号: RGPIN-2017-06053
• 负责人: Monahan, Philip
• 金额: $ 1.68万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679379
• 关键词: Predicting; Speech; Cortical; Oscillations; Dynamics

Speech is a complex and variable medium, and yet when we listen in our native language, comprehension appears effortless and automatic. Simultaneously, through experience with our native language, we have a wealth of knowledge about how it is structured at the level of sounds, words, and sentences. Recent work has begun to emphasize the importance of this knowledge during real-time language processing. One overarching mechanism that could shape the effortless nature of language comprehension is leveraging prior knowledge of our language's structure to predict upcoming information at all levels of linguistic analysis, thereby facilitating the processing of future information. My five-year research program is to identify how this prediction is encoded in the temporal and spatial dynamics of neural oscillations during speech perception: the earliest stages of language comprehension. In particular, this research builds on recent findings which have identified a brain marker of prediction during speech perception.******My long-term research goal is to incorporate findings from linguistics with cognitive neuroscience to understand the brain bases of speech perception. My short-term research goals focus on the “when”, “what” and “where” of prediction during speech perception and are centred around the following three research objectives:*** ***1. Spatial Localization of Prediction: Where in the brain is this brain marker for prediction, does it overlap with other networks involved in speech perception, and how does it relate to other indices of speech recognition?***2. Speech Cues that Drive Prediction: What aspects of speech sound representations contribute to driving this predictive mechanism? While my previous work has demonstrated a role for the contrastive articulatory properties of speech sounds, could other cues also be at play, including speech sound co-occurrences, statistical cues, and word structure?***3. Language Specificity of Prediction: Are these predictive mechanisms general or do they rely solely on the linguistic properties of speech sounds: music vs. speech, different speakers, different speaking rates? Is the prediction of different types of information encoded in distinct oscillatory brain patterns?******This research has applications for the construction of biologically plausible circuits that model language processing, necessary for Artificial Intelligence research, aid in the rehabilitation of various speech language motor-deficits by pinpointing the relevant speech properties necessary for the accurate perception and production, as well as in aphasias, where the deficit may partially result from an inability of aphasic brains to synchronize speech cues when talking to another individual, the latter two having major impacts in Canada both as we become more sensitive to speech-motor deficits in children and as our population ages, and with it, the number of cases of aphasia.

言语是一种复杂多变的媒介，但当我们用母语倾听时，理解似乎毫不费力，而且是自动的。同时，通过母语的经验，我们对母语在声音、单词和句子层面上的结构有了丰富的了解。最近的工作已经开始强调这方面的知识在实时语言处理的重要性。一个可以塑造语言理解的轻松性质的总体机制是利用我们语言结构的先验知识来预测语言分析各个层面的即将到来的信息，从而促进对未来信息的处理。我的五年研究计划是确定这种预测是如何编码在语音感知期间神经振荡的时间和空间动态中的：语言理解的最早阶段。特别是，这项研究建立在最近的发现基础上，这些发现已经确定了言语感知过程中预测的大脑标记。我的长期研究目标是将语言学的发现与认知神经科学相结合，以了解言语感知的大脑基础。我的短期研究目标集中在言语感知过程中预测的“何时”、“什么”和“何地”，并围绕以下三个研究目标：**1。预测的空间定位：这个预测的大脑标记在大脑的什么地方，它是否与涉及语音感知的其他网络重叠，以及它如何与语音识别的其他指标相关？2.驱动预测的语音线索：语音声音表征的哪些方面有助于驱动这种预测机制？虽然我以前的工作已经证明了语音的对比发音特性的作用，但其他线索也可能起作用，包括语音同现，统计线索和单词结构？3.预测的语言特异性：这些预测机制是通用的，还是仅仅依赖于语音的语言特性：音乐与语音，不同的说话者，不同的语速？对不同类型信息的预测是否编码在不同的振荡大脑模式中？这项研究应用于构建模拟语言处理的生物学上合理的电路，这是人工智能研究所必需的，通过精确定位准确感知和生产所需的相关语音特性来帮助各种语言运动缺陷的康复，以及失语症，其中缺陷可能部分地由失语症大脑在与另一个人谈话时不能同步语音提示引起，后两个在加拿大有着重要的影响，因为我们对儿童的语言运动缺陷越来越敏感，随着我们人口的老龄化，失语症的病例数量也随之增加。