Characterizing the generative mechanisms underlying the cortical tracking of natural speech

表征自然语音皮质跟踪背后的生成机制

• 批准号: 10710717
• 负责人: Edmund Lalor
• 金额: $ 38.23万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-07 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710717
• 关键词: Acoustics; Address; Adult; Area; Attention; Brain; Characteristics; Clinical; Clinical Research; Cognitive; Computer Models; Consensus; Data; Development; Dimensions; Electroencephalography; Electrophysiology (science); Experimental Designs; Frequencies; Future; Human; Knowledge; Life; Linear Models; Linguistics; Literature; Measures; Methods; Modeling; Nature; Neurons; Neurosciences; Patients; Pattern; Periodicity; Phase; Physiological; Population; Process; Risk; Running; Scalp structure; Sensory; Series; Side; Signal Transduction; Speech; Speech Acoustics; Stimulus; Structure; Testing; Work; auditory stimulus; design; experimental study; insight; language processing; lens; neural; neural network; neurophysiology; neurosurgery; response; speech processing; statistics; theories

PROJECT SUMMARY Speech is central to human life. Yet how the human brain converts patterns of acoustic speech energy into meaning remains unclear. This is particularly true for natural, continuous speech, which requires us to efficiently parse and process speech at multiple timescales in the context of our ongoing conversation and situational knowledge. Much progress has been made on this problem in recent years by the realization that the dynamics of cortical activity track those of natural speech. This has led to the development of new methods to study the neurophysiology of speech processing in more naturalistic paradigms. However, the field still lacks consensus regarding the precise physiological mechanisms and neurostructural origins of this tracking. In particular, two contrasting theories have been advanced that attempt to explain the genesis of this phenomenon. The first proposes that the quasi-rhythmic nature of continuous speech “entrains” intrinsic, endogenous oscillations in the brain as a way to parse that continuous speech into smaller units for further (linguistic) processing. Meanwhile, the second proposes that the cortical tracking of speech reflects the summation of a series of transient evoked responses from hierarchically organized neural networks that are tuned to the different acoustic and linguistic features of speech. The contrast between these two ideas is reflected in the emergence of two almost completely non-overlapping literatures in the field of speech electrophysiology. This is highly problematic as the design and interpretation of most studies on this important topic are now filtered through either one or the other of these theoretical lenses. Without a clear understanding of the true mechanisms involved, our collective work on this topic thus runs the risk of being distorted through misconception. This project aims to address this urgent need by critically examining these two frameworks side by side. We aim to do so by collecting scalp EEG from human adults as they listen to natural and manipulated speech. These manipulations will involve varying speech across several dimensions that should maximize the differences in the predictions made by each theory. We specifically aim to test the hypothesis that both evoked responses and entrained oscillations contribute to the cortical tracking of speech, with their relative contributions varying as a function of the statistics of the speech and attention. We will test this hypothesis by analyzing the EEG data with reference to computational models of both evoked and oscillatory activity. Furthermore, we will use the same analytical framework to model signals from different regions of the speech/language processing hierarchy acquired using intracranial recordings in neurosurgical patients. This will allow us to test the deeper hypothesis that evoked and oscillatory mechanisms operate differently in different cortical areas. We will also leverage these intracranial findings to develop a fuller understanding of our scalp EEG signals with a view to strengthening the applicability of our work to future cognitive and clinical research.

项目总结 语言是人类生活的中心。然而，人类大脑如何将声学语音能量模式转换为 其含义尚不清楚。对于自然的、连续的语音来说尤其如此，这需要我们有效地 在我们正在进行的对话和情景中，在多个时间尺度上分析和处理语音 知识。近年来在这个问题上已经取得了很大的进展，这是因为人们意识到 大脑皮质活动跟踪那些自然语言的活动。这导致了新方法的发展，以研究 更自然主义范式中的语音处理神经生理学。然而，该领域仍然缺乏共识。 关于这种追踪的确切生理机制和神经结构来源。特别是两个 已经提出了不同的理论，试图解释这一现象的起源。第一 提出连续言语的准节奏性“包含”了内在的、内生的振荡。 大脑作为一种方式，将连续的语音解析成更小的单位，以便进一步(语言)处理。同时， 第二种观点认为，语音的大脑皮层追踪反映了一系列瞬间诱发的总和 来自分层组织的神经网络的响应，这些神经网络调整到不同的声学和语言 语言的特点。这两种观念之间的反差几乎完全体现在两种观念的出现上。 语音电生理学领域中的非重叠文献。这是非常有问题的，因为设计和 对这一重要主题的大多数研究的解释现在都是通过其中的一种或另一种进行过滤 理论上的镜片。在不清楚所涉及的真正机制的情况下，我们在这方面的集体工作 因此，话题存在被误解而被扭曲的风险。该项目旨在解决这一紧迫需求 通过批判性地并排检查这两个框架。我们的目标是通过收集人类头皮脑电来做到这一点 成年人听自然的和人为的语言。这些操作将涉及不同的语音 应该最大限度地扩大每种理论预测差异的几个维度。我们特别指出 目的验证诱发反应和诱发振荡共同作用于大脑皮层追踪的假设 它们的相对贡献随着言语和注意力的统计而变化。我们 我将通过参考诱发和诱发的计算模型分析脑电数据来检验这一假设 振荡活动。此外，我们将使用相同的分析框架来模拟来自不同 神经外科中使用颅内记录获得的语音/语言处理层级的区域 病人。这将使我们能够测试更深层次的假设，即诱发和振荡机制起作用 在不同的皮质区域有不同的反应。我们还将利用这些颅内发现来开发更全面的 了解我们的头皮脑电信号，以期加强我们的工作对未来的适用性 认知和临床研究。