Neural basis of auditory-motor adaptation

项目概要/摘要 言语运动系统显示出基于言语运动的快速有效地学习动作的显著能力。 听觉反馈这种基于听觉反馈的学习的一个常见表现是听觉-运动 适应，这可以在涉及语音声学变化的日常说话情况下观察到， 说话者必须学会补偿这些噪声以保持可理解的讲话。尽管其基本 然而，在维持和微调语音产生方面， 对运动神经适应的理解。事实上，即使是适应的基本机制，例如 因素驱动学习（即，适应于补偿像共振峰移位的听觉扰动），以及 取消学习（即，在移除先前施加的听觉刺激之后返回到基线运动 扰动）过程在很大程度上仍不清楚。以前的研究假设， 自适应可以由最小化听觉预测误差（预测的听觉预测误差之间的差异 运动指令和实际听觉反馈的结果。然而，到目前为止，这一基本 令人惊讶的是，这一假设仍然没有得到充分的研究。因此，这项研究结合了计算 (Aim 1）和神经生理学（目的2）的方法，直接检查听觉预测错误是否 驱动言语习得的主要因素--运动适应。作为第一个具体目标的一部分， 语言运动控制的计算模型的一个版本，言语中任务的反馈意识控制 （FACTS）将被开发来模拟神经运动适应。新版本的FACTS将接受测试 并验证，以检查听觉预测错误的适应机制的作用。目标2将使用 脑磁图成像检查言语诱发抑制（SIS）-听觉抑制 与被动听到的语言相比，对自我产生的语言的反应被认为是 表示听觉预测误差。在这里，SIS的全脑数据驱动分析将在一个 一系列的运动神经适应任务。在这两个目标中，对光谱和运动的适应性都是一个重要的因素。 扰动（即，对扰动共振峰频率的适应）和时间扰动（即，适应 延长的声音开始时间）将被检查。总之，这种组合的计算建模和 神经生理学的方法将提供关键的机械见解的神经基础， 适应直接结果可能为听觉的关键作用提供第一个直接证据 预测错误的运动适应，具有深远的影响，理论和计算 语言运动控制模型这部作品的广泛影响可能为小说奠定了坚实的基础。 治疗策略，以提高临床人群的言语运动治疗效果。

PROJECT SUMMARY/ABSTRACT The speech motor system shows a remarkable ability to quickly and efficiently learn movements based on auditory feedback. One common manifestation of such auditory feedback-based learning is auditory-motor adaptation, which can be observed in everyday speaking situations that involve changes in speech acoustics, for which speakers must learn to compensate in order to maintain intelligible speech. Despite its fundamental nature in maintaining and fine-tuning speech production, however, there exist crucial knowledge gaps in the understanding of auditory-motor adaptation. In fact, even basic mechanisms in adaptation, such as which factors drive the learning (i.e., adapting to compensate for auditory perturbations like formant shifts) and unlearning (i.e., returning to the baseline movements upon the removal of the previously applied auditory perturbation) processes remain largely unclear. Previous studies have hypothesized that auditory-motor adaptation may result from minimizing auditory prediction errors–the discrepancies between predicted auditory consequences of motor commands and actual auditory feedback. To date, however, this fundamental hypothesis remains surprisingly underinvestigated. Hence, this proposed research combines computational (Aim 1) and neurophysiological (Aim 2) approaches to directly examine whether auditory prediction errors are the primary factor driving speech auditory-motor adaptation. As a part of the first specific aim, several new versions of a computational model of speech motor control, Feedback Awareness Control of Tasks in Speech (FACTS) will be developed to simulate auditory-motor adaptation. The new versions of FACTS will be tested and validated in order to examine the mechanistic role of auditory prediction errors in adaptation. Aim 2 will use magnetoencephalography imaging to examine Speaking-Induced Suppression (SIS)—suppression of auditory responses to self-produced speech compared to the responses to passively heard speech—which is thought to represent auditory prediction errors. Here, whole-brain data driven analyses of SIS will be examined during a series of auditory-motor adaptation tasks. In both aims, auditory-motor adaptation to both spectral perturbations (i.e., adaptation to perturbed formant frequencies) and temporal perturbations (i.e., adaptation to lengthened voice onset time) will be examined. Together, this combined computational modeling and neurophysiological approach will offer key mechanistic insights into the neural basis of auditory-motor adaptation. The immediate outcome may provide the first direct evidence for the critical role of auditory prediction errors in auditory-motor adaptation, with profound implications for theoretical and computational models of speech motor control. The broader impact of the work may establish a solid foundation for novel treatment strategies to improve speech motor treatment efficacy in clinical populations.