Trial-by-Trial Human Generalization of Sense into Action

描述（由申请人提供）：尽管运动学习对基础和临床神经科学至关重要，但我们对神经系统在感官转化为适应性控制中所起的定量作用知之甚少。这里提出的实验将挑战正常的人类受试者与扰动的不同强度，持续时间，频率的应用程序，偏见和空间复杂性内和跨运动。这些扰动的可变性将产生一个模板，我们将用它来识别逐个试验适应中的微妙之处。这些审讯，再加上新的状态空间分析，将使一个彻底的理解特定的感官经验转化为即时的，增量的适应预测控制，大大提高了我们对人类运动适应的定量理解。经验使我们能够建立我们运动环境的内部动态模型。这种内部动态适应的研究者假设了学习的两个组成部分：从以前的运动中提取错误信号，以及将这种错误应用于指定或概括整个运动空间的学习。迄今为止，人类一次又一次的适应性试验表明，适应性不断地与感知误差成比例，并在运动空间中广泛推广。然而，来自PI的初步结果发现了学习的两个组成部分的惊人的灵活性：感觉反馈可以引起与运动错误显著不成比例的适应，并且环境可以引起整个运动空间的泛化缩小。在这里，我们建议确定必要的感官体验，以诱导这些新建立的变化，人们执行的基本计算，将单一的运动感觉到增量适应。这些结果将阐明神经系统如何执行实时信号处理以提高运动性能。由此产生的模型将有助于神经科学和生物建模社区更好地将行为与其潜在的生理基础联系起来。这些见解也将有助于研究正常运动控制的全部功能，以及疾病状态下控制如何失败。我们的目标是制定康复如何最佳地帮助患者超越临床训练，以改善日常生活中的运动功能的科学基础。

DESCRIPTION (provided by applicant): Despite the centrality of motor learning to basic and clinical neuroscience, we know very little about the quantitative role neural systems play in the transformation of senses into adapted control. The experiments presented here will challenge normal human subjects with perturbations of varying strengths, durations, frequency of application, biases, and spatial complexities both within and across movements. The variability of these perturbations will generate a template with which we will identify subtleties in trial-by-trial adaptation. These interrogations, coupled with novel state space analyses, will enable a thorough understanding of the transformation of specific sensory experiences into immediate, incremental adaptations in predictive control, greatly enhancing our quantitative understanding of human motor adaptation. Experience enables us to build internal dynamic models of our movement environment. Investigators of this internal dynamic adaptation have hypothesized two components of learning: the abstraction of an error signal from previous movements and the application of this error to either specify or generalize learning across movement space. Human trial-by-trial adaptation has, to date, suggested that adaptation constantly scales with sensed error and generalizes broadly across movement space. However, preliminary results from the PI have discovered surprising flexibility in both components of learning: sensory feedback can induce adaptation strikingly disproportional to movement error, and environments can induce narrowing of generalization across movement space. Here we propose to identify the necessary sensory experiences to induce these newly established changes in the fundamental computations people execute to transform single movement sense into incremental adaptation. These results will illuminate how the nervous system performs real-timed signal processing to improve motor performance. The resultant models will help the neuroscience and biological modeling community to better connect behavior to its underlying physiological basis. These insights will also be of use to investigate the full repertoire of normal motor control and how control fails in disease states. We aim to formulate the scientific basis of how rehabilitation can optimally help patients generalize beyond clinical training to improve motor function in their daily lives.