Trial-by-Trial Human Generalization of Sense into Action

描述(申请人提供)：尽管运动学习是基础和临床神经科学的中心，但我们对神经系统在将感觉转化为适应的控制中所起的定量作用知之甚少。这里介绍的实验将用不同强度、持续时间、应用频率、偏见和运动内部和跨运动的空间复杂性的扰动来挑战正常的人类受试者。这些扰动的可变性将产生一个模板，我们将利用该模板来识别逐个试验适应中的微妙之处。这些询问，再加上新颖的状态空间分析，将使我们能够彻底理解特定感觉经验在预测控制中如何转变为即时的增量适应，从而极大地增强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人员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，以及应用这个错误来指定或概括跨运动空间的学习。到目前为止，人类一次又一次的适应表明，适应随着感觉到的错误而不断扩大，并在运动空间中广泛推广。然而，PI的初步结果发现，学习的两个组成部分都具有惊人的灵活性：感觉反馈可以导致适应与运动错误显著不成比例，而环境可以导致跨运动空间的泛化缩小。在这里，我们建议确定必要的感觉经验，以诱导这些新建立的基本计算变化，人们执行这些基本计算，将单一的运动感觉转变为增量适应。这些结果将阐明神经系统如何进行实时信号处理以提高运动性能。由此产生的模型将帮助神经科学和生物模型界更好地将行为与其潜在的生理基础联系起来。这些洞察力也将有助于研究正常运动控制的全部曲目，以及疾病状态下控制如何失败。我们的目标是为康复如何最好地帮助患者超越临床训练而改善日常生活中的运动功能提供科学基础。

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.