The role of uncertainty in human motor learning and adaptation

摘要 在这项拟议的研究中，我们将描述神经系统如何处理不确定性。 在运动学习方面。对象将光标从开始位置移动到目标位置 虚拟环境。视觉反馈将被操纵以引起关于状态的不确定性， 反馈，或者它的相关性。我们的实验将集中于探索由此产生的逐个试验 学习。关于不确定性对运动学习的影响的拟议分析是由 从统计框架中得出的强有力的假设。有了预期的结果，我们将要么 能够驳斥贝叶斯模型，这些模型将不确定性如何影响学习或驳斥状态 认为不确定性对学习没有影响的空间模型。重要的是，不确定性 是人类行为的中心因素，定量地理解它的作用是重要的 超越了任何特定的建模框架。这项研究计划的长期目标是 从计算的角度回答运动学习中的基本和重要问题 并为改善运动康复提供工具。神经系统需要在 在日常生活的功能中存在不确定性，在疾病存在的情况下。 基于统计学的洞察力，这项研究将测试影响紧张方式的关键因素 系统从视觉运动错误中学习。具体地说，我们将了解时代是如何， 错误的大小和视觉表现会影响运动学习。机器人领域的选择 修复方法导致如何使错误反馈变得有效和相关 通过最大限度地进行研究测试。当我们提出基本问题时，结果是 预计将推广到广泛的运动学习任务。

Abstract In the proposed research, we will characterize how the nervous system deals with uncertainty in motor learning. Subjects will move a cursor from a starting position to a target position in a virtual environment. Visual feedback will be manipulated to induce uncertainty about the state, the feedback, or its relevance. Our experiments will focus on probing the resulting trial-by-trial learning. The proposed analysis of the influence of uncertainty on motor learning is driven by strong hypotheses derived from a statistical framework. With the expected results we will either be able to refute Bayesian models that formalize how uncertainty affects learning or refute state space models that assume that uncertainty has no influence on learning. Importantly,uncertainty is a central factor for human behavior and quantitatively understanding its role is important beyond any specific modeling framework. The long term objectives of this research program are to answer basic and important questions in motor learning from a computational perspective and to provide tools for improving motor rehabilitation. The nervous system needs to learn in the presence of uncertainty within the functions of everyday life, and in the presence of disease. Based on statistical insights, this study will test key factors that affect the way the nervous system learns from visuo-motor errors. Specifically, we will understand how the times, magnitudes and the visual presentation of errors affect motor learning. Choices in robotic rehabilitation approaches result in how error feedback can be made effective and relevant through maximizing research testing. As we ask fundamental questions, the results are expected to generalize to a wide range of motor learning tasks.