Körding Konrad P, Wolpert Daniel M
Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, London, UK.
Novartis Found Symp. 2006;270:191-8; discussion 198-202, 232-7.
Uncertainty constitutes a fundamental constraint on human sensorimotor control. Our sensors are noisy and do not provide perfect information about all the properties of the world. Moreover, our muscles generate noisy outputs and many tasks we perform vary in an unpredictable way. Here we review the computations that the CNS uses in the face of such sensory, motor and task uncertainty. We show that the CNS reduces the uncertainty in estimates about the state of the world by using a Bayesian combination of prior knowledge and sensory feedback. It is shown that these mechanisms generalize to state estimation of ones own body during movement. We review how the CNS optimizes decisions based on these estimates, examining the error criterion that people optimize when performing targeted movements. Finally, we describe how signal-dependent noise on the motor output places constraints on performance. Goal-directed movement arises from a model in which the statistics of our actions are optimized. Together these studies provide a probabilistic framework for sensorimotor control.
不确定性是人类感觉运动控制的一个基本限制因素。我们的传感器存在噪声，无法提供关于世界所有属性的完美信息。此外，我们的肌肉产生有噪声的输出，并且我们执行的许多任务会以不可预测的方式变化。在这里，我们回顾中枢神经系统（CNS）在面对此类感觉、运动和任务不确定性时所使用的计算方法。我们表明，中枢神经系统通过将先验知识和感觉反馈进行贝叶斯组合，来降低对世界状态估计中的不确定性。结果表明，这些机制可推广到运动过程中对自身身体状态的估计。我们回顾中枢神经系统如何基于这些估计来优化决策，研究人们在进行目标导向运动时所优化的误差准则。最后，我们描述运动输出上的信号相关噪声如何对性能施加限制。目标导向运动源自一个对我们行动的统计数据进行优化的模型。这些研究共同为感觉运动控制提供了一个概率框架。
