Bayesian integration in sensorimotor learning
Bayesian integration in sensorimotor learning
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DOI：
10.1038/nature02169
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发表时间：
2004-01-15
期刊：
影响因子：
64.8
通讯作者：
Wolpert, DM
中科院分区：
文献类型：
--
作者：
Körding, KP;Wolpert, DM
当我们学习一项新的运动技能时，例如打一个接近的网球，我们的传感器和任务都具有可变性。我们的传感器提供的有关球速度的信息并不完善，因此我们只能估计它。组合来自多种模式的信息可以减少该估计中的误差(1-4)。在较长的时间尺度上，并非所有速度的先验概率都是相同的，并且在比赛过程中将会存在速度的概率分布。根据贝叶斯理论（5,6），最佳估计是通过将速度分布信息（先验）与感觉反馈的证据相结合而产生的。随着不确定性的增加，当在雾中或黄昏时玩耍时，系统应该越来越依赖先验知识。要使用贝叶斯策略，大脑需要表示感觉反馈中的先验分布和不确定性水平。在这里，我们控制新感觉运动任务的统计变化并操纵感觉反馈的不确定性。我们表明，受试者在内部代表了任务的统计分布及其感觉不确定性，并以与性能优化贝叶斯过程一致的方式将它们组合起来（4,5）。因此，中枢神经系统在感觉运动学习过程中采用概率模型。
When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate(1-4). On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory(5,6), an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process(4,5). The central nervous system therefore employs probabilistic models during sensorimotor learning.