Testing Bayesian models of human coincidence timing
Testing Bayesian models of human coincidence timing
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DOI：
10.1152/jn.01168.2004
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发表时间：
2005-07-01
影响因子：
2.5
通讯作者：
Nakajima, Y
中科院分区：
文献类型：
--
作者：
Miyazaki, M;Nozaki, D;Nakajima, Y
感觉运动控制任务通常需要准确估计外部目标到达的时间(例如，当击打投球时)。人类计时过程的传统研究忽略了目标计时的随机特征：例如，投球的速度通常不是恒定的，而是可变的。有趣的是，基于贝叶斯理论，最近的研究表明，人类的感觉运动系统通过将感觉信息与目标变化的概率结构的先验知识相结合来实现最优估计。在这项研究中，我们通过操纵目标计时的逐次试验变异性(即先验分布)，测试了在执行符合计时类型的感觉运动任务时是否也实现了贝叶斯整合。结果，在数百次学习试验中，受试者能够根据最优贝叶斯模型预测的先验分布的宽度产生系统的计时行为。考虑到以前的研究表明，人类感觉运动系统在间隔和力分级任务中使用贝叶斯积分，我们的结果表明，贝叶斯积分是人类感觉运动控制的各个方面的基础。此外，值得注意的是，当先验分布从宽到窄切换时，受试者都可以调整他们的行为，反之亦然，尽管前者的调整速度较慢。在与前人观察结果进行比较的基础上，我们讨论了贝叶斯感觉运动学习的灵活性和适应性。
A sensorimotor control task often requires an accurate estimation of the timing of the arrival of an external target (e.g., when hitting a pitched ball). Conventional studies of human timing processes have ignored the stochastic features of target timing: e.g., the speed of the pitched ball is not generally constant, but is variable. Interestingly, based on Bayesian theory, it has been recently shown that the human sensorimotor system achieves the optimal estimation by integrating sensory information with prior knowledge of the probabilistic structure of the target variation. In this study, we tested whether Bayesian integration is also implemented while performing a coincidence-timing type of sensorimotor task by manipulating the trial-by-trial variability (i.e., the prior distribution) of the target timing. As a result, within several hundred trials of learning, subjects were able to generate systematic timing behavior according to the width of the prior distribution, as predicted by the optimal Bayesian model. Considering the previous studies showing that the human sensorimotor system uses Bayesian integration in spacing and force-grading tasks, our result indicates that Bayesian integration is fundamental to all aspects of human sensorimotor control. Moreover, it was noteworthy that the subjects could adjust their behavior both when the prior distribution was switched from wide to narrow and vice versa, although the adjustment was slower in the former case. Based on a comparison with observations in a previous study, we discuss the flexibility and adaptability of Bayesian sensorimotor learning.