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贝叶斯方法在运动控制中的感觉整合。

Bayesian approaches to sensory integration for motor control.

机构信息

Northwestern University, Rehabilitation Institute of Chicago, Chicago, IL, USA.

出版信息

Wiley Interdiscip Rev Cogn Sci. 2011 Jul;2(4):419-428. doi: 10.1002/wcs.125. Epub 2011 Feb 9.

Abstract

The processing of sensory information is fundamental to the basic operation of the nervous system. Our nervous system uses this sensory information to gain knowledge of our bodies and the world around us. This knowledge is of great importance as it provides the coherent and accurate information necessary for successful motor control. Yet, all this knowledge is of an uncertain nature because we obtain information only through our noisy sensors. We are thus faced with the problem of integrating many uncertain pieces of information into estimates of the properties of our bodies and the surrounding world. Bayesian approaches to estimation formalize the problem of how this uncertain information should be integrated. Utilizing this approach, many studies make predictions that faithfully predict human sensorimotor behavior. WIREs Cogni Sci 2011 2 419-428 DOI: 10.1002/wcs.125 For further resources related to this article, please visit the WIREs website.

摘要

感觉信息的处理是神经系统基本运作的基础。我们的神经系统利用这些感觉信息来了解我们的身体和周围的世界。这些知识非常重要,因为它提供了成功进行运动控制所需的连贯和准确的信息。然而,由于我们只能通过嘈杂的传感器来获取信息,所以所有这些知识都具有不确定性。因此,我们面临着将许多不确定的信息整合到对我们身体和周围世界的属性的估计中的问题。贝叶斯估计方法将如何整合这些不确定信息的问题形式化。利用这种方法,许多研究做出了准确预测人类感觉运动行为的预测。WIREs Cogni Sci 2011 2 419-428 DOI: 10.1002/wcs.125 如需本文相关资源,请访问 WIREs 网站。

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