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自我中心视觉目标位置与速度编码:眼肌本体感觉的作用

Egocentric visual target position and velocity coding: role of ocular muscle proprioception.

作者信息

Gauthier G M, Vercher J L, Blouin J

机构信息

Laboratoire de Contrôles Sensorimoteurs, Université de Provence, Marseille, France.

出版信息

Ann Biomed Eng. 1995 Jul-Aug;23(4):423-35. doi: 10.1007/BF02584442.

Abstract

Limited knowledge is available regarding the processes by which the brain codes the velocity of visual targets with respect to the observer. Two models have been previously proposed to describe the visual target localization mechanism. Both assume that the necessary information is derived from the coding of the position of the eye in the orbit, either through a copy of the muscular activation (out flow model) or through eye muscle proprioception (in flow model). Eye velocity coding might be derived from velocity sensitive ocular muscle proprioceptors or from position coding signals through differentiation. We used techniques based on manual pointing and manual tracking of visual target, combined with passive deviation of one covered eye, to demonstrate that ocular muscle proprioception is involved in (i) eye-in-head position coding, hence in target localization function; (ii) long-term maintenance of ocular alignment (phoria); and (iii) sensing of visual target velocity with respect to the head. These observations support other data now available, describing the processes by which the brain codes position and velocity of visual targets. Such findings might interest engineers in the field of robotics who are facing the problem of providing robots with the ability to sense object position and velocity in order to create an internal model of their working environment.

摘要

关于大脑对视觉目标相对于观察者的速度进行编码的过程,目前所知有限。此前曾提出两种模型来描述视觉目标定位机制。这两种模型都假定必要信息来自于眼眶中眼睛位置的编码,要么通过肌肉激活的副本(流出模型),要么通过眼肌本体感觉(流入模型)。眼睛速度编码可能来自对速度敏感的眼肌本体感受器,或者通过微分从位置编码信号中得出。我们使用基于手动指向和手动跟踪视觉目标,并结合对一只被遮盖眼睛进行被动偏斜的技术,来证明眼肌本体感觉参与了:(i)头中眼位置编码,进而参与目标定位功能;(ii)眼位对准(隐斜)的长期维持;以及(iii)对视觉目标相对于头部速度的感知。这些观察结果支持了现有的其他数据,这些数据描述了大脑对视觉目标位置和速度进行编码的过程。这样的发现可能会引起机器人技术领域的工程师们的兴趣,他们正面临着为机器人赋予感知物体位置和速度的能力,以便创建其工作环境内部模型的问题。

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