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VISUAL INFORMATION FOR REACH AND GRASP

便于获取和掌握的视觉信息

基本信息

批准号：
2888522
负责人：
DANIEL J KERSTEN
金额：
$ 9.86万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
1996
资助国家：
美国
起止时间：
1996-07-01 至 2001-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/2888522
关键词：
arm behavioral /social science research tag body movement cues hand human subject neural information processing size perception visual depth perception visual feedback visual perception visual stimulus

项目摘要

DESCRIPTION: Knowledge of how visual information determines perceived depth and shape has grown considerably in the past few decades. We know less, however, about how this knowledge relates to performance in everyday tasks. It is widely appreciated that perceived depth is important for determining large scale spatial layout for a variety of actions, and that shape perception is important for object recognition. However, the visual information for depth and shape also provides critical image measurements for programming reach and grasp by the hand. Despite the importance of vision for prehension, we know surprisingly little about how multiple sources of visual information are used for hand movements. A central issue is the degree to which perceptual judgements of shape and depth predict measures of reaching and grasping. A common working hypothesis is that motor planning makes use of the full perceptual "reconstruction" of the scene to program its movements. The investigator will critically examine this assumption in a series of experiments. In particular, he plans to 1) compare visual processing for reaching with that for conscious perceptual judgements of depth under cue conflict situations; 2) understand how multiple cues for depth, size, and orientation combine to determine reach and grasp; 3) examine the role of dependence on learned views of an object for grasping. These studies will make use of recently acquired laboratory instrumentation which allows computer-controlled presentation of both real and synthetic visual stimuli together with real-time monitoring of movements.

描述：了解视觉信息如何决定感知深度在过去的几十年里，形状有了很大的发展。我们知道的更少，然而，关于这些知识如何与日常任务的表现相关。人们普遍认识到感知深度对于确定适合各种活动的大尺度空间布局，以及形状感知对于物体识别很重要。然而，视觉深度和形状信息还提供关键的图像测量用于用手进行编程和抓握。尽管很重要令人惊讶的是，我们对如何多重理解知之甚少视觉信息源用于手部动作。一个中心问题是对形状和深度的感知判断预测的程度达到和抓握的措施。一个常见的工作假设是运动规划利用了对运动的全面感知“重建” 场景来对其动作进行编程。调查员将严格审查这个假设是在一系列实验中得出的。他特别计划：1) 比较达到目标的视觉处理与有意识知觉的视觉处理提示冲突情况下的深度判断； 2）了解如何深度、大小和方向的多种线索结合起来确定到达范围并把握； 3）检查对学习到的对象视图的依赖的作用用于抓握。这些研究将利用最近获得的实验室允许计算机控制呈现真实的仪器和合成视觉刺激以及实时监控动作。