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：视觉信息如何确定感知深度的知识在过去的几十年中，形状已经大大发展。我们知道少，但是，关于这些知识与日常任务的性能如何相关。人们普遍认为，感知的深度对于确定很重要大规模的空间布局用于各种动作，而这种形状感知对于对象识别很重要。但是，视觉深度和形状的信息还提供了关键的图像测量用于编程触手可及的手。尽管很重要预性的愿景，我们对多重的多次了解很少知道视觉信息的来源用于手动运动。一个中心问题是形状和深度的知觉判断的程度达到和抓握的措施。一个常见的假设是运动计划利用了完整的感知“重建” 场景编程其动作。调查人员将批判性检查在一系列实验中的假设。特别是，他计划1）比较视觉处理以与有意识的感知相比在提示冲突情况下的深度判断； 2）了解如何深度，大小和方向的多个提示结合在一起以确定覆盖范围和掌握； 3）检查依赖对物体学习观点的作用抓握。这些研究将利用最近获得的实验室允许计算机控制的仪器对两者的真实表示和合成视觉刺激以及实时监控动作。