VISUAL INFORMATION FOR REACH AND GRASP

便于获取和掌握的视觉信息

• 批准号: 2711184
• 负责人: DANIEL J KERSTEN
• 金额: $ 9.48万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-01 至 2000-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2711184
• 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

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：关于视觉信息如何决定感知深度的知识