Visual Perception of Spatial Structure

空间结构的视觉感知

• 批准号: 8719934
• 负责人: Stephen Palmer
• 金额: $ 12.5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-03-15 至 1991-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8719934&HistoricalAwards=false
• 关键词: Visual; Perception; Spatial; Structure

Almost every object one sees is visually incomplete in the sense that at least part of it is hidden from view by its own visible surfaces and/or by those of closer objects. This project will study how people's visual systems accomplish this task. The research will employ the "primed matching paradigm", in which people match two figures, as quickly as possible, for physical identity shortly after seeing an initial "priming" display that depicts a figure partly hidden from view. The pattern of response times to different shapes in this task can then be used to infer how people have completed the image of the hidden object in the priming display. The results should be useful in answering questions about the process of visual completion and in constraining theories about how that process unfolds in time. Such questions include the following: (1) How long does the completion process take? (2) How "completely" are objects completed? (3) Are the texture and color of the object completed as well as its shape? (4) What factors influence the way that shapes are completed? This methodology can be extended to questions concerning other important problems of visual perception, such as how people see depth in scenes and how they perceive the transparency of objects.

几乎每个人看到的物体在视觉上都是不完整的 它至少有一部分被它自己的可见光所隐藏 表面和/或由更近的物体的表面和/或由更近的物体的表面和/或由更近的物体的表面和/或由更近的物体的表面。 该项目将 研究人类的视觉系统如何完成这项任务。 的 研究将采用“启动匹配范式”，其中 人们尽快将两个数字进行身体匹配 在看到最初的“启动”显示后不久， 描绘了一个部分隐藏在视野中的人物。 的格局 然后可以使用此任务中对不同形状的响应时间 来推断人们是如何完成隐藏物体的图像的 在启动显示屏上。 结果应该是有用的， 回答有关视觉完成过程的问题， 关于这个过程如何在时间上展开的理论。 这些问题包括：（1） 完成过程需要？ (2)对象有多“完全” 完成？ (3)物体的纹理和颜色是否完成 还有它的形状 (4)哪些因素会影响 形状已完成？ 这种方法可以扩展到 关于视觉的其他重要问题的问题 感知，例如人们如何在场景中看到深度， 感知物体的透明度。