Implicit Measures of Shape Learning and Shape Perception

感知似乎毫不费力，但它仍然是最具挑战性的科学课题之一。例如，我们如何在视觉感知上将沙滩椅与海滩分开，或者与拥挤的海滩场景中的人分开？事实上，我们如何在一个场景中看到与另一个和它们的背景分开的物体，这仍然是一个普遍的难题。最近的计算机模型只有在允许过去的经验发挥作用的情况下，才能成功地将图像中的物体分离出来。在国家科学基金会的支持下，玛丽·彼得森博士将沿着这条线索研究人类感知中的场景分离，特别是以前对物体的体验对后来对同一物体的感知的影响。更广泛的影响包括监督和培训来自代表性不足群体的学生，以及向当地高中开展推广活动。受资助的研究还将向亚利桑那大学提供用于数据分析和存储的设备。调查结果也将通过实验室网站发布。

Perception seems effortless and yet it remains one of the most challenging scientific topics. For instance, how do we separate beach chairs from the beach in visual perception, or from the people in a crowded beach scene? Indeed it remains a general puzzle how we may see objects in a scene separate from one another and from their backgrounds. Recent computer models succeed in segregating objects in pictures only when past experience is allowed to play a role. With NSF support Dr. Mary Peterson will follow this lead to investigate scene separation in human perception, specifically the effects of previous experience with objects on later perception of the same objects.Broader impacts include supervision and training of students from underrepresented groups and outreach activities to local high schools. The funded research will also supply equipment to the University of Arizona for data analysis and storage. Findings will also be disseminated through a laboratory website.