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中将海滩椅与海滩分开，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彼此分离的物体以及与它们的背景分离的物体，这仍然是一个普遍的难题。 只有当过去的经验被允许发挥作用时，最近的计算机模型才能成功地将图片中的对象分离出来。在NSF的支持下，玛丽·彼得森博士将继续研究人类感知中的场景分离，特别是以前对物体的经验对后来对同一物体的感知的影响。更广泛的影响包括对来自代表性不足的群体的学生的监督和培训，以及对当地高中的外展活动。 资助的研究还将为亚利桑那大学提供数据分析和存储设备。 调查结果还将通过实验室网站传播。

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.