Cue Reliability and Depth Calibration During Space Perception

空间感知期间的提示可靠性和深度校准

• 批准号: 7692268
• 负责人: BENJAMIN T BACKUS
• 金额: $ 22.88万
• 依托单位: STATE COLLEGE OF OPTOMETRY
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7692268
• 关键词: Address; Adult; Animal Behavior; Animals; Appearance; Autistic Disorder; Binocular Vision; Biological Models; Brain; Calibration; Cataract; Computer Vision Systems; Cues; Data; Devices; Diagnosis; Disease; Environment; Experimental Designs; Family history of; Food; Funding; Goals; Human; Image; Individual; Knowledge; Laboratories; Lead; Learning; Learning Disabilities; Literature; Location; Longevity; Machine Learning; Measures; Memory; Motion; Motion Perception; Names; Neurodegenerative Disorders; Neuronal Plasticity; Pathology; Perception; Perceptual learning; Persons; Positioning Attribute; Primates; Procedures; Process; Public Health; Recruitment Activity; Research; Retinal; Reversal Learning; Rotation; Shadowing (Histology); Shapes; Signal Transduction; Source; Space Perception; Stimulus; Surface; System; Testing; Texture; Time; Training; Translations; Trust; Ursidae Family; Vision; Vision Disparity; Visual; Visual Perception; Visual impairment; Visual system structure; Work; Workplace; area MT; base; classical conditioning; clinical application; computerized; congenital cataract; design; devices for the visually impaired; experience; improved; meetings; neuromechanism; new technology; novel; programs; relating to nervous system; research study; response; stereoscopic; theories; tool; visual information; visual learning; visual process; visual processing

DESCRIPTION (provided by applicant): The long-term objective of the proposed work is to understand how learning by the visual system helps it to represent the immediate environment during perception. Because perception is accurate, we can know spatial layout: the shapes, orientations, sizes, and spatial locations of the objects and surfaces around us. But this accuracy requires that the visual system learn over time how best to interpret visual "cues". These cues are the signals from the environment that the visual system extracts from the retinal images that are informative about spatial layout. Known cues include binocular disparity, texture gradients, occlusion relations, motion parallax, and familiar size, to name a few. How do these cues come to be interpreted correctly? A fundamental problem is that visual cues are ambiguous. Even if cues could be measured exactly (which they cannot, the visual system being a physical device) there would still be different possible 3D interpretations for a given set of cues. As a result, the visual system is forced to operate probabilistically: the way things "look" to us reflects an implicit guess as to which interpretation of the cues is most likely to be correct. Each additional cue helps improve the guess. For example, the retinal image of a door could be interpreted as a vertical rectangle or as some other quadrilateral at a non-vertical orientation in space, and the shadow cues at the bottom of the door helps the system know that it's a vertical rectangle. What mechanisms do the visual system use to discern which cues are available for interpreting images correctly? The proposed work aims to answer this fundamental question about perceptual learning. It was recently shown that the visual system can detect and start using new cues for perception. This phenomenon can be studied in the laboratory using classical conditioning procedures that were previously developed to study learning in animals. In the proposed experiments, a model system is used to understand details about when this learning occurs and what is learned. The data will be compared to predictions based on older, analogous studies in the animal learning literature, and interpreted in the context of Bayesian statistical inference, especially machine learning theory. The proposed work benefits public health by characterizing the brain mechanisms that keep visual perception accurate. These mechanisms are at work in the many months during which a person with congenital cataracts learns to use vision after the cataracts are removed, and it is presumably these mechanisms that go awry when an individual with a family history of synesthesia or autism develops anomalous experience-dependent perceptual responses. Neurodegenerative diseases may disrupt visual learning, in which case visual learning tests could be used to detect disease; understanding the learning of new cues in human vision could lead to better computerized aids for the visually impaired; and knowing what causes a new cue to be learned could lead to new technologies for training people to perceive accurately in novel work environments.

描述（由申请人提供）：拟议工作的长期目标是了解视觉系统的学习如何帮助其在感知过程中表示即时环境。因为感知是准确的，所以我们可以知道空间布局：我们周围的物体和表面的形状、方向、大小和空间位置。但这种准确性要求视觉系统随着时间的推移学习如何最好地解释视觉“线索”。这些线索是视觉系统从视网膜图像中提取的来自环境的信号，这些信号提供有关空间布局的信息。已知的线索包括双眼视差、纹理梯度、遮挡关系、运动视差和熟悉的尺寸等等。如何正确解释这些线索？一个根本问题是视觉线索不明确。即使可以精确测量线索（这是不可能的，视觉系统是物理设备），对于给定的一组线索仍然可能有不同的 3D 解释。结果，视觉系统被迫按概率运行：事物“看起来”的方式反映了我们对线索的哪种解释最有可能是正确的隐含猜测。每个额外的提示都有助于改进猜测。例如，门的视网膜图像可以被解释为垂直矩形或空间中非垂直方向的其他四边形，门底部的阴影提示可以帮助系统知道它是垂直矩形。视觉系统使用什么机制来辨别哪些线索可用于正确解释图像？拟议的工作旨在回答有关感知学习的基本问题。最近的研究表明，视觉系统可以检测并开始使用新的感知线索。这种现象可以在实验室中使用先前为研究动物学习而开发的经典调节程序进行研究。在所提出的实验中，模型系统用于了解有关学习何时发生以及学到什么的详细信息。这些数据将与基于动物学习文献中较早的类似研究的预测进行比较，并在贝叶斯统计推断（尤其是机器学习理论）的背景下进行解释。拟议的工作通过描述保持视觉感知准确的大脑机制来有益于公共健康。这些机制在患有先天性白内障的人在白内障摘除后学习使用视觉的几个月中发挥作用，并且当有联觉或自闭症家族史的个体出现异常体验时，这些机制可能会出错——依赖的知觉反应。神经退行性疾病可能会扰乱视觉学习，在这种情况下，视觉学习测试可用于检测疾病；了解人类视觉新线索的学习可以为视障人士提供更好的计算机辅助；了解导致学习新线索的原因可能会带来新技术，用于训练人们在新的工作环境中准确感知。