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

基本信息

批准号：
8139754
负责人：
BENJAMIN T BACKUS
金额：
$ 21.97万
依托单位：
STATE COLLEGE OF OPTOMETRY
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-05-01 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8139754
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

项目摘要

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代表直接环境。由于感知是准确的，我们可以知道空间布局：我们周围对象和表面的形状，方向，大小和空间位置。但是，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“提示”。这些提示是来自环境的信号，即视觉系统从视网膜图像中提取有关空间布局的信息。已知的提示包括双眼差异，纹理梯度，遮挡关系，运动视差和熟悉的大小，仅举几例。这些提示如何正确解释？一个基本问题是，视觉提示是模棱两可的。即使可以精确测量提示（它们不能，视觉系统是物理设备），对于给定的一组提示，仍然可能会有不同的3D解释。结果，视觉系统被迫概率地操作：对我们来说，事物“看起来”的方式反映了关于提示哪种解释最有可能是正确的猜测。每个其他提示都可以改善猜测。例如，门的视网膜图像可以解释为垂直矩形，也可以在空间非垂直方向上解释为其他四边形，门底部的阴影提示有助于系统知道它是垂直矩形。视觉系统使用哪些机制来辨别哪些提示可以正确解释图像？拟议的工作旨在回答有关感知学习的基本问题。最近显示，视觉系统可以检测并开始使用新的提示进行感知。可以在实验室中使用以前开发用于研究动物学习的经典调节程序在实验室中进行研究。在拟议的实验中，使用模型系统来了解有关何时进行学习以及学到的内容的细节。数据将与基于动物学习文献中较旧的类似研究的预测进行比较，并在贝叶斯统计推断的背景下进行解释，尤其是机器学习理论。提出的工作通过表征使视觉感知准确的大脑机制来使公共卫生受益。这些机制在删除白内障后的先天性白内障人士学会使用视觉的数月内正在起作用，当时这些机制是当具有共鸣症或自闭症家族史或自闭症家族史而产生异常经验经验的感知反应时，这些机制会出现问题。神经退行性疾病可能会破坏视觉学习，在这种情况下，视觉学习测试可用于检测疾病。了解人类视力中新提示的学习可能会为视力障碍带来更好的计算机化辅助工具；并且知道什么原因导致新的提示学习可能会导致新技术，以训练人们在新型的工作环境中准确感知。