Cue Reliability and Depth Calibration During Space Perception

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它在感知过程中表现眼前的环境。因为感知是准确的，我们可以知道空间布局：我们周围物体和表面的形状、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“提示”。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，这些图像提供了关于空间布局的信息。已知的线索包括双目视差、纹理梯度、遮挡关系、运动视差和熟悉的大小，仅举几例。这些暗示如何才能被正确解读？一个根本的问题是视觉提示是模棱两可的。即使线索可以被准确测量(他们不能，因为视觉系统是一个物理设备)，对于一组给定的线索，仍然会有不同的可能的3D解释。结果，视觉系统被迫以概率的方式运作：事物在我们看来“看起来”的方式反映了一种隐含的猜测，即对线索的哪种解释最可能是正确的。每一条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，而门底部的阴影提示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读图像？这项拟议的工作旨在回答关于知觉学习的这个基本问题。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。这一现象可以在实验室中使用经典的条件反射程序来研究，这些程序以前是为了研究动物的学习而开发的。在拟议的实验中，一个模型系统被用来理解这种学习何时发生以及学到了什么的细节。这些数据将与基于动物学习文献中更古老的类似研究的预测进行比较，并在贝叶斯统计推理，特别是机器学习理论的背景下进行解释。这项拟议的工作通过描述保持准确视觉感知的大脑机制，使公众健康受益。这些机制是在先天性白内障患者在白内障摘除后学习使用视觉的几个月中发挥作用的，当一个有联觉或自闭症家族史的人产生异常的经验依赖型知觉反应时，这些机制可能会出错。神经退行性疾病可能会扰乱视觉学习，在这种情况下，视觉学习测试可以用来检测疾病；了解人类视觉中新线索的学习可能会为视障人士带来更好的计算机化辅助；了解是什么导致学习新线索可能会导致新技术的出现，以训练人们在新的工作环境中准确感知。

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.