A Framework for Understanding How Humans Perceive the Depth of Moving Objects

项目摘要 大脑的主要功能之一是从一系列2D图像中表示世界的3D结构 投射到视网膜上。在观测者平移过程中，静止物体之间的相对图像运动 在没有双眼提示的情况下，不同的距离(运动视差，MP)提供了有效的深度信息。 然而，如果对象在世界中移动，这将使从MP计算深度变得复杂，因为 是与对象在世界上的运动相关的图像运动的附加组件。之前的实验 MP关于深度知觉的理论工作假设物体在世界上是静止的。我们 建议使用人类心理物理学和计算模型的组合来首次解决 人类如何在自我运动过程中推断移动物体的深度。 我们考虑了两种大脑可能从MP计算移动物体的深度的方法。首先，如果大脑 可以准确地将视网膜图像运动解析为与自身运动和物体运动相关的分量，然后是深度 可以从由自身运动引起的图像运动分量计算。在目标1中，我们测试了这一点 通过让受试者判断正在移动或静止的物体的深度符号(近与远)来进行假设 整个世界。我们假设受试者的深度判断会受到世界上物体运动的影响，因为 最近的研究表明，流解析并不完全准确。我们的初步数据支持这一假设。 其次，我们认为大脑可能不能准确地分离图像运动引起的分量 通过自我运动，因为在推断物体是否在世界上运动时存在不确定性。这条线索 我们假设，目标1中观察到的偏差可以通过将感知视为联合推理来解释 世界上既有深度又有物体运动的。在目标2中，我们通过让受试者回答两个问题来检验这一假设 问题：1)物体在世界上是运动的还是静止的？2)物体比凝视更远还是更近？ 要点？我们假设深度估计将取决于受试者对世界上物体运动的信念， 而且还应该系统地依赖于深度线索的可靠性。我们的初步结果支持预测 我们将把这些数据与贝叶斯理想观察者模型的预测进行比较。 该奖学金将通过以下途径为候选人提供计算和系统神经科学方面的培训 与研究导师、罗切斯特大学更广泛的神经科学界以及 正式的课程作业。这项拟议的研究与NEI的目标是一致的，即“了解大脑是如何处理的 视觉信息“(国家眼睛和视力研究计划)。此外，从这项工作中获得的知识 可能会帮助我们更好地了解影响深度知觉的各种神经和眼科疾病， 并协助开发人工视觉和导航系统。

Project Summary One of the brain’s major functions is to represent the 3D structure of the world from a sequence of 2D images projected onto the retinae. During observer translation, the relative image motion between stationary objects at different distances (motion parallax, MP) provides potent depth information, in the absence of binocular cues. However, if an object is moving in the world, this complicates the computation of depth from MP since there will be an additional component of image motion related to the object’s motion in the world. Previous experimental and theoretical work on depth perception from MP has assumed the objects are stationary in the world. We propose to use a combination of human psychophysics and computational modelling to address, for the first time, how humans infer the depth of moving objects during self-motion. We consider two ways that the brain might compute the depth of moving objects from MP. First, if the brain can accurately parse retinal image motion into components related to self-motion and object motion, then depth can be computed from the component of image motion that is caused by self-motion. In Aim 1, we test this hypothesis by asking subjects to judge the depth sign (near vs. far) of objects that are moving or stationary in the world. We hypothesize that subjects’ depth judgements will be biased by object motion in the world, since recent studies suggest that flow parsing is not completely accurate. Our preliminary data support this hypothesis. Second, we consider that the brain may not be able to accurately isolate the component of image motion caused by self-motion because there is uncertainty in inferring whether or not objects are moving in the world. This leads us to hypothesize that the biases observed in Aim 1 can be explained by considering perception as joint inference of both depth and object motion in the world. In Aim 2, we test this hypothesis by asking subjects to answer two questions: 1) is the object moving or stationary in the world? 2) is the object farther or nearer than the fixation point? We hypothesize that depth estimates will depend on the subject’s belief about object motion in the world, and should also depend systematically on the reliability of depth cues. Our preliminary results support predictions of a causal inference scheme, and we will compare the data to predictions of a Bayesian ideal observer model. This fellowship will provide the candidate training in computational and systems neuroscience through interactions with the research mentor, the broader neuroscience community at the University of Rochester, and formal coursework. The proposed research is consistent with NEI goals to “understand how the brain processes visual information” (National Plan for Eye and Vision Research). In addition, the knowledge gained from this work may help us better understand the various neural and ophthalmological diseases that affect depth perception, and assist in the development of artificial vision and navigation systems.