Cue reliability/depth calibration in space perception

空间感知中的提示可靠性/深度校准

• 批准号: 6736838
• 负责人: BENJAMIN T BACKUS
• 金额: $ 27.74万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 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 2006-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6736838
• 关键词: behavioral /social science research tag; binocular vision; clinical research; cues; form /pattern perception; human subject; mathematical model; motion perception; neural information processing; retinal adaptation; space perception; vision tests; visual depth perception

DESCRIPTION (provided by applicant): Humans routinely and confidently base their physical actions on the visual perception of space. We step off curbs but not cliffs, merge successfully with oncoming traffic, and dice chicken without chopping off our fingers. How does the visual system build representations of the environment that are so reliable? Recent work has shown that visual performance is in many ways nearly optimal. An important example of this occurs when multiple types of visual information (such as stereo, perspective, and motion parallax) are present, and the scene's layout could be determined from any of them. This is often the case in natural vision. In this situation, the visual system often constructs a percept that not only uses all the sources of information, but averages them together to create the perceived scene, with the most reliable sources given the greatest weight in the average. In principle, such weighted averaging should affect not only the appearance of the scene, but also the performance of tasks that use the percept. It is not yet known whether this is the case. The first study in the proposal quantifies the improvement in performance, using high quality visual displays and a task that is important for driving. There are also situations in which different sources of information could, in principle, be combined to give an extra boost to performance, above and beyond the use of a weighted average. This can happen because different cues excel at providing different sorts of information about shape and distance. If the information from different cues could be combined before each cue is used to estimate various aspects of the scene layout, a "nonlinear" improvement in performance could be realized. Does the visual system exploit this opportunity? The answer to this question is important for understanding the neural mechanisms of visual perception. The second study addresses this question by measuring performance in a task in which observers adjust the shapes of simulated objects. Finally, the visual system builds accurate percepts and is exquisitely sensitive to changes in spatial layout. This requires that the system be kept finely tuned. Any drift in its computational mechanisms must be quickly detected and corrected. How this is done is not understood, but there is reason to believe the visual system can compare the outputs from different mechanisms with each other, and recalibrate itself when discrepancies are found. We propose that this process can be understood using the same conceptual tools that have already been developed to understand cue combination. We exploit a depth recalibration phenomenon discovered forty years ago to test predictions about how fast different visual mechanisms will be recalibrated when they disagree with each other.

描述（由申请人提供）：人类通常和自信地将他们的身体动作建立在对空间的视觉感知上。我们走下路缘而不是悬崖，成功地与迎面而来的车辆汇合，切鸡肉而不切掉我们的手指。视觉系统是如何构建如此可靠的环境表征的？最近的研究表明，视觉表现在许多方面几乎是最佳的。当存在多种类型的视觉信息（如立体、透视和运动视差）时，会出现这种情况的一个重要示例，并且场景的布局可以从其中任何一个确定。在自然视觉中，情况往往如此。在这种情况下，视觉系统通常会构建一个概念，不仅使用所有的信息来源，而且将它们平均在一起以创建感知场景，最可靠的来源在平均值中具有最大的权重。原则上，这种加权平均不仅会影响场景的外观，而且会影响使用该对象的任务的性能。目前还不知道是否如此。提案中的第一项研究量化了性能的改善，使用高质量的视觉显示和对驾驶很重要的任务。在有些情况下，原则上可以将不同的信息来源结合起来，从而比使用加权平均数更能提高业绩。这可能是因为不同的线索擅长提供关于形状和距离的不同信息。如果来自不同线索的信息可以在每个线索被用于估计场景布局的各个方面之前被组合，则可以实现性能的“非线性”改进。视觉系统是否利用了这个机会？这个问题的答案对于理解视觉感知的神经机制非常重要。第二项研究通过测量观察者调整模拟物体形状的任务中的表现来解决这个问题。最后，视觉系统建立精确的感知，并对空间布局的变化非常敏感。这就要求对系统进行微调。它的计算机制中的任何漂移都必须被迅速检测和纠正。如何做到这一点是不理解的，但有理由相信，视觉系统可以比较输出从不同的机制与对方，并重新校准自己的差异时发现。我们建议，这个过程可以理解使用相同的概念工具，已经开发了理解线索组合。我们利用40年前发现的深度重新校准现象来测试不同视觉机制在相互不一致时重新校准的速度。