Cue reliability/depth calibration in space perception

描述（由申请人提供）：人类通常且自信地将他们的身体行为建立在对空间的视觉感知上。我们走下路边而不是悬崖，成功地与迎面而来的车辆合并，切鸡肉而不砍断手指。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，视觉表现在很多方面都接近最佳状态。一个重要的例子是，当多种类型的视觉信息（如立体、视角和运动视差）出现时，场景的布局可以根据其中的任何一种来确定。这在自然视觉中经常出现。在这种情况下，视觉系统通常构建一个感知，不仅使用所有的信息来源，而且将它们平均起来创建感知场景，其中最可靠的来源在平均值中被赋予最大的权重。原则上，这种加权平均不仅会影响场景的外观，还会影响使用感知的任务的性能。目前尚不清楚情况是否如此。提案中的第一项研究量化了性能的提高，使用高质量的视觉显示和对驾驶很重要的任务。在某些情况下，原则上可以将不同来源的信息结合起来，以在使用加权平均数之外额外提高性能。之所以会出现这种情况，是因为不同的线索擅长提供不同种类的形状和距离信息。如果在每个线索用于估计场景布局的各个方面之前，可以将来自不同线索的信息组合在一起，则可以实现性能的“非线性”改进。视觉系统会利用这个机会吗？这个问题的答案对于理解视觉感知的神经机制非常重要。第二项研究解决了这个问题，通过测量观察者在一个任务中的表现来调整模拟物体的形状。最后，视觉系统建立了准确的感知，对空间布局的变化非常敏感。这就要求系统保持良好的调节。它的计算机制中的任何偏差都必须迅速检测和纠正。这是如何做到的尚不清楚，但有理由相信视觉系统可以比较不同机制的输出，并在发现差异时重新调整自己。我们建议可以使用已经开发出来的理解线索组合的相同概念工具来理解这个过程。我们利用四十年前发现的深度重新校准现象来测试当不同的视觉机制彼此不一致时重新校准的速度。

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.