Efficiency of Heading Perception

9309820组人类能够快速通过复杂的环境，同时避开静止和移动的障碍物；显然，对于这项技能来说，最重要的感觉通道是视觉。人们使用视觉运动信息来引导导航的简便性掩盖了这项任务的潜在复杂性。在环境中的自我运动会在视网膜上产生一种称为光流场的运动模式。一个有影响的早期建议是通过定位流动的来源，即扩张的焦点来确定相对于障碍物的自我运动方向，但这项任务实际上要复杂得多。首先，由于2D运动的传感(首先需要得到光流场)本身就是一个困难的问题，所以不能假设无噪声的矢量场可用于计算观察者的运动。其次，由于人们在运动时通常会移动眼睛和头部，这些运动会掩盖扩张的焦点。第三，由于视觉场景通常包含观察者之外的移动对象，这些对象不会产生一致的焦点。尽管有这些复杂的问题，人们仍然非常有效地利用光学流场来判断相对于地标和其他移动物体的航向。这项研究主要是关于人类观察者如何有效地利用包含在光流场中的信息来确定其自身运动的参数。效率的衡量标准是比较执行任务的理想观察者和执行相同任务的人类观察者的表现。因为理想的观察者使用了流场中的所有信息，所以它的性能提供了一个严格的基准来比较人类的性能。具体的比较不仅可以衡量人的效率，还可以查明造成效率低下的一些原因。这项研究将展示各种变量，如显示器中的元素数量、所显示的流场类型、显示器的锐度和大小、视网膜上刺激的位置、眼睛/头部运动引起的旋转流动的存在以及场景几何知识如何影响人类确定自我运动方向的效率。虽然这项研究的成果将增强我们对空间和运动感知的理解，但它们也可能产生重要的实际影响。首先，由于生物系统已经进化出强大的机制来辅助视觉导航，更好地理解这是如何实现的应该会为移动机器人系统带来更好的算法。其次，开发一种理想的头部任务观察者将提供一个基准，用于比较计算机算法的性能。第三，由于相对于静止和移动物体的航向感知对驾驶和飞行至关重要，更好的理解可以改进筛选和培训驾驶员和飞行员以及设计仪表、驾驶舱、道路和跑道标记的程序。***

9309820 BANKS Humans are able to move rapidly through complex environments while avoiding stationary and moving obstacles; clearly the sensory modality most important for this skill is vision. The ease with which people use visual motion information to guide navigation belies the underlying complexity of the task. Self-motion through an environment produces a pattern of movement on the retina called the optic flow field. An influential early proposal was to identify the direction of self-motion with respect to obstacles by locating the source of flow, i.e., the focus of expansion, but the task is actually much more complicated. First, since the sensing of 2D motion (which is required to derive the optic flow field in the first place) is a difficult problem in its own right, one cannot assume that a noise-free vector field is available for calculation of observer motion. Second, since people commonly move their eyes and head while locomoting, these movements obliterate the focus of expansion. Third, since visual scenes often contain moving objects besides the observer, those objects do not produce a consistent focus. Despite these complications, people use the optic flow field very effectively to judge heading relative to landmarks and other moving objects. This research is mainly concerned with how efficiently human observers use the information contained in the optic flow field to determine the parameters of their self-motion. A measure of efficiency derives from the comparison of the performance of an ideal observer for heading tasks to that of human observers in the same tasks. Because the ideal observer uses all of the information in the flow field, its performance provides a rigorous benchmark against which to compare human performance. Specific comparisons will not only allow the measurement of human efficiency but also the identification of some of the causes of inefficiency. The research will demonstrate how variables such as number of elemen ts in the display, type of flow field displayed, sharpness and size of the display, position of stimulation on the retina, presence of rotational flow due to eye/head movements, and knowledge of the scene geometry affect human efficiency in determining the direction of self-motion. While the products of this research will enhance our understanding of space and motion perception, they may also have important practical consequences. For one thing, since biological systems have evolved robust mechanisms to subserve visually-guided navigation, a better understanding of how this is accomplished should lead to better algorithms for mobile robotic systems. Second, the development of an ideal observer for heading tasks will provide a benchmark against which to compare the performance of computer algorithms. Third, since perception of heading with respect to stationary and moving objects is crucial for driving and flying, a better understanding could lead to improved procedures for screening and training drivers and pilots and for designing instruments, cockpits, and roadway and runway markings. ***