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. ***
