In recent years, there has been an extraordinary number of physiological and psychophysical studies of the primate motion system, and endeavor encouraged by the National Plan for Vision Research 1994-1998 (p.273- 275). This work has established the main characteristics of local motion processing units. To be useful, the responses of these local motion units must be combined over space and time to guide locomotion and to aid object recognition. This proposal will examine how these local responses re combined. An object moving within a complex background is readily visible, and easily identifiable, despite occluding surfaces and the surrounding motion of other features. This observation suggests that the local motion units are organized along the slowly-changing paths taken by real objects, where rapid changes in direction are improbable. First, human psychophysical experiments will demonstrate increased visibility for targets that fall on a common motion path. Subsequent measurements will examine how this increased visibility is accomplished, and what information is lot by pooling the local motion signals into a network. These studies will improve basic scientific knowledge on human motion processing. Motion processing is critical for mobility in a structured environment. Thus, there results may assist in the design of devices and training procedures that enhanced mobility in individuals suffering from low vision.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
1R01EY012038-01A2
Application #
2859266
Study Section
Visual Sciences B Study Section (VISB)
Project Start
1999-05-01
Project End
2002-04-30
Budget Start
1999-05-01
Budget End
2000-04-30
Support Year
1
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Smith-Kettlewell Eye Research Institute
Department
Type
DUNS #
City
San Francisco
State
CA
Country
United States
Zip Code
94115
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Verghese, Preeti; McKee, Suzanne P (2002) Predicting future motion. J Vis 2:413-23
Verghese, P (2001) Visual search and attention: a signal detection theory approach. Neuron 31:523-35