Visual systems must be matched (via evolution and learning over the lifespan) to the natural tasks organisms perform to survive and reproduce. Thus, it is of fundamental importance to analyze visual systems with respect to natural tasks and with respect to the statistical properties of natural stimuli relevant to performing those tasks. In our lab we call this ?natural systems analysis.? This novel approach to vision science is composed of several steps: (1) identify natural tasks, (2) measure the natural scene statistics relevant for those tasks, (3) determine how to optimally use those statistics to perform the tasks, given appropriate biological constraints, and (4) use the first three steps to formulate principled hypotheses which are tested and refined in behavioral or physiological experiments. Using a unique suite of measurement devices, computational tools, and psychophysical paradigms developed in our laboratory, we propose to tackle (within the framework of natural systems analysis) several fundamental tasks involving estimation of local properties in natural scenes:
(Aim 1) detection of occluding and partially-occluded targets in natural images, (Aim 2) detection of depth edges created by occluding surfaces and estimation local 3D surface orientation at the non-depth edge locations within those surfaces, and (Aim 3) estimation of disparity and local 2D motion. Many of the proposed studies will be the first to precisely characterize the statistical constraints in natural images underlying the visual system's ability to perform these tasks accurately. Many of the proposed studies will also be the first to measure performance in these fundamental tasks using natural stimuli. The product of the studies will be not only unique new measurements, but principled new models that can predict human performance under natural conditions and guide future neurophysiological studies of the underlying mechanisms. Strong preliminary results have been obtained in the previous project period for many of the proposed studies.

Public Health Relevance

Ultimate goals of vision science are to understand vision in the real world and to mitigate the effects of visual dysfunction on real-world performance. The proposed studies based on measuring the task-relevant statistics of natural images, and determining how best to use those statistical properties in natural tasks, will provide rigorous steps toward those ultimate goals and may produce useful image-processing applications.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Mechanisms of Sensory, Perceptual, and Cognitive Processes Study Section (SPC)
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Wiggs, Cheri
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University of Texas Austin
Schools of Arts and Sciences
United States
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Sebastian, Stephen; Abrams, Jared; Geisler, Wilson S (2017) Constrained sampling experiments reveal principles of detection in natural scenes. Proc Natl Acad Sci U S A 114:E5731-E5740
Burge, Johannes; McCann, Brian C; Geisler, Wilson S (2016) Estimating 3D tilt from local image cues in natural scenes. J Vis 16:2
Paulun, Vivian C; Schütz, Alexander C; Michel, Melchi M et al. (2015) Visual search under scotopic lighting conditions. Vision Res 113:155-68
Burge, Johannes; Geisler, Wilson S (2015) Optimal speed estimation in natural image movies predicts human performance. Nat Commun 6:7900
Sebastian, Stephen; Burge, Johannes; Geisler, Wilson S (2015) Defocus blur discrimination in natural images with natural optics. J Vis 15:16
Bradley, Chris; Abrams, Jared; Geisler, Wilson S (2014) Retina-V1 model of detectability across the visual field. J Vis 14:
Burge, Johannes; Geisler, Wilson S (2014) Optimal disparity estimation in natural stereo images. J Vis 14:
Morgenstern, Yaniv; Geisler, Wilson S; Murray, Richard F (2014) Human vision is attuned to the diffuseness of natural light. J Vis 14:
Michel, Melchi M; Chen, Yuzhi; Geisler, Wilson S et al. (2013) An illusion predicted by V1 population activity implicates cortical topography in shape perception. Nat Neurosci 16:1477-83
D'Antona, Anthony D; Perry, Jeffrey S; Geisler, Wilson S (2013) Humans make efficient use of natural image statistics when performing spatial interpolation. J Vis 13:

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