Predictions of visual performance from the statistical properties of natural scenes
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1
University of Texas, United States
Five decades ago Horace Barlow argued that sensory scientists should explore the relationship between the design of an organism’s sensory circuits, the organism’s natural tasks, and the stimulus properties relevant to those tasks. In the past two decades advances in physical measurement technology, computational power and statistical modeling have made it possible to begin exploring this relationship in detail. In this talk I will briefly summarize our recent efforts to determine what stimulus features are optimal for performance in specific visual tasks, how those features should be combined to optimally perform those tasks, and how human performance compares with optimal performance. Our methods for determining optimal stimulus features and optimal performance are both based on the concepts of Bayesian statistical decision theory. Our results suggest that a quantitative analysis of the natural scene statistics that support natural tasks can often provide novel quantitative predictions of visual performance and deep insight into the design of the visual system.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Oral Presentation
Topic:
Oral presentations
Citation:
Geisler
W
(2010). Predictions of visual performance from the statistical properties of natural scenes.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00025
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Received:
17 Feb 2010;
Published Online:
17 Feb 2010.
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Correspondence:
Wilson Geisler, University of Texas, Austin, United States, geisler@psy.utexas.edu