Predictions of visual performance from the statistical properties of natural scenes

Abstract

Event Abstract Back to Event Predictions of visual performance from the statistical properties of natural scenes Wilson Geisler1* 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 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Feb 2010; Published Online: 17 Feb 2010. * Correspondence: Wilson Geisler, University of Texas, Austin, United States, geisler@psy.utexas.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Wilson Geisler Google Wilson Geisler Google Scholar Wilson Geisler PubMed Wilson Geisler Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.