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Johannes Burge; Predicting human performance in fundamental visual tasks with natural stimuli. Journal of Vision 2016;16(12):4. doi: 10.1167/16.12.4.
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© ARVO (1962-2015); The Authors (2016-present)
Understanding how vision works under natural conditions is a fundamental goal of vision science. Vision research has made enormous progress toward this goal by probing visual function with artificial stimuli. However, evidence is mounting that artificial stimuli may not be fully up to the task. The field is full of computational modelsfrom retina to behaviorthat beautifully account for performance with artificial stimuli, but that generalize poorly to arbitrary natural stimuli. On the other hand, research with natural stimuli is often criticized on the grounds that natural signals are too complex and insufficiently controlled for results to be interpretable. I will describe recent efforts to develop methods for using natural stimuli without sacrificing computational and experimental rigor. Specifically, I will discuss how we use natural stimuli, techniques for dimensionality reduction, and ideal observer analysis to tightly predict human estimation and discrimination performance in three tasks related to depth perception: binocular disparity estimation, speed estimation, and motion through depth estimation. Interestingly, the optimal processing rules for processing natural stimuli also predict human performance with classic artificial stimuli. We conclude that properly controlled studies with natural stimuli can complement studies with artificial stimuli, perhaps contributing insights that more traditional approaches cannot.
Meeting abstract presented at VSS 2016
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