Testing models of peripheral encoding using metamerism in an oddity paradigm.

By Thomas S. A. Wallis, Matthias Bethge, Felix Wichmann. 2016

46 citations recorded by the source corpus.

Most of the visual field is peripheral, and the periphery encodes visual input with less fidelity compared to the fovea. What information is encoded, and what is lost in the visual periphery? A systematic way to answer this question is to determine how sensitive the visual system is to different kinds of lossy image changes compared to the unmodified natural scene. If modified images are indiscriminable from the original scene, then the information discarded by the modification is not important for perception under the experimental conditions used. We measured the detectability of modifications of natural image structure using a temporal three-alternative oddity task, in which observers compared modified images to original natural scenes. We consider two lossy image transformations, Gaussian blur and Portilla and Simoncelli texture synthesis. Although our paradigm demonstrates metamerism (physically different images that appear the same) under some conditions, in general we find that humans can be capable of impressive sensitivity to deviations from natural appearance. The representations we examine here do not preserve all the information necessary to match the appearance of natural scenes in the periphery.

Subfields: Biophysics, Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

Excessive Invariance Causes Adversarial Vulnerability (2018, background): “long history in neuroscience that started in the context of color vision. Metamerism refers to the cognitive equivalent to the preimage in neuroscience (Wandell, 1995; Freeman & Simoncelli, 2011; Wallis et al., 2016). Here we generate metameric samples in artiﬁcial neural networks to explore the”
A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017, background): “…or structurally loss-less (in that they could both be considered samples from original images; Pappas, 2013), as opposed to being perceptually lossless (unable to be told apart) compared either to each other ( Freeman & Simoncelli, 2011) or the original source images ( Wallis et al., 2016).”