The effects of display time and eccentricity on the detection of amplitude and phase degradations in textured stimuli

Abstract

The amplitude and phase spectra of an image contain important information for perception, and a large body of work has investigated the effects of manipulating these spectra on the recognition or classification of image content. Here, we use a novel means of investigating sensitivity to amplitude and phase spectra properties, testing the ability of observers to detect degradations of the spectral content of synthetic images of textured surfaces that are broadband in the frequency domain. The effects of display time and retinal eccentricity on sensitivity to these two manipulations are compared using stimuli matched for difficulty of detection. We find no difference between the time courses for the detection of degradation in the two spectra; in both cases, accuracy rises above chance when display times are greater than 80 ms. Increasing retinal eccentricity to 8.7°, however, has a significantly stronger effect on the accuracy of detecting degradations of the amplitude spectrum than of the phase spectrum. Further, sensitivity to phase randomization that is restricted to low spatial frequencies is greater in the periphery (at 8.7° eccentricity) than in the fovea. These last two results imply that the fovea and periphery are specialized for the processing of phase spectrum information in distinct spatial frequency bands.

Early ERPs to faces and objects are driven by phase, not amplitude spectrum information: Evidence from parametric, test-retest, single-subject analyses

“…Using a general linear model approach, we were able to show that despite the presence of main amplitude effects in almost all subjects, these effects could be dissociated from phase effects: they differed in timing, strength, scalp distribution, and reliability, and they did not interact with image category. Our results converge with the explanation proposed by Clarke et al (2012), who suggested that cortical sensitivity to amplitude spectrum arises not because amplitude carries information about image category but because the visual system detects and responds to changes in spatial frequency content of the visual input, regardless of its category. In keeping with this idea, a recent study by Hansen, Johnson, and Table 3.…”

“…This idea is supported by the existence of computational algorithms that can efficiently classify images of natural scenes using nonlocalized or coarsely localized amplitude spectrum information (Oliva & Torralba, 2001). Furthermore, human observers can detect degradation in amplitude spectra in meaningless synthetic textures (Clarke, Green, & Chantler, 2012) or discriminate between wavelet textures using higher order statistics (Kingdom, Hayes, & Field, 2001). Hence, provided that amplitude spectrum information is available for the task at hand, human observers might be able to use it when categorizing objects and natural scenes.…”

Prediction shapes peripheral appearance

“…Peripheral vision is characterized by a number of limitations compared to central vision (for reviews, see Rosenholtz, 2016;Strasburger, Rentschler, & Jüttner, 2011). Most prominently, contrast sensitivity for higher spatial frequencies declines as a function of eccentricity (e.g., Hilz & Cavonius, 1974;Johnston, 1987), but the ability to detect blur (e.g., Clarke, Green, & Chantler, 2012;Maiello, Walker, Bex, & Vera-Diaz, 2017;Wang, Ciuffreda, & Irish, 2006) and the ability to detect image distortions (Bex, 2010) are reduced as well. This indicates that in peripheral vision we cannot access a large part of the information on which the sharp and precise representation of our central visual field is based.…”

Exploring V1 by modeling the perceptual quality of images

“…Such a paradox raises the question we discuss in this paper: What kind of phase is essential and how is the essential phase fused with the amplitude in the human visual system? Previous works have investigated the human response to artifact detection (Clarke, Green, & Chandler, 2012) and image classification (Joubert, Rousselet, Fabre-Thorpe, & Fize, 2009;Wichmann, Braun, & Gegenfurtner, 2006) under either phase or amplitude degradations. Recent functional imaging may detect the neural activity directly (Issa, Rosenberg, & Husson, 2008), but its spatial resolution is not as fine as neural organization and needs to be supplemented by mathematical models, for instance, multivariate analysis (Kamitani & Tong, 2005).…”