Flexible color perception depending on the shape and positioning of achromatic contours

Abstract

In this study, we present several demonstrations of color averaging between luminance boundaries. In each of the demonstrations, different black outlines are superimposed on one and the same colored surface. Whereas perception without these outlines comprises a blurry colored gradient, superimposing the outlines leads to a much clearer binary color percept, with different colors perceived on each side of the boundary. These demonstrations show that the color of the perceived surfaces is flexible, depending on the exact shape of the outlines that define the surface, and that different positioning of the outlines can lead to different, distinct color percepts. We argue that the principle of color averaging described here is crucial for the brain in building a useful model of the distal world, in which differences within object surfaces are perceptually minimized, while differences between surfaces are perceptually enhanced.

Color Afterimages in Autistic Adults

“…Second, the basic phenomenon of color afterimages is attributable to adaptation very early on in the visual stream, at the level of photoreceptors and ganglion cells in the retina (Zaidi et al 2012 ), whereas the effects of luminance contours which help to make the Spanish Castle effect so vivid reflect the contribution of areas of visual cortex modifying the afterimage signal received from the retina to create the perception of filling-in (Anstis et al 2012 ; van Lier et al 2009 ; Vergeer et al 2015 ). In contrast, previous demonstrations of attenuated adaptation in autism have been for faces and number, which are processed at a higher level, in regions of the temporal (i.e., fusiform face area—Kanwisher et al 1997 ) and parietal cortex (see Piazza and Izard 2009 , for a review), respectively.…”

Transfer of incompatible spatial mapping to the vertical Simon task generalizes across effectors but not stimulus features

“…The different result patterns for the color and shape versions of the Simon task in this study may also be a consequence of the specific stimuli used for the incompatible spatial task and transfer task. For example, because the visual system relies on sharp luminance changes to define the boundaries of objects (Vergeer, Anstis, & van Lier, 2015), the association formed during the incompatible practice may be more strongly related to shape and therefore transfer more readily to shape stimuli. Also, because the color of the stimulus changes between red and green for the color discrimination task, this salient trial-to-trial feature change during the transfer session may make the stimuli more perceptually distinct from the neutral color stimuli in the practice session, reducing transfer of the incompatible S-R association.…”

Convolutional Sketch Inversion

“…In this study we developed sketch datasets, complementing well known unconstrained benchmarking datasets [19,20], developed DNN models that can synthesize face images from sketches with state-of-the-art performance and proposed applications of our CSI model in fine arts, art history and forensics. We foresee further computer vision applications of the developed methods for nonface images and various other sketch-like representations, as well as cognitive neuroscience applications for the study of cognitive phenomena such as perceptual filling in [33,34] and the neural representation of complex stimuli [35,36] 14.1721 ± 0.4127 0.5608 ± 0.0232 0.7811 ± 0.0217 XM2GTS (6) 11.7158 ± 1.3050 0.4096 ± 0.0258 0.3817 ± 0.1341 All (18) 13.5030 ± 0.5639 0.5021 ± 0.0205 0.6641 ± 0.0658 Table 5: Comparison of physical (PSNR), perceptual (SSIM) and correlational (R) quality measures for the inverse sketches synthesized from the sketches in the CUFS database and its sub-databases with the line sketch model trained using feature loss alone. x ± m shows the mean ± the bootstrap estimate of the standard error of the mean.…”

“…In this study we developed sketch datasets, complementing well known unconstrained benchmarking datasets [19,20], developed DNN models that can synthesize face images from sketches with state-of-the-art performance and proposed applications of our CSI model in fine arts, art history and forensics. We foresee further computer vision applications of the developed methods for nonface images and various other sketch-like representations, as well as cognitive neuroscience applications for the study of cognitive phenomena such as perceptual filling in [33,34] and the neural representation of complex stimuli [35,36].…”