Afterimage watercolors: an exploration of contour-based afterimage filling-in

After-image formation by adaptation to dynamic color gradients

The eye's retinotopic exposure to an adapter typically produces an after-image. For example, an observer who fixates a red adapter on a gray background will see an illusory cyan after-image after removing the adapter. The after-image's content, like its color or intensity, gives insight into mechanisms responsible for adaptation and processing of a specific feature. To facilitate adaptation, vision scientists traditionally present stable, unchanging adapters for prolonged durations. How adaptation affects perception when features (e.g., color) dynamically change over time is not understood. To investigate adaptation to a dynamically changing feature, participants viewed a colored patch that changed from a color to gray, following either a direct or curved path through the (roughly) equiluminant color plane of CIE LAB space. We varied the speed and curvature of color changes across trials and experiments. Results showed that dynamic adapters produce after-images, vivid enough to be reported by the majority of participants. An after-image consisted of a color complementary to the average of the adapter's colors with a small bias towards more recent rather than initial adapter colors. The modelling of the reported after-image colors further confirmed that adaptation rapidly instigates and gradually dissipates. A second experiment replicated these results and further showed that the probability of observing an after-image diminishes only slightly when the adapter displays transient (stepwise, abrupt) color transitions. We conclude from the results that the visual system can adapt to dynamic colors, to a degree that is robust to the potential interference of transient changes in adapter content.

A Compound Computational Model for Filling-In Processes Triggered by Edges: Watercolor Illusions

The goal of our research was to develop a compound computational model with the ability to predict different variations of the "watercolor effects" and additional filling-in effects that are triggered by edges. The model is based on a filling-in mechanism solved by a Poisson equation, which considers the different gradients as "heat sources" after the gradients modification. The biased (modified) contours (edges) are ranked and determined according to their dominancy across the different chromatic and achromatic channels. The color and intensity of the perceived surface are calculated through a diffusive filling-in process of color triggered by the enhanced and biased edges of stimulus formed as a result of oriented double-opponent receptive fields. The model can successfully predict both the assimilative and non-assimilative watercolor effects, as well as a number of "conflicting" visual effects. Furthermore, the model can also predict the classic Craik-O'Brien-Cornsweet (COC) effect. In summary, our proposed computational model is able to predict most of the "conflicting" filling-in effects that derive from edges that have been recently described in the literature, and thus supports the theory that a shared visual mechanism is responsible for the vast variety of the "conflicting" filling-in effects that derive from edges.

A Model for a Filling-in Process Triggered by Edges Predicts "Conflicting" Afterimage Effects

The goal of our research was to develop a compound computational model that predicts the "opposite" effects of the alternating aftereffects stimuli, such as the "color dove illusion" (Barkan and Spitzer, 2017), and the "filling in the afterimage after the image" (van Lier et al., 2009). The model is based on a filling-in mechanism, through a diffusion equation where the color and intensity of the perceived surface are obtained through a diffusion process of color from the stimulus edges. The model solves the diffusion equation with boundary conditions that takes the locations of the chromatic edges of the chromatic inducer (chromatic stimulus) and the achromatic remaining contours into account. These contours (edges) trigger the diffusion process. The same calculations are done for both types of afterimage effects, with the only difference related to the location of the remaining contour. While a gradient toward the inducing color produces a perception of the complementary color, an opposite gradient yields the perception of the same color as that of the chromatic inducer. Furthermore, we show that the same computational model can also predict new alternating aftereffects stimuli, such as the spiral stimulus, and the averaging of colors in alternating afterimage stimuli described by Anstis et al. (2012). The suggested model is able to predict most of the additional properties related to the "conflicting" phenomena that have been recently described in the literature, and thus supports the idea that a shared visual mechanism is responsible for both the positive and the negative effects.

Neural circuits for long-range color filling-in

Surface color appearance depends on both local surface chromaticity and global context. How are these inter-dependencies supported by cortical networks? Combining functional imaging and psychophysics, we examined if color from long-range filling-in engages distinct pathways from responses caused by a field of uniform chromaticity. We find that color from filling-in is best classified and best correlated with appearance by two dorsal areas, V3A and V3B/KO. In contrast, a field of uniform chromaticity is best classified by ventral areas hV4 and LO. Dynamic causal modeling revealed feedback modulation from area V3A to areas V1 and LO for filling-in, contrasting with feedback from LO modulating areas V1 and V3A for a matched uniform chromaticity. These results indicate a dorsal stream role in color filling-in via feedback modulation of area V1 coupled with a cross-stream modulation of ventral areas suggesting that local and contextual influences on color appearance engage distinct neural networks.

Contour adaptation reduces the spreading of edge induced colors

Brief exposure to flickering achromatic outlines of an area causes a reduction in the brightness contrast of the surface inside the area. This contour adaptation to achromatic contours does not reduce surface contrast when the surface is chromatic (the saturation or colorimetric purity of the surface is maintained). In addition to reducing the brightness of physical luminance contrast, contour adaptation also reduces (or even reverses) the illusory brightness contrast seen in the Craik-O'Brien-Cornsweet illusion, in which two physically identical grey areas appear different brightness because of a sharp luminance edge separating them. Chromatic color spreading illusions also occur with chromatic inducing edges, and an unanswered question is whether contour adaptation can reduce the perceived contrast of illusory color spreading from edges, even though it cannot reduce the perceived contrast of physical surface color. The current studies use a color spreading illusion known as the watercolor effect in order to test whether illusory color spreading is affected by contour adaptation. The general findings of physical achromatic contrast being reduced and chromatic contrast being robust to contour adaptation were replicated. However, both illusory brightness and color were reduced by contour adaptation, even when the illusion edges only differed in chromatic contrast with each other and the background. Additional studies adapting to chromatic contours showed opposite effects on illusory color contrast than achromatic adaptation.

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

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.