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

Abstract

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.

The rebound response plays a role in the motion mechanisms and perception

“…For this purpose, we used a noise mask (Haber and Standing 1970;Felsten and Wasserman 1980), and tested whether abrogating the aftereffect affected the reverse-phi perception. The results presented in Fig 3, demonstrated that the reverse-phi illusion (aftereffect mechanism) was indeed abolished by the noise mask, and therefore, it indicates that the aftereffect, which is resulted by the rebound mechanism (Grossberg 1976;Francis 2010;Zaidi et al 2012;Cohen-Duwek and Spitzer 2018), is essential for perception of the reverse-phi motion.…”

“…It has been suggested that the mechanism for the aftereffect is the rebound response (Grossberg 1976;Francis 2010;Zaidi et al 2012;Cohen-Duwek and Spitzer 2018). Earlier studies showed that a noise mask can abolish the aftereffect phenomenon (Haber and Standing 1970;Felsten and Wasserman 1980).…”

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

“…This process is modeled by the Poisson equation. The diffusion process is actually the same mechanism described for the afterimage effect (Cohen-Duwek and Spitzer, 2018).…”

“…In addition, we assume that in cases such as the watercolor stimuli, the visual system performs filling-in processes in order to make an “educated guess” and to reconstruct surfaces. (B) Each edge triggers a diffusion process and determines its color (Cohen-Duwek and Spitzer, 2018). (C) The trigger for the diffusion process is determined by the interactions between the gradients of the image, i.e., the gradients between the inner contour (IC) and the outer contour (OC), the gradients between the IC and the background, and between the OC and the background.…”

“…The filling-in process is expressed by the diffusion (or heat) Equation (10) (Weickert, 1998), and is determined according to the weighted triggers, Equation (8), Figure 1E. The model assumes that the filling-in process represents “isomorphic diffusion” (von der Heydt et al, 2003; Cohen-Duwek and Spitzer, 2018), although it does not necessarily negate other possible filling-in mechanisms, such as “edge integration” (Rudd, 2014). This filling-in process is reminiscent of the physical diffusion process, where the signals spread in all directions, until “blocked” by another heat source (image edges).…”