Using afterimages for orientation and color to explore mechanisms of visual filling-in

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.

Contour Erasure and Filling-in: Old Simulations Account for Most New Observations

Three recent studies used similar stimulus sequences to investigate mechanisms for brightness perception. Anstis and Greenlee (2014) demonstrated that adaptation to a flickering black and white outline erased the visibility of a subsequent target shape defined by a luminance increment or decrement. Robinson and de Sa (2012, 2013) used a flickering disk or annulus to show a similar effect. Here, a neural network model of visual perception (Francis & Kim, 2012), that previously explained properties of scene fading, is shown to also explain most of the erasure effects reported by Anstis and Greenlee and by Robinson and de Sa. The model proposes that in normal viewing conditions a brightness filling-in process is constrained by oriented boundaries, which thereby define separate regions of a visual scene. Contour adaptation can weaken the boundaries and thereby allow brightness signals to merge together, which renders target stimuli indistinguishable from the background. New simulations with the stimuli used by Anstis and Greenlee and Robinson and de Sa produce model output very similar to the perceptual experience of human observers. Finally, the model predicts that adaptation to illusory contours will not produce contour erasure.

Crowding, grouping, and object recognition: A matter of appearance

In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of object recognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing.

Simulations of induced visual scene fading with boundary offset and filling-in

Blurred images can appear to fade to uniform brightness and color when viewed with some types of visual transient stimuli. Simons et al. (2006) identified the conditions where such scene fading occurs and noted that their findings were inconsistent with mechanisms that have been used to explain other fading effects. We show that their empirical findings are consistent with a neural model of visual perception that hypothesizes filling-in of brightness and color that is constrained by signals from a boundary contour system. Certain types of transients can weaken the boundary responses and thereby induce scene fading. The simulations explain how even small transient changes can produce scene fading effects across large parts of an image.

Modeling filling-in of afterimages

Van Lier, Vergeer, and Anstis (2009) reported that color information in a visual afterimage could spread across regions that were not colored in the inducing stimulus. The perceived color and shape of the afterimage could be manipulated by drawn contours that apparently trap the spread of afterimage color signals. They further hypothesized that the observed effects indicated a common mechanism for afterimage color filling-in and real-color filling-in phenomena. New simulations of the existing boundary contour system/feature contour system model of visual perception (Grossberg & Mingolla, 1985a, 1985b) demonstrate the connection between these phenomena.

Orientation tuning of a two-stimulus afterimage: Implications for theories of filling-in

Sequential viewing of 2 orthogonally related gratings produces an afterimage related to the firstgrating (Vidyasagar, Buzas, Kisyarday, & Eysel, 1999; Francis & Rothmayer, 2003). We investigated how the appearance of the afterimage depended on the relative orientations of the 2 stimulus gratings. We firstanalyzethetheoretical explanation of the appearance of the afterimage that was proposed by Francis and Rothameyer (2003). From the analysis, we show that the model must predict a rapid drop in afterimage occurrence as the gratings deviate from orthogonal. We also show that the model predicts that the shape of the afterimage should always be orthogonal to the second grating. We then report on 2 experiments that test the properties of the model and find that the experimental data are strikingly different from the model predictions. From these discrepancies we identify the key deficits of the current version of the model.

The time course of visual afterimages: data and theory

Sequential viewing of two orthogonally related patterns produces an afterimage of the first pattern (Vidyasagar et al, 1999 Nature 399 422-423; Francis and Rothmayer, 2003 Perception and Psychophysics 65 508-522). We investigated how the timing between the first stimulus (a vertical bar grating) and the second stimulus (a horizontal bar grating) affected the visibility of the afterimage (a perceived vertical grating). As the duration from offset of the first stimulus increased, reports of afterimages decreased. Holding fixed the total time from offset of the first stimulus and increasing the duration from offset of the second stimulus while decreasing the time between the first and second stimuli, caused a decrease in afterimage reports. We interpret this finding in terms of Grossberg's BCS - FCS (boundary contour system--feature contour system) theory. In this theory, the afterimage percept is the result of color complement after-responses in the FCS system interacting with orientation after-responses in the BCS system. The two types of after-responses interact at a stage of neural filling-in to produce the afterimage percept. As the duration between the stimuli increases, the color after-responses weaken so that visible filling-in is less likely to occur. A similar effect occurs for the orientation after-responses but at a faster time scale. Simulations of the model match the experimental data.