The Color Dove Illusion

Spitzer

2018

Front. Neurosci.

Self Cite

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.

Section: Resultsmentioning

confidence: 99%

Section: The Chromatic Edges and The Remaining Contoursmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Spitzer

2018

Front. Neurosci.

Self Cite

“…We further used our model to demonstrate and critically explore three important visual phenomena: (1) color constancy, in which an object' perceived color is perceived under varying lighting conditions [13]; (2) the color assimilation grid illusion, in which the color of a grid is assimilated into the underlying black and white surfaces; and (3) ambiguous color perception (e.g., #TheDress and #TheShoe). Interestingly, perceptual filling-in-driven visual illusions, featuring chromatic and achromatic phenomena, have been long known for shedding new light on neural mechanisms in the visual system [14][15][16][17][18][19]. For example, extensive research has been conducted on color constancy [20], deciphering it as a result of either high-level processing with which color is estimated in accordance with prior experience [21,22], or low-level retinal [23] and V1 [24][25][26] processing.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of color perception with biologically plausible spiking neural networks

Slovin²,

Tsur³

2022

PLoS Comput Biol

Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons’ spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-driven model for the reconstruction of colorful images from retinal inputs. We compared our results to experimentally obtained V1 neuronal activity maps in a macaque monkey using voltage-sensitive dye imaging and used the model to demonstrate and critically explore color constancy, color assimilation, and ambiguous color perception. Our parametric implementation allows critical evaluation of visual phenomena in a single biologically plausible computational framework. It uses a parametrized combination of high and low pass image filtering and SNN-based filling-in Poisson processes to provide adequate color image perception while accounting for differences in individual perception.

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

Spitzer

2019

Front. Neurosci.

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.