Brightness contrast-contrast induction model predicts assimilation and inverted assimilation effects

Empirical evaluation of computational models of lightness perception

Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes similar to human illusory percepts, allowing for demonstrable assessment of the applied mechanisms and principles. We tested 8 computational models on 13 typical displays used in lightness research (11 Illusions and 2 Mondrians), and compared them with results from human participants (N = 85). Results show that HighPass and MIR models predict empirical results for simultaneous lightness contrast (SLC) and its close variations. ODOG and its newer variants (ODOG-2 and L-ODOG) in addition to SLC displays were able to predict effect of White's illusion. RETINEX was able to predict effects of both SLC displays and Dungeon illusion. Dynamic decorrelation model was able to predict obtained effects for all tested stimuli except two SLC variations. Finally, FL-ODOG model was best at simulating human data, as it was able to predict empirical results for all displays, bar the Reversed contrast illusion. Finally, most models underperform on the Mondrian displays that represent most natural stimuli for the human visual system.

Dynamic decorrelation as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.

Slow Temporal Dynamics of Motion-Induced Brightness Shift Reveals Impact of Adaptation

Brightness of an object is determined by various factors including ambient illumination, surface reflectance of the object, and spatial and temporal relation between the object and its surrounding context. Recently, it has been demonstrated that the motion of an object alters its own and nearby object’s appearance such as brightness and color. This study aims to unveil mechanisms of the motion-induced brightness shift by measuring its temporal dynamics. We found that the motion-induced brightness shift occurred instantaneously with the motion onset when the motion was introduced abruptly. However, the brightness of a stationary object was altered gradually by a nearby moving object in about 2 s time window when the stationary dot was introduced abruptly. Two distinct temporal dynamics (slow vs. fast) of the motion-induced brightness shift demonstrate that both slow neural adaptation and fast neural normalization processes determine the brightness shift induced by the object’s motion.

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.

Multi-scale texture-based level-set segmentation of breast B-mode images

Automatic segmentation of ultrasonographic breast lesions is very challenging, due to the lesions' spiculated nature and the variance in shape and texture of the B-mode ultrasound images. Many studies have tried to answer this challenge by applying a variety of computational methods including: Markov random field, artificial neural networks, and active contours and level-set techniques. These studies focused on creating an automatic contour, with maximal resemblance to a manual contour, delineated by a trained radiologist. In this study, we have developed an algorithm, designed to capture the spiculated boundary of the lesion by using the properties from the corresponding ultrasonic image. This is primarily achieved through a unique multi-scale texture identifier (inspired by visual system models) integrated in a level-set framework. The algorithm׳s performance has been evaluated quantitatively via contour-based and region-based error metrics. We compared the algorithm-generated contour to a manual contour delineated by an expert radiologist. In addition, we suggest here a new method for performance evaluation where corrections made by the radiologist replace the algorithm-generated (original) result in the correction zones. The resulting corrected contour is then compared to the original version. The evaluation showed: (1) Mean absolute error of 0.5 pixels between the original and the corrected contour; (2) Overlapping area of 99.2% between the lesion regions, obtained by the algorithm and the corrected contour. These results are significantly better than those previously reported. In addition, we have examined the potential of our segmentation results to contribute to the discrimination between malignant and benign lesions.

Single additive mechanism predicts lateral interactions effects-computational model

The mechanism underlying the lateral interactions (LI) phenomenon is still an enigma. Over the years, several groups have tried to explain the phenomenon and suggested models to predict its psychophysical results. Most of these models comprise both inhibitory and facilitatory mechanisms for describing the LI phenomenon. Their studies' assumption that a significant inhibition mechanism exists is based on the classical interpretation of the threshold elevation perceived in psychophysical experiments. In this work, we suggest a different interpretation of the threshold elevation obtained experimentally. Our model proposes and demonstrates how a facilitatory additive mechanism can solely predict both the facilitation and "inhibition" aspects of the phenomenon, without the need for an additional inhibitory mechanism, at least for the two flankers' configurations. Though the model is simple it succeeds to predict the LI effect under a large variety of stimuli configurations and parameters. The model is in agreement with both classical and recent psychophysical and neurophysiological results. We suggest that the LI mechanism plays a role in creating an educated guess to form a continuation of gratings and textures based on the surrounding visual stimuli.

A Neurocomputational account of the role of contour facilitation in brightness perception

A new filling-in model is proposed in order to account for challenging brightness illusions, where inducing background elements are spatially separated from the gray target such as dungeon, cube and grating illusions, bullseye display and ring patterns. This model implements the simple idea that neural response to low-contrast contour is enhanced (facilitated) by the presence of collinear or parallel high-contrast contours in its wider neighborhood. Contour facilitation is achieved via dendritic inhibition, which enables the computation of maximum function among inputs to the node. Recurrent application of maximum function leads to the propagation of the neural signal along collinear or parallel contour segments. When a strong global-contour signal is accompanied with a weak local-contour signal at the same location, conditions are met to produce brightness assimilation within the Filling-in Layer. Computer simulations showed that the model correctly predicts brightness appearance in all of the aforementioned illusions as well as in White's effect, Benary's cross, Todorović's illusion, checkerboard contrast, contrast-contrast illusion and various variations of the White's effect. The proposed model offers new insights on how geometric factors (contour colinearity or parallelism), together with contrast magnitude contribute to the brightness perception.

The impact of stereoscopic depth on the Munker-White illusion

The current study investigated the impact of stereoscopic depth information on adults' perception of a coloured version of the Munker-White illusion. In one half of the illusory figure red patches were embedded in black stripes and flanked by yellow stripes. In the other half of the illusory figure red patches were embedded in yellow stripes and flanked by black stripes. The red patches either remained in the same depth plane as the black and yellow inducing stripes (zero horizontal disparity condition) or were shifted into the foreground (crossed horizontal disparity condition) or into the background (uncrossed horizontal disparity condition). According to the results, the illusory effect was robust across all viewing conditions. The illusion mainly consisted of a subjective darkening of the red patches superimposed on the yellow stripes, a perceived hue shift of the red patches superimposed on the black stripes toward yellow, and a subjective saturation decrease in both kinds of red patches. Moreover, the study established a partial confirmation of Anderson's scission theory, according to which the Munker-White illusion should be largest in the crossed horizontal disparity condition, intermediate in the zero horizontal disparity condition, and smallest in the uncrossed horizontal disparity condition.

Perceptual consequence of normalization revealed by a novel brightness induction

The human brain is renowned for its dynamic regulation of sensory inputs, which enables our brain to operate under an enormous range of physical energy with sensory neurons whose processing range is limited. Here we present a novel and strong brightness induction that reflects neural mechanisms underlying this dynamic regulation of sensory inputs. When physically identical, stationary and moving objects are viewed simultaneously, the stationary and moving objects appear largely different. Experiments reveal that normalization at multiple stages of visual processing provides a plausible account for the large shifts in perceptual experiences, observed in both the stationary and the moving objects. This novel brightness induction suggests that brightness of an object is influenced not only by variations in surrounding light (i.e. simultaneous contrast) but also by dynamically changing neural responses associated with stimulus motion.