A feedback model of figure-ground assignment

Abstract

A computational model is proposed in order to explain how bottom-up and top-down signals are combined into a unified perception of figure and background. The model is based on the interaction between the ventral and the dorsal stream. The dorsal stream computes saliency based on boundary signals provided by the simple and the complex cortical cells. Output from the dorsal stream is projected to the surface network which serves as a blackboard on which the surface representation is formed. The surface network is a recurrent network which segregates different surfaces by assigning different firing rates to them. The figure is labeled by the maximal firing rate. Computer simulations showed that the model correctly assigns figural status to the surface with a smaller size, a greater contrast, convexity, surroundedness, horizontal-vertical orientation and a higher spatial frequency content. The simple gradient of activity in the dorsal stream enables the simulation of the new principles of the lower region and the top-bottom polarity. The model also explains how the exogenous attention and the endogenous attention may reverse the figural assignment. Due to the local excitation in the surface network, neural activity at the cued region will spread over the whole surface representation. Therefore, the model implements the object-based attentional selection.

Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention

“…Despite biological evidence that the ventral and dorsal pathway share feedback connections to operate a figure-ground segmentation [17,54], there are only a few approaches available that integrate both streams for the control of spatio-temporal attention [36,38]. Still these rely on a pixel-to-pixel combination of the two pathways and therefore are difficult to reconcile with the object-based nature of attention.…”

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

“…The input image (A) is augmented with a spatial gradient for which each location in the network receives activity of a different amplitude (B). Domijan and Šetić (2008) argued that such spatial biasing arises from visual field anisotropies that might explain why there is a tendency to assign figural status to surfaces in the lower visual field or with a wide bottom part and narrow top. Recurrent processing within the network results in a spatial representation in which each surface is labeled with a distinct activity amplitude or firing rate (C) due to lateral inhibition.…”

“…The interaction between anisotropic diffusion and its containment within the borders defined by orientation-selective responses in early visual cortex explains many properties of color and brightness perception (Cohen & Grossberg, 1984;Grossberg & Todorović, 1988). Domijan and Šetić (2008) generalized this computational scheme to attentional spreading, where, instead of brightness, abstract attentional labels also fill in the surface representation and form the neural substrate for objectbased attentional selection (Roelfsema, 2006).…”

“…Moschovakis et al discovered a graded strength of anatomical projections from distinct superior colliculus cells to burst generators in the cat oculo-motor system. Based on this work, Domijan and Šetić (2008) proposed a computational model that used a spatial gradient to explain a tendency in the perceptual organization to assign figural status to surfaces in the lower part of the visual field. Furthermore, Ivey et al (2011) showed how the spatial gradient helps to explain the formation of plans for navigation through an environment.…”

Perceptual Reversal Guided by Integration Between Bottom-Up Input and Top-Down Feedback Over Time Course

“…Criticizing these serial processing views and making a compromise between two opposing explanations, an interactive approach in which bottom-up input interacts with top-down input in a hierarchical system was suggested (Domijan & Setic, 2008;Vecera & O'reilly, 1998). In this explanation, inputs from a lower layer continuously activate outputs in a higher layer, and activation from a higher layer provides feedback to units in a lower layer, allowing the model to converge to a stable state.…”