A Neurodynamic Model of Feature-Based Spatial Selection

Accentuation, Boolean maps and perception of (dis)similarity in a neural model of visual segmentation

We developed an interactive cortical circuit for visual segmentation that integrates bottom-up and top-down processing to segregate or group visual elements. A bottom-up pathway incorporates stimulus-driven saliency computation, top-down feature-based weighting by relevance and winner-take-all selection. A top-down pathway encompasses multiscale feedback projections, an object-based attention network and a visual segmentation network. Computer simulations have shown that a salient element in the stimulus guides spatial attention and further influences the decomposition of the nearby object into its parts, as postulated by the principle of accentuation. By contrast, when no single salient element is present, top-down feature-based attention highlights all locations occupied by the attended feature and the model forms a Boolean map, i.e., a spatial representation that makes the feature-based grouping explicit. The same distinction between bottom-up and top-down influences in perceptual organization can also be applied to texture perception. The model suggests that the principle of accentuation and feature-based similarity grouping are two manifestations of the same cortical circuit designed to detect similarities and dissimilarities of visual elements in a stimulus.

An interactive cortical architecture for perceptual organization by accentuation

Accentuation has been proposed as a general principle of perceptual organization. Here, we have developed a neurodynamic architecture to explain how accentuation affects boundary segmentation and shape perception. The model consists of bottom-up and top-down pathways. Bottom-up processing involves a set of feature maps that compute bottom-up salience of surfaces, boundaries, boundary completions, and junctions. Then, a feature-based winner-take-all network selects the most salient locations. Top-down processing includes an object-based attention stage that allows enhanced neural activity to propagate from the most salient locations to all connected locations, and a visual segmentation stage that employs inhibitory connections to segregate boundaries into distinct maps. The model was tested on a series of computer simulations showing how the position of the accent affects boundary segregation in the square-diamond and the pointing illusion. The model was also tested on a variety of texture segregation tasks, showing that its performance was comparable to that of human observers. The model suggests that there is an intermediate stage of visual processing between perceptual grouping and object recognition that helps the visual system choose between competing percepts of the ambiguous stimulus.

A multi-scale neurodynamic implementation of incremental grouping

Incremental grouping is a process entailing serial binding of distal image elements into a unified object representation. At the neural level, incremental grouping involves propagation of the enhanced firing rate among feature-tuned neurons in the early visual cortex. Here, we developed a multi-resolution neural model of incremental grouping. In the model, propagation of the enhanced firing rate is achieved by computing the activity difference between two sets of units: attentional or A-units, whose firing rate is modulated by their horizontal collaterals, and non-attentional or N-units that receive only feedforward input. The activity difference is computed on dendrites that act as independent computational subunits. The proposed model employs multiple spatial scales to account for a variable speed of incremental grouping. In addition, the model incorporates the L-junction detection network that enables incremental grouping over L-junctions. Computer simulations show that the timing of attentional modulations in the model is comparable with neurophysiological measurements in monkey primary visual cortex.

Neural dynamics of spreading attentional labels in mental contour tracing

Behavioral and neural data suggest that visual attention spreads along contour segments to bind them into a unified object representation. Such attentional labeling segregates the target contour from distractors in a process known as mental contour tracing. A recurrent competitive map is developed to simulate the dynamics of mental contour tracing. In the model, local excitation opposes global inhibition and enables enhanced activity to propagate on the path offered by the contour. The extent of local excitatory interactions is modulated by the output of the multi-scale contour detection network, which constrains the speed of activity spreading in a scale-dependent manner. Furthermore, an L-junction detection network enables tracing to switch direction at the L-junctions, but not at the X- or T-junctions, thereby preventing spillover to a distractor contour. Computer simulations reveal that the model exhibits a monotonic increase in tracing time as a function of the distance to be traced. Also, the speed of tracing increases with decreasing proximity to the distractor contour and with the reduced curvature of the contours. The proposed model demonstrated how an elaborated version of the winner-takes-all network can implement a complex cognitive operation such as contour tracing.