Abstract
The dynamic properties of a neural network model of boundary segmentation, called the Boundary Contour System, explains characteristics of metacontrast masking. Computer simulations of the model, with a single set of parameters, demonstrate that it accounts for the key findings of metacontrast masking; strongest masking occurs at positive stimulus onset asynchronies (SOA); masking is weak for negative SOAs; masking effects weaken with spatial separation. The properties of metacontrast masking arise from interactions between positive feedback and lateral inhibition in cortical neural circuits. The model links properties of metacontrast masking with aspects of visual persistence, spatial vision, and cells in visual cortex.
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