Abstract
The dynamic properties of a neural network model of visual perception, called the boundary contour system, explain characteristics of metacontrast visual masking. Computer simulations of the model, with a single set of parameters, demonstrate that it accounts for 9 key properties of metacontrast masking: Metacontrast masking is strongest at positive stimulus onset asynchronies (SOAs); decreasing target luminance changes the shape of the masking curve; increasing target duration weakens masking; masking effects weaken with spatial separation; increasing mask duration leads to stronger masking at shorter SOAs; masking strength depends on the amount and distribution of contour in the mask; a second mask can disinhibit the masking of the target; such disinhibition depends on the SOA of the 2 masks; and such disinhibition depends on the spatial separation of the 2 masks. No other theory provides a unified explanation of these data sets. Additionally, the model suggests a new analysis of data related to the SOA law and makes several testable predictions.
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