Modelling the statistical processing of visual information
Recent psychophysical investigations showed that humans have the ability to compute the mean size of a set of visual objects. The investigations suggest that the visual system is able to form an overall, statistical representation of a set of objects, while the information about individual members of the set is lost. We proposed a neural model that computes the mean size of a set of similar objects. The model is a feedforward, two-dimensional neural network with three layers. Computer simulations showed that the presented model of statistical processing is able to form abstract numerical representation and to compute the mean size independently from the visual appearance of objects. This is achieved in a fast, parallel manner without serial scanning of the visual field. The mean size is computed indirectly by comparing the total activity in the input layer and in the third layer. Therefore, the information about the size of individual elements is lost. An extended model is able to hold statistical information in the working memory and to handle the computation of the mean size for surfaces with empty interiors.
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