Data-Dependent Structural Risk Minimization for Perceptron Decision Trees

A novel neural network model of pre-attention processing in visual(cid:173) search tasks is presented. Using displays of line orientations taken from Wolfe's experiments [1992], we study the hypothesis that the distinction between parallel versus serial processes arises from the availability of global information in the internal representations of the visual scene. The model operates in two phases. First, the visual displays are compressed via principal-component-analysis. Second, the compressed data is processed by a target detector mod(cid:173) ule in order to identify the existence of a target in the display.