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Refined time-to-detection model using shunting neural networks

The purpose of this work is to provide a model for the average time to detection for observers searching for targets in photo-realistic images of cluttered scenes. The current work proposes to extend previous results of modeling time to detection that used a simple decaying fixation memory. While the aforementioned results were encouraging in showing a strong effect of fixation memory, there were also discrepancies. The main discrepancy was the tendency of immediate refixation, which was not accounted for at all by the original model. The present paper describes how the original fixation memory model is extended using a shunting neural network. Shunting neural networks are neurally plausible mechanisms for modeling various brain functions. Furthermore, this shunting neural network can then be extended in a simple manner to incorporate effects of spatial relationships, which were completely ignored in the original model. The model described is testable on experimental data, and is being calibrated using both analytical and experimental methods.