The Artificial Attention Model and Algorithm

Computational models of attention can be used to mitigate data overload especially when multiple sensors provide feeds to a human observer who is not present in the same environment as the network of sensors. Computational models of attention use a variety of functional components to find a balance between reorienting to new events and stimuli and focusing on currently active and relevant events by guiding one or more sampling processes. This research reports the results from tests of the performance of several functional components of one computational attention model that has been designed to address overload from multiple sensor feeds. The functional components tested include parallel center and surround sampling processes, exploratory drive, and temporal dependencies. The tests map algorithm sampling behavior over its parameter space independent of environmental input – establishing baseline performance prior to testing performance when multiple objects move and new events occur.