Sequential recognition of superimposed patterns with top-down selective attention

Top–down attention is a cognitive mechanism to ,lter out irrelevant information from sensory input. Unlike bottom–up attention based on the sensory signal itself the top–down attention process is originated from the higher brain, which consists of previous knowledge about the sensory signals. A simple computational model is developed for the top–down attention. In this model an attention gain coe1cient is assigned to each input feature, and all the attention gain coe1cients are dynamically adjusted based on previous knowledge. A multilayer Perceptron is used to model the knowledge in the higher brain. The developed model demonstrates excellent capability of extracting and recognizing each pattern sequentially from superimposed dual-class patterns studied in visual perception. c © 2004 Elsevier B.V. All rights reserved.