Modelling divided visual attention with a winner-take-all network

• Published in: Neural networks Vol. 18; no. 5; pp. 620 - 627
• Main Authors: Standage, Dominic I., Trappenberg, Thomas P., Klein, Raymond M.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Oxford Elsevier Ltd 01-07-2005; Elsevier Science
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Attention - physiology; Biological and medical sciences; Computer science; control theory; systems; Computer Simulation; Computerized, statistical medical data processing and models in biomedicine; Connectionism. Neural networks; Exact sciences and technology; Medical computing and teaching; Medical sciences; Models, Neurological; Neural Networks (Computer); Spatial saliency; Visual attention; Visual Perception - physiology; Winner-take-all; Spatial saliency; Winner-take-all; Visual attention; Spatial distribution; Working memory; Neural network; Winner take all; Modeling; Implementation
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2005.06.015

Experimental evidence on the distribution of visual attention supports the idea of a spatial saliency map, whereby bottom-up and top-down influences on attention are integrated by a winner-take-all mechanism. We implement this map with a continuous attractor neural network, and test the ability of our model to explain experimental evidence on the distribution of spatial attention. The majority of evidence supports the view that attention is unitary, but recent experiments provide evidence for split attentional foci. We simulate two such experiments. Our results suggest that the ability to divide attention depends on sustained endogenous signals from short term memory to the saliency map, stressing the interplay between working memory mechanisms and attention.