title: Modelling divided visual attention with a winner-take-all network.
abstract: 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.
authors.affiliation: Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada. standage@cs.dal.ca
authors.forename: Dominic I
authors.initials: DI
authors.lastname: Standage
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Trappenberg
authors.forename: Raymond M
authors.initials: RM
authors.lastname: Klein
mesh_terms: D000465:Algorithms; D001288:Attention / Q000502:physiology*; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D014796:Visual Perception / Q000502:physiology*