A continuous attractor neural network model of divided visual attention

The biologically realistic model of selective visual attention by Deco et al. uses a continuous attractor neural network to simulate a saliency map in posterior parietal cortex. We test the ability of the model to explain experimental evidence on the distribution of spatial attention. The majority of evidence supports the view that attention is a unitary construct, but recent experiments provide evidence for split attention 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. Our results also point to a mechanism whereby inhibitory endogenous signals may play a role in dividing attention, suggesting a possible mechanism for inhibition of return.