  Fast reactions to changes in the surrounding visual environment require
efficient attention mechanisms to reallocate computational resources to most
relevant locations in the visual field. While current computational models keep
improving their predictive ability thanks to the increasing availability of
data, they still struggle approximating the effectiveness and efficiency
exhibited by foveated animals. In this paper, we present a
biologically-plausible computational model of focus of attention that exhibits
spatiotemporal locality and that is very well-suited for parallel and
distributed implementations. Attention emerges as a wave propagation process
originated by visual stimuli corresponding to details and motion information.
The resulting field obeys the principle of "inhibition of return" so as not to
get stuck in potential holes. An accurate experimentation of the model shows
that it achieves top level performance in scanpath prediction tasks. This can
easily be understood at the light of a theoretical result that we establish in
the paper, where we prove that as the velocity of wave propagation goes to
infinity, the proposed model reduces to recently proposed state of the art
gravitational models of focus of attention.
