  Selective attention plays an essential role in information acquisition and
utilization from the environment. In the past 50 years, research on selective
attention has been a central topic in cognitive science. Compared with unimodal
studies, crossmodal studies are more complex but necessary to solve real-world
challenges in both human experiments and computational modeling. Although an
increasing number of findings on crossmodal selective attention have shed light
on humans' behavioral patterns and neural underpinnings, a much better
understanding is still necessary to yield the same benefit for computational
intelligent agents. This article reviews studies of selective attention in
unimodal visual and auditory and crossmodal audiovisual setups from the
multidisciplinary perspectives of psychology and cognitive neuroscience, and
evaluates different ways to simulate analogous mechanisms in computational
models and robotics. We discuss the gaps between these fields in this
interdisciplinary review and provide insights about how to use psychological
findings and theories in artificial intelligence from different perspectives.
