What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective
Frontiers in Integrative Neuroscience
Volume 14,
Number 10,
pages 10,
doi: 10.3389/fnint.2020.00010
- Feb 2020
Associated documents :
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 intelligent computational 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.
@Article{FWYKNBWLW20,
author = {Fu, Di and Weber, Cornelius and Yang, Guochun and Kerzel, Matthias and Nan, Weizhi and Barros, Pablo and Wu, Haiyan and Liu, Xun and Wermter, Stefan},
title = {What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective},
journal = {Frontiers in Integrative Neuroscience},
number = {10},
volume = {14},
pages = {10},
year = {2020},
month = {Feb},
publisher = {},
doi = {10.3389/fnint.2020.00010},
}