TY - JOUR
T1 - A critical review of selective attention
T2 - An interdisciplinary perspective
AU - Lee, Kangwoo
AU - Choo, Hyunseung
PY - 2013/6
Y1 - 2013/6
N2 - During the last half century, significant efforts have been made to explore the underlying mechanisms of visual selective attention using a variety of approaches - psychology, neuroscience, and computational models. Among them, the computational approach emerged on the stage with the development of computer science and computer vision focusing researchers interests in this area. However, computer scientists often face the difficulty of how to construct a computational model of selective attention working on their own purpose. Here, we critically review studies of selective attention from a multidisciplinary perspective to take lessons from psychological and biological studies of attention. We consider how constraints from those studies can be imposed on computational models of selective attention.
AB - During the last half century, significant efforts have been made to explore the underlying mechanisms of visual selective attention using a variety of approaches - psychology, neuroscience, and computational models. Among them, the computational approach emerged on the stage with the development of computer science and computer vision focusing researchers interests in this area. However, computer scientists often face the difficulty of how to construct a computational model of selective attention working on their own purpose. Here, we critically review studies of selective attention from a multidisciplinary perspective to take lessons from psychological and biological studies of attention. We consider how constraints from those studies can be imposed on computational models of selective attention.
KW - Computational model
KW - Multidisciplinary approach
KW - Selective attention
UR - https://www.scopus.com/pages/publications/84878107013
U2 - 10.1007/s10462-011-9278-y
DO - 10.1007/s10462-011-9278-y
M3 - Review article
AN - SCOPUS:84878107013
SN - 0269-2821
VL - 40
SP - 27
EP - 50
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 1
ER -