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What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective
Fu, Di1,2,3; Weber, Cornelius3; Yang, Guochun1,2; Kerzel, Matthias3; Nan, Weizhi4; Barros, Pablo3; Wu, Haiyan1,2; Liu, Xun1,2; Wermter, Stefan3
First AuthorFu, Di
Correspondent Emailxun liu liux@psych.ac.cn
Contribution Rank1
Abstract

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.

Keywordselective attention visual attention auditory attention crossmodal learning computational modeling deep learning
2020-02-27
Language英语
DOI10.3389/fnint.2020.00010
Source PublicationFRONTIERS IN INTEGRATIVE NEUROSCIENCE
ISSN1662-5145
Volume14Pages:18
Subtypearticle
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China (NSFC)[61621136008] ; German Research Foundation (DFG) under project Transregio Crossmodal Learning[TRR 169] ; CAS-DAAD
PublisherFRONTIERS MEDIA SA
WOS KeywordHUMAN AUDITORY-CORTEX ; SUPERIOR-COLLICULUS ; MULTISENSORY INTEGRATION ; STIMULUS-DRIVEN ; TOP-DOWN ; NEURAL MECHANISMS ; SPATIAL ATTENTION ; COGNITIVE CONTROL ; VISUAL-ATTENTION ; SALIENCY
WOS Research AreaBehavioral Sciences ; Neurosciences & Neurology
WOS SubjectBehavioral Sciences ; Neurosciences
WOS IDWOS:000526713900001
QuartileQ3
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/31553
Collection中国科学院行为科学重点实验室
Corresponding AuthorLiu, Xun
Affiliation1.Chinese Acad Sci, Key Lab Behav Sci, Inst Psychol, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
3.Univ Hamburg, Dept Informat, Hamburg, Germany
4.Guangzhou Univ, Sch Educ, Dept Psychol, Ctr Brain & Cognit Sci, Guangzhou, Peoples R China
First Author AffilicationKey Laboratory of Behavioral Science, CAS
Corresponding Author AffilicationKey Laboratory of Behavioral Science, CAS
Recommended Citation
GB/T 7714
Fu, Di,Weber, Cornelius,Yang, Guochun,et al. What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective[J]. FRONTIERS IN INTEGRATIVE NEUROSCIENCE,2020,14:18.
APA Fu, Di.,Weber, Cornelius.,Yang, Guochun.,Kerzel, Matthias.,Nan, Weizhi.,...&Wermter, Stefan.(2020).What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective.FRONTIERS IN INTEGRATIVE NEUROSCIENCE,14,18.
MLA Fu, Di,et al."What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective".FRONTIERS IN INTEGRATIVE NEUROSCIENCE 14(2020):18.
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