What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective | |
Fu, Di2,3,4; Weber, Cornelius4; Yang, Guochun2,3; Kerzel, Matthias4; Nan, Weizhi1; Barros, Pablo4; Wu, Haiyan2,3; Liu, Xun2,3; Wermter, Stefan4 | |
刊名 | FRONTIERS IN INTEGRATIVE NEUROSCIENCE |
2020-02-27 | |
卷号 | 14页码:18 |
关键词 | selective attention visual attention auditory attention crossmodal learning computational modeling deep learning |
ISSN号 | 1662-5145 |
DOI | 10.3389/fnint.2020.00010 |
产权排序 | 1 |
文献子类 | article |
英文摘要 | 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. |
资助项目 | National Natural Science Foundation of China (NSFC)[61621136008] ; German Research Foundation (DFG) under project Transregio Crossmodal Learning[TRR 169] ; CAS-DAAD |
WOS关键词 | HUMAN AUDITORY-CORTEX ; SUPERIOR-COLLICULUS ; MULTISENSORY INTEGRATION ; STIMULUS-DRIVEN ; TOP-DOWN ; NEURAL MECHANISMS ; SPATIAL ATTENTION ; COGNITIVE CONTROL ; VISUAL-ATTENTION ; SALIENCY |
WOS研究方向 | Behavioral Sciences ; Neurosciences & Neurology |
语种 | 英语 |
出版者 | FRONTIERS MEDIA SA |
WOS记录号 | WOS:000526713900001 |
资助机构 | National Natural Science Foundation of China (NSFC) ; German Research Foundation (DFG) under project Transregio Crossmodal Learning ; CAS-DAAD |
内容类型 | 期刊论文 |
源URL | [http://ir.psych.ac.cn/handle/311026/31552] |
专题 | 心理研究所_中国科学院行为科学重点实验室 |
通讯作者 | Liu, Xun |
作者单位 | 1.Guangzhou Univ, Sch Educ, Dept Psychol, Ctr Brain & Cognit Sci, Guangzhou, Peoples R China 2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 3.Chinese Acad Sci, Key Lab Behav Sci, Inst Psychol, Beijing, Peoples R China 4.Univ Hamburg, Dept Informat, Hamburg, Germany |
推荐引用方式 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. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论