Institutional Repository of Key Laboratory of Behavioral Science, CAS
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![]() ![]() ![]() | |
第一作者 | Fu, Di |
通讯作者邮箱 | xun liu liux@psych.ac.cn |
心理所单位排序 | 1 |
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. |
关键词 | selective attention visual attention auditory attention crossmodal learning computational modeling deep learning |
2020-02-27 | |
语种 | 英语 |
DOI | 10.3389/fnint.2020.00010 |
发表期刊 | FRONTIERS IN INTEGRATIVE NEUROSCIENCE
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ISSN | 1662-5145 |
卷号 | 14页码:18 |
期刊论文类型 | article |
收录类别 | SCI |
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 |
出版者 | FRONTIERS MEDIA SA |
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 |
WOS类目 | Behavioral Sciences ; Neurosciences |
WOS记录号 | WOS:000526713900001 |
Q分类 | Q3 |
资助机构 | National Natural Science Foundation of China (NSFC) ; German Research Foundation (DFG) under project Transregio Crossmodal Learning ; CAS-DAAD |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/31553 |
专题 | 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.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 |
第一作者单位 | 中国科学院行为科学重点实验室 |
通讯作者单位 | 中国科学院行为科学重点实验室 |
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. |
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