A biologically inspired model of emotion eliciting from visual stimuli
Ren, Dongchun1; Wang, Peng2; Qiao, Hong1; Zheng, Suiwu1
Source PublicationNEUROCOMPUTING
2013-12-09
Volume121Pages:328-336
SubtypeArticle
AbstractSeveral emotion eliciting models have been proposed in the literature, however most of them are still artificial models which ignore the biological basis. We propose an emotion (without awareness) eliciting model from visual stimuli, which is inspired by biology: we describe an emotion eliciting process that follows the circuits of emotion in the brain derived through the results of neuroscience and the three major modules in the process, visual perception, emotion-eliciting region and emotional valence elicited by the region, are all supported by biology research. In our work, visual perception works with visual stimuli from coarse to the finer level according to human visual system. The elicited emotion in coarse level is also capable of affecting the emotion valence in the finer level. Based on psychophysical research, the emotion-eliciting region is selected out through color preference. The emotion is elicited by the emotion-eliciting region rather than overall visual context, which has been first introduced to computational modeling of emotion eliciting from image stimuli. The emotional valence elicited by the region is calculated on coarseness and directionality by comparing with stored image representations. In the experiments, two types of visual stimuli are considered: (1) natural scenes stimuli and (2) natural scenes and mutilation scenes stimuli. We compare the performance of our model with International Affective Picture System (IAPS), a large set of emotionally evocative color photographs that includes pleasure and arousal ratings made by men and women. Experimental results show that our model can generate human-like emotion based on natural scenes stimuli and obtain the positive or negative emotion as people feel on natural scenes and mutilation scenes stimuli. (C) 2013 Elsevier B.V. All rights reserved.
KeywordEmotion Eliciting Model Emotion-eliciting Region Emotional Valence Emotion Circuits Visual Information
WOS HeadingsScience & Technology ; Technology
WOS KeywordATTENTION ; RECOGNITION ; PERCEPTION ; ACTIVATION ; COGNITION ; AMYGDALA ; CORTEX ; INTELLIGENCE ; VALENCE ; BRAIN
Indexed BySCI ; SSCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000325303800033
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3041
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ren, Dongchun,Wang, Peng,Qiao, Hong,et al. A biologically inspired model of emotion eliciting from visual stimuli[J]. NEUROCOMPUTING,2013,121:328-336.
APA Ren, Dongchun,Wang, Peng,Qiao, Hong,&Zheng, Suiwu.(2013).A biologically inspired model of emotion eliciting from visual stimuli.NEUROCOMPUTING,121,328-336.
MLA Ren, Dongchun,et al."A biologically inspired model of emotion eliciting from visual stimuli".NEUROCOMPUTING 121(2013):328-336.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ren, Dongchun]'s Articles
[Wang, Peng]'s Articles
[Qiao, Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ren, Dongchun]'s Articles
[Wang, Peng]'s Articles
[Qiao, Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ren, Dongchun]'s Articles
[Wang, Peng]'s Articles
[Qiao, Hong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.