Learning emotion recognition from canines? Two for the road

被引:10
作者
Stetina, Birgit U. [1 ]
Turner, Karoline
Burger, Eva
Glenk, Lisa M. [2 ]
McElheney, Julia C.
Handlos, Ursula [3 ]
Kothgassner, Oswald D. [1 ]
机构
[1] Univ Vienna, Dept Clin Biol & Differential Psychol, A-1010 Vienna, Austria
[2] Vet Univ Vienna, Dept Neurophysiol, A-1030 Vienna, Austria
[3] Vienna Sch City Board, Vienna, Austria
关键词
emotion recognition; animal-assisted therapy; animal-assisted intervention; basic emotions; emotional enhancement; dogs; DOG; COMMUNICATION; COMPREHENSION; FAMILIARIS; PATTERNS; INSIGHTS; LUPUS;
D O I
10.1016/j.jveb.2010.11.004
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
The ability to recognize emotions is a prerequisite for interpersonal interaction and essential for emotional competencies. The present study aimed at exploring the possibility of enhancing the emotion recognition capability of human beings by using an animal-assisted intervention focusing on emotional expressions of dogs. A pre-post design was applied to 32 children aged 5-7 years and 34 adults aged 19-45 years, who received the multiprofessional animal-assisted intervention in groups of 6-12 participants. To measure emotion recognition, a computerized test, Vienna Emotion Recognition Tasks, was used to identify the emotion recognition capacity of the participants before and after 12 weeks of training. The hypotheses were tested using a general linear model with repeated measures, and effect sizes were calculated to gain further insight. The effect size eta square was used to analyze the variables on practical relevance. Results showed that the highest changes with relevant effect sizes in the adult group concerned the correct identification of anger and fear as well as the overall number of correctly identified facial expressions, including a decrease in latency to respond. The children also significantly increased their capacity for the recognition of anger and fear as well as disgust and neutral facial expressions. Additionally, they identified 5 more emotions correctly after the training and also decreased their latency to respond. The participants identified more emotions correctly and decreased their latency to respond significantly, even though "only" facial and/or emotional expressions of dogs were part of the program. A generalization process from human-dog interaction to human-human interaction seems to occur. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:108 / 114
页数:7
相关论文
共 50 条
[41]   EMOTION RECOGNITION FROM DYNAMIC EMOTIONAL DISPLAYS IN CHILDREN WITH ADHD [J].
Ludlow, Amanda K. ;
Garrood, Amy ;
Lawrence, Kate ;
Gutierrez, Roberto .
JOURNAL OF SOCIAL AND CLINICAL PSYCHOLOGY, 2014, 33 (05) :413-427
[42]   Emotion Recognition in Songs via Bayesian Deep Learning [J].
Nayal, Jeevan Singh ;
Joshi, Abhishek ;
Kumar, Bijendra .
PROCEEDINGS OF THE 14TH INTERNATIONAL AUDIO MOSTLY CONFERENCE, AM 2019: A Journey in Sound, 2019, :235-238
[43]   Articulation constrained learning with application to speech emotion recognition [J].
Mohit Shah ;
Ming Tu ;
Visar Berisha ;
Chaitali Chakrabarti ;
Andreas Spanias .
EURASIP Journal on Audio, Speech, and Music Processing, 2019
[44]   Progressive Neural Networks for Transfer Learning in Emotion Recognition [J].
Gideon, John ;
Khorram, Soheil ;
Aldeneh, Zakaria ;
Dimitriadis, Dimitrios ;
Provost, Emily Mower .
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION, 2017, :1098-1102
[45]   Human-in-the-loop Reinforcement Learning for Emotion Recognition [J].
Tan, Swee Yang ;
Yau, Kok-Lim Alvin .
2024 IEEE 14TH SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS, ISCAIE 2024, 2024, :21-26
[46]   Articulation constrained learning with application to speech emotion recognition [J].
Shah, Mohit ;
Tu, Ming ;
Berisha, Visar ;
Chakrabarti, Chaitali ;
Spanias, Andreas .
EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, 2019, 2019 (01)
[47]   DEEP MULTIMODAL LEARNING FOR EMOTION RECOGNITION IN SPOKEN LANGUAGE [J].
Gu, Yue ;
Chen, Shuhong ;
Marsic, Ivan .
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, :5079-5083
[48]   Respiration-based emotion recognition with deep learning [J].
Zhang, Qiang ;
Chen, Xianxiang ;
Zhan, Qingyuan ;
Yang, Ting ;
Xia, Shanhong .
COMPUTERS IN INDUSTRY, 2017, 92-93 :84-90
[49]   Multiview Supervised Dictionary Learning in Speech Emotion Recognition [J].
Gangeh, Mehrdad J. ;
Fewzee, Pouria ;
Ghodsi, Ali ;
Kamel, Mohamed S. ;
Karray, Fakhri .
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2014, 22 (06) :1056-1068
[50]   Facial Expression Recognition with Identity and Emotion Joint Learning [J].
Li, Ming ;
Xu, Hao ;
Huang, Xingchang ;
Song, Zhanmei ;
Liu, Xiaolin ;
Li, Xin .
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2021, 12 (02) :544-550