Blocking lower facial features reduces emotion identification accuracy in static faces and full body dynamic expressions

阻塞(统计) 心理学 面部表情 鉴定(生物学) 认知心理学 沟通 语音识别 计算机科学 计算机网络 植物 生物
作者
Ryan Lundell-Creagh,María Monroy,Joseph Ocampo,Dacher Keltner
出处
期刊:Cognition & Emotion [Taylor & Francis]
卷期号:: 1-12
标识
DOI:10.1080/02699931.2025.2477745
摘要

During COVID, much of the world wore masks covering their lower faces to prevent the spread of disease. These masks cover lower facial features, but how vital are these lower facial features to the recognition of facial expressions of emotion? Going beyond the Ekman 6 emotions, in Study 1 (N = 372), we used a multilevel logistic regression to examine how artificially rendered masks influence emotion recognition from static photos of facial muscle configurations for many commonly experienced positive and negative emotions. On average, masks reduced emotion recognition accuracy by 17% percent for negative emotions and 23% for positive emotions. In Study 2 (N = 338), we asked whether these results generalised to multimodal full-body expressions of emotions, accompanied by vocal expressions. Participants viewed videos from a previously validated set, where the lower facial features were blurred from the nose down. Here, though the decreases in emotion recognition were noticeably less pronounced, highlighting the power of multimodal information, we did see important decreases for certain specific emotions and for positive emotions overall. Results are discussed in the context of the social and emotional consequences of compromised emotion recognition, as well as the unique facial features which accompany certain emotions.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
lynn_zhang完成签到,获得积分10
1秒前
1秒前
细心咖啡发布了新的文献求助10
2秒前
2秒前
鲲鹏发布了新的文献求助10
2秒前
2秒前
小李完成签到,获得积分10
3秒前
标致尔蓝发布了新的文献求助10
3秒前
Ava应助grain采纳,获得10
3秒前
懦弱的灵萱完成签到,获得积分10
4秒前
一澜发布了新的文献求助50
5秒前
忧郁觅柔发布了新的文献求助10
6秒前
7秒前
伊斯塔发布了新的文献求助10
7秒前
信仰发布了新的文献求助10
7秒前
8秒前
8秒前
10秒前
11秒前
打打应助伊斯塔采纳,获得10
11秒前
香蕉觅云应助练习者采纳,获得10
11秒前
12秒前
Samuel发布了新的文献求助10
12秒前
overmind发布了新的文献求助10
13秒前
yout驳回了tcf应助
13秒前
科研通AI2S应助优秀醉易采纳,获得10
13秒前
俞绯发布了新的文献求助10
13秒前
李健的小迷弟应助xinanan采纳,获得10
14秒前
HAO完成签到,获得积分10
14秒前
细心咖啡完成签到,获得积分10
14秒前
14秒前
studystudy完成签到,获得积分10
15秒前
15秒前
15秒前
希望天下0贩的0应助yj采纳,获得10
16秒前
正直的友容完成签到,获得积分10
16秒前
16秒前
领导范儿应助xiaoaoni采纳,获得10
16秒前
16秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Machine Learning Methods in Geoscience 1000
Resilience of a Nation: A History of the Military in Rwanda 888
Musculoskeletal Pain - Market Insight, Epidemiology And Market Forecast - 2034 666
Crystal Nonlinear Optics: with SNLO examples (Second Edition) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3734958
求助须知:如何正确求助?哪些是违规求助? 3278816
关于积分的说明 10011931
捐赠科研通 2995493
什么是DOI,文献DOI怎么找? 1643460
邀请新用户注册赠送积分活动 781225
科研通“疑难数据库(出版商)”最低求助积分说明 749320