Mapping the Passions: Toward a High-Dimensional Taxonomy of Emotional Experience and Expression

被引:111
作者
Cowen, Alan [1 ]
Sauter, Disa [2 ]
Tracy, Jessica L. [3 ]
Keltner, Dacher [1 ]
机构
[1] Univ Calif Berkeley, Dept Psychol, 2121 Berkeley Way, Berkeley, CA 94704 USA
[2] Univ Amsterdam, Fac Social & Behav Sci, Amsterdam, Netherlands
[3] Univ British Columbia, Dept Psychol, Vancouver, BC, Canada
基金
欧洲研究理事会;
关键词
emotion; affect; expression; face; voice; signal; semantic space; SPONTANEOUS FACIAL EXPRESSIONS; MULTIMODAL EXPRESSION; POSITIVE EMOTIONS; VOCAL EXPRESSION; SOCIAL FUNCTIONS; BASIC EMOTIONS; BRAIN-REGIONS; DISCRETE; COMMUNICATION; RECOGNITION;
D O I
10.1177/1529100619850176
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
What would a comprehensive atlas of human emotions include? For 50 years, scientists have sought to map emotion-related experience, expression, physiology, and recognition in terms of the "basic six"-anger, disgust, fear, happiness, sadness, and surprise. Claims about the relationships between these six emotions and prototypical facial configurations have provided the basis for a long-standing debate over the diagnostic value of expression (for review and latest installment in this debate, see Barrett et al., p. 1). Building on recent empirical findings and methodologies, we offer an alternative conceptual and methodological approach that reveals a richer taxonomy of emotion. Dozens of distinct varieties of emotion are reliably distinguished by language, evoked in distinct circumstances, and perceived in distinct expressions of the face, body, and voice. Traditional models-both the basic six and affective-circumplex model (valence and arousal)-capture a fraction of the systematic variability in emotional response. In contrast, emotion-related responses (e.g., the smile of embarrassment, triumphant postures, sympathetic vocalizations, blends of distinct expressions) can be explained by richer models of emotion. Given these developments, we discuss why tests of a basic-six model of emotion are not tests of the diagnostic value of facial expression more generally. Determining the full extent of what facial expressions can tell us, marginally and in conjunction with other behavioral and contextual cues, will require mapping the high-dimensional, continuous space of facial, bodily, and vocal signals onto richly multifaceted experiences using large-scale statistical modeling and machine-learning methods.
引用
收藏
页码:69 / 90
页数:22
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