Cross-cultural and cultural-specific production and perception of facial expressions of emotion in the wild
Open accessAbstract
Automatic recognition of emotion from facial expressions is an intense area of research, with a potentially long list of important application. Yet, the study of emotion requires knowing which facial expressions are used within and across cultures in the wild, not in controlled lab conditions; but such studies do not exist. Which and how many cross-cultural and cultural-specific facial expressions do people commonly use? And, what affect variables does each expression communicate to observers? If we are to design technology that understands the emotion of users, we need answers to these two fundamental questions. In this paper, we present the first large-scale study of the production and visual perception of facial expressions of emotion in the wild. We find that of the 16,384 possible facial configurations that people can theoretically produce, only 35 are successfully used to transmit emotive information across cultures, and only 8 within a smaller number of cultures. Crucially, we find that visual analysis of cross-cultural expressions yields consistent perception of emotion categories and valence, but not arousal. In contrast, visual analysis of cultural-specific expressions yields consistent perception of valence and arousal, but not of emotion categories. Additionally, we find that the number of expressions used to communicate each emotion is also different, e.g., 17 expressions transmit happiness, but only 1 is used to convey disgust.
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The notion that discrete emotion categories can be reliably identified by specific behavioural patterns and/or physiological features has been seriously called into doubt by marked variability within instances of the same category as well as similarities across categories.
“The notion that discrete emotion categories can be reliably identified by specific behavioural patterns and/or physiological features has been seriously called into doubt by the marked variability within instances of the same category as well as similarities across categories.”
From A new science of emotion: implications for functional neurological disorder (2022) · cites this paper as [56]
Section 2.6.2 reports multiple facial configurations for the same emotion and widely varying agreement on category labels, supporting the concern about variability in behavioural patterns. The paper also finds substantial cross-cultural consistency in identifying emotion categories; these passages do not establish the claimed similarities across categories or address physiological features. Page numbers were not available in our copy. (LLM, conf=0.85)
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Cho, Y., & Jung, E.-C. (2024) · Archives of Design Research · doi:10.15187/adr.2024.05.37.2.317 · Cited by 2 · see citing works
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Introduction to Applied Affective Computing
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502400 · Cited by 1 · see citing works
Emotion Recognition in the Wild
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502404 · Cited by 2 · see citing works
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Leitzke, B. T., Cochrane, A., Stein, A. G., DeLap, G. A. L., Green, C. S., & Pollak, S. D. (2024) · Affective Science · doi:10.1007/s42761-024-00279-5 · Cited by 1 · see citing works
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Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502405 · Cited by 1 · see citing works
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Enabling Fairness in Cloud Computing Infrastructures
Kannan, R. S. (2019) · Deep Blue (University of Michigan) · Cited by 1 · see citing works
Excavating “Excavating AI”: The Elephant in the GalleryPreprint
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Analysis of Contextual Emotions Using Multimodal Data
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Future of Affective Computing and Applied Affective Computing
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502411 · Cited by 1 · see citing works
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Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502403
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502399
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502412
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- Authors
- Srinivasan, R., & Martinez, A. M.
- Affiliations
- The Ohio State University (US)
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2021-07-01
- Volume
- 12
- Issue
- 3
- Pages
- 707-721
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2885291642
- Cited by
- 94
- References
- 52
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 11 · 2023: 9 · 2022: 27 · 2021: 21 · 2020: 10 · 2019: 4
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (R01-DC-014498, R01-EY-020834)
- Human Frontier Science Program (RGP0036/2016)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Social Sciences › Psychology › Social Psychology › Cultural Differences and Values
- Keywords
- emotional facial expressions, cross-cultural emotion, emotion recognition, facial expression production, emotion perception, valence and arousal, emotion categories, happiness, disgust
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