Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Open accessAbstract
It is commonly assumed that a person’s emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions . This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view , and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.
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Multimodal recognition extends inference to speech prosody, facial movement and physiology, but facial expressions are context-sensitive social cues rather than emotion readouts.
“Multimodal recognition extends inference to speech prosody, facial movement and physiology, but facial expressions are context-sensitive social cues rather than emotion readouts [29]”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [29]
The Summary and Recommendations section describes facial movements as context-dependent social information, not reliable readouts of particular emotions. Its Recommendations for Future Scientific Research also propose combining facial movement, tone of voice, and physiological measurements, but present this as a research approach rather than an established multimodal recognition capability. Page numbers are not provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe findings allow no conclusions about whether the smiles were genuine expressions of felt joy.
“However, our findings allow no conclusions about whether the smiles were “genuine” as expressions of felt joy (Feldman Barrett et al., 2019).”
From Existential threat and responses to emotional displays of ingroup and outgroup members (2023) · cites this paper as Feldman Barrett et al., 2019
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 [57]
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- Widen SC (2016). The development of children's concepts of emotion In Barrett LF, Lewis M, J. M., & Haviland-Jones (Eds.), Handbook of emotions (4th ed., pp. 307– 318). New York: Guilford Press.
- Yin Y, Nabian M, Ostadabbas S, Fan M, Chou C, & Gendron M (2018). Facial expression and peripheral physiology fusion to decode individualized affective experiences. 2nd IJCAI Workshop on Artificial Intelligence in Affective Computing: Proceedings of Marching Learning Research, 1, 1– 16.
- Zafeiriou S, Papaioannou A, Kotsia I, Nicolaou M, & Zhao G (2016). Facial Affect‘‘In-The-Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 36– 47).
Cited by
Sorted by citations
Sustainable AI: AI for sustainability and the sustainability of AI
van Wynsberghe, A. (2021) · AI and Ethics · doi:10.1007/s43681-021-00043-6 · Cited by 939 · see citing works
Veale, M., & Zuiderveen Borgesius, F. (2021) · Computer Law Review International · doi:10.9785/cri-2021-220402 · Cited by 805 · see citing works
Towards a standard for identifying and managing bias in artificial intelligence
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548 · see citing works
“The basis for drawing conclusions about emotional state from facial characteristics ranges from unscientific and debunked theories to emerging experimental studies [177], presenting concerning challenges to AI systems that claim to make such judgements.”
BackgroundEmotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354 · see citing works
Self-citationArtificial intelligence and the future of psychiatry: Insights from a global physician survey
Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253 · see citing works
McStay, A. (2020) · Big Data & Society · doi:10.1177/2053951720904386 · Cited by 217 · see citing works
Kshetri, N. (2021) · Management Research Review · doi:10.1108/mrr-03-2020-0168 · Cited by 180 · see citing works
A new science of emotion: implications for functional neurological disorder
Jungilligens, J., Paredes-Echeverri, S., Popkirov, S., Barrett, L. F., & Perez, D. L. (2022) · Brain · doi:10.1093/brain/awac204 · Cited by 164 · see citing works
Self-citationContext-aware experience sampling reveals the scale of variation in affective experience
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69180-y · Cited by 124 · see citing works
Self-citationDeveloping an understanding of emotion categories: Lessons from objects
Hoemann, K., Wu, R., LoBue, V., Oakes, L. M., Xu, F., & Barrett, L. F. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.10.010 · Cited by 123 · see citing works
Self-citationMascaro, J. S., Florian, M. P., Ash, M. J., Palmer, P. K., Frazier, T., Condon, P., & Raison, C. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.547241 · Cited by 118 · see citing works
Durán, J. I., & Fernández-Dols, J.-M. (2021) · Emotion · doi:10.1037/emo0001015 · Cited by 101 · see citing works
Srinivasan, R., & Martinez, A. M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2887267 · Cited by 94 · see citing works
Allostasis, action and affect in depression: Insights from the theory of constructed emotion
Shaffer, C., Westlin, C., Quigley, K. S., Whitfield-Gabrieli, S., & Barrett, L. F. (2022) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-081219-115627 · Cited by 94 · see citing works
Self-citationDemystifying the Draft EU Artificial Intelligence Act
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Le Mau, T., Hoemann, K., Lyons, S. H., Fugate, J. M. B., Brown, E. N., Gendron, M., & Barrett, L. F. (2021) · Nature Communications · doi:10.1038/s41467-021-25352-6 · Cited by 74 · see citing works
Self-citationPhysiological linkage during shared positive and shared negative emotion
Chen, K.-H., Brown, C. L., Wells, J. L., Rothwell, E. S., Otero, M. C., Levenson, R. W., & Fredrickson, B. L. (2021) · Journal of Personality and Social Psychology · doi:10.1037/pspi0000337 · Cited by 72 · see citing works
Emotion perception in Hadza hunter-gatherers
Gendron, M., Hoemann, K., Crittenden, A. N., Mangola, S. M., Ruark, G. A., & Barrett, L. F. (2020) · Scientific Reports · doi:10.1038/s41598-020-60257-2 · Cited by 52 · see citing works
Self-citationOn manipulation by emotional AI: UK adults’ views and governance implications
Bakir, V., Laffer, A., McStay, A., Miranda, D., & Urquhart, L. (2024) · Frontiers in Sociology · doi:10.3389/fsoc.2024.1339834 · Cited by 34 · see citing works
People can identify the likely owner of heartbeats by looking at individuals’ faces
Galvez-Pol, A., Antoine, S., Li, C., & Kilner, J. M. (2022) · Cortex · doi:10.1016/j.cortex.2022.03.003 · Cited by 21 · see citing works
How could we make a social robot? A virtual bargaining approach
Chater, N. (2023) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2022.0040 · Cited by 14 · see citing works
Children track probabilistic distributions of facial cues across individuals
Woodard, K., Plate, R. C., & Pollak, S. D. (2022) · Journal of Experimental Psychology: General · doi:10.1037/xge0001087 · Cited by 9 · see citing works
Self-citationExistential threat and responses to emotional displays of ingroup and outgroup members
Wessler, J., van der Schalk, J., Hansen, J., Klackl, J., Jonas, E., Fons, M., Doosje, B., & Fischer, A. (2023) · Group Processes & Intergroup Relations · doi:10.1177/13684302221128229 · Cited by 3 · see citing works
“However, our findings allow no conclusions about whether the smiles were “genuine” as expressions of felt joy (Feldman Barrett et al., 2019).”
ResultCaring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration
Xu, J., Wu, X., Zhang, Z., Xu, Y., Yang, Y., Zhu, J., Xiao, L., Wu, W., & He, L. (2026) · arXiv · doi:10.48550/arxiv.2606.18259
“Multimodal recognition extends inference to speech prosody, facial movement and physiology, but facial expressions are context-sensitive social cues rather than emotion readouts [29], and the value of multimodality lies in cautious cue integration rather than confident labelling [30].”
Algorithmic monoculture and social welfare
(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202
- Authors
- Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D.
- Affiliations
- Northeastern University (US)
- California Institute of Technology (US)
- Department of Electrical and Computer Engineering and Center for Cognitive and Brain Sciences, The Ohio State University
- University of Wisconsin–Madison (US)
- Corresponding author
- Barrett, Lisa Feldman
- Venue
- Psychological Science in the Public Interest
- Publisher
- SAGE Publications
- Published
- 2019-07
- Volume
- 20
- Issue
- 1
- Pages
- 1-68
- ISSN
- 1529-1006 (ISSN-L)
- 2160-0031
- PMC
- PMC6640856
- OpenAlex
- W2912483755
- Semantic Scholar
- c489b6787c5af8aca97f4761343a66f3f189b35d
- Cited by
- 1,748
- References
- 446
- Citations by year
- 2026: 238 · 2025: 266 · 2024: 230 · 2023: 302 · 2022: 269 · 2021: 227 · 2020: 186 · 2019: 26 · 2018: 3 · 2015: 1
- FWCI
- 102.82
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-11-15)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychological Science in the Public Interest (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH113234, P50MH094258, R01MH109464, R01MH061285)
- National Eye Institute (R01EY020834)
- National Cancer Institute (U01CA193632)
- National Institute on Deafness and Other Communication Disorders (R01DC014498)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD090256)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
- Keywords
- facial expressions, emotion recognition, facial motion, emotion categories, mixed emotions, social communication, emotion perception, anger, disgust, fear, happiness, sadness, surprise
- MeSH terms
- Facial Recognition, Emotions, Facial Expression, Female, Humans, Interpersonal Relations, Judgment, Male, Movement, Psychomotor Performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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