Children track probabilistic distributions of facial cues across individuals
Open accessAbstract
Children face a difficult task in learning how to reason about other people's emotions. How intensely facial configurations are displayed can vary not only according to what and how much emotion people are experiencing, but also across individuals based on differences in personality, gender, and culture. To navigate these sources of variability, children may use statistical information about other's facial cues to make interpretations about perceived emotions in others. We examined this possibility by testing children's ability to adjust to differences in the intensity of facial cues across different individuals. In the present study, children (6- to 10-year-olds) categorized the information communicated by facial configurations of emotion varying continuously from "calm" to "upset," with differences in the intensity of each actor's facial movements. We found that children's threshold for categorizing a facial configuration as "upset" shifted depending on the statistical information encountered about each of the different individuals. These results suggest that children are able to track individual differences in facial behavior and use these differences to flexibly update their interpretations of facial cues associated with emotion. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Cites
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835 · see citing works
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138 · see citing works
The NimStim set of facial expressions: Judgments from untrained research participants.
Tottenham, N., Tanaka, J. W., Leon, A. C., McCarry, T., Nurse, M., Hare, T. A., Marcus, D. J., Westerlund, A., Casey, B., & Nelson, C. (2009) · Psychiatry Research · doi:10.1016/j.psychres.2008.05.006 · Cited by 3,454 · see citing works
Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019) · Psychological Science in the Public Interest · doi:10.1177/1529100619832930 · Cited by 1,748 · see citing works
Self-citationSex differences in emotion: Expression, experience, and physiology.
Kring, A. M., & Gordon, A. H. (1998) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.74.3.686 · Cited by 1,289 · see citing works
Sex differences in emotion: Expression, experience, and physiology.
Kring, A. M., & Gordon, A. H. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.74.3.686 · Cited by 1,067 · see citing works
sjPlot - Data Visualization for Statistics in Social Science.
Lüdecke, D. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1308157 · Cited by 756 · see citing works
Aviezer, H., Hassin, R. R., Ryan, J. D., Grady, C. L., Susskind, J., Anderson, A. K., Moscovitch, M., & Shlomo Bentin, A. (2008) · Psychological Science · doi:10.1111/j.1467-9280.2008.02148.x · Cited by 652 · see citing works
Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel.
Kleinschmidt, D., & Jaeger, T. F. (2015) · Psychological Review · doi:10.1037/a0038695 · Cited by 638 · see citing works
Emotion Perception from Face, Voice, and Touch: Comparisons and Convergence
Schirmer, A., & Adolphs, R. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.001 · Cited by 383 · see citing works
Universals and cultural variations in 22 emotional expressions across five cultures.
Cordaro, D. T., Sun, R., Keltner, D., Kamble, S., Huddar, N., & McNeil, G. (2018) · Emotion · doi:10.1037/emo0000302 · Cited by 343 · see citing works
Perceptual learning for speech
Samuel, A. G., & Tanya Kraljic, A. (2009) · Attention Perception & Psychophysics · doi:10.3758/app.71.6.1207 · Cited by 307 · see citing works
Thompson‐Schill, S. L., Ramscar, M., & Chrysikou, E. G. (2009) · Current Directions in Psychological Science · doi:10.1111/j.1467-8721.2009.01648.x · Cited by 272 · see citing works
Lucas, C. G., Bridgers, S., Griffiths, T. L., & Gopnik, A. (2014) · Cognition · doi:10.1016/j.cognition.2013.12.010 · Cited by 240 · see citing works
Influence of intensity on children’s sensitivity to happy, sad, and fearful facial expressions
Gao, X., & Maurer, D. M. (2009) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2008.11.002 · Cited by 204 · see citing works
Arciuli, J., & Simpson, I. C. (2011) · Developmental Science · doi:10.1111/j.1467-7687.2009.00937.x · Cited by 181 · see citing works
What Basic Emotion Theory Really Says for the Twenty-First Century Study of Emotion
Keltner, D. J., Tracy, J. L., Sauter, D. A., & Cowen, A. (2019) · Journal of Nonverbal Behavior · doi:10.1007/s10919-019-00298-y · Cited by 172 · see citing works
The inherently contextualized nature of facial emotion perception.
Aviezer, H., Ensenberg, N. S., & Hassin, R. R. (2017) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2017.06.006 · Cited by 147 · see citing works
Linguistic Processing of Accented Speech Across the Lifespan
Cristià, A., Seidl, A. H., Vaughn, C. R., Schmale, R., Bradlow, A. R., & Floccia, C. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00479 · Cited by 146 · see citing works
Prevalence-induced concept change in human judgment
Levari, D. E., Gilbert, D. T., Wilson, T. D., Sievers, B., Amodio, D. M., & Wheatley, T. P. (2018) · Science · doi:10.1126/science.aap8731 · Cited by 145 · see citing works
Raviv, L., & Arnon, I. (2017) · Developmental Science · doi:10.1111/desc.12593 · Cited by 136 · see citing works
Speech Perception and Generalization Across Talkers and Accents
Weatherholtz, K., & Jaeger, T. F. (2016) · Oxford Research Encyclopedia of Linguistics · doi:10.1093/acrefore/9780199384655.013.95 · Cited by 127 · see citing works
Structure in talker variability: How much is there and how much can it help?
Kleinschmidt, D. (2018) · Language Cognition and Neuroscience · doi:10.1080/23273798.2018.1500698 · Cited by 122 · see citing works
The role of peripheral vision in implicit contextual cuing
Castelo-Branco, M., & Asselen, M. V. (2009) · Estudo Geral (Universidade de Coimbra) · doi:10.3758/app · Cited by 103 · see citing works
Toddlers recognize words in an unfamiliar accent after brief exposure
Schmale, R., Cristià, A., & Seidl, A. H. (2012) · Developmental Science · doi:10.1111/j.1467-7687.2012.01175.x · Cited by 100 · see citing works
Friedman, H. S., DiMatteo, M. R., & Taranta, A. (1980) · Journal of Research in Personality · doi:10.1016/0092-6566(80)90018-5 · Cited by 61 · see citing works
Developmental changes in the primacy of facial cues for emotion recognition.
Leitzke, B. T., & Pollak, S. D. (2016) · Developmental Psychology · doi:10.1037/a0040067 · Cited by 50 · see citing works
Self-citationProbabilistic learning of emotion categories
Plate, R. C., Wood, A., Woodard, K., & Pollak, S. D. (2019) · Journal of Experimental Psychology: General · doi:10.1037/xge0000529 · Cited by 45 · see citing works
Self-citationAtias, D., Todorov, A. T., Liraz, S., Eidinger, A., Dror, I., Maymon, Y., & Aviezer, H. (2018) · Journal of Experimental Psychology General · doi:10.1037/xge0000535 · Cited by 43 · see citing works
Feelings and contexts: socioecological influences on the nonverbal expression of emotion
Niedenthal, P. M., Rychlowska, M., & Wood, A. (2017) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2017.07.025 · Cited by 34 · see citing works
Categorization of Vocal Emotion Cues Depends on Distributions of Input
Woodard, K. A., Plate, R. C., Morningstar, M. A., Wood, A., & Pollak, S. D. (2021) · Affective Science · doi:10.1007/s42761-021-00038-w · Cited by 15 · see citing works
Self-citationDrift in children's categories: when experienced distributions conflict with prior learning
Kalish, C. W., Zhu, X., & Rogers, T. T. (2014) · Developmental Science · doi:10.1111/desc.12280 · Cited by 14 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Kleinschmidt D, & Jaeger TF (2011). A Bayesian belief updating model of phonetic recalibration and selective adaptation. Association for Computational Linguistics.
- Montirosso R, Peverelli M, Frigerio E, Crespi M, & Borgatti R (2010). The development of dynamic facial expression recognition at different intensities in 4‐to 18‐year‐olds. Social Development, 19(1), 71– 92.
- Ruba AL, & Pollak SD (2020). The development of emotion reasoning in infancy and early childhood. Annual Review of Developmental Psychology, 2, 503– 531.
- Schuler KD, Yang C, & Newport EL (2016). Testing the Tolerance Principle: Children form productive rules when it is more computationally efficient to do so. In CogSci (Vol. 38, pp. 2321– 2326).
- Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
- Woodard K, Plate RC, & Pollak SD (2021, April 12). Children Track Probabilistic Distributions of Facial Cues Across Individuals. Retrieved from osf.io/uec4m PMC8923917 34570561
Cited by
Sorted by citations
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
Barrett, L. F. (2022) · American Psychologist · doi:10.1037/amp0001054 · Cited by 100 · see citing works
The COVID‐19 pandemic, mask‐wearing, and emotion recognition during late‐childhood
Chester, M. A., Plate, R. C., Powell, T., Rodriguez, Y., Wagner, N. J., & Waller, R. E. (2022) · Social Development · doi:10.1111/sode.12631 · Cited by 25 · see citing works
Self-citationFoster, J. C., Hodges, H. R., Beloborodova, A. V., Cohodes, E. M., Phillips, M. Q., Anderson, E. W., Fagbenro, B., & Gee, D. G. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101399 · Cited by 13 · see citing works
Category Flexibility in Emotion Learning
Plate, R. C., Woodard, K. A., & Pollak, S. D. (2023) · Affective Science · doi:10.1007/s42761-023-00192-3 · Cited by 6 · see citing works
Self-citationHuang, S., Pollak, S. D., & Xie, W. (2025) · Nature Communications · doi:10.1038/s41467-025-62210-1 · Cited by 4 · see citing works
Self-citationStatistical learning across cognitive and affective domains: a multidimensional review
Wang, Y., Lu, L., & Wu, M. (2025) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2025.1460471 · Cited by 5 · see citing works
Faster adult implicit probabilistic statistical learning following childhood adversity
Farkas, B. C., Brezóczki, B., Vékony, T., Jacquet, P. O., & Németh, D. (2026) · Royal Society Open Science · doi:10.1098/rsos.252007
- Authors
- Woodard, K., Plate, R. C., & Pollak, S. D.
- Venue
- Journal of Experimental Psychology: General
- Published
- 2022-02
- Volume
- 151
- Issue
- 2
- Pages
- 506-511
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- PMC
- PMC8923917
- OpenAlex
- W3201711748
- Cited by
- 9
- References
- 26
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 1
- FWCI
- 0.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Experimental Psychology General (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- National Institute of Mental Health (MH61285)
- National Institute of Child Health and Human Development (U54 HD090256)
- University of Wisconsin
- National Science Foundation (DGE-1256259)
- Sponsor name not included
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Psychology › Clinical Psychology › Child and Adolescent Psychosocial and Emotional Development
- Keywords
- emotion perception, facial expressions, children, probabilistic reasoning, statistical learning, individual differences, emotion classification, social cognition development
- MeSH terms
- Child, Cues, Emotions, Face, Facial Expression, Humans, Learning
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