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Reconsidering the Duchenne Smile: Formalizing and Testing Hypotheses About Eye Constriction and Positive Emotion

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Abstract

The common view of emotional expressions is that certain configurations of facial-muscle movements reliably reveal certain categories of emotion. The principal exemplar of this view is the Duchenne smile, a configuration of facial-muscle movements (i.e., smiling with eye constriction) that has been argued to reliably reveal genuine positive emotion. In this paper, we formalized a list of hypotheses that have been proposed regarding the Duchenne smile, briefly reviewed the literature weighing on these hypotheses, identified limitations and unanswered questions, and conducted two empirical studies to begin addressing these limitations and answering these questions. Both studies analyzed a database of 751 smiles observed while 136 participants completed experimental tasks designed to elicit amusement, embarrassment, fear, and physical pain. Study 1 focused on participants’ self-reported positive emotion and Study 2 focused on how third-party observers would perceive videos of these smiles. Most of the hypotheses that have been proposed about the Duchenne smile were either contradicted by or only weakly supported by our data. Eye constriction did provide some information about experienced positive emotion, but this information was lacking in specificity, already provided by other smile characteristics, and highly dependent on context. Eye constriction provided more information about perceived positive emotion, including some unique information over other smile characteristics, but context was also important here as well. Overall, our results suggest that accurately inferring positive emotion from a smile requires more sophisticated methods than simply looking for the presence/absence (or even the intensity) of eye constriction.

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Notes

  1. Note that an earlier version of Study 1 was previously published as a conference paper (Girard et al., 2019).

  2. FACS includes instructions for determining whether an image includes AU6, AU12, or both (Ekman et al., 2002, pp. 188–193).

  3. Varying effects are also called “random effects.”

  4. Population-level effects are also called “fixed effects.”

  5. In this context, a simplex is a vector where each element is a real number between 0 and 1 and all the elements add up to 1.

  6. Inter-rater reliability under ICC model 2A equals \( {\hat{\sigma}}_o^2/\left({\hat{\sigma}}_o^2+\left({\hat{\sigma}}_r^2+{\hat{\sigma}}_e^2\right)/k\right) \) where \( {\hat{\sigma}}_o^2 \) is the estimated object (i.e., video) variance, \( {\hat{\sigma}}_r^2 \) is the estimated rater variance, \( {\hat{\sigma}}_e^2 \) is the estimated residual variance, and k is the number of raters whose scores are being averaged per object.

  7. McNeish and Wolf (2020) provide compelling arguments for why this approach of estimating a CFA model with freely estimated factor loadings and residuals is preferable to using a simpler approach, such as sum or mean scores, even with highly inter-correlated indicators.

  8. Testing this theory is beyond the scope of this paper (and would require looking beyond just smiles). However, we note that the non-significant partial effects of eye constriction in the joke task are problematic for this theory.

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Authors and Affiliations

Authors

Contributions

JFC and LY designed, collected, and provided consultation about the BP4D+ dataset. JFC and JMG managed the process of annotating the dataset for facial action units. JMG conceived the present studies, wrote the code, collected the perceptual ratings, ran the statistical analyses, and wrote the initial draft of the manuscript under the advisement of LPM. All authors contributed to editing and approved the submitted manuscript.

Corresponding author

Correspondence to Jeffrey M. Girard.

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Funding

This material is based upon work partially supported by the National Science Foundation (1629716, 1629898, 1722822, 1734868) and National Institutes of Health (MH096951). Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or National Institutes of Health, and no official endorsement should be inferred.

Data Availability

The BP4D+ dataset, including videos and FACS annotations, is available from Binghamton University. Because access to this data requires signing a license, instead of providing direct access to the data as part of this work, we are providing code to reproducibly derive our data from the distributed dataset. This code, as well as other materials not covered by the BP4D+ license, is available online at https://osf.io/k3g2e/ under an open source license.

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

The collection of the BP4D+ dataset was approved and overseen by the Institutional Review Board at Binghamton University. The collection of perceptual ratings of videos from the BP4D+ dataset was approved and overseen by the Institutional Review Board at Carnegie Mellon University.

Consent to Participate

All participants in the BP4D+ dataset provided informed consent to participate and to have their data shared with other researchers, shown to participants in further studies, and printed in scientific journals.

Code Availability

All code used to derive our measures from the BP4D+ dataset and analyze the data is available online at the aforementioned link.

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Handling Editor: Jonathan Gratch

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Girard, J.M., Cohn, J.F., Yin, L. et al. Reconsidering the Duchenne Smile: Formalizing and Testing Hypotheses About Eye Constriction and Positive Emotion. Affec Sci 2, 32–47 (2021). https://doi.org/10.1007/s42761-020-00030-w

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