Perception of Discrete Emotions in Others: Evidence for Distinct Facial Mimicry Patterns

Wingenbach, Tanja S. H.; Brosnan, Mark; Pfaltz, Monique C.; Peyk, Peter; Ashwin, Chris

doi:10.1038/s41598-020-61563-5

Cited by 28 publications

(42 citation statements)

References 45 publications

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“…Overall, the results confirm our main prediction, that is, self-resembling faces are more likely to be mimicked than other-resembling faces. More specifically, participants mimicked a smile, i.e., we observed an overall larger Zygomaticus than Corrugator activity (see e.g., Wingenbach et al, 2020) to self-resembling faces displaying happiness. Likewise, self-resembling faces displaying anger provoked a convergent facial response, i.e., larger activation of Corrugator than Zygomaticus, although the changes were relatively smaller compared to the happiness.…”

Section: Discussionmentioning

confidence: 74%

“…For example, using surface EMG allows for the detection of changes to the Zygomaticus major (which pulls up lip corners), as the participant's muscle is flexed when they observe other people's smiles, and relatively relaxed when observing anger. In contrast, the Corrugator supercilii muscle (which lowers the eyebrows) is typically flexed in response to anger and relaxed when the participant is watching other people smile (Dimberg, 1982;Hess et al, 2017;Wingenbach et al, 2020).…”

Section: Emotional Mimicry Of Socially Resemblant Othersmentioning

confidence: 99%

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The Effect of Facial Self-Resemblance on Emotional Mimicry

et al. 2022

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Social resemblance, like group membership or similar attitudes, increases the mimicry of the observed emotional facial display. In this study, we investigate whether facial self-resemblance (manipulated by computer morphing) modulates emotional mimicry in a similar manner. Participants watched dynamic expressions of faces that either did or did not resemble their own, while their facial muscle activity was measured using EMG. Additionally, after each presentation, respondents completed social evaluations of the faces they saw. The results show that self-resemblance evokes convergent facial reactions. More specifically, participants mimicked the happiness and, to a lesser extent, the anger of self-resembling faces. In turn, the happiness of non-resembling faces was less likely mimicked than in the case of self-resembling faces, while anger evoked a more divergent, smile-like response. Finally, we found that social evaluations were in general increased by happiness displays, but not influenced by resemblance. Overall, the study demonstrates an interesting and novel phenomenon, particularly that mimicry can be modified by relatively subtle cues of physical resemblance.

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Section: Discussionmentioning

confidence: 74%

Section: Emotional Mimicry Of Socially Resemblant Othersmentioning

confidence: 99%

The Effect of Facial Self-Resemblance on Emotional Mimicry

et al. 2022

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“…The results may particularly be relevant to the shape of eyebrows in the sad cartoon style, and in this study, the inward eyebrow ends were raised in an exaggerated way, which might have an impact on the obtained results. Another critical factor that should be taken into consideration is that the findings of this study might not be generalized to the detection of cartoon expressions in non-Eastern cultures where observers are less likely to judge facial emotions with the upper part of the face ( Yuki et al, 2007 ; Jack et al, 2009 ; Wingenbach et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

et al. 2021

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Cartoon faces are widely used in social media, animation production, and social robots because of their attractive ability to convey different emotional information. Despite their popular applications, the mechanisms of recognizing emotional expressions in cartoon faces are still unclear. Therefore, three experiments were conducted in this study to systematically explore a recognition process for emotional cartoon expressions (happy, sad, and neutral) and to examine the influence of key facial features (mouth, eyes, and eyebrows) on emotion recognition. Across the experiments, three presentation conditions were employed: (1) a full face; (2) individual feature only (with two other features concealed); and (3) one feature concealed with two other features presented. The cartoon face images used in this study were converted from a set of real faces acted by Chinese posers, and the observers were Chinese. The results show that happy cartoon expressions were recognized more accurately than neutral and sad expressions, which was consistent with the happiness recognition advantage revealed in real face studies. Compared with real facial expressions, sad cartoon expressions were perceived as sadder, and happy cartoon expressions were perceived as less happy, regardless of whether full-face or single facial features were viewed. For cartoon faces, the mouth was demonstrated to be a feature that is sufficient and necessary for the recognition of happiness, and the eyebrows were sufficient and necessary for the recognition of sadness. This study helps to clarify the perception mechanism underlying emotion recognition in cartoon faces and sheds some light on directions for future research on intelligent human-computer interactions.

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“…2 shows the position of channels on the face and neck muscles of subjects. Wingenbach et al [18] stated that chick movements express happiness and smile, and forehead movements express sad and angry emotions. Zygomaticusmajor muscle cause chick movement.…”

Section: B Emg Signal Acquisitionmentioning

confidence: 99%

Recognition of Emotion in Indian Classical Dance Using EMG Signal

Mithbavkar¹,

Shah²

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Indian classical dance forms like Kathak are an enrichment of Indian culture and tradition. These dance forms glorify its beauty by expressing nine emotions (Navras) such as Adbhut (amazed), Bhayanaka (fearful), Hasya (humorous), Karuna (tragic), Raudra (fierce), Shringar (loving smile), Veer (heroic), Bibhatsa (disgusted), and Shant (peaceful). Identifying correct emotions is an important task. The objective of this research work is to recognize Navras in the Kathak dance. Proposed research work can assist dance teachers in an accurate and unbiased evaluation process of dance examination. This research work analyzed the Electromyogram (EMG) signals acquired from eleven subjects. The EMG signals collected from the various locations on the face and neck represent the emotions and head movement. The EMG signals are processed to extract integrated EMG (IEMG) features. This research introduced a new feature named 'difference in IEMG feature' for improving the accuracy of emotion recognition. For the classification of nine emotions, the Least Square Support Vector Machine (LSSVM), Nonlinear Autoregressive Exogenous Network (NARX), and Long-and Short-Term Memory (LSTM) classifiers were used. The classifiers' performance is judged with head motion and without head motion. The classification accuracies are calculated using a maximum, variance, and mean of the feature. LSSVM, NARX, and LSTM classifiers achieved 60.80%, 81.67%, and 92.28% classification accuracies, respectively, using the IEMG feature and head motion. Using the new feature, LSSVM, NARX, and LSTM classifiers achieved 64.29%, 81.27%, and 93.63% classification accuracies, respectively. The overall classification accuracy improved by 1.46% by using the new feature.

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Perception of Discrete Emotions in Others: Evidence for Distinct Facial Mimicry Patterns

Cited by 28 publications

References 45 publications

The Effect of Facial Self-Resemblance on Emotional Mimicry

The Effect of Facial Self-Resemblance on Emotional Mimicry

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

Recognition of Emotion in Indian Classical Dance Using EMG Signal

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