The different shades of laughter: when do we laugh and when do we mimic other's laughter?

Putting a label on someone: impact of schizophrenia stigma on emotional mimicry, liking, and interpersonal closeness

Affiliation is both an antecedent and a consequence of emotional mimicry (i.e. imitating a counterpart's emotional expression). Thus, interacting with a disliked partner can decrease emotional mimicry, which in turn can further decrease liking. This perpetuating circle has not been investigated in the context of mental health stigma yet. The present study tested the influence of the label "schizophrenia" on liking, interpersonal closeness, and emotional mimicry. In an online experiment (n = 201), participants recruited from the general population saw several videos of actors displaying emotional expressions. Actors were described with one of four labels: "schizophrenia", "healthy", "diabetes", and a negative adjective (e.g. "hot-tempered"). Emotional mimicry was measured using OpenFace 2.2. Liking and interpersonal closeness were assessed with questionnaires. Overall, compared to other labels, participants reported less liking and interpersonal closeness to the actor with the schizophrenia label. However, no effect on emotional mimicry was found. The decreased liking of the schizophrenia actors was explained by a lack of knowledge about schizophrenia and the explicit stigma of schizophrenia. Our study contributes to the literature by highlighting the need to reduce the stigma of schizophrenia.

Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding

Although electromyography (EMG) remains the standard, researchers have begun using automated facial action coding system (FACS) software to evaluate spontaneous facial mimicry despite the lack of evidence of its validity. Using the facial EMG of the zygomaticus major (ZM) as a standard, we confirmed the detection of spontaneous facial mimicry in action unit 12 (AU12, lip corner puller) via an automated FACS. Participants were alternately presented with real-time model performance and prerecorded videos of dynamic facial expressions, while simultaneous ZM signal and frontal facial videos were acquired. Facial videos were estimated for AU12 using FaceReader, Py-Feat, and OpenFace. The automated FACS is less sensitive and less accurate than facial EMG, but AU12 mimicking responses were significantly correlated with ZM responses. All three software programs detected enhanced facial mimicry by live performances. The AU12 time series showed a roughly 100 to 300 ms latency relative to the ZM. Our results suggested that while the automated FACS could not replace facial EMG in mimicry detection, it could serve a purpose for large effect sizes. Researchers should be cautious with the automated FACS outputs, especially when studying clinical populations. In addition, developers should consider the EMG validation of AU estimation as a benchmark.