Most of the existing sets of facial expressions of emotion contain static photographs. While increasing demand for stimuli with enhanced ecological validity in facial emotion recognition research has led to the development of video stimuli, these typically involve full-blown (apex) expressions. However, variations of intensity in emotional facial expressions occur in real life social interactions, with low intensity expressions of emotions frequently occurring. The current study therefore developed and validated a set of video stimuli portraying three levels of intensity of emotional expressions, from low to high intensity. The videos were adapted from the Amsterdam Dynamic Facial Expression Set (ADFES) and termed the Bath Intensity Variations (ADFES-BIV). A healthy sample of 92 people recruited from the University of Bath community (41 male, 51 female) completed a facial emotion recognition task including expressions of 6 basic emotions (anger, happiness, disgust, fear, surprise, sadness) and 3 complex emotions (contempt, embarrassment, pride) that were expressed at three different intensities of expression and neutral. Accuracy scores (raw and unbiased (Hu) hit rates) were calculated, as well as response times. Accuracy rates above chance level of responding were found for all emotion categories, producing an overall raw hit rate of 69% for the ADFES-BIV. The three intensity levels were validated as distinct categories, with higher accuracies and faster responses to high intensity expressions than intermediate intensity expressions, which had higher accuracies and faster responses than low intensity expressions. To further validate the intensities, a second study with standardised display times was conducted replicating this pattern. The ADFES-BIV has greater ecological validity than many other emotion stimulus sets and allows for versatile applications in emotion research. It can be retrieved free of charge for research purposes from the corresponding author.

There has been much research on sex differences in the ability to recognise facial expressions of emotions, with results generally showing a female advantage in reading emotional expressions from the face. However, most of the research to date has used static images and/or ‘extreme’ examples of facial expressions. Therefore, little is known about how expression intensity and dynamic stimuli might affect the commonly reported female advantage in facial emotion recognition. The current study investigated sex differences in accuracy of response (Hu; unbiased hit rates) and response latencies for emotion recognition using short video stimuli (1sec) of 10 different facial emotion expressions (anger, disgust, fear, sadness, surprise, happiness, contempt, pride, embarrassment, neutral) across three variations in the intensity of the emotional expression (low, intermediate, high) in an adolescent and adult sample (N = 111; 51 male, 60 female) aged between 16 and 45 (M = 22.2, SD = 5.7). Overall, females showed more accurate facial emotion recognition compared to males and were faster in correctly recognising facial emotions. The female advantage in reading expressions from the faces of others was unaffected by expression intensity levels and emotion categories used in the study. The effects were specific to recognition of emotions, as males and females did not differ in the recognition of neutral faces. Together, the results showed a robust sex difference favouring females in facial emotion recognition using video stimuli of a wide range of emotions and expression intensity variations.

These results provide independent validation from molecular data for the heritability of reporting environmental measures, and show that this heritability is in part due to both common variants and the confounding effect of personality.

Covert facial mimicry involves subtle facial muscle activation in observers when they perceive the facial emotional expressions of others. It remains uncertain whether prototypical facial features in emotional expressions are being covertly mimicked and also whether covert facial mimicry involves distinct facial muscle activation patterns across muscles per emotion category, or simply distinguishes positive versus negative valence in observed facial emotions. To test whether covert facial mimicry is emotion-specific, we measured facial electromyography (EMG) from five muscle sites (corrugator supercilii, levator labii, frontalis lateralis, depressor anguli oris, zygomaticus major) whilst participants watched videos of people expressing 9 different basic and complex emotions and a neutral expression. This study builds upon previous research by including a greater number of facial muscle measures and emotional expressions. It is the first study to investigate activation patterns across muscles during facial mimicry and to provide evidence for distinct patterns of facial muscle activation when viewing individual emotion categories, suggesting that facial mimicry is emotion-specific, rather than just valence-based.

A B S T R A C TBackground: A plethora of research on facial emotion recognition in autism spectrum disorders (ASD) exists and reported deficits in ASD compared to controls, particularly for negative basic emotions. However, these studies have largely used static high intensity stimuli. The current study investigated facial emotion recognition across three levels of expression intensity from videos, looking at accuracy rates to investigate impairments in facial emotion recognition and error patterns ('confusions') to explore potential underlying factors. Method: Twelve individuals with ASD (9 M/3F; M(age) = 17.3) and 12 matched controls (9 M/3F; M(age) = 16.9) completed a facial emotion recognition task including 9 emotion categories (anger, disgust, fear, sadness, surprise, happiness, contempt, embarrassment, pride) and neutral, each expressed by 12 encoders at low, intermediate, and high intensity. Results: A facial emotion recognition deficit was found overall for the ASD group compared to controls, as well as deficits in recognising individual negative emotions at varying expression intensities. Compared to controls, the ASD group showed significantly more, albeit typical, confusions between emotion categories (at high intensity), and significantly more confusions of emotions as 'neutral' (at low intensity). Conclusions: The facial emotion recognition deficits identified in ASD, particularly for negative emotions, are in line with previous studies using other types of stimuli. Error analysis showed that individuals with ASD had difficulties detecting emotional information in the face (sensitivity) at low intensity, and correctly identifying emotional information (specificity) at high intensity. These results suggest different underlying mechanisms for the facial emotion recognition deficits at low vs high expression intensity.

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According to embodied cognition accounts, viewing others’ facial emotion can elicit the respective emotion representation in observers which entails simulations of sensory, motor, and contextual experiences. In line with that, published research found viewing others’ facial emotion to elicit automatic matched facial muscle activation, which was further found to facilitate emotion recognition. Perhaps making congruent facial muscle activity explicit produces an even greater recognition advantage. If there is conflicting sensory information, i.e., incongruent facial muscle activity, this might impede recognition. The effects of actively manipulating facial muscle activity on facial emotion recognition from videos were investigated across three experimental conditions: (a) explicit imitation of viewed facial emotional expressions (stimulus-congruent condition), (b) pen-holding with the lips (stimulus-incongruent condition), and (c) passive viewing (control condition). It was hypothesised that (1) experimental condition (a) and (b) result in greater facial muscle activity than (c), (2) experimental condition (a) increases emotion recognition accuracy from others’ faces compared to (c), (3) experimental condition (b) lowers recognition accuracy for expressions with a salient facial feature in the lower, but not the upper face area, compared to (c). Participants (42 males, 42 females) underwent a facial emotion recognition experiment (ADFES-BIV) while electromyography (EMG) was recorded from five facial muscle sites. The experimental conditions’ order was counter-balanced. Pen-holding caused stimulus-incongruent facial muscle activity for expressions with facial feature saliency in the lower face region, which reduced recognition of lower face region emotions. Explicit imitation caused stimulus-congruent facial muscle activity without modulating recognition. Methodological implications are discussed.

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