Facial Reactions Research Articles

Advancing Virtual Interviews: AI-Driven Facial Emotion Recognition for Better Recruitment

Behavior analysis involves the detailed process of identifying, modeling, and comprehending the various nuances and patterns of emotional expressions exhibited by individuals. It poses a significant challenge to accurately detect and predict facial emotions, especially in contexts like remote interviews, which have become increasingly prevalent. Notably, many participants struggle to convey their thoughts to interviewers with a happy expression and good posture, which may unfairly diminish their chances of employment, despite their qualifications. To address this challenge, artificial intelligence techniques such as image classification offer promising solutions. By leveraging AI models, behavior analysis can be applied to perceive and interpret facial reactions, thereby paving the way to anticipate future behaviors based on learned patterns to the participants. Despite existing works on facial emotion recognition (FER) using image classification, there is limited research focused on platforms like remote interviews and online courses. In this paper, our primary focus lies on emotions such as happiness, sadness, anger, surprise, eye contact, neutrality, smile, confusion, and stooped posture. We have curated our dataset, comprising a diverse range of sample interviews captured through participants' video recordings and other images documenting facial expressions and speech during interviews. Additionally, we have integrated existing datasets such as FER 2013 and the Celebrity Emotions dataset. Through our investigation, we explore a variety of AI and deep learning methodologies, including VGG19, ResNet50V2, ResNet152V2, Inception-ResNetV2, Xception, EfficientNet B0, and YOLO V8 to analyze facial patterns and predict emotions. Our results demonstrate an accuracy of 73% using the YOLO v8 model. However, we discovered that the categories of happy and smile, as well as surprised and confused, are not disjoint, leading to potential inaccuracies in classification. Furthermore, we considered stooped posture as a non-essential class since the interviews are conducted via webcam, which does not allow for the observation of posture. By removing these overlapping categories, we achieved a remarkable accuracy increase to around 76.88% using the YOLO v8 model.

How emotion is experienced and expressed in multiple cultures: a large-scale experiment across North America, Europe, and Japan.

Core to understanding emotion are subjective experiences and their expression in facial behavior. Past studies have largely focused on six emotions and prototypical facial poses, reflecting limitations in scale and narrow assumptions about the variety of emotions and their patterns of expression. We examine 45,231 facial reactions to 2,185 evocative videos, largely in North America, Europe, and Japan, collecting participants' self-reported experiences in English or Japanese and manual and automated annotations of facial movement. Guided by Semantic Space Theory, we uncover 21 dimensions of emotion in the self-reported experiences of participants in Japan, the United States, and Western Europe, and considerable cross-cultural similarities in experience. Facial expressions predict at least 12 dimensions of experience, despite massive individual differences in experience. We find considerable cross-cultural convergence in the facial actions involved in the expression of emotion, and culture-specific display tendencies-many facial movements differ in intensity in Japan compared to the U.S./Canada and Europe but represent similar experiences. These results quantitatively detail that people in dramatically different cultures experience and express emotion in a high-dimensional, categorical, and similar but complex fashion.

Computer Vision for E-learning Student Face Reaction Detection for Improving Learning Rate

In the advent of e-learning, understanding student engagement and reaction is crucial for improving the quality of education and enhancing the learning rate. With the advancement of computer vision technologies, there is a significant opportunity to analyze and interpret student reactions in a non-intrusive manner. This study proposes a novel framework employing Faster R-CNN integrated with DenseNet architecture for real-time detection of student facial reactions during e-learning sessions. The proposed method leverages the strengths of Faster R-CNN in generating high-quality region proposals for object detection tasks, coupled with the DenseNet’s efficiency in feature propagation and reduction in the number of parameters, which is well-suited for processing the intricate patterns in facial expressions. Our approach begins with the application of Faster R-CNN to extract potential facial regions with high accuracy and reduced computational cost. The integration of DenseNet as a backbone for feature extraction within Faster R-CNN capitalizes on its densely connected convolutional networks, ensuring maximum information flow between layers in the network. By doing so, the system becomes exceptionally adept at recognizing subtle changes in facial features that indicate various student reactions, such as confusion, engagement, or boredom. We conducted a series of experiments using a diverse dataset of e-learning interactions, collected under various lighting conditions and involving multiple ethnicities to ensure robustness and generalizability. The model was trained and validated on this dataset, and the results demonstrate a significant improvement in detection rates of student reactions compared to existing methods.

Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study

BackgroundAtypical anticipation of social reward has been shown to lie at the core of the social challenges faced by individuals with autism spectrum disorder (ASD). However, previous research has yielded inconsistent results and has often overlooked crucial characteristics of stimuli. Here, we investigated ASD reward processing using social and nonsocial tangible stimuli, carefully matched on several key dimensions. MethodsWe examined the anticipation and consumption of social (interpersonal touch) and nonsocial (flavored milk) rewards in 25 high-functioning individuals with ASD and 25 neurotypical adult individuals. In addition to subjective ratings of wanting and liking, we measured physical energetic expenditure to obtain the rewards, brain activity with neuroimaging, and facial reactions through electromyography on a trial-by-trial basis. ResultsParticipants with ASD did not exhibit reduced motivation for social or nonsocial rewards; their subjective ratings, motivated efforts, and facial reactions were comparable to those of neurotypical participants. However, anticipation of higher-value rewards increased neural activation in lateral parietal cortices, sensorimotor regions, and the orbitofrontal cortex. Moreover, participants with ASD exhibited hyperconnectivity between frontal medial regions and occipital regions and the thalamus. ConclusionsIndividuals with ASD who experienced rewards with tangible characteristics, whether social or nonsocial, displayed typical subjective and objective motivational and hedonic responses. Notably, the observed hyperactivations in sensory and attentional nodes during anticipation suggest atypical sensory overprocessing of forthcoming rewards rather than decreased reward value. While these atypicalities may not have manifested in observable behavior here, they could impact real-life social interactions that require nuanced predictions, potentially leading to the misperception of reduced interest in rewarding social stimuli in ASD.

Age-related disgust responses to signs of disease

ABSTRACT Previous studies found similarities in adults’ disgust responses to benign (e.g. obesity) and actual disease signs (e.g. influenza). However, limited research has compared visual (i.e. benign and actual) to cognitive (i.e. disease label) disease cues in different age groups. The current study investigated disgust responses across middle childhood (7–9 years), late childhood (10–12 years), adolescence (13–17 years), and adulthood (18+ years). Participants viewed individuals representing a benign visual disease (obese), sick-looking (staphylococcus), sick-label (cold/flu), and healthy condition. Disgust-related outcomes were: (1) avoidance, or contact level with apparel the individual was said to have worn, (2) disgust facial reactions, and (3) a combination of (1) and (2). Avoidance was greater for the sick-looking and sick-label than the healthy and obese conditions. For facial reaction and combination outcomes, middle childhood participants responded with greater disgust to the sick-looking than the healthy condition, while late childhood participants expressed stronger disgust towards the sick-looking and obese conditions than the healthy condition. Adolescents and adults exhibited stronger disgust towards sick-label and sick-looking than obese and healthy conditions. Results suggest visual cues are central to children’s disgust responses whereas adolescents and adult responses considered cognitive cues.

Infants' facial electromyographic responses to the sight of emotional interpersonal touch.

Adult studies have shown that observed interpersonal touch provides crucial information about others' emotional states. Yet, despite the unique communicative function of touch during development, very little is known about infants' sensitivity to the emotional valence of observed touches. To investigate this issue, we measured facial electromyographic (EMG) activity in response to positive (caress) and negative (scratches) observed touches in a sample of 11-month-old infants. Facial EMG activity was measured over the zygomaticus major (ZM) and corrugator supercilii muscles, respectively involved in positive (i.e., smiling) and negative (i.e., frowning) facial expressions. Results have shown distinct activations of the ZM during the observation of scratches and caresses. In particular, significantly greater activation of the ZM (smiling muscle) emerged specifically in response to the observation of caresses compared to scratches. Our finding suggests that, in infancy, observed affective touches can evoke emotional facial reactions.

Facial reactions to face representations in art: An electromyography study.

Facial reactions to face representations in art: An electromyography study.

Learning Person-Specific Cognition From Facial Reactions for Automatic Personality Recognition

This paper proposes to recognise the true (self-reported) personality traits from the target subject's cognition simulated from facial reactions. This approach builds on the following two findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions, individuals' nonverbal behaviours are influenced by their conversational partner's behaviours. In this context, we hypothesise that during a dyadic interaction, a target subject's facial reactions are driven by two main factors: their internal (person-specific) cognitive process, and the externalised nonverbal behaviours of their conversational partner. Consequently, we propose to represent the target subject's (defined as the listener) person-specific cognition in the form of a person-specific CNN architecture that has unique architectural parameters and depth, which takes audio-visual non-verbal cues displayed by the conversational partner (defined as the speaker) as input, and is able to reproduce the target subject's facial reactions. Each person-specific CNN is explored by the Neural Architecture Search (NAS) and a novel adaptive loss function, which is then represented as a graph representation for recognising the target subject's true personality. Experimental results not only show that the produced graph representations are well associated with target subjects' personality traits in both human-human and human-machine interaction scenarios, and outperform the existing approaches with significant advantages, but also demonstrate that the proposed novel strategies help in learning more reliable personality representations.

Emotional Expressivity is a Reliable Signal of Surprise

We consider the problem of inferring what happened to a person in a social task from momentary facial reactions. To approach this, we introduce several innovations. First, rather than predicting what (observers think) someone feels, we predict objective features of the event that immediately preceded the facial reactions. Second, we draw on appraisal theory, a key psychological theory of emotion, to characterize features of this immediately-preceded event. Specifically, we explore if facial expressions reveal if the event is expected, goal-congruent, and norm-compatible. Finally, we argue that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emotional expressivity</i> serves as a better feature for characterizing momentary expressions than traditional facial features. Specifically, we use supervised machine learning to predict third-party judgments of emotional expressivity with high accuracy, and show this model improves inferences about the nature of the event that preceded an emotional reaction. Contrary to common sense, “genuine smiles” failed to predict if an event advanced a person's goals. Rather, expressions best revealed if an event violated expectations. We discussed the implications of these findings for the interpretation of facial displays and potential limitations that could impact the generality of these findings.

A brainstem circuit for the expression of defensive facial reactions in rat

The brainstem houses neuronal circuits that control homeostasis of vital functions. These include the depth and rate of breathing1,2 and, critically, apnea, a transient cessation of breathing that prevents noxious vapors from entering further into the respiratory tract. Current thinking is that this reflex is mediated by two sensory pathways. One known pathway involves vagal and glossopharyngeal afferents that project to the nucleus of the solitary tract.3,4,5 Yet, apnea induced by electrical stimulation of the nasal epithelium or delivery of ammonia vapors to the nose persists after brainstem transection at the pontomedullary junction, indicating that the circuitry that mediates this reflex is intrinsic to the medulla.6 A second potential pathway, consistent with this observation, involves trigeminal afferents from the nasal cavity that project to the muralis subnucleus of the spinal trigeminal complex.7,8 Notably, the apneic reflex is not dependent on olfaction as it can be initiated even after disruption of olfactory pathways.9 We investigated how subnucleus muralis cells mediate apnea in rat. By means of electrophysiological recordings and lesions in anesthetized rats, we identified a pathway from chemosensors in the nostrils through the muralis subnucleus and onto both the preBötzinger and facial motor nuclei. We then monitored breathing and orofacial reactions upon ammonia delivery near the nostril of alert, head-restrained rats. The apneic reaction was associated with a grimace, characterized by vibrissa protraction, wrinkling of the nose, and squinting of the eyes. Our results show that a brainstem circuit can control facial expressions for nocifensive and potentially pain-inducing stimuli.

Hybrid heuristic mechanism for occlusion aware facial expression recognition scheme using patch based adaptive CNN with attention mechanism

In computer vision, the recognition of expressions from partially occluded faces is one of the serious problems. By the prior recognizing techniques it can solve the issue by various assumptions. A benchmark-guided branch was proposed for detecting and eliminating the manipulated features from the occluded regions since the human visual system is proficient for eliminating the occlusion and the appropriate focal point was obtained on the non-occluded areas. In recent years deep learning has attained a great place in the recognition of facial reactions Still, the precision of facial expression is affected by the occlusion and large skew. In this research work, a deep structure-based occlusionaware facial expression recognition mechanism is introduced to provide superior recognition results. Firstly, the required image is taken from publically provided online sources and the gathered images are subjected to the face extraction method. The face extraction method is done via the ViolaJones method for the extraction of redundant patterns from the original images. Secondly, the extracted face features are given to the pattern recognition stage, where the Adaptive CNN with Attention Mechanism (ACNN-AM) is introduced. This mechanism automatically forms the occluded region of the face and the focal point was on the most discriminative un-occluded regions. Moreover, the hidden patterns in the Occlusion aware facial expressions are identified through the Hybrid Galactic Swarm Yellow Saddle Goatfish Optimization (HGSYSGO). Finally, the overall effectiveness of the developed occlusion aware facial expression recognition model is examined through the comparative analysis of different existing baseline recognition techniques.

Facial Reactivity to Emotional Stimuli is Related to Empathic Concern, Empathic Distress, and Depressive Symptoms in Social Work Students.

Helping professionals are exposed daily to the emotional burden of their vulnerable clients and are at risk of unconscious emotional contagion that may lead to stress and emotional distress. Being aware of their own susceptibility to emotional contagion, however, can improve their well-being. This study aimed to propose an objective measure of emotional contagion, complementary to the Emotional Contagion Scale, and to evaluate its construct and predictive validity. To do so, we turned to FACET, an automatic facial coding software using the Facial Action Coding System, to measure participants' facial expressions as they watched movie clips eliciting specific emotional responses. Results show that both tools to measure emotional contagion (objective and self-reported) are complementary, but they do not measure the same psychosocial constructs. Also, the new objective measure of emotional contagion seems to predict emotional empathy and the risk of developing depressive symptoms among this study's participants.

Eye-Blink Event Detection Using a Neural-Network-Trained Frame Segment for Woman Drivers in Saudi Arabia

Women have been allowed to drive in Saudi Arabia since 2018, revoking a 30-year ban that also adhered to the traffic rules provided in the country. Conventional drivers are often monitored for safe driving by monitoring their facial reactions, eye blinks, and expressions. As driving experience and vehicle handling features have been less exposed to novice women drivers in Saudi Arabia, technical assistance and physical observations are mandatory. Such observations are sensed as images/video frames for computer-based analyses. Precise computer vision processes are employed for detecting and classifying events using image processing. The identified events are unique to novice women drivers in Saudi Arabia, assisting with their vehicle usage. This article introduces the Event Detection using Segmented Frame (ED-SF) method to improve the abnormal Eye-Blink Detection (EBD) of women drivers. The eye region is segmented using variation pixel extraction in this process. The pixel extraction process requires textural variation identified from different frames. The condition is that the frames are to be continuous in the event detection. This method employs a convolution neural network with two hidden layer processes. In the first layer, continuous and discrete frame differentiations are identified. The second layer is responsible for segmenting the eye region, devouring the textural variation. The variations and discrete frames are used for training the neural network to prevent segment errors in the extraction process. Therefore, the frame segment changes are used for Identifying the expressions through different inputs across different texture luminosities. This method applies to less-experienced and road-safety-knowledge-lacking woman drivers who have initiated their driving journey in Saudi-Arabia-like countries. Thus the proposed method improves the EBD accuracy by 9.5% compared to Hybrid Convolutional Neural Networks (HCNN), Long Short-Term Neural Networks (HCNN + LSTM), Two-Stream Spatial-Temporal Graph Convolutional Networks (2S-STGCN), and the Customized Driving Fatigue Detection Method CDFDM.

The effect of the severity of neurocognitive disorders on emotional and motor responses to music.

The successful design of musical interventions for dementia patients requires knowledge of how rhythmic abilities change with disease severity. In this study, we tested the impact of the severity of the neurocognitive disorders (NCD) on the socioemotional and motor responses to music in three groups of patients with Major NCD, Mild NCD, or No NCD. Patients were asked to tap to a metronomic or musical rhythm while facing a live musician or through a video. We recorded their emotional facial reactions and their sensorimotor synchronization (SMS) abilities. Patients with No NCD or Mild NCD expressed positive socioemotional reactions to music, but patients with Major NCD did not, indicating a decrease in the positive emotional impact of music at this stage of the disease. SMS to a metronome was less regular and less precise in patients with a Major NCD than in patients with No NCD or Mild NCD, which was not the case when tapping with music, particularly in the presence of a live musician, suggesting the relevance of live performance for patients with Major NCD. These findings suggest that the socioemotional and motor reactions to music are negatively affected by the progression of the NCD.

The sight of one's own body: Could qEEG help predict the treatment response in anorexia nervosa?

Aims of the studyThe study aims to identify the differences in brain activity between participants with anorexia nervosa and healthy control using visual stimulus conditions combined with the quantitative dense-array EEG recording analysis method called Brain Activation Sequences (BAS).Materials and methods23 participants with anorexia nervosa and 21 healthy controls were presented with visual stimuli, including the subject’s facial expressions and body images. The 128-channel EEG data were processed using BAS and displayed as activity in up to 66 brain regions. Subsequent cluster analysis was used to identify groups of participants exhibiting area-specific activation patterns.ResultsCluster analysis identified three distinct groups: one including all healthy controls (HC) and two consisting of all participants with anorexia (AN-I with 19 participants and AN-II with four participants). The AN-I and AN-II groups differed in their response to treatment. Comparisons of HC vs. AN confirmed the dominance of the right cerebral hemisphere in participants with anorexia nervosa in two of the three reported conditions. The facial expressions condition, specifically the facial reaction expressing disgust, indicates the existence of a social attentional bias toward faces, whereas emotions remained undetected in participants. High limbic activity, medial frontal gyrus involvement, low fusiform cortex activity, and milder visual cortex activity in healthy controls compared to participants indicate that the facial expression stimulus is perceived by healthy subjects primarily as an emotion, not as the face itself. In the body image condition, participants showed higher activity in the fusiform gyrus and right insula, indicating activation of the brain’s “fear network.”ConclusionThe study describes a specific pattern of brain activation in response to facial expression of disgust and body images that likely contributes to social-cognitive and behavioral impairments in anorexia. In addition, the substantial difference in the pattern of brain activation within the participants with AN and its association with treatment resistance deserves special attention because of its potential to develop a clinically useful prediction tool and identify potential targets for, for example, neuromodulatory treatments and/or individualized psychotherapy.

Facial reactions to emotional films in young children with conduct problems and varying levels of callous-unemotional traits.

Elevated levels of callous-unemotional (CU) traits have proven useful for identifying a distinct subgroup of children whose conduct problems (CP) are early emerging, severe, persistent, and underpinned by aberrant emotional processing. The early childhood emotional experiences and expressions of CP subtypes are poorly understood, despite their importance to understanding the problematic attachments and atypical social affiliation experienced by children with elevated CU traits. The current study aimed to test for differences in facial emotional reactions to mood-inducing film clips in children with CP and varying levels of CU traits. We compared facial emotional reactions during a developmentally appropriate mood induction task in a mixed-sex sample of clinic-referred preschool children (Mage = 3.64 years, SD = 0.63, 66.9% male) classified as CP with elevated levels of CU traits (CP + CU; n = 25) versus low CU traits (CP-only; n = 47), and typically developing children (TD; n = 28). Relative to TD children, children with clinical CP showed less congruent and more incongruent facial emotional expressions to sad and happy film clips, controlling for child sex, age, and ethnicity. Consistent with older samples, young children with CP show atypical facial emotional expressions in response to positive and negative emotional stimuli. Findings have implications for developmental models of childhood antisocial behavior and can inform the development of targeted interventions.

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm.

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

Investigating the Relationship between Facial Mimicry and Empathy.

Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in the empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient, capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, this study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and after the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social and healthcare providers.

Monitoring the emotional facial reactions of individuals with antisocial personality disorder during the retrieval of self-defining memories.

While a deficit in the recognition of facial expression has been demonstrated in persons with antisocial personality disorder (ASPD), few studies have investigated how individuals with ASPD produce their own emotional facial expressions. This study examines the production of facial emotional expressions of male inpatients with ASPD in a forensic hospital compared with a control group as they retrieve autobiographical memories. This design constitutes a specific ecological experimental approach fostering the evocation of personal feelings. Two indicators characterizing the activation of facial expression were used: activation of emotional action units and emotional dominance. The results showed that individuals with ASPD 1) activated angrier facial expressions than control participants for both indicators, 2) displayed a higher dominance of angry facial expressions during the retrieval of positive self-defining memories than control participants and 3) recalled significant memories that were less associated with neutral facial states than the control sample, regardless of the valence of their memories. These findings highlight the core role of anger in ASPD and the possible development of pathological anger, which would distinguish trajectories toward anxious or mood disorders and trajectories characterized by external disorders.

Automatic facial reactions to emotional body expressions are not driven by emotional experience.

Perceiving emotional expressions automatically triggers a tendency to react with a matching facial expression. Although it is considered fundamental for healthy social interactions, the mechanism behind it is unclear. One prevalent explanation suggests that perceiving emotional expressions induces emotions in the observer and that it is these emotions that elicit the facial reactions. This study directly tested this hypothesis, investigating whether emotion elicitation is what drives the effect. Two experiments used a facial stimulus-response compatibility (SRC) paradigm-a widely used measure of the tendency to facially match emotional expressions-in which the irrelevant stimuli were happy and angry body postures. Reaction times were measured using facial electromyography. Experiment 1 replicated the known SRC effect to body postures using a simpler task with only one, prespecified, response. This established a novel variant of the paradigm in which the facial effects cannot be attributed to motor matching or response selection and which focuses specifically on the automatic components of the effect. Experiment 2 then added to this paradigm a habituation protocol and self-report ratings of affective valence. Results indicated that emotional body postures elicited limited emotional reactions, which were further habituated following repeated presentations. However, the facial SRC effect did not undergo such habituation, suggesting that reducing emotional reaction to observed expressions does not reduce the tendency to match those expressions. Our findings do not support the emotion elicitation hypothesis and suggest that automatic facial reactions to emotional body postures are not driven by emotional reactions to the stimuli. (PsycInfo Database Record (c) 2022 APA, all rights reserved).