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Stretch syncope (SS) is a benign, uncommon, distinct condition described mainly in adolescent males. It is responsible for paroxysmal events started by stereotyped stretching actions with neck hyperextension, culminating in alteration of consciousness. Motor manifestations are often present and may be associated with a generalized slowing of the electroencephalographic activity, challenging the diagnosis. Despite a few cases reported in the literature, different mechanisms have been implied in the pathogenesis, involving both local and systemic hemodynamic phenomena. Here, we report on an 8-year-old girl with self-induced SS, providing new insights into the related neurophysiological profile and discussing the possible etiology. Our evidence of transient and dynamic vascular impairment supports the hypothesis of SS as a multifactorial disorder. [ABSTRACT FROM AUTHOR]

Background: Childhood adversity has been associated with alterations in threat-related information processing, including heightened perceptual sensitivity and attention bias towards threatening facial expressions, as well as hostile attributions of neutral faces, although there is a large degree of variability and inconsistency in reported findings. Methods: Here, we aimed to implicitly measure neural facial expression processing in 120 adolescents between 12 and 16 years old with and without exposure to childhood adversity. Participants were excluded if they had any major medical or neurological disorder or intellectual disability, were pregnant, used psychotropic medication or reported acute suicidality or an ongoing abusive situation. We combined fast periodic visual stimulation with electroencephalography in two separate paradigms to assess the neural sensitivity and responsivity towards neutral and expressive, i.e. happy and angry, faces. Linear mixed effects models were used to assess the impact of childhood adversity on facial expression processing. Results: Sixty-six girls, 53 boys and one adolescent who identified as 'other', between 12 and 16 years old (M = 13.93), participated in the current study. Of those, 64 participants were exposed to childhood adversity. In contrast to our hypotheses, adolescents exposed to adversity show lower expression-discrimination responses for angry faces presented in between neutral faces and higher expression-discrimination responses for happy faces presented in between neutral faces than unexposed controls. Moreover, adolescents exposed to adversity, but not unexposed controls, showed lower neural responsivity to both angry and neutral faces that were simultaneously presented. Conclusions: We therefore conclude that childhood adversity is associated with a hostile attribution of neutral faces, thereby reducing the dissimilarity between neutral and angry faces. This reduced threat-safety discrimination may increase risk for psychopathology in individuals exposed to childhood adversity. [ABSTRACT FROM AUTHOR]

ABSTRACT Cracking the non‐verbal “code” of human emotions has been a chief interest of generations of scientists. Yet, despite much effort, a dictionary that clearly maps non‐verbal behaviours onto meaning remains elusive. We suggest this is due to an over‐reliance on language‐related concepts and an under‐appreciation of the evolutionary context in which a given non‐verbal behaviour emerged. Indeed, work in other species emphasizes non‐verbal effects (e.g. affiliation) rather than meaning (e.g. happiness) and differentiates between signals, for which communication benefits both sender and receiver, and cues, for which communication does not benefit senders. Against this backdrop, we develop a “non‐verbal effecting” perspective for human research. This perspective extends the typical focus on facial expressions to a broadcasting of multisensory signals and cues that emerge from both social and non‐social emotions. Moreover, it emphasizes the consequences or effects that signals and cues have for individuals and their social interactions. We believe that re‐directing our attention from verbal emotion labels to non‐verbal effects is a necessary step to comprehend scientifically how humans share what they feel. [ABSTRACT FROM AUTHOR]

Human emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. This phenomenon reflects the complexity and richness of human states, where facial features dynamically convey a combination of feelings. This study embarks on a pioneering exploration of Compound Facial Expression Recognition (CFER), with a distinctive emphasis on leveraging the Label Distribution Learning (LDL) paradigm. This strategic application of LDL aims to address the ambiguity and complexity inherent in compound expressions, marking a significant departure from the dominant Single Label Learning (SLL) and Multi‐Label Learning (MLL) paradigms. Within this framework, we rigorously investigate the potential of LDL for a critical challenge in Facial Expression Recognition (FER): recognizing compound facial expressions in uncontrolled environments. We utilize the recently introduced RAF‐CE dataset, meticulously designed for compound expression assessment. By conducting a comprehensive comparative analysis pitting LDL against conventional SLL and MLL approaches on RAF‐CE, we aim to definitively establish LDL's superiority in handling this complex task. Furthermore, we assess the generalizability of LDL models trained on RAF‐CE by evaluating their performance on the EmotioNet and RAF‐DB Compound datasets. This demonstrates their effectiveness without domain adaptation. To solidify these findings, we conduct a comprehensive comparative analysis of 12 cutting‐edge LDL algorithms on RAF‐CE, S‐BU3DFE, and S‐JAFFE datasets, providing valuable insights into the most effective LDL techniques for FER in‐the‐wild. [ABSTRACT FROM AUTHOR]

Introduction: Emotion recognition impairments and a tendency to misclassify neutral faces as negative are common in schizophrenia. A possible explanation for these deficits is aberrant salience attribution. To explore the possibility of salience driven emotion recognition deficits, we implemented a novel facial emotion salience task (FEST). Methods: Sixty-six healthy participants with variations in psychometric schizotypy completed the FEST. In the FEST, we manipulated physical salience (FEST-1: contrast, FEST-2: saturation) of emotionally salient (positive, i.e., happy and negative, i.e., fearful) and non-salient (neutral) facial expressions. Results: When salience was high (increased contrast), participants recognized negative facial expressions faster, whereas neutral faces were recognized more slowly and were more frequently misclassified as negative. When salience was low (decreased saturation), positive expressions were recognized more slowly. These measures were not associated with schizotypy in our sample. Discussion: Our findings show that the match between physical and emotional salience influences emotion recognition and suggest that the FEST is suitable to simulate aberrant salience processing during emotion recognition in healthy participants. [ABSTRACT FROM AUTHOR]

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains. [ABSTRACT FROM AUTHOR]

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists' emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups' emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups' emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists' emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework. [ABSTRACT FROM AUTHOR]

Background: The use of self-report pain scales in persons with aphasia can be challenging due to communication and cognitive problems, while for assessing pain self-report pain is considered the gold standard (Harrison RA, Field TS. Post stroke pain: identification, assessment, and therapy. Cerebrovasc Dis. 2015;39(3–4):190–201.). An observational scale may be used as an alternative. This study examines the validity and reliability of the observational Pain Assessment in Impaired Cognition (PAIC15) scale in persons with aphasia. Methods: Persons with aphasia were observed during rest and transfer by two observers using the PAIC15. The PAIC15 comprises 15 items covering the three domains of facial expressions, body movements, and vocalizations. When able, the participant completed four self-report pain scales after each observation. The observations were repeated within one week. For criterion validity, correlations between the PAIC15 and self-report pain scales were calculated and for construct validity, three hypotheses were tested. Reliability was determined by assessing internal consistency, and intra- and interobserver agreement. Results: PAIC15 observations were obtained for 71 persons (mean age 75.5 years) with aphasia. Fair positive correlations (rest: 0.35–0.50; transfer: 0.38–0.43) were reported between PAIC15 and almost all self-report pain scales. Results show that significantly more pain was observed in persons with aphasia during transfer than during rest. No differences were found for observed pain between persons with aphasia who use pain medication and those without, or persons who have joint diseases compared to those without. Results showed acceptable internal consistency. Intra- and interobserver agreement was high for most PAIC15 items, particularly for the domains body movements and vocalizations during rest and transfer. Conclusions: Recognition of pain in persons aphasia using the PAIC15 showed mixed yet promising results. [ABSTRACT FROM AUTHOR]

Background: Rosacea has a high incidence, significantly impacts quality of life, and lacks sufficient diagnostic techniques. This study aimed to investigate the feasibility of laser speckle contrast imaging (LSCI) for measuring facial blood perfusion in patients with rosacea and to identify differences in blood flow among various facial regions associated with different rosacea subtypes. Methods: From June to December 2023, 45 patients were recruited, with 9 excluded, leaving 36 subjects: 12 with erythematotelangiectatic rosacea (ETR), 12 with papulopustular rosacea (PPR), and 12 healthy controls. The Think View multispectral imaging analyzer assessed inflammation via gray reading values across the full face and five facial areas: forehead, nose, cheeks, and chin. LSCI measured and analyzed blood perfusion in the same areas. Plasma biomarkers interleukin-6 (IL-6), IL-1β, and tumor necrosis factor-a (TNF-a) were tested in different groups. Results: Both ETR and PPR groups showed increased average blood perfusion and facial inflammation intensity by gray values compared to controls, with statistically significant differences. Average blood perfusion of ETR and PPR groups showed increased values in the forehead, cheeks, and nose, compared to controls, and the values in the cheeks were statistically different between ETR and PPR. The facial inflammation intensity of the ETR group showed increased values in the forehead and cheeks, and the PPR group showed increased gray values in the forehead, cheeks, nose, and chin compared to controls, and the values for the cheeks, nose, and chin were statistically significantly different between ETR and PPR. Plasma biomarkers IL-6, IL-1β, and TNF-α were significantly elevated in both ETR and PPR groups compared to controls. Conclusion: LSCI is a valuable, non-invasive tool for assessing blood flow dynamics in rosacea, providing a data foundation for clinical research. Different rosacea subtypes exhibit distinct lesion distribution and blood flow patterns, and both ETR and PPR could affect all facial areas, particularly the cheeks in ETR and the forehead, nose, and chin in PPR. [ABSTRACT FROM AUTHOR]

Facial expressions are critical for conveying emotions and facilitating social interaction. Yet, little is known about how accurately sighted individuals recognize emotions facially expressed by people with visual impairments in online communication settings. This study aimed to investigate sighted individuals’ ability to understand facial expressions of six basic emotions in people with visual impairments during Zoom calls. It also aimed to examine whether education on facial expressions specific to people with visual impairments would improve emotion recognition accuracy. Sighted participants viewed video clips of individuals with visual impairments displaying facial expressions. They then identified the emotions displayed. Next, they received an educational session on facial expressions specific to people with visual impairments, addressing unique characteristics and potential misinterpretations. After education, participants viewed another set of video clips and again identified the emotions displayed. Before education, participants frequently misidentified emotions. After education, their accuracy in recognizing emotions improved significantly. This study provides evidence that education on facial expressions of people with visual impairments can significantly enhance sighted individuals’ ability to accurately recognize emotions in online settings. This improved accuracy has the potential to foster more inclusive and effective online interactions between people with and without visual disabilities. [ABSTRACT FROM AUTHOR]

Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature of current methods. This study aimed to evaluate the effectiveness of an AI system designed for automatic facial recognition by comparing its performance with the expert opinion of health care provider. This is a secondary analysis from an eye-tracking study, assessing neonatal pain evaluations by healthcare professionals. The performance of AI software, FaceReader 9, was compared to experts' evaluations using a visual-analog scale, focusing on identifying specific facial action units associated with different pain levels. The study found significant differences in AI-generated metrics—arousal and valence—across three stimulus types: non-noxious thermal, short-noxious, and prolonged-noxious, with p-values below 0.001. A strong correlation (r = 0.84, p ≤.001) was observed between AI metrics and expert ratings. Eleven facial action units were identified as relevant to describe neonatal pain. The findings highlight the AI system's potential in accurately detecting and analyzing newborn facial expressions in response to varying pain intensities, demonstrating a significant correlation with healthcare professionals' assessments. This suggests that AI technology could enhance objective pain assessment in neonates. [ABSTRACT FROM AUTHOR]

Background: Individuals on the autism spectrum commonly have differences from non-autistic people in expressing their emotions using communicative behaviors, such as facial expressions. However, it is not yet clear if this reduced expressivity stems from reduced physiological reactivity in emotional contexts or if individuals react internally, but do not show these reactions externally to others. We hypothesized that autism is characterized by a discordance between in-the-moment internal psychophysiological arousal and external communicative expressions of emotion. Methods: Forty-one children on the autism spectrum and 39 non-autistic, typically developing (TD) children of two age groups (2–4 and 8–12 years) participated in a low-level stress task whilst wearing a wireless electrocardiogram. Children's negative emotional expressions (facial, vocal, bodily) were coded following standardized protocols. Alexithymia traits were assessed using the Children's Alexithymia Measure with school-aged children only. Data analyses involved ANOVAs, correlations, and sensitivity analyses. Results: There were no group differences in physiological arousal (heart rate) or in communicative expressions of stress to the stress task. For TD preschoolers, physiological arousal during the stress task was associated with vocal expressions and for TD school-aged children, they were associated with facial and bodily expressions. By contrast, for children on the autism spectrum, physiological arousal during the stress tasks was not associated with communicative expressions across age groups. Conclusions: Our findings suggest that children on the autism spectrum might experience emotional disconcordance, in that their physiological arousal does not align with their communicative expressions. Therefore, the internally experienced stress of children on the autism spectrum may be inadvertently missed by teachers and caregivers and, consequently, learning opportunities for teaching emotional communication and regulation may be also missed. Our results support the use of wearable biosensors to facilitate such interventions in children on the autism spectrum. [ABSTRACT FROM AUTHOR]

This essay examines the detailed process of isolating facial data from the context of its emergence through the early work of psychologist Paul Ekman in the 1960s. It explores how Ekman's data practices have been developed, criticized, and compromised by situating them within the political and intellectual landscape of his early career. This essay follows Ekman's journey from the Langley Porter Neuropsychiatric Institute to New Guinea, highlighting his brief but notable collaborations with psychologist Charles E. Osgood and NIH researchers D. Carleton Gajdusek and E. Richard Sorenson. It argues that the different meanings assigned to the human face resulted in how each group developed their studies – examining facial expressions either in interaction, where they shape reciprocal actions in interpersonal communication, or in isolation, where faces surface from the individual's unconscious interior. [ABSTRACT FROM AUTHOR]

This study aimed to build on existing research on the implicit relational assessment procedure (IRAP), which has identified different patterns of IRAP effects associated with responses to happy and angry facial expressions. In particular, the study aimed to (1) replicate the previously observed happiness superiority effect, but with a far larger participant group than previous studies and (2) explore whether the IRAP effect would be influenced by self-reported levels of depression. One hundred twenty-two participants completed an IRAP that presented pairs of emotional human faces in a 2x2 crossover design yielding four trial-types: happy face–happy face, happy face–angry face, angry face–happy face and angry face–angry face. The results showed a clear happiness superiority effect, with the IRAP effect for trial-type 1 (happy face–happy face) larger than the IRAP effect for trial-type 4 (angry face–angry face). Self-reported depression appeared to moderate responding on the trial-type 1 alone, with low depressed individuals producing a larger D-IRAP score on the trial-type 1 relative to high depressed individuals. The findings support recent arguments that the stimulus function properties of all elements within an IRAP should be taken into account when predicting and interpreting behavioral patterns produced on the procedure. Considerations for use of the IRAP to predict behaviors in the natural environment are discussed. [ABSTRACT FROM AUTHOR]

Objective: Affective flexibility, the capacity to respond to life's varying environmental changes in a dynamic and adaptive manner, is considered a central aspect of psychological health in many psychotherapeutic approaches. The present study examined whether affective two-dimensional (i.e., arousal and valence) temporal variability extracted from voice and facial expressions would be associated with positive changes over the course of psychotherapy, at the session, client, and treatment levels. Method: A total of 22,741 mean vocal arousal and facial expression valence observations were extracted from 137 therapy sessions in a sample of 30 clients treated for major depressive disorder by nine therapists. Before and after each session, the clients self-reported their level of well-being on the outcome rating scale. Session-level affective temporal variability was assessed as the mean square of successive differences between consecutive two-dimensional affective measures. Results: Session outcome was positively associated with temporal variability at the session level (i.e., within clients, between sessions) and at the client level (i.e., between clients). Importantly, these associations held when controlling for average session- and client-level valence scores. In addition, the expansion of temporal variability throughout treatment was associated with steeper positive session outcome trajectories over the course of treatment. Conclusions: The continuous assessment of both vocal and facial affective expressions and the ability to extract measures of affective temporal variability from within-session data may enable therapists to better respond and modulate clients' affective flexibility; however, further research is necessary to determine whether there is a causal link between affective temporal variability and psychotherapy outcomes. What is the public health significance of this article?: The current findings highlight the potential of computerized facial expressions and vocal analyses to capture moment-by-moment processes in psychotherapy sessions. The results suggest that clients' affective flexibility, which in this project was measured by depressed clients' capacity to dynamically shift their emotional states and their arousal levels from one moment to the next, was associated with better session outcomes. The findings support therapeutic interventions aimed at enhancing clients' affective flexibility. Future work is needed to determine the causal links between affective flexibility in depression and outcomes. [ABSTRACT FROM AUTHOR]

With their unique electrical, mechanical, and surface properties, gold nanoparticles (AuNPs) open up new possibilities for sensor technology. In particular, conductive thin films constructed from ligand‐stabilized AuNPs are considered an ideal sensing platform due to their high surface area, excellent conductivity, and biocompatibility. However, most methods for making conductive AuNPs thin‐film sensors with excellent sensitivity require expensive equipment. In this work, an innovative resistive strain sensor consisting of AuNPs and poly (allylamine hydrochloride) (PAH) based on the mutual adsorption of positive and negative charges using a low‐cost layer‐by‐layer self‐assembly (LBL‐SA) approach on a flexible polyester substrate is developed. The conductance changes at low temperatures of the AuNPs/PAH agree with the Arrhenius‐type activation of charge transport. Additionally, the maximum gauge factor of the sensor is shown experimentally to be ≈656 when 1% strain is applied to the sensor film. This work demonstrates that the sensor detects body motions, eyeball movements, and facial micro‐expressions. For detecting eyeball movements and facial micro‐expressions, the macro‐recall can reach 91.5% and 98.8%. Simultaneously, the sensor can control the virtual avatar's eye movements and human facial micro‐expressions in VR. Therefore, nanoparticle‐based sensors can be extensively used in future applications related to healthcare and human‐computer interaction. [ABSTRACT FROM AUTHOR]

Purpose: Determine physiological and behavioral stress in preterm infants during bodysuit application in the Neonatal Intensive Care Unit (NICU). Design: Randomized crossover design using two clothing interventions: 1) hospital blanket swaddling and 2) a short-sleeve bodysuit. Sample: Preterm infants (N = 8) at 320 /7 to 346 /7 weeks postmenstrual age (PMA) at testing. Main Outcome Variables: Heart rate (HR), oxygen saturation (O2 ), and Neonatal Pain, Agitation, and Sedation Scale (N-PASS) measured in three phases: baseline before a diaper change, intervention after the diaper change, and recovery. Results: An increase in HR above normal limits and a clinically significant increase in N-PASS scores were observed when the infants were dressed in a bodysuit compared to swaddled only. Donning a bodysuit did not affect O2 levels. Dressing a preterm infant in a bodysuit while in the NICU added temporary but consistent stress and should be avoided. [ABSTRACT FROM AUTHOR]

Background: Emotion categorization has often been studied in the relapsing-remitting form of multiple sclerosis (RR-MS), suggesting an impairment in the recognition of emotions. The production of facial emotional expressions in RR-MS has not been considered, despite their importance in non-verbal communication. Method: Twenty-five RR-MS patients and twenty-five matched controls completed a task of emotional categorization during which their faces were filmed. The stimuli were dynamic (sound or visual), expressed by adults (women or men), and expressing happy (laughing or smiling) or negative emotion. Two independent blinded raters quantified the happy facial expressions produced. The categorization task was used as a proxy for emotional categorization, while the happy facial expressions produced assessed the production of emotions. Results: The main analysis indicated impaired categorization of RR-MS for happy stimuli selectively, whereas their happy facial expressions were not statistically different from those of the control group. More specifically, this group effect was found for smiles (and not laughter) and for happy stimuli produced by men. Analysis of individual patient profiles suggested that 77% of patients with impaired judgments produced normal facial expressions, suggesting a high prevalence of this dissociation. Only 8% of our samples showed reverse dissociation, with happy facial expressions significantly different from those of the control group and normal emotional judgments. Conclusion: These results corroborated the high prevalence of emotional categorization impairment in RR-MS but not for negative stimuli, which can probably be explained by the methodological specificities of the present work. The unusual impairment found for happy stimuli (for both emotional categorization and facial congruence) may be linked to the intensity of the perceived happy expressions but not to the emotional valence. Our results also indicated a mainly preserved production of facial emotions, which may be used in the future sociocognitive care of RR-MS patients with impaired emotional judgments. [ABSTRACT FROM AUTHOR]

A healthy man in his 40s was referred to the Oral Surgery and Implantology Unit of Geneva University Hospital for diagnosis and management of facial swelling affecting the right side of his lower jaw. The patient's history revealed that the patient had been hit by several punches to the face a few months earlier. To investigate the swelling, an intra-oral radiograph, an orthopantomographic radiograph, and computed tomography were performed, which revealed no fracture of the lower jaw but the presence of a partly impacted fractured wisdom tooth (third molar). This finding, together with the clinical status, indicated cellulitis most likely related to the presence of a fractured wisdom tooth. The decision was made to proceed with tooth extraction, and follow-up at 3 weeks showed good healing and complete resolution of facial swelling. This case highlights that odontogenic infection can also occur as a result of necrosis following the fracture of an impacted wisdom tooth. [ABSTRACT FROM AUTHOR]

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings. [ABSTRACT FROM AUTHOR]

Understanding the developmental trajectories for recognizing facial expressions is important for a better understanding of development of psychiatric disorders. In this study, we examined the recognition of emotional and neutral facial expressions in 93 typically developing adolescents and adults. The Emotion Intensity Rating task required participants to rate the intensity of emotional expression in happy, neutral, and sad faces on a scale from 1 to 9. A score of '5' had to be assigned to neutral faces, scores between '6' (slightly happy) and '9' (very happy) to happy faces, and scores between '4' (slightly sad) and '1' (very sad) to sad faces. Mixed effects models were used to examine the effects of age and emotion on recognition accuracy, reaction time (RT), and emotional intensity. Participants tended to misjudge neutral faces as sad. Adolescents were less accurate than adults for neutral face recognition. There were significant quadratic effects of age on accuracy (negative quadratic effect) and RT (positive quadratic effect). The most accurate and fastest performance was observed in 25- to 35-year-old subjects. This trajectory may be associated with prefrontal cortex maturation, which provides top–down control over the heightened amygdala response to ambiguity that may be misinterpreted as emotional content. [ABSTRACT FROM AUTHOR]

Person pose transfer has shown great application prospects and potential in recent years. However, this research mainly focuses on posture transfer and does not pay much attention to other human body attributes. In addition, there are problems such as blurred facial expressions and inconsistent facial texture. In order to solve these problems and improve user experience, this paper proposes a multi-feature transfer person image generation model, which combines facial expression transfer and pose transfer and completes the lighting reconstruction of person images. Our model can simultaneously migrate and supplement facial expressions while migrating a person's posture, ensuring the consistency of characters' overall migration. Our model can freely switch personal body lighting to adapt to different scenes, greatly improving the user experience. The experimental results show that our method's preservation rate for face identity is improved by nearly 35% compared with the benchmark, and the similarity index for the overall person body is improved by nearly 14%. The evaluation results indicate that our method performs well in person posture transfer. The experimental results show that the proposed method is superior to the existing algorithms in person posture transformation. [ABSTRACT FROM AUTHOR]

Objective To investigate the application effect of "buried-guidance" low external fixation technique in the transconjunctival orbital septum fat reset blepharoplasty. Methods From October 2022 to October 2023, the clinical datas of patients who requested comprehensive treatment of lower eyelid were retrospectively analyzed. The "buried-guidance" low external fixation technique was applied in the transconjunctival orbital septum fat reset blepharoplasty. This technique effectively prevented the release of the orbital septum fat flap from returning to the preseptal space to form a pouch. The upward rebound rate and complications of the pedicled orbital septum fat flap after release were observed and analyzed. Results There were significant improvements in the lower eyelid pouch and the tear groove in 42 cases. Twelve cases of intraoperative conjunctival hemorrhage occurred during anesthetic injection, which improved after 2 weeks without special treatment. In 2 cases, the bilateral lower eyelid area was flat when there was no facial expression 2 weeks after operation, while the lower eyelid area was suspected to be low eyelid pouch when there was intense facial dynamic expression, without special treatment. Two patients still had lower eyelid drag sensation at the first 2 months after surgery, which disappeared after no special treatment. All patients were followed up for 3 to 12 months after surgery, and none of them released orbital septal fat to rebound into the preorbital space to re-form lower eyelid pouch. The satisfaction rates of the senior doctor, nurse and beauty seeker were 90.48%, 95.24% and 85.71%, respectively. Conclusion The application of "buried-guidance" low external fixation technique in transconjunctival orbital septum fat reset blepharoplasty can achieve a flat lower eyelid appearance, and has a good correction effect on pouch and tear trough depression. The incidence of orbital septum fat rebound to form pouch is low, and the satisfaction of both patients and doctors is high. [ABSTRACT FROM AUTHOR]

Facial expressions are important in pain recognition in horses, but current observation‐based pain scales remain subjective. A promising technique to quantitatively measure subtle changes in expression patterns, including changes invisible to the human eye, is surface electromyography (sEMG). To achieve high‐quality and reliable sEMG signals, unilateral placement of bipolar electrodes is required in relation to the motor endplates (MEP). We aimed to localize the nerve entry points (NEPs; where the nerve branch first pierced the muscle belly) and the direction of the terminal nerve endings to estimate MEP locations of the innervating nerves in five equine facial muscles involved in pain expression. Three cadaveric Dutch Warmblood horse heads were dissected to identify the NEPs in the musculi caninus, levator anguli oculi medialis, nasolabialis, masseter and zygomaticus. These points were marked with pins and measured in relation to a reference line between two anatomical landmarks near the origin and insertion of the respective muscle. Relative distances were calculated from the most caudally situated landmark. NEPs were located at 33%–38% (caninus), 69%–86% (levator anguli oculi medialis) and 0%–18% (zygomaticus) from the caudal landmark. The nasolabialis showed two innervations zones. Its NEPs were located at 47%–72% (dorsal muscle branch) and 52%–91% (ventral branch). All terminal nerve endings were found to run in rostral direction. The masseter showed numerous NEPs diffusely spread within the muscle belly. Therefore, calculation of relative positions was not performed. These results could form the basis for feasibility studies and standardization of bipolar electrode positioning in vivo to measure facial muscle activity patterns in horses. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) technology advances, its application in the field of psychology has witnessed significant advancements. In this paper, with the assistance of AI, 80 university students with depression and 80 university students with normal psychology were selected as the subjects. The facial expression feature data were extracted through OpenFace, and the action feature data were extracted based on a Kinect camera. Then, the convolutional neural network-long short-term memory (CNN-LSTM) and temporal convolutional neural network (TCN) approaches were designed for recognition. Finally, a weighted fusion recognition method was proposed. The results showed that compared with the support vector machine, back-propagation neural network, and other approaches, the CNN-LSTM and TCN methods showed better performance in the recognition of single feature data, and the accuracy reached 0.781 and 0.769, respectively. After weighted fusion, the accuracy reached the highest at 0.875. The results verify that the methods designed in this paper are effective in identifying depressive emotions through facial expressions and actions among university students and have the potential for practical application. [ABSTRACT FROM AUTHOR]

In this paper, we study facial expression recognition (FER) using three modalities obtained from a light field camera: sub-aperture (SA), depth map, and all-in-focus (AiF) images. Our objective is to construct a more comprehensive and effective FER system by investigating multimodal fusion strategies. For this purpose, we employ EfficientNetV2-S, pre-trained on AffectNet, as our primary convolutional neural network. This model, combined with a BiGRU, is used to process SA images. We evaluate various fusion techniques at both decision and feature levels to assess their effectiveness in enhancing FER accuracy. Our findings show that the model using SA images surpasses state-of-the-art performance, achieving 88.13% ± 7.42% accuracy under the subject-specific evaluation protocol and 91.88% ± 3.25% under the subject-independent evaluation protocol. These results highlight our model's potential in enhancing FER accuracy and robustness, outperforming existing methods. Furthermore, our multimodal fusion approach, integrating SA, AiF, and depth images, demonstrates substantial improvements over unimodal models. The decision-level fusion strategy, particularly using average weights, proved most effective, achieving 90.13% ± 4.95% accuracy under the subject-specific evaluation protocol and 93.33% ± 4.92% under the subject-independent evaluation protocol. This approach leverages the complementary strengths of each modality, resulting in a more comprehensive and accurate FER system. [ABSTRACT FROM AUTHOR]

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection. [ABSTRACT FROM AUTHOR]

Simulation theories predict that the observation of other's expressions modulates neural activity in the same centres controlling their production. This hypothesis has been developed by two models, postulating that the visual input is directly projected either to the motor system for action recognition (motor resonance) or to emotional/interoceptive regions for emotional contagion and social synchronization (emotional resonance). Here we investigated the role of frontal/insular regions in the processing of observed emotional expressions by combining intracranial recording, electrical stimulation and effective connectivity. First, we intracranially recorded from prefrontal, premotor or anterior insular regions of 44 patients during the passive observation of emotional expressions, finding widespread modulations in prefrontal/insular regions (anterior cingulate cortex, anterior insula, orbitofrontal cortex and inferior frontal gyrus) and motor territories (Rolandic operculum and inferior frontal junction). Subsequently, we electrically stimulated the activated sites, finding that (i) in the anterior cingulate cortex and anterior insula, the stimulation elicited emotional/interoceptive responses, as predicted by the 'emotional resonance model'; (ii) in the Rolandic operculum it evoked face/mouth sensorimotor responses, in line with the 'motor resonance' model; and (iii) all other regions were unresponsive or revealed functions unrelated to the processing of facial expressions. Finally, we traced the effective connectivity to sketch a network-level description of these regions, finding that the anterior cingulate cortex and the anterior insula are reciprocally interconnected while the Rolandic operculum is part of the parieto-frontal circuits and poorly connected with the former. These results support the hypothesis that the pathways hypothesized by the 'emotional resonance' and the 'motor resonance' models work in parallel, differing in terms of spatio-temporal fingerprints, reactivity to electrical stimulation and connectivity patterns. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) in the wild is one of the most challenging visual tasks owing to various uncontrolled factors such as occlusion, pose, and subtle variation in real scenes. These factors can directly affect the robust performance of current networks, especially as most single-feature learning space methods lack the extraction of potential discriminative features and fail to provide a deeper understanding of expressions. To address the above issues, we propose a novel hybrid attention-aware learning network (HALNet), which comprises a feature compactness network (FCN), a hybrid attention enhancement network (HAEN), and a joint loss optimization strategy. First, FCN performs basic expression feature extraction and optimizes intra- and inter-class distributions simultaneously. Afterward, HAEN constructs a multi-level feature enhancement space by fusing hybrid attention based on CNN and transformer in parallel to effectively improve the profound understanding of expressions. Finally, the expression classification is performed by supervised optimization with joint loss. Extensive experiments are assessed on some of the widest employed wild expression datasets, and results indicate our method is superior to several present state-of-the-art methods, obtaining accuracies of 90.29%, 90.04%, and 61.75% on RAF-DB, FERPlus, and AffectNet, respectively. The cross-dataset and occlusion and pose variation datasets assessment further substantiate our approach's sound generalization and robustness. [ABSTRACT FROM AUTHOR]

The ability of a person to communicate with other people and engage with the outside environment makes it crucial to a person's existence. It can be challenging for all the people who have hearing loss to communicate without the aid of lip reading, facial expression, or signals. Also, without instruction, it is challenging for them to comprehend lip movements, which makes it challenging for them to hear spoken words. The visual speech recognition devices for lip reading were developed to solve this issue and help the deaf understand speech. Recently, Visual Voice Recognition research has focused a lot on deep learning advancements. Here, we suggested the LSTM and CNN 3D hybrid model for MIRACL VC1 Dataset visual speech recognition. When a person's lip features are extracted for pattern detection, the text without audio is converted in this process. A model used to automatically recognize speech is called visual speech recognition. The proposed work stands apart from the previous work by incorporation the hybrid model, the combination of the models would yield a better and efficient accuracy rate comparatively and also the focal novelty of the proposed work was that the concatenation of the extracted features to provide an input to the system was a challenging one so has to manage the weights in the entire process of extraction for a huge number of dataset. It was suggested that two separate models, CNN 3D and LSTM, be combined to create hybrid model in mandate to progress the overall accuracy in this case. In comparison with the individual CNN 3D model, whose testing accuracy is 79%, and the LSTM model, whose accuracy was 85% when we employed the English Dataset, the hybrid model's training, test, and validation accuracy were all 98, 85, and 86%, respectively. [ABSTRACT FROM AUTHOR]

Deep learning techniques have attained substantial progress in various face‐related tasks, such as face recognition, face inpainting, and facial expression recognition. To prevent infection or the spread of the virus, wearing of masks in public places has been mandated following the COVID‐19 epidemic, which has led to face occlusion and posed significant challenges for face recognition systems. Most prominent masked face recognition solutions rely on mask segmentation tasks. Therefore, segmentation can be used to mitigate the negative impacts of wearing a mask and improve recognition accuracy. Mask region segmentation suffers from two main problems: there is no standard type of masks that people wear, they come in different colours and designs, and there is no publicly available masked face dataset with appropriate ground truth for the mask region. In order to address these issues, we propose an encoder–decoder framework that utilizes a boundary‐aware attention network combined with a new hybrid loss to provide a map, patch, and pixel‐level supervision. We also introduce a dataset called MFSD, with 11,601 images and 12,758 masked faces for masked face segmentation. Furthermore, we compare the performance of different cutting‐edge deep learning semantic segmentation models on the presented dataset. Experimental results on the MSFD dataset reveal that the suggested approach outperforms state‐of‐the‐art, algorithms with 97.623% accuracy, 93.814% IoU, and 96.817% F1‐score rate. Our dataset of masked faces with mask region labels and source code will be available online. [ABSTRACT FROM AUTHOR]

Bilateral transcutaneous auricular vagus nerve stimulation (taVNS) - a non-invasive neuromodulation technique – has been investigated as a safe and feasible technique to treat many neuropsychiatric conditions. such as epilepsy, depression, anxiety, and chronic pain. Our aim is to investigate the effect of taVNS on neurophysiological processes during emotional and Go/No-Go tasks, and changes in frontal alpha asymmetry. We performed a randomized, double-blind, sham-controlled trial with 44 healthy individuals who were allocated into two groups (the active taVNS group and the sham taVNS group). Subjects received one session of taVNS (active or sham) for 60 min. QEEG was recorded before and after the interventions, and the subjects were assessed while exposed to emotional conditions with sad and happy facial expressions, followed by a Go/No-Go trial. The results demonstrated a significant increase in N2 amplitude in the No-Go condition for the active taVNS post-intervention compared to the sham taVNS after adjusting by handedness, mood, and fatigue levels (p = 0.046), significantly reduced ERD during sad conditions after treatment (p = 0.037), and increased frontal alpha asymmetry towards the right frontal hemisphere during the emotional task condition (p = 0.046). Finally, we observed an interesting neural signature in this study that suggests a bottom-up modulation from brainstem/subcortical to cortical areas as characterized by improved lateralization of alpha oscillations towards the frontal right hemisphere, and changes in ERP during emotional and Go/No-Go tasks that suggests a better subcortical response to the tasks. Such bottom-up effects may mediate some of the clinical effects of taVNS. [ABSTRACT FROM AUTHOR]

Guided by theories of communicative disenfranchisement (TCD) and memorable messages (ToMM), this study analyzes 60 open-ended survey responses detailing experiences of grief following the onset of invisible, physical illness (e.g., chronic overlapping pain conditions, autoimmune rheumatic diseases). Employing reflexive thematic analysis, we identify (1) illness-related losses that are disenfranchised by discourses surrounding grief and (2) opportunities to enfranchise these losses via memorable messages. Situated at two points in time, before and after the onset of illness, we identify disenfranchised losses as what once was (e.g., physical [in]dependence, relational connection, images of self, and trust in institutions) and what will never be (e.g., family planning and career dreams). Next, we identify opportunities to disrupt this disenfranchisement through memorable messages of presence (e.g., nonverbal acknowledgment and verbal expressions of belief) and finding the fit (e.g., physical restructuring and community connection). In doing so, we extend theoretical understanding of both memorable messages, in the form that they can take (of experience) and as points of (dis)enfranchisement, and communicative (dis)enfranchisement. This study contributes to a growing body of health communication research on disenfranchising experiences in chronic illness by highlighting a resulting experience of illness not often talked about – illness-related loss and its grief process. Theoretical and practical implications, as well as limitations and future directions, are discussed. [ABSTRACT FROM AUTHOR]

Paranoid ideation is a transdiagnostic construct that is associated with social impairment and often occurs in psychotic spectrum disorders. Little research has examined how paranoid ideation is related to social behaviors that underlie social impairment and may ultimately lead to social rejection. It is important to consider that negative symptoms and sleep problems also contribute to social impairment. No research has assessed the unique and combined influence of paranoid ideation, negative symptoms, and sleep problems on social impairment. Therefore, the current study examined how paranoid ideation, negative symptoms, and sleep problems contribute to poorer social skills and social rejection in a transdiagnostic sample of persons with psychosis and community members (N = 112). Assessments included diagnostic and symptom interviews, questionnaires, behavioral ratings of social skill and facial displays of affect, and naive observer reactions utilizing thin-slice methodology. Greater paranoid ideation, negative symptoms, and sleep problems were each related to poorer social skill and more negative reactions from observers. When considered in path analyses, negative symptoms were associated with observer reports of less willingness to interact with participants through poorer social skill. These findings demonstrate the symptom correlates of social rejection and how interpersonal behavior may contribute to social exclusion. [ABSTRACT FROM AUTHOR]

Aim: To identify specific facial expressions associated with pain behaviors using the PainChek application in residents with dementia. Design: This is a secondary analysis from a study exploring the feasibility of PainChek to evaluate the effectiveness of a social robot (PARO) intervention on pain for residents with dementia from June to November 2021. Methods: Participants experienced PARO individually five days per week for 15 min (once or twice) per day for three consecutive weeks. The PainChek app assessed each resident's pain levels before and after each session. The association between nine facial expressions and the adjusted PainChek scores was analyzed using a linear mixed model. Results: A total of 1820 assessments were completed with 46 residents. Six facial expressions were significantly associated with a higher adjusted PainChek score. Horizontal mouth stretch showed the strongest association with the score, followed by brow lowering parting lips, wrinkling of the nose, raising of the upper lip and closing eyes. However, the presence of cheek raising, tightening of eyelids and pulling at the corner lip were not significantly associated with the score. Limitations of using the PainChek app were identified. Conclusion: Six specific facial expressions were associated with observational pain scores in residents with dementia. Results indicate that automated real‐time facial analysis is a promising approach to assessing pain in people with dementia. However, it requires further validation by human observers before it can be used for decision‐making in clinical practice. Impact: Pain is common in people with dementia, while assessing pain is challenging in this group. This study generated new evidence of facial expressions of pain in residents with dementia. Results will inform the development of valid artificial intelligence‐based algorithms that will support healthcare professionals in identifying pain in people with dementia in clinical situations. Reporting Method: The study adheres to the CONSORT reporting guidelines. Patient or Public Contribution: One resident with dementia and two family members of people with dementia were consulted and involved in the study design, where they provided advice on the protocol, information sheets and consent forms, and offered valuable insights to ensure research quality and relevance. Trial Registration: Australian and New Zealand Clinical Trials Registry number (ACTRN12621000837820). [ABSTRACT FROM AUTHOR]

The human brain is fundamentally a social brain, with its perceptual and cognitive systems evolved to support a diverse set of processes aimed at enabling our complex social function. And while in the last decades the field has advanced considerably in understanding the mechanisms by which the perceptual and cognitive faculties support human social behaviour, there remains a lack of knowledge about how social information is communicated between individuals. In my work, I have studied nonverbal visual social signalling via faces and eyes. In this article, I summarize the results from three lines of my research which show that (a) limiting face visibility decreases its social value, (b) eyes transmit key social messages, and (c) group size modulates social messages. Together, these data show that visual signals from faces and eyes convey complex social messages and represent an important vehicle for the communication of social information. [ABSTRACT FROM AUTHOR]

Wearing facial masks became a common practice worldwide during the COVID-19 pandemic. This study investigated (1) whether facial masks that cover adult faces affect 4- to 6-year-old children's recognition of emotions in those faces and (2) whether the duration of children's exposure to masks is associated with emotion recognition. We tested children from Switzerland (N = 38) and Brazil (N = 41). Brazil represented longer mask exposure due to a stricter mandate during COVID-19. Children had to choose a face displaying a specific emotion (happy, angry, or sad) when the face wore either no cover, a facial mask, or sunglasses. The longer hours of mask exposure were associated with better emotion recognition. Controlling for the hours of exposure, children were less likely to recognise emotions in partially hideen faces. Moreover, Brazilian children were more accurate in recognising happy faces than Swiss children. Overall, facial masks may negatively impact children's emotion recognition. However, prolonged exposure appears to buffer the lack of facial cues from the nose and mouth. In conclusion, restricting facial cues due to masks may impair kindergarten children's emotion recognition in the short run. However, it may facilitate their broader reading of facial emotional cues in the long run. [ABSTRACT FROM AUTHOR]

Apart from verbal communication among humans, non-verbal interactions also play a significant role in conveying meaningful information. Non-verbal cues mainly comprise gestures, body postures, and facial expressions. Hand gestures constitute the preferred mechanism for non-verbal communication, and today, they also find utility in human–computer interaction (HCI), gaming, virtual reality, robotics, sign language, etc. While extensive research has been conducted on utilizing deep learning for hand gesture recognition, there has been a notable scarcity of efforts focused on leveraging the sparse characteristics of deeply acquired features to distinguish hand postures, even in the presence of challenges such as varying hand sizes, diverse spatial positions within images, and background clutter. We demonstrate the effect of data augmentation, transfer learning, and sparsity on the performance of the proposed algorithm using publicly available hand gesture datasets. We also provide a quantitative comparative analysis of the proposed approach with state-of-the-art algorithms for static hand gesture recognition. We illustrate a noteworthy finding wherein dictionary learning through LC-KSVD, when applied to fine-tuned features extracted from a deep architecture, outperforms the results achieved by state-of-the-art architectures in the context of hand gesture classification. We have realized substantial enhancements with our proposed methodology when compared to a baseline convolutional model. For instance, in the case of the EgoGesture dataset, we attained an accuracy of 94.9 % , as opposed to the baseline accuracy of 63.3 % , through the utilization of sparsity in deep features. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a vital role for applications based on human–computer interaction. In the past few years, many deep learning models have been proposed for FER, but their performance is limited due to challenges such as variation in head pose, occlusion, illumination, etc. Moreover, existing models consider input image through holistic view without giving attention to features relevant to expressions. In this work, we propose a deep fusion framework for FER which employs self-attention mechanism to resolve this issue. Furthermore, to improve feature representation of the images, the proposed model transforms the input image to wavelet domain through discrete wavelet transform. The framework employs two parallel branches for shallow and deep features which are fused for better feature representation. The proposed model is evaluated on posed and in-the-wild datasets: CK+, JAFFE, MUG, YALE, RAF, and experimental results validate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Objective: Anorexia nervosa (AN) has been associated with alterations in the processing of socio‐emotional information, including impairments in the recognition of emotions in other people's faces. However, adolescents with AN might not show the impairments found in adult patients. The present study investigated facial emotion recognition in adolescents with AN, aiming to replicate our previous results of superior emotion recognition abilities in adolescents with AN compared to adolescents without mental disorders. Method: Adolescent girls (12–18 years) with AN (n = 33) were compared to girls without mental disorders (n = 41). Participants completed one task requiring identification of emotions (happy, sad, afraid, angry, neutral) in faces and one control task. Results: As expected, adolescents with AN showed superior emotion recognition, with higher accuracy rates specifically for afraid faces. Conclusion: This is the first study replicating previous results on basic emotion recognition in adolescents with AN using (almost) the same methodology. The results suggest that, in contrast to adults, adolescents with AN do not show impairments in facial emotion recognition. The impairments may arise in the longer course of the illness, however, longitudinal studies are necessary to confirm this assumption. Highlights: Adolescent girls with anorexia nervosa (AN) did not show impairments in facial emotion recognition compared to adolescents girls with no mental disordersOn the contrary, adolescent with AN showed superior emotion recognition specifically for afraid faces (and a trend for sad faces)The results replicate those of a previous study and suggest that emotion recognition impairments may arise with prolonged illness duration [ABSTRACT FROM AUTHOR]

Objective: The study investigated interpersonal distance in patients with anorexia nervosa (AN), focussing on the role of other's facial expression and morphology, also assessing physiological and subjective responses. Method: Twenty‐nine patients with AN and 30 controls (CTL) were exposed to virtual characters either with an angry, neutral, or happy facial expression or with an overweight, normal‐weight, or underweight morphology presented either in the near or far space while we recorded electrodermal activity. Participants had to judge their preferred interpersonal distance with the characters and rated them in terms of valence and arousal. Results: Unlike CTL, patients with AN exhibited heightened electrodermal activity for morphological stimuli only, when presented in the near space. They also preferred larger and smaller interpersonal distances with overweight and underweight characters respectively, although rating both negatively. Finally, and similar to CTL, they preferred larger interpersonal distance with angry than neutral or happy characters. Discussion: Although patients with AN exhibited behavioural response to emotional stimuli similar to CTL, they lacked corresponding physiological response, indicating emotional blunting towards emotional social stimuli. Moreover, they showed distinct behavioural and physiological adjustments in response to body shape, confirming the specific emotional significance attached to body shape. Highlights: Patients with anorexia nervosa (AN) show a regulation of interpersonal distances to social emotional stimuli similar to healthy controls (CTL) but lack the corresponding physiological response.Patients with AN, not CTL, prefer shorter interpersonal distance with underweight social stimuli and larger interpersonal distance with overweight ones and show increased physiological response to both of them.Patients with AN show a bias towards morphological rather than emotional cues when processing information to optimise social interaction. [ABSTRACT FROM AUTHOR]

This study examined the effects of an intervention package using high-tech augmentative and alternative communication (AAC) with Naturalistic Instruction (NI), time delay, and prompting during social routines for preschoolers on the autism spectrum. The AAC was a communication application on a tablet programmed with color photo representations of participants and communication partners engaged in social routines. The primary dependent measure was a linguistic communication turn on the AAC device completing the instructor's cloze phrase related to the routine. A secondary dependent measure was prelinguistic communication –nonspecific vocalizations and smiling – within each turn of the routine. Results indicated that all communication types increased and maintained for all three participants with two participants meeting mastery criteria within the minimal number of sessions. These results are significant in providing additional evidence for the effectiveness of AAC intervention beyond teaching simple object requests and in teaching more socially meaningful communication to children on the autism spectrum. Additionally, this study supports the use of color photo representations to promote social interaction. Future research should continue to evaluate such interventions on a larger scale and include naturally occurring communication partners, such as siblings and peers. [ABSTRACT FROM AUTHOR]

DailyConnect is a visual-based mobile application that supports children with autism spectrum disorder (ASD) in recalling memories by reviewing photos through discrete trial training (DTT) to understand situation-based emotions. To assess DailyConnect and its adaptability to a child's characteristics and emotional situations, a pilot study was conducted that included 15 children with ASD and their parents and teachers. The DTT steps—memory recall and situation recognition, emotion recognition, emotion cues, facial expression recognition, and response behavior—were reliable in assessing the understanding of emotional situations when compared before and after the intervention in four categories of emotional situations, namely, happiness, sadness, anger, and fear. The results revealed that DailyConnect improves the understanding of situation-based emotions, particularly negative emotions (e.g., sadness: mean diff = −.687, sig. <.01, T = −3.866, d = 1.006; anger: mean diff = −.952, sig. <.01, T = −6.187, d =.705; fear: mean diff = −.961, sig. <.01, T = −5.522, d =.627); however, its effectiveness varied for children in different emotional situations. Furthermore, subjective feedback from participants and users (parents and teachers) provided insights into design considerations for similar mobile aids. [ABSTRACT FROM AUTHOR]

Although the development of prosocial behavior has been widely studied from the behavioral aspect, the neural mechanisms underlying prosocial behavior in the early stages of development remain unclear. Therefore, this study investigated the neural mechanisms underlying the emergence of prosocial behavior in 3-year-old children. Brain activity in the medial pFC and right TPJ (rTPJ) and facial expression activity, which are related to the ability to infer others' mental states (mentalizing), during the observation of prosocial and antisocial scenes were measured using functional near-infrared spectroscopy and electromyography, respectively. Subsequently, the children's helping and comforting behaviors toward an experimenter were assessed to examine prosocial behavioral tendencies. A correlation analysis revealed that the children who showed stronger activity levels in the rTPJ while observing prosocial scenes had more immediate helping behaviors toward others than those who did not show stronger response levels. Moreover, the amount of facial expression activity correlated with prosocial behavior, including both helping and comforting behaviors. These results suggest that the development of mentalizing ability and the social evaluation of others' actions, mediated by the rTPJ, contribute to the emergence of prosocial behavior. [ABSTRACT FROM AUTHOR]

The aim of the current study was to examine facial and body posture emotion recognition among deaf and hard-of-hearing (DHH) and hearing young adults. Participants were (N = 126) DHH (n = 48) and hearing (n = 78) college students who completed two emotion recognition tasks in which they were shown photographs of faces and body postures displaying different emotions of both high and low intensities and had to infer the emotion being displayed. Compared to hearing participants, DHH participants performed worse on the body postures emotion task for both high and low intensities. They also performed more poorly on the facial emotion task, but only for low-intensity emotional facial expressions. On both tasks, DHH participants whose primary mode of communication was Signed English performed significantly more poorly than those whose primary mode was American Sign Language (ASL) or spoken English. Moreover, DHH participants who communicated using ASL performed similarly to hearing participants. This suggests that difficulties in affect recognition among DHH individuals occur when processing both facial and body postures that are more subtle and reflective of real-life displays of emotion. Importantly, this also suggests that ASL as a primary form of communication in this population may serve as a protective factor against emotion recognition difficulties, which could, in part, be due to the complex nature of this language and its requirement to perceive meaning through facial and postural expressions with a wide visual lens. [ABSTRACT FROM AUTHOR]

Depictions of sadness are commonplace, and here we aimed to discover and catalogue the complex and nuanced ways that people interpret sad facial expressions. We used a rigorous qualitative methodology to build a thematic framework from 3,243 open-ended responses from 41 people who participated in 2020 and described what they thought sad expressors in 80 images were thinking, feeling, and/or intending to do. Face images were sourced from a novel set of naturalistic expressions (ANU Real Facial Expression Database), as well as a traditional posed expression database (Radboud Faces Database). The resultant framework revealed clear themes around the expressors' thoughts (e.g., acceptance, contemplation, disbelief), social needs (e.g., social support or withdrawal), social behaviours/intentions (e.g., mock or manipulate), and the precipitating events (e.g., social or romantic conflict). Expressions that were perceived as genuine were more frequently described as thinking deeply, reflecting, or feeling regretful, whereas those perceived as posed were more frequently described as exaggerated, overamplified, or dramatised. Overall, findings highlight that facial expressions — even with high levels of consensus about the emotion category they belong to — are interpreted in nuanced and complex ways that emphasise their role as other-oriented social tools, and convey semantically related emotion categories that share smooth gradients with one another. Our novel thematic framework also provides an important foundation for future work aimed at understanding variation in the social functions of sadness, including exploring potential differences in interpretations across cultural settings. [ABSTRACT FROM AUTHOR]

Although the same emotion can be related to different facial prototypes, little is known about the impact of differences in these prototypes on emotion perception. The present research aimed to investigate the impact of different facial prototypes on emotion perception, focusing specifically on anger and disgust. Four major prototypes of anger and six major prototypes of disgust by Asian and White targets were generated by FaceGen and MetaHuman programs. These prototypes were presented to participants from Canada and China, who were asked to identify the corresponding emotions. The results revealed that emotional signals conveyed by different facial prototypes were not uniform. While some prototypes cued distinctive emotions, others cued mixed emotions. Moreover, the signal value of specific prototypes varied across perceiver culture, with prototypes generally perceived as more mixed in Chinese compared to Canadian participants. Notably, emotional signals were also influenced by the facial generation software. Together these findings highlight the importance of considering the impact of specific facial prototypes on emotion perception, especially in cross-cultural contexts, and caution researchers to be mindful of the facial generation software when examining potential variability in emotional decoding associated with different facial prototypes. [ABSTRACT FROM AUTHOR]

To clinically evaluate previously developed pain scales [Donkey Chronic Pain Composite Pain Scale (DCP-CPS), Donkey Chronic Pain Facial Assessment of Pain (DCP-FAP) and combined Donkey Chronic Pain Scale (DCPS)], including behavioural and facial expression–based variables, for the assessment of chronic pain in donkeys. Prospective, blinded clinical study. A group of 77 donkeys (34 patients and 43 healthy control animals). Animals were assessed by two observers that were blinded to the condition of the animals. Both DCP-CPS and DCP-FAP, and resulting combined DCPS scores, showed good interobserver reliability [intraclass correlation coefficient (ICC) = 0.91, 95% confidence interval (CI) = 0.86–0.95, p < 0.001; ICC = 0.71, CI = 0.50–0.83, p < 0.001 and ICC = 0.84, CI = 0.72–0.91, p < 0.001, respectively]. All scores (DCP-CPS, DCP-FAP and the resulting combined DCPS) were significantly higher for patients than for controls at all time points (p < 0.001 for all three scales). Sensitivity and specificity for identification of pain (cut-off value >3) was 73.0% and 65.1% for DCP-CPS, and 60.9% and 83.3% for DCP-FAP, respectively. For the combined DCPS, sensitivity was 87.0% and specificity 90.9% (cut-off value >6). Based on behavioural and facial expression–based variables, DCPS proved a promising and reproducible tool to assess different types of chronic pain in donkeys. The combination of behavioural and facial expression–based variables showed the best discriminatory characteristics in the current study. Further studies are needed for refinement of these tools. [ABSTRACT FROM AUTHOR]

Introduction: Patients with chronic headaches and chronic oro‐facial pain commonly present psychosocial issues that can affect social interactions. A possible reason could be that patients with these disorders might present impairments in facial recognition, laterality judgement and also alexithymia. However, a systematic review summarizing the effects of facial emotion recognition, laterality judgement and alexithymia in individuals with headaches and oro‐facial pain is still not available. Aim: The main objective of this systematic review (SR) and meta‐analysis (MA) was to compile and synthesize the evidence on the occurrence of alexithymia, deficits in laterality or left–right (LR) recognition and/or facial emotion recognition (FER) in patients with chronic headache and facial pain. Methods: Electronic searches were conducted in five databases (up to September 2023) and a manual search to identify relevant studies. The outcomes of interest were alexithymia scores, speed and accuracy in LR and/or FER, or any other quantitative data assessing body image distortions. The screening process, data extraction, risk of bias and data analysis were performed by two independent assessors following standards for systematic reviews. Results: From 1395 manuscripts found, only 34 studies met the criteria. The overall quality/certainty of the evidence was very low. Although the results should be interpreted carefully, individuals with chronic headaches showed significantly higher levels of alexithymia when compared to healthy individuals. No conclusive results were found for the other variables of interest. Conclusion: Although the overall evidence from this review is very low, people with chronic primary headaches and oro‐facial pain could be regularly screened for alexithymia to guarantee appropriate management. [ABSTRACT FROM AUTHOR]

Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation. We compared Baby FaceReader 9 output to manual micro-coding of positive, negative, or neutral facial expressions in a longitudinal dataset of 58 infants at 4 and 8 months of age during naturalistic face-to-face interactions with the mother, father, and an unfamiliar adult. Baby FaceReader 9's global emotional valence formula yielded reasonable classification accuracy (AUC =.81) for discriminating manually coded positive from negative/neutral facial expressions; however, the discrimination of negative from neutral facial expressions was not reliable (AUC =.58). Automatically detected a priori action unit (AU) configurations for distinguishing positive from negative facial expressions based on existing literature were also not reliable. A parsimonious approach using only automatically detected smiling (AU12) yielded good performance for discriminating positive from negative/neutral facial expressions (AUC =.86). Likewise, automatically detected brow lowering (AU3+AU4) reliably distinguished neutral from negative facial expressions (AUC =.79). These results provide initial support for the use of selected automatically detected individual facial actions to index positive and negative affect in young infants, but shed doubt on the accuracy of complex a priori formulas. [ABSTRACT FROM AUTHOR]