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Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI's emotion understanding, employing "response engineering" to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value. [ABSTRACT FROM AUTHOR]

While prior work has examined the effects of emotions on consumers' attitudes toward ads, little research has explored how the joint presence of positive and negative emotions affects ad-related outcomes and to what extent such ads are common. This research leverages recent advances in emotion recognition technology to identify emotions based on the facial expressions of actors in Super Bowl ads aired over the past five years. We examine how the joint presence of happiness—a basic positive emotion—and sadness, disgust, anger, and fear—basic negative emotions—in these ads predict USA TODAY Ad Meter scores (the premier tracker of public opinion on Super Bowl ads). Results show that the joint presence of happiness and sadness enhances ad liking, the presence of happiness and fear reduces ad liking, while the presence of happiness and anger or happiness and disgust do not affect ad liking. We discuss the theoretical and methodological implications of these findings and future research directions. [ABSTRACT FROM AUTHOR]

IntroductionAlthough frontotemporal dementia (FTD) with right anterior temporal lobe (RATL) predominance has been recognized, a uniform description of the syndrome is still missing. This multicenter study aims to establish a cohesive clinical phenotype.MethodsRetrospective clinical data from 18 centers across 12 countries yielded 360 FTD patients with predominant RATL atrophy through initial neuroimaging assessments.ResultsCommon symptoms included mental rigidity/preoccupations (78%), disinhibition/socially inappropriate behavior (74%), naming/word-finding difficulties (70%), memory deficits (67%), apathy (65%), loss of empathy (65%), and face-recognition deficits (60%). Real-life examples unveiled impairments regarding landmarks, smells, sounds, tastes, and bodily sensations (74%). Cognitive test scores indicated deficits in emotion, people, social interactions, and visual semantics however, lacked objective assessments for mental rigidity and preoccupations.DiscussionThis study cumulates the largest RATL cohort unveiling unique RATL symptoms subdued in prior diagnostic guidelines. Our novel approach, combining real-life examples with cognitive tests, offers clinicians a comprehensive toolkit for managing these patients.HighlightsThis project is the first international collaboration and largest reported cohort. Further efforts are warranted for precise nomenclature reflecting neural mechanisms. Our results will serve as a clinical guideline for early and accurate diagnoses.

BACKGROUND AND HYPOTHESIS: People with serious mental illness (SMI; psychotic and affective disorders with psychosis) are at an increased risk of suicide, yet there is limited research on the correlates of suicide in SMI. Social cognitive impairments are common among people with SMI and several studies have examined social cognition and suicidal ideation (SI) and behavior. This systematic review aims to evaluate the links between various domains of social cognition, SI, and suicidal behavior in SMI. STUDY DESIGN: Electronic databases (PubMed and PsycInfo) were searched through June 2023. Records obtained through this search (N = 618) were screened by 2 independent reviewers according to inclusion criteria. Relevant data were extracted, and study quality was assessed. STUDY RESULTS: Studies (N = 16) from 12 independent samples were included in the systematic review (N = 2631, sample sizes ranged from N = 20 to N = 593). Assessments of social cognition and SI and behavior varied widely between studies. Broadly, effects were mixed. Better emotion recognition of negative affect was linked to SI and a history of suicide attempts, though there is little consistent evidence for the relationship of emotion recognition and SI or behavior. On the other hand, better theory of mind ability was linked to SI and a history of suicide attempts. Furthermore, negative attributional bias was linked to current SI, but not a history of SI or attempt. CONCLUSIONS: This review suggests mixed associations between social cognition, SI, and behavior in SMI. Future research should evaluate additional mediators and moderators of social cognition and suicide, employing prospective designs.

Content created by employing artificial intelligence (AI) algorithms, also known as synthetic content, promises to radically change the advertising and marketing landscape in the coming decades, presumably for the better. It is fundamental for advertising and marketing scholars and practitioners to have solid knowledge of how synthetic content is perceived by consumers before widespread adoption is promoted. Across three experimental studies we tested how consumers in charitable giving contexts reacted to advertising messages featuring content generated by an AI neural network. We show that potential donors responded differently to children's faces when they knew they had been generated by AI. Study 1 established that awareness of the falsity of a face or its status as an AI-generated image has a negative impact on donation intentions. This negative impact is serially mediated by empathy and anticipatory guilt and empathy and emotion perception. Study 2 investigated several motives for employing AI-generated images and indicated that charities employing those images can benefit by making their ethical motives salient. Finally, Study 3 revealed that under extraordinary circumstances the use of AI images by charities is considered acceptable by consumers and is likely to lead to similar outcomes as the use of real images. Therefore, we recommend a cautious approach to the adoption of synthetic content. [ABSTRACT FROM AUTHOR]

Schizophrenia, a complex neuropsychiatric disorder, manifests severe impairments in social cognition, notably in Theory of Mind (ToM), empathy, and emotion recognition, which significantly influence social competence and overall functioning. These aspects are crucial for prognosis in individuals diagnosed with schizophrenia (SZ). This study validates a comics strip paradigm for ToM and empathy assessment, the Montreal Affective Voices (MAV) for measuring emotion recognition, and a Go-NoGo task for inhibition control estimation in individuals diagnosed with SZ, comparing their performance with healthy controls. SZ participants exhibited diminished abilities in the comics strip task, especially in ToM and empathy conditions, alongside challenges in identifying emotions from vocal cues in MAV. They responded slower and tended to be less accurate in the Go-NoGo task. The validated behavioral battery addresses the limitations of previous measures and emerges as a promising tool for future investigations into the neural systems underlying social cognition in schizophrenia. Such insights can lead to the development of long-needed treatment for negative symptoms and social dysfunctions in schizophrenia. [ABSTRACT FROM AUTHOR]

Speech emotion recognition holds significant importance as it enables machines to understand and respond to human emotions, enhancing human-computer interaction and personalized experiences. Accurate identification and interpretation of emotional states from speech signals enable various benefits, including enhanced personalized experiences, effective monitoring of mental health, and improved human-computer interfaces. However, recognizing emotions from speech is a difficult task primarily because there exists a significant disparity between acoustic features and human emotions. Both vocal cues and spoken words play significant roles in determining a person's emotional state. Therefore, in order to accurately identify human emotions from speech, it is essential to extract distinct and meaningful acoustic features. In this paper, we propose a novel approach to infer human emotional states. Human emotional state recognition has a wide range of applications ranging from customer service to mental health. Our proposed approach extracts a set of features from the speech signals, and employs a framework known as deep stride convolutional neural network using bi-directional LSTM. Our proposed model achieved a high accuracy of 95% which is almost 20% higher when compared to the state of the art on the RAVDESS dataset, while also minimizing the loss. [ABSTRACT FROM AUTHOR]

We investigated neural correlates of Emotion Recognition Accuracy (ERA) using the Assessment of Contextualized Emotions (ACE). ACE infuses context by presenting emotion expressions in a naturalistic group setting and distinguishes between accurately perceiving intended emotions (signal), and bias due to perceiving additional, secondary emotions (noise). This social perception process is argued to induce perspective taking in addition to pattern matching in ERA. Thirty participants were presented with an fMRI-compatible adaptation of the ACE consisting of blocks of neutral and emotional faces in single and group-embedded settings. Participants rated the central character’s expressions categorically or using scalar scales in consequent fMRI scans. Distinct brain activations were associated with the perception of emotional vs. neutral faces in the four conditions. Moreover, accuracy and bias scores from the original ACE task performed on another day were associated with brain activation during the scalar (vs. categorical) condition for emotional (vs. neutral) faces embedded in group. These findings suggest distinct cognitive mechanisms linked to each type of emotional rating and highlight the importance of considering cognitive bias in the assessment of social emotion perception. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. [ABSTRACT FROM AUTHOR]

Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusionsAttention Deficit Hyperactivity Disorder (ADHD) is defined as a persistent pattern of inattention and hyperactivity-impulsivity that interferes with functioning anofd development. Increased emotional reactivity and impaired emotion regulation are established findings in children with ADHD. Impairments in executive functions such as impulse control and working memory, in turn, have also been suggested to have a negative effect on emotion recognition. However, studies exploring suspected deficits in the ability to recognise facial emotions in ADHD have to date yielded controversial results. We sought to clarify the mechanism of possible emotion recognition dysfunction in children with ADHD.Sixty-one children diagnosed with ADHD (aged 10.36 ± 1.89 years) and a control group (N = 78; aged 9.6 ± 1.8 years) were evaluated with questionnaires and computerized tests for cognitive and facial emotion recognition capacity.The ADHD group displayed more behavioural issues and performed worse in cognitive tests compared to the control group. Group status (i.e. ADHD vs. control group) did not predict facial emotion recognition when controlled for age, IQ and sex in linear regression models. Performance in Divided Attention predicted facial emotion recognition in linear regression in the ADHD group.Individuals with ADHD showed facial emotion recognition capacity similar to a typically developing control group. Good performance in a cognitive test assessing divided attention predicted capacity for facial emotion recognition, but only in the ADHD group. [ABSTRACT FROM AUTHOR]

Music is a powerful language capable of eliciting a variety of emotions in individuals. Understanding and recognizing these emotions is pivotal for applications ranging from personalized music recommendations and music therapy to automatic music composition and affective computing. Presently, deep learning for music emotion recognition is gaining popularity, primarily relying on timbre features to capture local spatial information. However, there is an untapped potential in incorporating other pertinent audio features and global correlations in the feature space to capture the repetitive temporal information of music for emotion classification. This study introduces GlocalEmoNet as a method to capture both local and global correlations in music, utilizing timbre and Chroma audio features for tasks related to emotion classification and segmentation. The neural network underwent training and testing on approximately six thousand music audio samples, encompassing six music-emotion categories. The utilization of a genetic algorithm is employed for optimizing the hyperparameters of the proposed neural networks, aiming to attain optimal performance, efficiency, and generalization. The best classifier demonstrated superior performance, surpassing previously published results by a significant margin of approximately 14%. The optimal classifier achieved an accuracy score of 81.66%, an f1-score of 0.812, and an area under the curve score of 0.956. The evaluation of classification and segmentation outcomes also involved the use of visual representations. [ABSTRACT FROM AUTHOR]

Image captioning has emerged as a prominent approach for generating verbal descriptions of images that humans can read and understand. Numerous techniques and models in this domain have predominantly focused on analyzing the factual elements present within an image, employing convolutional neural networks (CNN) and long short-term memory (LSTM) networks to generate captions. However, an inherent limitation of these existing approaches is their failure to consider the emotional aspects exhibited by the main subject within an image, thereby potentially leading to inaccuracies in reflecting the conveyed emotional content. Acknowledging this limitation, this paper endeavors to construct an improved model dedicated to extracting human emotions from images and seamlessly embedding emotional attributes into the accompanying captions. In our research, we employ the widely accessible benchmarking image captioning dataset, Flickr8k. Our ultimate objective is to establish a more appropriate and impactful model for images containing human faces that provide more accurate and impacting captions. [ABSTRACT FROM AUTHOR]

Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints. [ABSTRACT FROM AUTHOR]

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors. [ABSTRACT FROM AUTHOR]

Facial expression detection technology is a flexible tool utilized in many different industries that allows for a better understanding of human behavior and emotional responses. Its applications span a broad spectrum of societal requirements, from enhancing marketing strategies to bolstering security protocols and assisting in the diagnosis of mental health issues. By using the power of artificial intelligence and computer vision, facial expression recognition (FER) helps businesses to adapt, grow and build deeper interactions between people and the technology and environments they live in. Its primary goal remains to facilitate better understanding, communication and decision-making in order to bring in a new wave of technical innovation that is driven by people’s needs. Facial emotion recognition, which makes use of computer vision and artificial intelligence, not only improves the capacity to understand and react to human emotions but also opens the door to more personalized and empathic interactions in an increasingly digital environment. In this paper, unique method of facial emotion recognition based on sophisticated feature extraction techniques combined with a hybrid ensemble learning technique. In particular, hierarchical cascade regression neural networks (HCRNN) for face landmark identification, VGG-19 for feature extraction and Random Forests are considered as classification techniques. In order to accomplish reliable and accurate emotion recognition from facial images, this method takes advantage of the complementary capabilities of both approaches. Initially, we utilized a deep convolutional neural network (CNN) called VGG-19 to extract high-level features from face images. Pre-trained on extensive image datasets, VGG-19 has proven to be highly effective at capturing complex visual representations. These characteristics function as an all-inclusive description of facial expressions, encompassing both general trends and minute details. HCRNN for facial landmark detection, which precisely locates important face features including the mouth, nose and eyes. Facial landmark predictions are iteratively improved by this hierarchical cascade architecture, which also handles occlusions and changes in illumination and position. Lastly, we use an ensemble learning technique called Random Forests to classify emotions. Multiple decision trees’ predictions are combined by random forests, which offer robustness against overfitting and efficiently handle high-dimensional feature fields. High accuracy and generalization capabilities are achieved in the facial emotion classification process by Random Forests, which combine the feature representations retrieved by VGG-19 with the localized facial landmarks recognized by HCRNN. We tested the suggested strategy on benchmark face emotion detection datasets and compared its results with the most advanced techniques available. Performance measures like accuracy of 98.89%, precision 96.52%, recall 96%, F1-score 99% and receiver operating characteristic (ROC) curve of 97.24% analysis were used to evaluate how well hybrid strategy worked and how much better it was at identifying emotions in facial images. The outcomes of experiments show how method has practical applications in human–computer interaction, healthcare and other fields. [ABSTRACT FROM AUTHOR]

Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods. • A data-driven approach to capturing signals when emotions are triggered. Network parameters for different subjects were obtained through PLV calculations, forming a tensor. Tucker decomposition was then used to detect the brain network state of EEG signals, automatically identifying EEG segments during emotional activation. This method effectively screens signals in the state of emotional arousal for improved emotion recognition. • Using tensor to extract brain functional connectivity patterns under emotional states. Through tensor sparse representation, the functional brain connectivity network can be obtained to represent the emotional excitation state, and then the network connection features can be extracted. • Fusion features offer richer compensatory information for emotion recognition by combining activation difference features with connectivity features. [ABSTRACT FROM AUTHOR]

Multi-channel Electroencephalogram (EEG) based emotion recognition is focused on several analysis of frequency bands of the acquired signals. In this paper, spectral properties appeared on five EEG bands (δ , θ , α , β , γ ) and gated transformer network (GTN) based emotion recognition using EEG signal are proposed. Spectral energies and differential entropies of 62-channel signals are converted to 3D (sequence-channel-trial) form to feed the GTN. The GTN with enhanced gated two tower based transformer architecture is fed by 3D sequences extracted from SEED and SEED-IV emotional datasets. 15 participants' states in session 1–3 are evaluated using the proposed GTN based sequence classification, and the results are repeated by 3 × shuffling. Totally, 135 times training and testing are performed on each dataset, and the results are presented. The proposed GTN model achieves mean accuracy rates of 98.82% on the SEED dataset and 96.77% on the SEED-IV dataset for three and four emotional state recognition tasks, respectively. The proposed emotion recognition model can be employed as a promising approach for EEG emotion recognition. [ABSTRACT FROM AUTHOR]

Background: Alexithymia, characterized by difficulty identifying and describing emotions and an externally oriented thinking style, is a personality trait linked to various mental health issues. Despite its recognized importance, research on alexithymia in early childhood is sparse. This study addresses this gap by investigating alexithymia in preschool-aged children and its correlation with psychopathology, along with parental alexithymia. Methods: Data were analyzed from 174 parents of preschoolers aged 3 to 6, including 27 children in an interdisciplinary intervention program, all of whom attended regular preschools. Parents filled out online questionnaires assessing their children's alexithymia (Perth Alexithymia Questionnaire–Parent Report) and psychopathology (Strengths and Difficulties Questionnaire), as well as their own alexithymia (Perth Alexithymia Questionnaire) and emotion recognition (Reading Mind in the Eyes Test). Linear multivariable regressions were computed to predict child psychopathology based on both child and parental alexithymia. Results: Preschool children's alexithymia could be predicted by their parents' alexithymia and parents' emotion recognition skills. Internalizing symptomatology could be predicted by overall child alexithymia, whereas externalizing symptomatology was predicted by difficulties describing negative feelings only. Parental alexithymia was linked to both child alexithymia and psychopathology. Conclusions: The findings provide first evidence of the importance of alexithymia as a possible risk factor in early childhood and contribute to understanding the presentation and role of alexithymia. This could inform future research aimed at investigating the causes, prevention, and intervention strategies for psychopathology in children. [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]

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]

The task of object detection in computer vision revolves around the identification of objects within images or videos. A specific subtask within object detection is face detection, which focuses on detecting human faces. Within the realm of face detection, an important research area is facial feature detection, which has diverse applications ranging from facial recognition to emotion detection and facial expression analysis. The crucial step in facial feature detection is the identification and localization of key facial features such as the eyes, eyebrows, nose, mouth, and chin, which can also be called facial region detection. Face region detection can be done in two ways: landmark detection and Bounding box- based detection. Bounding boxes offer computational benefits such as increased speed and efficiency. They are preferable when the objective is to accurately detect and locate the presence of an object or face in an image or video frame. Although most of the existing algorithms for facial feature detection based on bounding box predictions typically treat the eyes as a single entity, our approach using YOLOv5 addresses the separation of left and right eye detection. In this research study, we conducted experiments using YOLOv5, which provides bounding box predictions. We used a subset of LFW (Labelled Faces in the Wild) Dataset which we augmented using GFP-GAN, Gaussian Noise, Image Sharpening, and CLAHE. We explored the effectiveness of different backbone architectures when applied to YOLOv5 for the task of facial region detection. We evaluated three popular backbone networks: EfficientNet-b0, GhostNet, and CSP-Darknet53. Our objective was to identify the most suitable backbone architecture that yields accurate detection of facial features, including the left eye, right eye, nose, and lips. Our experiments show that when GhostNet is used as a backbone in the YOLOv5 architecture, it produces superior results for the detection and classification of features as compared to the other backbones. We present a detailed evaluation of our findings, including discussions of the experimental results using different IOU thresholds and backbone combinations. Our proposed methodology and findings make valuable contributions to the field of facial feature extraction and provide meaningful insights into the potential and performance of YOLOv5 for detecting and localizing key facial elements. [ABSTRACT FROM AUTHOR]

Emotion plays a vital part in the life of humankind. Emotion Recognition (ER) is the necessary task to find one's inner Emotion through speech, text or face. ER process has inevitable places in applications such as education, healthcare, defence, forensics, automation and scientific-based purposes. However, the recognition models face various issues during the process. Due to the large feature sets, the model's computational time and complexity have been increased; it also produced high errors in recognition, which degrades the efficiency of the system. So, a novel Monkey-based V-Net Framework (MbVF) was developed for this research work to classify the Emotion and sentiment patterns using healthcare data. Initially, the healthcare Artificial Intelligence (AI) conversation data from the Coronavirus Tweets NLP-Text Classification dataset was collected and trained for the system. Next, stemming, stop-words removal, tokenization, and noise feature elimination process were performed in the preprocessing step. Then, the Term Frequency based-Inverse Document Frequency (TF-IDF) features were used to choose the best features depending on the fitness features of the proposed MbVF approach. Finally, the ER process was performed to recognize the emotions, such as positive, neutral and negative, using sentiment analysis with the emotion state transition and emotion pattern. The model attained 99.98% accuracy in predicting emotions, and is higher than the other model. [ABSTRACT FROM AUTHOR]

EEG-based classification from human states is still challenging in human-computer interaction (HCI). Since it reflects brain activity directly, electroencephalography (EEG) has significant advantages in emotion classification research. This study recorded EEG signals from 12 participants while they perceived emotional audio-visual stimulation movie clips for four minutes. The six emotions studied were anger, excitement, fear, happiness, sadness, and a neutral state. We also perform raw data preprocessing to obtain clean data, extracting the power spectrum using Fast Fourier Transform (FFT) to generate feature vectors. In addition, we conduct extensive experiments to validate the classification of human states using subject-independent machine-learning techniques. As a result, the LSTM network achieved the highest classification accuracy of 81.46% for six emotional states, while the SVM network achieved only 68.64%. In addition, we achieved 82.89% accuracy in the Bi-LSTM network with two layers when applying the deep learning methods to different layers. In conclusion, extensive experiments were conducted on our collected dataset. Experimental results indicate that the LSTM network, a time-sequence-related model, has superior classification results of 82.89% than other methods. It also shows that the long duration of EEG signals is crucial for detecting the emotional state changes of various subject types, including individual subjects and cross-subjects. In future research, we will investigate multiple evaluation experiments utilizing deep learning models or propose novel EEG-based emotion classification features to improve the accuracy of emotion classification. [ABSTRACT FROM AUTHOR]

Emotional events are often remembered better than neutral ones; however, emotion can also spill over and affect our memory for neutral experiences that precede an emotional event. Theories suggest that emotion can retroactively enhance memory for preceding neutral events that are considered high-priority while impairing memory for events deemed low-priority. However, the impact of conceptual relationships (i.e., semantic connections) between preceding neutral information and emotional events on memory for the preceding information has received little attention. This study investigated the influence of conceptual relatedness on the retroactive effects of emotion on memory. Participants sequentially encoded pairs of images that were high or low in conceptual relatedness, each comprising a neutral object followed by either a negative or neutral image. Participants returned the next day for a recognition memory assessment. The results indicated an interactive effect of emotion and conceptual relatedness on memory: In a “discovery” sample, memory was poorer for images preceding conceptually unrelated negative (vs. neutral) images, while the opposite pattern was seen for conceptually related images. In a “replication” sample, these effects were partially replicated, with the former impairment effect statistically observed but not the latter augmentation effect. Hence, conceptual relatedness affects how negative emotion influences memory. [ABSTRACT FROM AUTHOR]

Background: COVID-19, as a significant public health issue, has had a major impact on the mental health of people worldwide. Research shows a significant positive correlation between individuals' risk perception levels and negative emotions during the outbreak of COVID-19. However, some studies also suggest that the relationship between the two is not significant. Therefore, we will conduct a meta-analysis to explore the relationship between risk perception and negative emotions from cultural, temporal, and individual psychological perspectives. Methods: Searches were conducted in the Web of Science, Pub Med, Google Scholar, PsycINFO, Scopus, and China National Knowledge Infrastructure databases, focusing on publications from January 2020 onwards, specifically targeting studies examining the relationship between risk perception and negative emotion during COVID-19. Results: A total of 58 papers with 85 effect sizes were meta-analyzed using Comprehensive Meta-Analysis 3.0 software, with a combined sample of 83,948 individuals. Risk perception of COVID-19 showed a moderate positive correlation with negative emotions (r = 0.211, 95%CI [0.18, 0.24]). There was no moderating effect of tight-loose cultures on the relationship between risk perception of COVID-19 and negative emotions. However, the epidemic period, gender ratio, and measurement methods did have moderating effects on the relationship between risk perception of COVID-19 and negative emotions. Conclusion: In future research, we can further develop theories related to the risk perception of COVID-19 and negative emotions, and based on these, formulate interventions to promote people's mental health. [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]

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person's emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%. [ABSTRACT FROM AUTHOR]

Children's ability to accurately recognize the external emotional signals produced by those around them represents a milestone in their socioemotional development and is associated with a number of important psychosocial outcomes. A plethora of individual studies have examined when, and in which order, children acquire emotion knowledge over the course of their development. Yet, very few attempts have been made to summarize this body of work quantitatively. To address this, the present meta-analysis examined the age-related trajectories of emotion recognition across childhood and the extent to which typically developing children's recognition of external emotional cues (in the face, voice, and body) is influenced by a host of participant-, task-, and stimulus-related factors. We analyzed children's emotion recognition overall (independent of specific emotion categories) and for specific basic emotions. In total, k = 129 individual studies, investigating a total of N = 31,101 2–12-year-old children's emotion recognition abilities were included in our analyses. Children's recognition accuracy across all emotion categories was significantly above chance and improved with age in the same manner for all emotions. Emotion recognition accuracy was also moderated by region of study and task type. The order in which children became proficient at identifying specific emotions was consistent with previous qualitative reviews: Happiness was the easiest emotion to recognize, and disgust and fear were the most difficult to recognize across age. Task- and stimulus-related moderator variables also influenced specific emotion categories in different ways. We contextualize these results with regard to children's socioemotional development more broadly, and we discuss how our findings can be used to guide researchers and practitioners interested in children's social skills. Public Significance Statement: The accurate recognition of others' emotions is a fundamental social skill, relevant for navigating the social world from early childhood. The present meta-analysis demonstrates that children's emotion recognition accuracy increases across childhood in the same manner for different emotions (i.e., happiness, sadness, fear, anger, surprise, and disgust). In addition, children's emotion recognition accuracy was found to depend on tasks that are used to test these abilities. These findings suggest that child development researchers and practitioners should carefully consider age as well as the tests they use when assessing emotion recognition in childhood. [ABSTRACT FROM AUTHOR]

Individuals suffering from schizophrenia demonstrate deficits in recognizing emotion based on voice. The present study aimed to develop a vocal emotion recognition test with good psychometric properties and use it for evaluation in schizophrenia. Data from 82 healthy participants and 161 stable inpatients diagnosed with schizophrenia were collected. The internal consistency and test-retest reliability of the vocal emotion recognition test (VER-42) were acceptable. Patients were less accurate than the healthy group in identifying emotion based on the VER-42 total score and were less accurate than healthy individuals across all emotions. The VER-42 total score correlated with cognitive functions and social cognition or function; and was associated negatively with negative symptoms of patients suffering from schizophrenia. The VER-42 was shown to be a valid and reliable instrument for evaluating vocal emotion recognition ability and could thus contribute to the screening of patients in the future. [ABSTRACT FROM AUTHOR]

Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday communication, but also a central focus in the field of human–computer interaction. However, SER faces several challenges, including difficulties in detecting subtle emotional nuances and the complicated task of recognizing speech emotions in noisy environments. To effectively address these challenges, we introduce a Transformer-based model called MelTrans, which is designed to distill critical clues from speech data by learning core features and long-range dependencies. At the heart of our approach is a dual-stream framework. Using the Transformer architecture as its foundation, MelTrans deciphers broad dependencies within speech mel-spectrograms, facilitating a nuanced understanding of emotional cues embedded in speech signals. Comprehensive experimental evaluations on the EmoDB (92.52%) and IEMOCAP (76.54%) datasets demonstrate the effectiveness of MelTrans. These results highlight MelTrans's ability to capture critical cues and long-range dependencies in speech data, setting a new benchmark within the context of these specific datasets. These results highlight the effectiveness of the proposed model in addressing the complex challenges posed by SER tasks. [ABSTRACT FROM AUTHOR]

EEG-based emotion classification is considered to separate and observe the mental state or emotions. Emotion classification using EEG is used for medical, security and other purposes. Several deep learning and machine learning strategies are employed to classify the EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here, the input EEG signals are taken from two benchmark datasets,namely DEAP and K-EmoCon datasets. Then, the input EEG signals are pre-processed,and the features are extracted utilizing ContextNet with Global Principal Component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks method. RSTAGNN weight parameters are optimized under the Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The efficacy of the proposed method using the DEAP dataset attains higher accuracy by 24.05%, 12.64% related to existing systems, like Multi-domain feature fusion for emotion classification (DWT-SVM-EEGEA- DEAP), EEG emotion finding utilizing fusion mode of graph CNN with LSTM (GCNN-LSTM-EEG-EA-DEAP) respectively. The efficiency of the proposed method using the K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like Toward Robust Wearable Emotion Realization along Contrastive Representation Learning (CAT-EEG-EA-K-EmoCon) and Human Emotion Recognition using Physiological Signals (CATEEG- EA-K-EmoCon) respectively. [ABSTRACT FROM AUTHOR]

Depression is one of the most frequent conditions within the general population, ranking among the most burdensome in terms of public health expenses, productivity loss and social costs. The condition has been linked with cognitive deficits that may even continue in remission phases, social cognition being one potentially affected cognitive function. In our study, the Penn's Emotional Acuity Test included in the CogtestTM battery was used to assess emotion recognition accuracy and processing speed. The Structured Clinical Interview for the Montgomery-Asberg Depression Rating Scale was used to assess the intensity of the symptomatology. A total of 48 depressed individual individuals (65% females), with a mean age of 49.8 ± 10.4 years, and 40 healthy controls (75% females), with a mean age of 35.2 ± 6.9 years were included. As compared to controls, depressive participants recorded significantly less correct answers (9.3 ± 3.8 vs. 11.2 ± 3.6, p = 0.019 -- Student's t test) and slower processing speeds (6795 ± 3366 vs. 4042 ± 1623, p < 0.001 -- Mann-Whitney U test) in emotion processing. Furthermore, symptom severity significantly influences only processing speed and not accuracy in emotion recognition tasks. Thus, we conclude that depressive individuals have a significant deficit in processing speed and accuracy when identifying the intensity of other peoples' emotions. [ABSTRACT FROM AUTHOR]

In smart car applications, monitoring drivers involves assessing drivers' concentration, fatigue, distraction, and emotional state to ensure safe driving and accident prevention. In recent years, several research projects have focused on the impact of emotions on driving safety. Despite the models proposed and the performance achieved, FER2013, one of the well-known datasets, still presents performance challenges. This research paper proposes a CNN-based model to classify seven driver emotions using the FER2013 dataset. After rigorous pre-processing including image label correction, cleaning, balancing with SMOTE-EEN, and data augmentation, our CNN model was trained on the FER2013 dataset. The experiment's outcomes were compared to established facial emotion recognition methods developed over FER2013, revealing that the proposed model achieved an improved accuracy of 98.41%, precision of 98%, F1-score of 94%, and recall of 96%, surpassing the performance of prior research in this field. Moreover, our approach outperforms the most existing CNN pre-trained models, including MobileNet, VGG16, VGG19, ResNET50, DensNet121, DensNet169, MobileNetV3Small, and EfficientNet, all assessed using the FER2013 dataset. [ABSTRACT FROM AUTHOR]

The widespread applications of emotion recognition (ER) in various fields have recently attracted much attention from researchers. Consequently, an array of advanced techniques has emerged, driven by enhancing the accuracy and robustness of these recognition systems. As emotional dialogue comprises sound and spoken content, the proposed model encodes the information from audio and text sequences using two separate channels and merges them for emotional classification. The two channels used inputs from audio and text modalities. The audio channel is encoded using a deep convolutional neural network with residual connections and further transformed using a self-attention-based multihead attention network called channel-wise global head pooling. Unlike the vanilla multihead attention network, an adaptive global pooling is used after concatenating all the heads. The text channel is encoded using a pre-trained BERT model. The proposed ER method is validated on four benchmark databases: Interactive Emotional Dyadic Motion Capture in English, the Berlin emotional speech dataset in the German language, Ryerson Audio-Visual Database of Emotional Speech and Song in North American English and Crowd-sourced Emotional Multimodal Actors Dataset in English. The classification accuracy on the above emotional corpora is 85.71%, 79.52%, 76.71% and 73.91%, respectively. Furthermore, cross-corpus analysis is presented to understand the variability of speech and text features and the robustness of the proposed architecture. [ABSTRACT FROM AUTHOR]

This study sought to investigate if there was a significant difference between the Huntington's Disease gene expansion carriers who were impaired on the cognitive domains, social cognition and executive functions. Also, it was investigated which of the cognitive domains could predict the decrease in total functional capacity over a 6-year follow-up period. Premanifest and motor-manifest Huntington's Disease gene expansion carriers (N = 98), were examined with a neurological and neuropsychological examination at Time 1 (year 2012–2013). Regression-based normative data was used to classify impairments on the two cognitive domains. Follow-up participants (N = 80) had their functional capacity reexamined at Time 2 (year 2018–2020), to examine which cognitive domain could predict the decrease in functional capacity over the 6-year follow-up. More than 50% of the participants were impaired on the domain of social cognition. These participants were significantly different from the participants who were impaired on executive functions. The motor function and impairments on social cognition significantly predicted the decline in functional capacity. The Emotion Hexagon test was the only significant social cognitive task, that predicted the decline in functional capacity. Social cognition includes unique and separate functions in Huntington's Disease, unaffected by executive functions. This study emphasizes the importance of regular assessment of social cognition in Huntington's Disease and the clinical relevance of impaired social cognitive function. [ABSTRACT FROM AUTHOR]

Traditional music emotion recognition (MER) faces problems such as lack of contextual information, inaccurate recognition of music emotions, and difficulty in handling nonlinear relationships. This article first used long short-term memory (LSTM) networks to capture global information and contextual relationships of music. Subsequently, the DCNN was chosen to process sequence data and capture global dependencies to improve the accuracy of MER. Finally, a MER model was constructed based on DCNN to recognize and classify music emotions. This article obtained the impact of different parameter values on model training iterations by adjusting hyperparameters related to training. The optimal values for learning rate μ , momentum coefficient α , weight attenuation coefficient γ , and Dropout coefficient were 0.01, 0.7, 0.0003, and 0.5, respectively. The DCNN used in this article was iteratively trained with recurrent neural networks, convolutional recurrent neural networks, and transform domain neural networks for audio spectrograms, and the results were compared. The experimental findings indicated that the spectral recognition accuracy of DCNN was stable at 95.68%, far higher than the other three different networks. The results showed that the DCNN method used in this article could more accurately distinguish different negative emotions and positive emotions. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent popularity of EEG-based emotion recognition. However, due to the non-stationary nature of EEG, inter-subject variabilities become obstacles for recognition models to well adapt to different subjects. In this paper, we propose a novel approach called semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) for cross-subject emotion recognition, which offers two significant advantages. Firstly, SBGASS adaptively learns a bipartite graph to characterize the underlying relationships between labeled and unlabeled EEG samples, effectively implementing the semantic connection for samples from different subjects. Secondly, we employ active sample selection technique in this paper to reduce the impact of negative samples (outliers or noise in the data) on bipartite graph construction. Drawing from the experimental results with the SEED-IV data set, we have gained the following three insights. (1) SBGASS actively rejects negative labeled samples, which helps mitigate the impact of negative samples when constructing the optimal bipartite graph and improves the model performance. (2) Through the learned optimal bipartite graph in SBGASS, the transferability of labeled EEG samples is quantitatively analyzed, which exhibits a decreasing tendency as the distance between each labeled sample and the corresponding class centroid increases. (3) Besides the improved recognition accuracy, the spatial-frequency patterns in emotion recognition are investigated by the acquired projection matrix. [ABSTRACT FROM AUTHOR]

Background: The accuracy of decoding fine motor imagery (MI) tasks remains relatively low due to the dense distribution of active areas in the cerebral cortex. Methods: To enhance the decoding of unilateral fine MI activity in the brain, a weight-optimized EEGNet model is introduced that recognizes six types of MI for the right upper limb, namely elbow flexion/extension, wrist pronation/supination and hand opening/grasping. The model is trained with augmented electroencephalography (EEG) data to learn deep features for MI classification. To address the sensitivity issue of the initial model weights to classification performance, a genetic algorithm (GA) is employed to determine the convolution kernel parameters for each layer of the EEGNet network, followed by optimization of the network weights through backpropagation. Results: The algorithm's performance on the three joint classification is validated through experiment, achieving an average accuracy of 87.97%. The binary classification recognition rates for elbow joint, wrist joint, and hand joint are respectively 93.92%, 90.2%, and 94.64%. Thus, the product of the two-step accuracy value is obtained as the overall capability to distinguish the six types of MI, reaching an average accuracy of 81.74%. Compared to commonly used neural networks and traditional algorithms, the proposed method outperforms and significantly reduces the average error of different subjects. Conclusions: Overall, this algorithm effectively addresses the sensitivity of network parameters to initial weights, enhances algorithm robustness and improves the overall performance of MI task classification. Moreover, the method is applicable to other EEG classification tasks; for example, emotion and object recognition. [ABSTRACT FROM AUTHOR]

Patients with schizophrenia tend to have deficits in emotion recognition (ER) that affect their social function. However, the commonly-used ER measures appear incomprehensive, unreliable and invalid, making it difficult to comprehensively evaluate ER. The purposes of this study were to develop the Computerized Emotion Recognition Video Test (CERVT) evaluating ER ability in patients with schizophrenia. This study was divided into two phases. First, we selected candidate CERVT items/videos of 8 basic emotion domains from a published database. Second, we validated the selected CERVT items using Rasch analysis. Finally, the 269 patients and 177 healthy adults were recruited to ensure the participants had diverse abilities. After the removal of 21 misfit (infit or outfit mean square > 1.4) items and adjustment of the item difficulties of the 26 items with severe differential item functioning, the remaining 217 items were finalized as the CERVT items. All the CERVT items showed good model fits with small eigenvalues (≤ 2) based on the residual-based principal components analysis for each domain, supporting the unidimensionality of these items. The 8 domains of the CERVT had good to excellent reliabilities (average Rasch reliabilities = 0.84–0.93). The CERVT contains items of the 8 basic emotions with individualized scores. Moreover, the CERVT showed acceptable reliability and validity, and the scores were not affected by examinees' gender. Thus, the CERVT has the potential to provide a comprehensive, reliable, valid, and gender-unbiased assessment of ER for patients with schizophrenia. [ABSTRACT FROM AUTHOR]

This paper introduces EmoBone, a comprehensive audio‐only emotional bone‐conducted speech dataset featuring speakers from various countries. The dataset comprises speeches from 28 individuals representing 10 different nations, with each participant delivering 10 sentences designed to evoke distinct emotions. In addition to an air‐conducted microphone, the recordings utilized bone conduction technology, transmitting sound directly to the speakers' inner ears, ensuring high‐quality emotional speech recordings. To assess the validity of the dataset, 80 university students from Bangladesh listened to the recordings and successfully identified the expressed emotions with an accuracy exceeding 76%. Statistical methods were also employed to evaluate the reliability of the dataset, revealing a high level of agreement among raters. EmoBone, with a cumulative duration surpassing 19 h and 15 680 unique utterances, stands as the most extensive emotional speech dataset available. This makes it a valuable tool for studying how emotional speech varies across cultures. Furthermore, due to its utilization of bone conduction technology, EmoBone facilitates the study of acoustic features in emotional speech from diverse dimensions. The data that supports the findings of this study is available upon reasonable request. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [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]