Basic Emotions Research Articles

Parental depression moderates the relationship between childhood maltreatment and the recognition of children expressions of emotions.

Sensitivity plays a crucial role in parenting as it involves the ability to perceive and respond appropriately to children's signals. Childhood maltreatment and depression can negatively impact adults' ability to recognize emotions, but it is unclear which of these factors has a greater impact or how they interact. This knowledge is central to developing efficient, targeted interventions. This paper examines the interaction between parents' depressive symptoms and childhood maltreatment and its influence on their ability to recognize the five basic emotions (happiness, anger, sadness, fear, and disgust) in children's faces. The sample consisted of 52 parents. Depressive symptoms were measured by the depression subscale of the Brief Symptom Inventory-18 (BSI-18), and maltreatment history was assessed by the Childhood Trauma Questionnaire (CTQ). Children's emotional stimuli were morphed images created using The Child Affective Facial Expression (CAFE) database. Our findings indicate that depressive symptoms moderate the relationship between parents' history of childhood maltreatment and emotion recognition skills. Parents with higher depressive symptoms had lower emotion recognition accuracy when they had not experienced maltreatment. When childhood maltreatment was severe, emotion recognition skills were more consistent across all levels of depression. The relationship between depression and emotion recognition was primarily linked to recognizing sadness in children's faces. These findings highlight how different experiences can affect parental abilities in emotion recognition and emphasize the need for interventions tailored to individual profiles to improve their effectiveness.

A Tale of Three Advantages: Cross-Cultural Recognition of Basic Emotions Through Virtual Stickers

This study examined recognition of the six basic emotions communicated by 72 stickers provided on Chinese and American social media. Results demonstrate that sadness, happiness, and anger are recognized more accurately than disgust, fear, and surprise. An in-group advantage exists among Chinese instead of American participants, as the latter group recognizes emotions rendered by American and Chinese stickers with similar accuracy. Women outperform men in recognizing anger and fear expressed by stickers. A dynamic advantage manifests in recognizing anger, fear, and happiness rendered by animated stickers, in relation to static stickers. Three-way interaction effects show that emotions rendered by Chinese static stickers are more easily recognized compared to American static stickers. Moreover, women and Chinese participants recognize emotions rendered by Chinese and American animated stickers at similar accuracy levels. By contrast, men and American participants deliver better performances in recognizing emotions expressed by American animated stickers than Chinese animated stickers.

Improving masked communication: the case for transparent masks

Clear and compassionate communication is tantamount to quality of care and patient wellbeing. Yet, much of communication in healthcare occurs with lower faces visually hidden behind opaque face masks. Visual occlusion of the face by masks impairs both verbal and nonverbal communication, including recognition of basic emotions, perceptions of trustworthiness, emotional understanding, and empathic responding. This piece presents a brief overview of the new COVID-19 inspired literature which shows that visual face occlusion creates significant communication barriers which can be reduced by wearing transparent instead of opaque masks.

Effectiveness of Group-Play Training on Emotion Recognition for Young Children Who Are Deaf or Hard of Hearing

Abstract: Deaf and hard of hearing (DHH) children are developmentally delayed in facial emotion recognition (FER). This study aimed to explore the possibility of enhancing the FER ability of DHH preschoolers through a group-play intervention. Nineteen children with a cochlear implant or hearing aid were enrolled in a 4-week intervention; six DHH children were assigned to a control group. The training program included a learning procedure for four basic emotions: happiness, sadness, anger, and fear. A pretest/posttest design was used to measure the DHH children's FER performance. The results indicated that although the two groups performed comparably on the FER task before the training (pretest), the performance of the intervention group was significantly better than that of the control group in the posttest. Moreover, the DHH children in the intervention group showed the greatest improvement in FER of happiness.

Separated at birth: Rediscovering the lost emotions in Luria's Working Brain

Aleksandr Luria repeatedly emphasised the importance of emotions and the right hemisphere in his neuropsychological writings. It is surprising, therefore, that Luria's most influential book, The Working Brain, appears to lack an explicit section on these topics. This is especially notable because of a comment in the book's English-language Introduction, by Karl Pribram, referencing Luria's thoughts about precisely this material. Remarkably, it seems that Luria did write such an explicit chapter, in the original Russian edition. However, in the English-language version, the relevant sections were separated, embedded elsewhere without chapter headings, and altered, presumably following an explicit translation decision. The present paper tracks the nature of these changes and, 50 years later, presents the material for the first time translated and reunited in English, as Luria intended. After the translation, we offer a brief commentary, on the ways in which Luria's ideas were in some respects prescient, and in other respects less well-informed about the brain basis of emotions and the right hemisphere. This reunification offers an interesting time capsule on the opinions of one of neuropsychology's greatest minds, on a topic which Luria admits had, at the time, only a modest empirical foundation.

Automatic detection of facial expressions during the Cyberball paradigm in Borderline Personality Disorder: a pilot study.

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios.

The meaning of “Emotion” (情) in Confucian philosophy

Abstract“Emotion” (情) is a very complex concept in Confucianism, but it can be interpreted in five dimensions. Interpreted as “innate instinct,” emotion refers to the cognitive attributes and physiological basis of emotion. Interpreted in terms of desire, it emphasizes the regulation and restriction of emotion. Construed in terms of Qi (氣), it indicates the source and path of emotion. The interpretation of emotion by nature (性) is most commonly used by Confucians and constitutes the fundamental position of Confucianism. Confucianism can both unify “nature” and “emotion” and also downgrade the status of emotion. The interpretation of emotion by Yin (陰) and Yang (陽) explains the nature and principle of emotion with the help of the rules of heaven. Ritual is based on emotional psychology, and the most basic emotional psychology of ethical life is the premise of Confucian social ritual.

Evaluating ChatGPT's Performance in Answering Questions About Allergic Rhinitis and Chronic Rhinosinusitis.

This study aims to evaluate the accuracy of ChatGPT in answering allergic rhinitis (AR) and chronic rhinosinusitis (CRS) related questions. This is a cross-sectional study. Each question was inputted as a separate, independent prompt. Responses to AR (n = 189) and CRS (n = 242) related questions, generated by GPT-3.5 and GPT-4, were independently graded for accuracy by 2 senior rhinology professors, with disagreements adjudicated by a third reviewer. Overall, ChatGPT demonstrated a satisfactory performance, accurately answering over 80% of questions across all categories. Specifically, GPT-4.0's accuracy in responding to AR-related questions significantly exceeded that of GPT-3.5, but distinction not evident in CRS-related questions. Patient-originated questions had a significantly higher accuracy compared to doctor-originated questions when utilizing GPT-4.0 to respond to AR-related questions. This discrepancy was not observed with GPT-3.5 or in the context of CRS-related questions. Across different types of content, ChatGPT excelled in covering basic knowledge, prevention, and emotion for AR and CRS. However, it experienced challenges when addressing questions about recent advancements, a trend consistent across both GPT-3.5 and GPT-4.0 iterations. Importantly, the accuracy of responses remained unaffected when questions were posed in Chinese. Our findings suggest ChatGPT's capability to convey accurate information for AR and CRS patients, and offer insights into its performance across various domains, guiding its utilization and improvement.

Трансляция эмоций в аудиодескрипции к немецкоязычным детским анимационным фильмам

The aim of the study is to identify linguistic means and methods for representing emotions in specialized sound descriptions and commentaries (audio descriptions) intended for blind and visually impaired recipients and accompanying feature films. The article examines the features of a children’s ani-mated fairy tale film as a cinematographic genre. Considerable attention is paid to the characteristics of the film text as a semiotic system in which emotional meanings are formed at the verbal and non-verbal level. The scientific novelty of the study is due to the systematization of linguistic means that manifest basic emotions in the German-language audio description to the children’s animated film adaptation. As a result of the study, it was found that the transformation of visual emotional information into verbal information in the audio description text is based on the use of simple syntactic constructions, the models “verb + adjective”, “verb + adverb of manner”, “verb + participle”, as well as the use of phraseological units, colloquial expressions, similes and epithets, which is due to the age and sensory characteristics of young recipients with visual impairment, and serves to facilitate the perception of the transmitted information.

КОГНИТИВНЫЙ НЕОАССОЦИАНИЗМ:РОЛЬ ОТРИЦАТЕЛЬНЫХ ЭМОЦИЙ

The relevance of the topic “Cognitive neo-associationism: the role of negative emotions” is that, despite the time, the question of aggression and the way to calm down still stands and has not been completely resolved due to the fact that it is impossible to completely select an individual plan for combating aggression. Methods with elements of art, relaxation exercises and even sports were used as the main solutions to the problem. In the aspect of cognitive assessment, basic and suprabasic emotions are considered. If basic emotions are triggered by innate triggers, then supra-basic ones are triggered by those acquired during the life of a particular person. Superbasic or socialized emotions most often have a cognitive nature of origin and their appearance in the psyche. Such emotions are conscious in nature, since they appear upon the fact of a cognitive assessment of the stimulus; in turn, the appearance of basic emotions based on innate stimuli is most often not realized by a person. The purpose of the study was to overcome constructs in the scientific literature about the dominance of cognitive mechanisms of the origin of emotions. The object of the study was emotions and the nature of their origin. The subject of the study was a critique of the cognitive approach to the origin of emotions and the substantiation of the importance of the biological approach, where an important role is given to the innate and genetic mechanisms of the appearance of emotions. The methodological basis of the study was the analysis of scientific research on the problem of the nature and appearance of basic emotions in the psyche.

A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments

This research aims to compare the performance of three classification algorithms, namely Random Forest, XGBoost, and LightGBM, in classifying emotions in Reddit comments. Emotion classification in Reddit comments is a complex classification problem due to its numerous variations and ambiguities. This research utilizes the GoEmotions Fine-Grained dataset, filtered down to 7,325 Reddit comments with 5 different basic emotion labels. In this study, data preprocessing steps, feature extraction using CountVectorizer and TF-IDF, and hyperparameter tuning using GridSearchCV for each algorithm are conducted. Subsequently, model evaluation is performed using Cross-Validation and confusion matrix. The results of the study indicate that Random Forest outperforms the XGBoost and LightGBM algorithm with an accuracy of 75.38% compared to XGBoost with 69.05% accuracy and LightGBM with 66.63% accuracy.

Family resilience and vulnerability of patients at diagnosis of lung cancer: A qualitative study.

To explore and analyse the adaptation process of patients and their families at the point of lung cancer diagnosis. Totally 23 operable lung cancer patients were included in this study. Colaizzi's method of phenomenology was employed for data analysis. This study found two different aspects of family adaptation at the diagnosis of lung cancer. For family resilience, three themes emerged: (1) Positive family belief systems (giving meaning to a cancer diagnosis and maintaining a positive/optimistic attitude), (2) Flexible family organizational patterns (maintaining the stability of family structure and function, adjusting the relationship between patients and family members and receiving external support and help) and (3) Good communication and problem-solving strategies (open communication on an equal basis, positive and open expression of emotions and collaborative problem-solving). For family vulnerability, three themes were as follows: (1) Negative family belief systems (negative attitudes and concealment and self-isolation due to stigma), (2) Rigid family organizational patterns (adaptation lost, conflicts between family support and patients' willingness and pressure upon social support) and (3) Unhealthy communication and problem-solving (poor communication, emotional asymmetry of family members and tendency to solve problems alone). The study highlights the existence of the family resilience and family vulnerability at the point of lung cancer diagnosis and provides patient's perspective for understanding family resilience in specific cultural contexts. The data were collected through face-to-face interviews. ChiCTR2300074801.

Prosocial emotions predict individual differences in economic decision-making during ultimatum game with dynamic reciprocal contexts.

Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.

Emotions unveiled: detecting COVID-19 fake news on social media

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models’ effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features.

Emotion Processing in Peripheral Neuropathic Pain: An Observational Study.

In clinical practice, the implementation of tailored treatment is crucial for assessing the patient's emotional processing profile. Here, we investigate all three levels of analysis characterizing emotion processing, i.e., recognition, representation, and regulation, in patients with peripheral neuropathic pain (PNP). Sixty-two patients and forty-eight healthy controls underwent quantitative sensory testing, i.e., psychophysical tests to assess somatosensory functions such as perception of cold (CDT), heat-induced pain (HPT), and vibration (VDT), as well as three standardized tasks to assess emotional processing: (1) the Ekman 60-Faces Test (EK-60F) to assess recognition of basic facial emotions, (2) the Reading the Mind in the Eyes Test (RME) to assess the ability to represent the feelings of another person by observing their eyes, and (3) the 20-item Toronto Alexithymia Scale (TAS-20) to assess emotional dysregulation, i.e., alexithymia. General Linear Model analysis revealed a significant relationship between left index finger VDT z-scores in PNP patients with alexithymia. The RME correlated with VDT z-scores of the left little finger and overall score for the EK-60F. In patients with PNP, emotion processing is impaired, which emphasizes the importance of assessing these abilities appropriately in these patients. In this way, clinicians can tailor treatment to the needs of individual patients.

Speech Emotion Recognition Based on Machine Learning

The speech is the most effective means of communication, to recognize the emotions in speech is the most crucial task. In this paper we are using the Artificial Neural Network to recognize the emotions in speech. Hence, providing an efficient and accurate technique for speech based emotion recognition is also an important task. This study is focused on seven basic human emotions (angry, disgust, fear, happy, neutral, surprise, sad). The training and validating accuracy and also lose can be seen in a graph while training the dataset.According to it confusion matrix for model is created. The features frequency, pitch, amplitude and format of speech is used ti recognize seven basic emotions from speech. Keywords: Emotion detection, Emotion classification, verbal emotion classification.

Impact of the War in Ukraine on the Ability of Children to Recognize Basic Emotions.

This study assessed emotion recognition skills in school-age children in wartime conditions in Ukraine. An online survey based on the concept of basic emotions was administrated to a sample of 419 schoolchildren from Ukraine and a control group of 310 schoolchildren from the Czech Republic, aged 8 to 12. There is no difference in judging the intensity of anger and fear by Ukrainian children, compared with the control group. There is no evidence that the emotions of anger, fear, and sadness were better recognized in the Ukrainian group. Children from Ukraine were better at recognizing positive emotions than Czech children. Increased risks of threats and wartime experience do not impair the accuracy of identification of emotions like fear or the assessment of intensity of basic emotions by children who experience war in Ukraine. Still, it is important to continue studying the long-term consequences of military conflicts in order to deepen the understanding of their impact on human mental functioning.

Neuroanatomical markers of social cognition in neglected adolescents

Growing up in neglectful households can impact multiple aspects of social cognition. However, research on neglect's effects on social cognition processes and their neuroanatomical correlates during adolescence is scarce. Here, we aimed to comprehensively assess social cognition processes (recognition of basic and contextual emotions, theory of mind, the experience of envy and Schadenfreude and empathy for pain) and their structural brain correlates in adolescents with legal neglect records within family-based care. First, we compared neglected adolescents (n = 27) with control participants (n = 25) on context-sensitive social cognition tasks while controlling for physical and emotional abuse and executive and intellectual functioning. Additionally, we explored the grey matter correlates of these domains through voxel-based morphometry. Compared to controls, neglected adolescents exhibited lower performance in contextual emotional recognition and theory of mind, higher levels of envy and Schadenfreude and diminished empathy. Physical and emotional abuse and executive or intellectual functioning did not explain these effects. Moreover, social cognition scores correlated with brain volumes in regions subserving social cognition and emotional processing. Our results underscore the potential impact of neglect on different aspects of social cognition during adolescence, emphasizing the necessity for preventive and intervention strategies to address these deficits in this population.

Superior facial emotion recognition in adolescents with anorexia nervosa - A replication study.

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. Adolescent girls (12-18years) 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. As expected, adolescents with AN showed superior emotion recognition, with higher accuracy rates specifically for afraid faces. 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.

Speech Emotion Recognition Using Machine Learning

The speech is the most effective means of communication, to recognize the emotions in speech is the most crucial task. In this paper we are using the Artificial Neural Network to recognize the emotions in speech. Hence, providing an efficient and accurate technique for speech based emotion recognition is also an important task. This study is focused on seven basic human emotions (angry, disgust, fear, happy, neutral, surprise, sad). The training and validating accuracy and also lose can be seen in a graph while training the dataset. According to it confusion matrix for model is created. The seven features Frequency, Pitch, Amplitude and formant of speech is used to recognize seven basic emotions from speech. Keywords: Emotion detection, Emotion classification, verbal emotion classification.