Emotion Intelligence Research Articles

Sentient Rationality in the Digital Age: Integration of Neuroscience and Philosophy for Decision Making

This article delves into the intriguing intersection between human rationality and digital technologies, with a specific focus on the influence of emotion and artificial intelligence on decision making. Drawing from the theory of sentient rationality, it examines how emotional and somatic components shape rationality in a digital context. The implications of these insights for understanding human cognition are thoroughly discussed. Moreover, the article proposes innovative transemiotic approaches that integrate neuroscience and philosophy, inviting the reader to delve deeper into these novel methods. The conclusion presents practical recommendations for future research, underscoring their potential applications in the rapidly evolving digital age.

Intelligent emotion recognition in product design using multimodal physiological signals and machine learning

Identifying emotional responses in products is essential for product design and user research. Traditional methods, such as interviews and surveys, for gathering product experience data are time-consuming and resource-intensive, and often fail to capture users’ genuine emotional intentions. This article introduces an intelligent method for accurately identifying user-product emotions using multimodal physiological signals and machine learning techniques. The study involves designing experiments with 63 representative product images, and collecting various physiological signals (eye movement, electrodermal activity, and pulse). Using the K-means algorithm, we establish efficacy-arousal assessment labels to create a product emotion dataset. Preprocessing methods, including time–frequency analysis and wavelet transformation, ensure high-quality signal analysis data. By extracting temporal, frequency, and time–frequency features from multimodal physiological signals, the Relief algorithm determines the optimal feature combination. Finally, the study evaluates the performance of five machine learning classifiers, with results indicating an 81.31% recognition accuracy for the proposed product emotion recognition framework. This framework proves effective for objectively analyzing user emotions in product experiences.

Deep neural network-based emotion recognition using facial landmark features and particle swarm optimization

Relating specifically to human–computer interaction (HCI), computer vision research has placed a substantial emphasis on intelligent emotion recognition in recent years. The primary emphasis lies in investigating speech aspects and bodily motions, while the knowledge of recognizing emotions from facial expressions remains relatively unexplored. Automated facial emotion detection allows a machine to assess and understand a person's emotional state, allowing the system to predict intent by analyzing facial expressions. Therefore, this research provides a novel parameter selection strategy using swarm intelligence and a fitness function for intelligent recognition of micro emotions. This paper presents a novel method based on geometric visual representation obtained from facial landmark points. We employ the Deep Neural Networks (DNN) model to analyze the input features from the normalized angle and distance values derived from these landmarks. The results of the experiments show that Particle Swarm Optimization (PSO) worked very well by using only a few carefully chosen features. The method achieved a recognition success rate of 98.76% on the MUG dataset and 97.79% on the GEMEP datasets.

Role Emotion Intelligence Towards Work-Life Balance with Support Family as Mediator Variables

Role Emotion Intelligence Towards Work-Life Balance with Support Family as Mediator Variables

AN OPTIMIZED RESIDUAL NETWORK FOR EMOTION RECOGNITION BASED ON A MULTI-FEATURES FUSION TECHNOLOGY AND ELECTROENCEPHALOGRAPHY SIGNALS

Presently, a significant number of students enrolled in colleges and universities encounter mental health challenges, including academic stress and difficulties in interpersonal relationships. These factors contribute to a decline in mental well-being and necessitate prompt intervention and assistance. The automation of mental health identification in students greatly benefits from the implementation of intelligent emotion recognition systems. Through the analysis of students’ emotional states, educational institutions can gain a deeper understanding of students’ mental well-being, enabling them to promptly identify and address any issues and offer more complete care and support to students. This paper proposes a method for automated emotion recognition that is driven by data. The method is developed based on the provided background information. The proposed approach incorporates a feature fusion mechanism and an attention mechanism into the ResNet-34 model. This integration enhances the model’s capability to analyze intricate details and subsequently improves its classification performance when applied to electroencephalography (EEG) signals. This paper introduces several key innovations. First, an optimization technique is applied to the input component of the model, enabling multifeature fusion. Second, an attention mechanism is incorporated after the residual network module, enabling the model to prioritize parts that contribute to classification and enhance feature extraction. Finally, the network parameters are optimized using both softmax loss and center loss functions. The findings from the analysis of the sentiment EEG public data SJTU Emotion EEG Dataset (SEED) indicate that the proposed sentiment recognition approach not only enhances the classification performance of the model on the sentiment EEG data but also improves the stability of the results. This paper presents a novel approach that enables automatic and efficient recognition of students’ emotions on commonly used platforms. The findings of this study hold significant implications for mental health assessment and detection in real-life production settings, offering substantial reference value in this domain.

Emotional intelligence and job performance: A study of public sector management schools in Pakistan

In today’s dynamic world, higher education plays a vital role in the development of societies and in the socio-economic development of countries. In this context, many developed nations invest in the development of teaching faculty in the universities by improving their emotional intelligence and job performance. This research intends to construct a model for investigating the relationship between Emotional Intelligence and its key dimensions and job performance in public sector management schools of Pakistan. In Pakistan, there are 77 management schools with 1247 regular employees. The proposed model has one primary and four secondary hypotheses. In this proposed model, stratified random sample is employed. The teaching faculty is divided into 4 strata: professor, associate professor, assistant professor and lecturer. Data was collected from 305 randomly selected respondents from the target population through personally administered and officially emailed questionnaires in order to test the hypothesized model. In the study, regression model is employed using SPSS 2.1 software to test the hypotheses. Results shows positive and significant relationship of Emotional Intelligence with its four dimensions: Self-Emotion Appraisal, Other-Emotion Appraisal, Self-Emotion Regulation, and Other Emotion Regulation, with Job Performance. The research has addressed the existing gap on scarcity of research on Emotion Intelligence with its key dimensions, in the public sector universities and the management schools as a target population. Moreover, this research also has some limitations. The research findings have both theoretical and practical implications. In order to foster job performance, the study recommends the creation and promotion of an organizational environment wherein faculty members could share their workplace emotions with ease and interest. Lastly, suggestions for further research are provided.

An Intelligence Emotions, Behavior and Cognition

The American Psychological Association defines "any transient emotional state, typically characterized by moderate intensity." Moods are differentiated from emotions by their non-provocative nature and their potential to emerge spontaneously. Anger can be triggered by various factors, such as insults or even arise without any discernible cause. The complete definition of emotions remains elusive. The investigation of our emotional constitution remains a subject of considerable scholarly interest, as numerous academics propose divergent theories to account for this phenomenon. However, there exists a significant amount of data to analyze when examining the topic. There has been a growing interest within the field of neuroscience regarding the neurological foundations of human emotion, which has been largely driven by advancements in functional neuroimaging techniques. The investigation into the interplay between emotions and other cognitive processes in the brain, such as attention, memory, and reasoning, is gaining increasing significance. The primary focus of this article pertains to the psychological consequences and strategies employed for regulating cognitive processes associated with emotions.

A Review of Researches on the Relationship Between Critical Thinking and Emotion at Home and Abroad

Critical thinking is a key component of essential literacy skills, and it plays a crucial role in students' learning and development. Research shows that emotions have a significant influence on individual thinking activities, and they are important non-intellectual factors in developing students' critical thinking. Up to now, research scholars at home and abroad have conducted a lot of empirical studies to explore the possible relationship between the two variables. However, there is still a lack of research to systematically provide a holistic view. The current study is trying to present a comprehensive network of critical thinking and emotions through the discussion on the four-aspect correlation, that is, the correlation between critical thinking and negative emotion, critical thinking and emotional intelligence, critical thinking, emotion and academic achievement, and critical thinking, emotion and psychological cognition. The study also puts forward the limitations of the current research from the perspectives of research methods, research objects, and research contents, as well as the prospect of future research.

The Relationship Between Emotional Intelligence and Coping Behaviors among Nurses in the Intensive Care Unit.

Nurses are expected to experience chronic stress and be involved in unhealthy actions more frequently due to the rising demands of medical care, which impact every aspect of their individual and professional lives. Individuals with higher emotional intelligence could cope and adjust to environmental changes and social constraints better than those with lower emotional intelligence. The objective of this study was to assess the relationship between the emotional intelligence and coping behaviors of nurses in the intensive care units (ICUs) in the West Bank. The study was a cross-sectional method done on convenience sample of 266 ICU nurses. Data collection was performed by "Schutte Self Report Emotional Intelligence Test" (SSEIT) and the "Coping Behavior Inventory". Statistical analysis was completed using SPSS version 23. Pearson correlation was used to measure the relationship between the emotional intelligence and coping behaviors. The analysis indicated that emotional intelligence was high (mean ± SD = 150.8 ± 2.1, range 33-165). Also, the analysis indicated that coping behavior of the participants was moderate (mean ± SD = 29.3 ± 15.2, range 0-76). Furthermore, the analysis indicated that there was a moderate positive relationship between nurses' emotional intelligence and their coping behaviors. Coping behavior was indicated to have a moderately positive relationship with emotional intelligence. However, social skills or managing others' emotions indicated low positive relationship and emotional intelligence. There is a negligible correlation between emotional intelligence and perception of emotions, managing emotions in the self, and utilizing emotions. Training in emotional intelligence is essential for nurses since it improves both their personal and professional life and coping mechanisms.

Эмоционально-личностные детерминанты субъективного благополучия военнослужащих, участвовавших в боевых действиях

Studying the dynamics of subjective well-being in extreme conditions associated with combat operations and the role of intrapersonal factors in its achievement is an urgent task of modern psychology. Presented are the results of an empirical study, the purpose of which is to study the peculiarities of the relationship between emotional and personal formations and the subjective well-being of military personnel who participated in combat operations. The study involved 477 male respondents aged from 20 to 53 years: military personnel with combat experience and those who did not take part in military operations. Questionnaire methods were used for diagnosing personal characteristics, subjective well-being, difficulties in personal reflection of emotions, identifying and assessing neurotic states, emotional intelligence, and conscious self-regulation. The design of the empirical study included assessing differences in the studied indicators between those who took part in combat and those who did not take part in combat operations, as well as analyzing the relationship between these indicators using structural modeling. It was confirmed that being in a combat zone influenced the level of subjective well-being and the studied emotional and personal characteristics. Military personnel consider themselves less prosperous; they have more pronounced difficulties in personal reflection of emotions associated with the tolerance and expression of emotions, a tendency to use physiologically oriented defenses, as well as neurotic depression and obsessive-phobic disorders. They have lower values for extraversion, emotional stability, conscientiousness, and such regulatory processes and properties as modeling and flexibility. Revealed is the structure of relationships between emotional and personal predictors of the subjective wellbeing of combatants, presented in the form of a model. The central place in the model belongs to the processes and properties of conscious self-regulation, which directly and indirectly influence subjective well-being due to the mediating role of indicators of emotional intelligence and conscious suppression of emotions. Basic personality traits have only an indirect effect on subjective well-being through the ability to manage one’s own and others’ emotions and understand one’s emotions, neuroticism and conscious suppression of emotions. The leading role here belongs to emotional stability.

Adaptive Spatial–Temporal Aware Graph Learning for EEG-Based Emotion Recognition

An intelligent emotion recognition system based on electroencephalography (EEG) signals shows considerable potential in various domains such as healthcare, entertainment, and education, thanks to its portability, high temporal resolution, and real-time capabilities. However, the existing research in this field faces limitations stemming from the nonstationary nature and individual variability of EEG signals. In this study, we present a novel EEG emotion recognition model, named GraphEmotionNet, designed to enhance the accuracy of EEG-based emotion recognition through the incorporation of a spatiotemporal attention mechanism and transfer learning. The proposed GraphEmotionNet model can effectively learn the intrinsic connections between EEG channels and construct an adaptive graph. This graph’s adaptive nature is crucial in optimizing spatial–temporal graph convolutions, which in turn enhances spatial–temporal feature characterization and contributes to the process of emotion classification. Moreover, an integration of domain adaptation aligns the extracted features across different domains, further alleviating the impact of individual EEG variability. We evaluate the model performance on two benchmark databases, employing two types of cross-validation protocols: within-subject cross-validation and cross-subject cross-validation. The experimental results affirm the model’s efficacy in extracting EEG features linked to emotional semantics and demonstrate its promising performance in emotion recognition.

The effectiveness of learning strategies for the development of Emotional Intelligence in undergraduate nursing students: A systematic review protocol

AimThis protocol proposes a systematic review to identify and disclose learning strategies and evaluation tools to improve Emotion Intelligence (EI) in undergraduate nursing students. BackgroundEI improves the ability to manage emotions and their consequences. Management of emotions is fundamental in nursing both at inter- and intra-professional levels, and with the patients and their family carers. DesignSystematic review protocol of effectiveness based on the Joanna Briggs Institute (JBI) guidelines. Review MethodsAll articles based on educational programmes and/or activities to develop EI in nursing curricula will be included in the review. It will analyse the principle EI attributes and outcomes such as burnout, intention to leave and problem-solving skills. Based on the review question, seven databases will be searched: MEDLINE, The Cochrane Library, SCOPUS, CINAHL, EMBASE, PsycINFO and ERIC. Also, grey literature (Google Scholar and Open Gray) will be searched. Since no similar systematic reviews are present in the literature, no time limits will be set. To establish the quality of studies JBI tools will be used. When appropriate, we will meta-analyse prevalence and incidence estimates or we will meta-synthesize themes and findings. Expected ResultsThis systematic review is expected to provide an overview of educational and training methods, pedagogical strategies and evaluation tools to address EI in nursing and provide high-quality care to patients and their families. Results of the review will support nursing educators and leaders in implementing teaching and learning strategies to improve the Emotional Intelligence of undergraduate nursing students. ConclusionsMastering emotions is fundamental for nurses. Implementing education courses based on EI according to the best training methods and pedagogical strategies could become key for nursing curricula. Moreover, after the review, it will be possible to identify the best training methods and pedagogical strategies for implementing continuing professional development (CPD) courses in EI in the courses of following graduation.

Intelligent emotion recognition application in university students’ psychological intervention by using mobile voice terminal

Intelligent emotion recognition application in university students’ psychological intervention by using mobile voice terminal

Facial emotion recognition using geometrical features based deep learning techniques

In recent years, intelligent emotion recognition is active research in computer vision to understand the dynamic communication between machines and humans. As a result, automatic emotion recognition allows the machine to assess and acquire the human emotional state to predict the intents based on the facial expression. Researchers mainly focus on speech features and body motions; identifying affect from facial expressions remains a less explored topic. Hence, this paper proposes a novel approach for intelligent facial emotion recognition using optimal geometrical features from facial landmarks using VGG-19s (FCNN). Here, we utilize Haarcascade to detect the subject face and determine the distance and angle measurements. The entire process is to classify the facial expressions based on extracting relevant features with the normalized angle and distance measures. The experimental analysis shows high accuracy on the MUG dataset of 94.22% and 86.45% on GEMEP datasets, respectively.

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities

We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people’s current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.

The role of emotion-related individual differences in enjoyment and masking smile judgment

BackgroundWhile some research indicates that individuals can accurately judge smile authenticity of enjoyment and masking smile expressions, other research suggest modest judgment rates of masking smiles. The current study explored the role of emotion-related individual differences in the judgment of authenticity and recognition of negative emotions in enjoyment and masking smile expressions as a potential explanation for the differences observed.MethodsSpecifically, Experiment 1 investigated the role of emotion contagion (Doherty in J Nonverbal Behav 21:131–154, 1997), emotion intelligence (Schutte et al. in Personality Individ Differ 25:167–177, 1998), and emotion regulation (Gratz and Roemer in J Psychopathol Behav Assess 26:41–54, 2004) in smile authenticity judgment and recognition of negative emotions in masking smiles. Experiment 2 investigated the role of state and trait anxiety (Spielberger et al. in Manual for the state-trait anxiety inventory, Consulting Psychologists Press, Palo Alto, 1983) in smile authenticity judgment and recognition of negative emotions in the same masking smiles. In both experiments, repeated measures ANOVAs were conducted for judgment of authenticity, probability of producing the expected response, for the detection of another emotion, and for emotion recognition. A series of correlations were also calculated between the proportion of expected responses of smile judgement and the scores on the different subscales.ResultsResults of the smile judgment and recognition tasks were replicated in both studies, and echoed results from prior studies of masking smile judgment: participants rated enjoyment smiles as happier than the masking smiles and, of the masking smiles, participants responded “really happy” more often for the angry-eyes masking smiles and more often categorized fear masking smiles as “not really happy”.ConclusionsOverall, while the emotion-related individual differences used in our study seem to have an impact on recognition of basic emotions in the literature, our study suggest that these traits, except for emotional awareness, do not predict performances on the judgment of complex expressions such as masking smiles. These results provide further information regarding the factors that do and do not contribute to greater judgment of smile authenticity and recognition of negative emotions in masking smiles.

The first steps for adapting an artificial intelligence emotion expression recognition software for emotional management in the educational context

AbstractTo test the suitability of an automatic system for emotional management in the classroom following the control‐value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close‐ups of their faces and completing the AEQ emotional self‐report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ‐36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self‐reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed. Practitioner notesWhat is already known about this topic Student motivation and involvement in the learning process are highly related to appropriate emotional regulation, which can be associated with particular educational activities, strategies and methodologies. Deep learning technology based on convolutional neural networks feeds automatic systems focused on facial expression recognition from image analysis. What this paper adds There is high coherence between the emotional expressions detected by the AI system and the students' emotional self‐reports, but the AI system provides just emotional valences, insufficient to meet the CVT framework. Both emotional self‐reports and the emotion recognition software suggest students' preference for active educational activities as opposed to passive ones. Volitional competencies seem to influence the emotional response of students in the educational context. Implications for practice and/or policy It is possible to use automatic systems to effectively monitor the emotional response of students in the learning process. Only if sensitivity improved, a real‐time, easy‐to‐interpret emotional expression recognition software interface could be implemented to assist teachers with the emotional management of their classes within the CVT framework, maximizing their motivation and engagement.

Innovative Measures of Verhulst Diagram for Emotion Recognition using Eye-Blinking Variability

Background: The human body continuously reveals the status of several organs through biomedical signals. Over time, biomedical signal acquisition, monitoring, and analysis have captured the attention of many scientists for further prediction, diagnosis, decision-making, and recognition. Recently, building an intelligent emotion recognition system has become a challenging issue using the application of signal processing. Frequently, human emotion classification was proposed utilizing the internal body status in dealing with affective provocations. However, external states, such as eye movements, have been claimed to convey practical information about the participant’s emotions. In this study, we proposed an automatic emotion recognition scheme through the analysis of a single-modal eye-blinking variability. Methods: Initially, the signal was transformed into a 2D space using the Verhulst diagram, a simple analysis based on the signal’s dynamics. Next, some innovative features were introduced to characterize the maps. Then, the extracted measures were inputted to the support vector machine (SVM) and k-nearest neighbor (kNN). The former classifier was evaluated with three kernel functions, including RBF, linear, and polynomial. The latter performances were examined with different values for k. Moreover, the classification results were assessed in two feature-set partitioning modes: a 5-fold and 10-fold cross-validation. Results: The results showed a statistically significant difference between neutral/fear and neutral/sadness for all Verhulst indices. Also, the average values of these characteristics were higher for fear and sadness than those of other emotions. Our results indicated a maximum rate of 100% for the fear/neutral classification. Therefore, the suggested Verhulst-based approach was supremely talented in emotion classification and analysis using eye-blinking signals. Conclusion: The novel biomarkers set the scene for designing a simple accurate emotion recognition system. Additionally, this experiment could fortify the territory of ocular affective computing, and open a new horizon for diagnosing or treating various emotion deficiency disorders.

The Dark Triad and Trait Emotional Intelligence as Predictors of Problematic Social Media use and Engagement: The Mediating Role of the Fear of Missing Out.

Problematic social media use is becoming a significant social and clinical concern, and there is growing research interest in the psychological factors involved, such as personality predispositions and the fear of missing out (FOMO). The present study investigated both the dark triad (DT, namely, narcissism, Machiavellianism, and psychopathy) and trait emotion intelligence (trait EI) in relation to the problematic use of technology and social media engagement and the mediating role of FOMO. A total of 788 people between 18 and 35 years old (M = 24.22; SD = 3.91; 75% women) were surveyed. The results showed that social media engagement was positively related to problematic social media use and negatively related to trait EI. In addition, problematic social media use was positively related to DT and negatively related to trait EI. Fear of missing out was positively related to social media engagement, problematic social media use, and DT, and negatively related to trait EI. Fear of missing out had a mediating effect on the relationship between personality dimensions, problematic social media use, and social media engagement. The extent to which personality traits may underpin problematic use of social media and the practical implications of the findings are discussed.