Emotional Model Research Articles

Analyzing Crowd Emotional Contagion in Metro Emergencies Through the Lens of the Weber–Fechner Law: Predictions Based on Computational Techniques Applied to Science

The spread of panic can swiftly trigger group behaviors, leading to public security incidents and significant social hazards. Increasing attention is being paid to the impact of human psychology and behavior on the evolution and management of emergencies. Drawing on the Weber–Fechner Law, we proposed an emotional contagion model to explore the dynamics of crowd panic during metro emergencies, focusing on the interplay of emotional levels and stimuli. Key influencing factors such as crowd density, personality traits, official interventions, and evacuation rates are analyzed. Additionally, a case study is conducted to validate the model’s effectiveness in quantifying emotions and characterizing the emotional contagion of crowd panic. Numerical results reveal that the initial intensity of panic stimuli significantly impacts peak panic levels, while contagion duration plays a minor role. Panic intensifies with increased crowd density, with sensitive individuals being more susceptible to extreme emotions, escalating negative contagion. Official intervention proves crucial in mitigating panic, though its effect is transient in enclosed environments. Evacuation rate minimally affects emotional contagion during the train’s motion but becomes pivotal post-arrival. Highly panicked passengers evacuate quickly, necessitating timely interventions to prevent secondary panic on platforms. This highlights the importance of immediate, effective control measures to manage panic dynamics and ensure public safety.

Disabled people, arts festivals and emotional timescapes: An exploratory case study

ABSTRACT This exploratory study contributes to the leisure studies literature in three main ways: through foregrounding the emotionality of intellectually disabled people, an under-represented group within the context of events; through focusing on an arts festival, a form of event overlooked in comparison to other commonly studied types of events; and finally, through the testing of a new methodological framework – Brown’s emotional timescapes for events, itself adapted from Maguire and Geiger’s work on servicescapes. In this New Zealand-based study, we explore the emotions experienced by attendees, performers, volunteers, support workers and event organisers. Data from 20 interviews, participant observation and autoethnography was deductively analysed with a focus on expressions of emotion. We crafted a narrative for each individual, allowing us to identify the influence of the eventscape and map temporal patterns of emotional responses across the festival timescape. Findings show that participants experienced strong positive emotions. This was influenced by elements of the eventscape. In particular, interaction with others (including audience reaction) played a significant role in emotional timescapes. While the emotional timescape model was useful for broadly representing intensity of emotion over the duration of an event, we also identified limitations and propose a way to expand Brown’s theory.

El bienestar y la salud emocional del docente universitario. Reflexión desde la ecología emocional

The role that university teachers play depends largely on their emotional and physical health and well-being; with factors that define the optimal performance of their functions. This article is based on a novel approach to emotional intelligence, called “Emotional Ecology”, a model created by Ma. Mercè Conangla and Jaume Soler in 2002; who adapted the essence of environmental ecology for the management of human emotions. This work had the objective: “To present in a descriptive way, the Emotional Ecology Model (MEE), as an effective tool to achieve well-being and improve the emotional health of university teachers”. This is an academic writing, expository essay type, made with the intention of informing and objectively explaining to readers the aspects that are handled in the “Emotional Ecology” model, to be implemented in the context of university teachers; which shows particular points of view on the part of the authors, around the subject. As a most important conclusion, it is inferred after this research, that the implementation of this tool, from an alternative program, based on the principles, strategies and resources of the "Emotional Ecology Model", would be ideal for university teachers to reach an optimal level of well-being and emotional health.

A Novel Dual-Task Model for EEG-Based Emotion and Cognition Recognition

A Novel Dual-Task Model for EEG-Based Emotion and Cognition Recognition

Emotion‐aware psychological first aid: Integrating BERT‐based emotional distress detection with Psychological First Aid‐Generative Pre‐Trained Transformer chatbot for mental health support

AbstractMental health disorders have a global prevalence of 25%, according to the WHO, and this is exacerbated by factors such as stigma, geographical location, and a worldwide shortage of practitioners. Mental health chatbots have been developed to address these barriers, but these systems lack key features such as emotion recognition, personalisation, multilingual support, and ethical appropriateness. This paper introduces an innovative mental health support system that integrates BERT‐based emotional distress detection with a psychological first aid (PFA)‐generative pre‐trained transformer (PFA‐GPT) model, providing an emotion‐aware PFA chatbot. The methodology leverages deep learning models, utilising bidirectional encoder representations from transformers (BERT) for emotional distress detection and fine‐tuning GPT‐3.5 on therapy transcripts for PFA chatbot development. The findings demonstrate BERT's superior accuracy (93%) for emotional distress detection compared to bidirectional long short‐term memory. The multilingual PFA chatbot developed using the PFA‐GPT model demonstrated superior BERT scores (exceeding 83%) and proficiently provided ethical PFA. A proof of concept has been developed to illustrate the integration of the emotional distress detection model with the novel generative conversational agent for PFA. This integrated approach holds significant potential in overcoming existing barriers to mental health support and has the potential to transform mental health support, offering timely and accessible care through AI‐powered psychological interventions.

Emotional Boundaries and Intensity Aware Model for Incomplete Multimodal Sentiment Analysis

Emotional Boundaries and Intensity Aware Model for Incomplete Multimodal Sentiment Analysis

Neurostructural correlates of harm action/outcome aversion: The role of empathy.

Harm aversion is essential for normal human functioning; however, the neuroanatomical mechanisms underlying harm aversion remain unclear. To explore this issue, we examined the brain structures associated with the two distinct dimensions of harm aversion (harm action/outcome aversion) and the potential mediating role of the four aspects of empathy: fantasy, perspective-taking, empathic concern, and personal distress. A sample of 214 healthy young adults underwent structural magnetic resonance imaging. Voxel-based morphometry was used to assess regional gray matter volume (rGMV) and regional gray matter density (rGMD). Whole-brain multiple regression analysis revealed significant correlations between harm action aversion and rGMV/rGMD in various brain regions, including the inferior frontal gyrus (IFG) and precuneus for both rGMV and rGMD, the cerebellum for rGMV, and the superior frontal gyrus for rGMD. The rGMV/rGMD in the IFG and the rGMD in the primary somatosensory cortex (S1) were correlated with harm outcome aversion. Utilizing 10-fold balanced cross-validation analysis, we confirmed the robustness of these significant associations between rGMV/rGMD in these brain regions and harm action/outcome aversion. Importantly, mediation analysis revealed that empathic concern mediated the relationship between rGMV/rGMD in the precuneus and harm action aversion. Additionally, empathic concern, personal distress, and total empathy mediated the relationship between rGMD in the S1 and harm outcome aversion. These findings enhance our understanding of the neural mechanism of harm aversion by integrating insights from the brain structure, harm aversion, and the personality hierarchy models while also extending the frontal asymmetry model of Emotion.

Machine learning and confirmatory factor analysis show that buprenorphine alters motor and anxiety-like behaviors in male, female, and obese C57BL/6J mice.

Buprenorphine is an opioid approved for medication-assisted treatment of opioid use disorder. Used off-label, buprenorphine has been reported to contribute to the clinical management of anxiety. Although human anxiety is a highly prevalent disorder, anxiety is a latent construct that cannot be directly measured. The present study combined machine learning techniques and artificial intelligence with confirmatory factor analysis to evaluate the hypothesis that buprenorphine alters motor and anxiety-like behavior in C57BL/6J (B6) mice (n = 30) as a function of dose, sex, and body mass. After administration of saline (control) or buprenorphine, mice were placed on an elevated zero maze (EZM) for 5 min. Digital video of mouse behavior was uploaded to the cloud, and mouse position on the maze was tracked and analyzed with supervised machine learning and artificial intelligence. ANOVA and post hoc test showed that buprenorphine significantly altered five motor behaviors. Confirmatory factor analysis revealed that the latent construct of anxiety-like behavior accounted for a statistically significant amount of variance in all five motor behaviors.NEW & NOTEWORTHY Machine learning and pose estimation using a convolutional neural network accurately detected and objectively scored buprenorphine-induced changes in locomotor behaviors of mice on an elevated zero maze (EZM). Confirmatory factor analysis supports the interpretation that the anxiety-like construct accounted for the buprenorphine-induced changes in motor behavior. The results have noteworthy implications for the relationship between Darwin's story model of mammalian emotions and computational models of anxiety-like behavior in mice.

Using Simple Algorithms for Social Media Sentiment Analysis: Analyzing User Emotions on Social Platforms

Under the background of the vigorous development of social media, it is very important for business decision-making and social governance to accurately capture and analyze users' emotions. This article aims to explore a simple and effective way to analyze users' emotions on social media by using the basic NLP (Natural Language Processing) technology, especially the word frequency statistics method. Taking Weibo as the data source, this article constructs an emotional analysis model. By collecting data, preprocessing, constructing an emotion dictionary and calculating the emotion score, the model realizes the emotion tendency recognition of Weibo's posts. The experimental results show that the model can accurately identify the dominant emotional tendency in Weibo's posts, and perform well in capturing emotional trends. The research in this article not only provides a feasible solution for the emotional analysis of social media, but also provides reference for the follow-up research.

Rational emotive behavioral education model to reduce aggressive behavior in adolescents

Aggressive behavior is behavior that is manifested in the form of destroying objects or attacking other people verbally or non-verbally. This research tested the effectiveness of the Rational Emotive Behavior Education Model in reducing aggressive behavior in 36 class XI students at SMKN 2 Tasikmalaya. The selected students had high levels of aggressive behavior based on the Buss-Perry Aggression Questionnaire (BPAQ) scale. The experimental study with Time Series Design was conducted for 5 weeks, with one REBE intervention session per week for 40-60 minutes. The results showed a significant decrease in overall aggressive behavior scores from pre-intervention (M = 104.35) to post-intervention (M = 84.94). A significant decrease was also observed in the scores of each dimension of aggressive behavior, namely physical (29.93 to 24.42), verbal (17.68 to 14.24), anger (25.94 to 21.25), and hostility (30.49 to 24.64). These results indicate that REBE is effective in reducing aggressive behavior in students, helping them to think rationally, reducing emotional disturbances, increasing self-understanding, developing more adaptive coping strategies, and improving self-concept and sense of responsibility.

The Development of the Two-Dimensional Model of Emotion Based on Both Architectural Emotion Words and Design Elements in China

The purpose of this paper is to construct and validate a model centered on architectural emotion, to explore the role of emotion in architectural design, and to provide theoretical support for emotion-oriented design. This study collected 614 terms related to architectural emotion, screened 30 core terms, constructed a two-dimensional architectural emotion model, and verified the scientific and practicality of the model through three measurement methods. First, the one-dimensional scale analysis identified two dimensions of pleasure and charm, which portrayed the range of word variation; second, the Principal Component Analysis confirmed the periodic ordering pattern of words, which revealed its systematic relationship; and, lastly, the Multidimensional Scaling Analysis demonstrated the distribution of emotion words based on cognitive similarity in the multidimensional space. Based on this model, this paper proposes a three-layer circular model of “architectural emotion-architectural cognition-architectural elements”, which constructs the correspondence between architectural emotion and design elements, as well as how architectural cognition and architectural elements can synergize to create a spatial experience that triggers specific emotions. The model provides theoretical support for emotion-oriented architectural design and evaluation, and helps designers to better understand the relationship between emotion and space so as to create more valuable architectural works.

The use of anthropomorphic robots for developing empathic disposition as a personal trait among medical university students: a literature review.

Objective. The aim of this literature review is to analyze the psychological mechanisms that influence the development of empathy in medical students through interaction with humanoid robots. This study seeks to identify the factors that contribute to the formation of empathy as a personal trait in future physicians. Main Points. Empathy is a crucial aspect of medical practice, fostering trustful relationships between doctors and patients. Recent research indicates that humanoid robots can be an effective tool for enhancing empathetic skills in medical students. Interaction with these robots activates the same neural networks as human-to-human interactions, including the anterior cingulate cortex, the medial prefrontal cortex, and the mirror neuron system. Cognitive and affective processes, such as emotional intelligence, imitation, and behavioral modeling, play a significant role in shaping empathy. Personal characteristics, such as openness to experience and extraversion, also have a notable impact on students’ ability to empathize when interacting with robots. Conclusion. Anthropomorphic robots can be effectively integrated into educational programs to foster the development of empathy in students. They provide a safe environment for practicing communication and technical skills, while also encouraging emotional engagement and social cognition. Further research may help refine current technologies and methodologies, allowing for a deeper understanding of the neurobiological foundations of empathy in the context of human-robot interaction.

Emotion inference in conversations based on commonsense enhancement and graph structures.

In the task of emotion inference, a common issue is the lack of common sense knowledge, particularly in the context of dialogue, where traditional research has failed to effectively extract structural features, resulting in lower accuracy in emotion inference. To address this, this paper proposes a dialogue emotion inference model based on Common Sense Enhancement and Graph Model (CEICG). This model integrates external common sense with graph model techniques by dynamically constructing nodes and defining diverse edge relations to simulate the evolution of dialogue, thereby effectively capturing the structural and semantic features of the conversation. The model employs two methods to incorporate external common sense into the graph model, overcoming the limitations of previous models in understanding complex dialogue structures and the absence of external knowledge. This strategy of integrating external common sense significantly enhances the model's emotion inference capabilities, improving the understanding of emotions in dialogue. Experimental results demonstrate that the CEICG model outperforms six existing baseline models in emotion inference tasks across three datasets.

Turkish teachers' values with rational and non-rational truth and teacher emotions in teaching.

The aim of this study was to clarify the relationships between personal values, truth-related values and emotions among Turkish teachers. The Schwartz's Value Theory and Frenzel's teacher emotion model were used as the theoretical framework. This study used a cross-sectional correlational research design. The data were collected from 279 teachers with the Portrait Value Questionnaire (PVQ) where rational truth and non-rational truth values were added, and with the Teacher Emotions Scale (TES). The circular structure of the Schwartz Value Theory was tested by multidimensional scaling. The data analysis aimed to uncover relationships between personal values, truth-related values, and emotions. Rational truth emerged near self-direction and self-transcendence, yet items measuring non-rational truth were scattered among values. Females had higher regard for self-direction and hedonism than males. Non-rational truth was negatively correlated with enjoyment, while it was positively correlated with anxiety. The implications for gender roles in Turkish society are discussed.

ФРАЗЕОЛОГИЧЕСКАЯ ВЕРБАЛИЗАЦИЯ ЭМОЦИОНАЛЬНОЙ МЕТАФОРЫ В АНГЛОЯЗЫЧНОЙ И РУССКОЯЗЫЧНОЙ ЛИНГВОКУЛЬТУРАХ

The paper deals with the analysis of the emotional metaphor as a means of emotion conceptualization in phraseology. The interest to such idioms is accounted for by their capacity to reflect the understanding of emotions based on the socio-cultural experience of the language community, as well as to illustrate the national peculiarities of world perception through metaphoric meanings. On the material of English and Russian, metaphoric models of the emotional state are characterized, the role of metaphor in mapping the emotional domain is considered. In the context of a comparative study the authors find it relevant to expand the scope of understanding of the emotional model of the world formation by different linguocultures. Metaphors widely used to explicate the emotional concepts are orientational and ontological ones. A special attention is paid to anthropomorphic and zoomorphic metaphoric types. It is argued that the most productive metaphor is the anthropomorphic one, which can be explained by the anthropocentric mode of human cognition. Anthropomorphic metaphor assigns typically human properties or actions to emotions. Physiological metaphor also seems to be frequent in emotion conceptualization, based on similarity of physiological symptoms and emotional states. Zoomorphic metaphor is also actively used in metaphorical structuring the emotional sphere due to the special significance of animal images for people. In such idioms, animal images are metaphorically patterned to give characteristics of human emotions. The analysis of the material allows to make a conclusion that English and Russian phraseology represent nationally and culturally specific ways of metaphorical mapping of the human emotional experience. The common features in the metaphoric emotion representation are revealed in universal domain spheres for emotional metaphors. The distinctive features reveal themselves in different ways and frequency value, used in the process of figurative representation of positive and negative emotions in the phraseology of English and Russian, differences and gaps in metaphoric meanings, cognitive patterns of emotional concepts.

The role of meta-cognitive and emotional beliefs, and negative repetitive thinking in the relationship between emotional abuse and psychological distress in psychiatric outpatients: A serial multiple mediation model

BackgroundAlthough meta-cognitive and emotional beliefs are linked to higher psychological distress, little is known about the potential antecedents of these beliefs. On the other hand, the emotional schema model and metacognitive therapy posit that negative meta-cognitive and emotional beliefs generate negative repetitive thinking (NRTs) that lead to the development of psychological distress. In a meta-cognitive integrative model, we examined: (a) whether the link between emotional abuse and psychological distress was mediated serially by meta-cognitive beliefs (MCBs) and NRTs; (b) whether the relationship between emotional abuse and psychological distress was mediated serially by negative beliefs about emotional (NBEs) and NRTs. MethodsBetween 2021 and 2024, 514 outpatients (Mage = 29.29; SD = 10.88) completed the Emotional Abuse Questionnaire (EAQ), Metacognitions Questionnaire (MCQ-30), Leahy Emotional Schema Scale (LESS), Perseverative Thinking Questionnaire (PTQ), and Kessler Psychological Distress Scale (K10). The PROCESS SPSS macro was used to conduct serial multiple mediation analysis. ResultsThe multilevel mediation model demonstrated that the positive relationship between emotional abuse and psychological distress was partially mediated by MCBs, NBEs, and NRTs, and serially mediated by MCBs and then NRTs. The results also showed that the link between emotional abuse and psychological distress was mediated serially by NBEs and NRTs after controlling for age and gender. The model explained much of the variance in psychological distress (R2 = 0.52). ConclusionThe current study contributes to the literature by validating a conceptual model to confirm the association between emotional abuse and psychological distress. The hypothesized model in the present study also suggested the salience of the emotional schema model and metacognitive model in predicting psychological distress. Our results also support that NRTs are strategies utilized to cope with MCBs and NBEs.

Exploring the role of social capital in enhancing physical activity among college and university students: A systematic review.

College and university students often exhibit insufficient levels of physical activity, which negatively impacts their health, mental well-being, and academic performance. Social capital has emerged as a potential factor in promoting physical activity. This systematic review, conducted in line with PRISMA guidelines, examines the existing literature on the role of social capital in enhancing physical activity among college and university students. A search of the Web of Science, Scopus, SportDiscus, and PsychINFO databases identified 10 studies published by August 1, 2024, involving 2,700 students, primarily from North America and Europe. The overall quality of the included studies was high, with all scoring above 85% on quality assessments. The findings indicate that social capital, particularly strong social networks, support from family and friends, and high levels of social cohesion, is significantly associated with higher levels of physical activity among college and university students. Social capital may facilitate physical activity in group settings through mechanisms such as emotional support, role modeling, and social norms. Despite limitations within the existing research evidence, such as reliance on self-reported data and cross-sectional study designs, this review suggests that social capital holds potential for promoting physical activity in this population. Future research should prioritize the use of objective measurement tools and longitudinal designs to more accurately assess the long-term effects of social capital and explore how these findings can inform effective interventions.

Contextual Emotional Transformer-Based Model for Comment Analysis in Mental Health Case Prediction

Mental health (MH) assessment and prediction have become critical areas of focus in healthcare, leveraging developments in natural language processing (NLP). Recent advancements in machine learning have facilitated the exploration of predictive models for MH based on user-generated comments that overlooked the integration of emotional attention mechanisms. The methods often struggle with contextual nuances and emotional subtleties, leading to suboptimal predictions. The prevailing challenge lies in accurately understanding the emotional context embedded within textual comments, which is crucial for effective prediction and intervention. In this research, we introduce a novel approach employing contextual emotional transformer-based models (CETM) for comment analysis in MH case prediction. CETM leverages state-of-the-art transformer architectures enhanced with contextual embedding layers and emotional attention mechanisms for MH case prediction. By incorporating contextual information and emotional cues, CETM captures the underlying emotional states and MH indicators expressed in user comments. Through extensive experimentation and evaluation, both Roberta and bidirectional encoder representations from transformers (BERT) models exhibited enhanced accuracy, precision, recall, and F1 scores compared to their counterparts lacking emotional attention. Notably, the Roberta model attained a greater accuracy of 94.5% when matched to BERT’s 87.6% when emotional attention was employed. Hence, by incorporating emotional context into the predictive model, we achieved significant improvements, which offers promising avenues for more precise and personalized MH interventions.

Narrative nursing for negative emotions in patients with acute pancreatitis: Based on model construction and application.

Acute pancreatitis (AP), as a common acute abdomen disease, has a high incidence rate worldwide and is often accompanied by severe complications. Negative emotions lead to increased secretion of stress hormones, elevated blood sugar levels, and enhanced insulin resistance, which in turn increases the risk of AP and significantly affects the patient's quality of life. Therefore, exploring the intervention effects of narrative nursing programs on the negative emotions of patients with AP is not only helpful in alleviating psychological stress and improving quality of life but also has significant implications for improving disease outcomes and prognosis. To construct a narrative nursing model for negative emotions in patients with AP and verify its efficacy in application. Through Delphi expert consultation, a narrative nursing model for negative emotions in patients with AP was constructed. A non-randomized quasi-experimental study design was used in this study. A total of 92 patients with AP with negative emotions admitted to a tertiary hospital in Nantong City of Jiangsu Province, China from September 2022 to August 2023 were recruited by convenience sampling, among whom 46 patients admitted from September 2022 to February 2023 were included in the observation group, and 46 patients from March to August 2023 were selected as control group. The observation group received narrative nursing plan, while the control group was given with routine nursing. Self-rating anxiety scale (SAS), self-rating depression scale (SDS), positive and negative affect scale (PANAS), caring behavior scale, patient satisfaction scale and 36-item short form health survey questionnaire (SF-36) were used to evaluate their emotions, satisfaction and caring behaviors in the two groups on the day of discharge, 1-and 3-month following discharge. According to the inclusion and exclusion criteria, a total of 45 cases in the intervention group and 44 cases in the control group eventually recruited and completed in the study. On the day of discharge, the intervention group showed significantly lower scores of SAS, SDS and negative emotion (28.57 ± 4.52 vs 17.4 ± 4.44, P < 0.001), whereas evidently higher outcomes in the positive emotion score, Caring behavior scale score and satisfaction score compared to the control group (P < 0.05). Repeated measurement analysis of variance showed that significant between-group differences were found in time effect, inter-group effect and interaction effect of SAS and PANAS scores as well as in time effect and inter-group effect of SF-36 scores (P < 0.05); the SF-36 scores of two groups at 3 months after discharge were higher than those at 1 month after discharge (P < 0.05). The application of narrative nursing protocols has demonstrated significant effectiveness in alleviating anxiety, ameliorating negative emotions, and enhancing satisfaction among patients with AP.

Emotional analysis of broadcasting and hosting speech by integrating grid PSO-SVR and PAD models

Traditional speech emotion analysis techniques are influenced by factors such as environment and speech speed. Given this, this study assumes the combination of grid particle swarm optimization algorithm and support vector regression, combined with a pleasure-arousal-dominance emotion dimension model, to improve the accuracy of speech emotion analysis. This paper first constructs a speech emotion model that combines the pleasure-arousal-dominance dimensions of particle swarm optimization and support vector regression. Then, a comparative analysis is conducted on the performance of the sentiment analysis model in this dimension and its applicability in the direction of broadcasting hosting. In the experiment of standard deviation and average value of pleasure-arousal-dominance dimension, the scoring average curve of the dimension of the research algorithm was almost a straight line, and the components of pleasure-arousal-dominance were normally distributed. The minimum standard deviation scores of pleasures, dominance, and arousal were 0.40, 0.56, and 0.82. The results demonstrate that the model exhibits high reliability in all dimensions of pleasure-arousal-dominance prediction, thereby substantiating its potential as a pivotal tool in the analysis of speech emotions exhibited by broadcast hosts.