Emotional Model Research Articles

Visual Emotion Recognition Through Multimodal Cyclic-Label Dequantized Gaussian Process Latent Variable Model

A multimodal cyclic-label dequantized Gaussian process latent variable model (mCDGP) for visual emotion recognition is presented in this paper. Although the emotion is followed by various emotion models that describe cyclic interactions between them, they should be represented as precise labels respecting the emotions’ continuity. Traditional feature integration approaches, however, are incapable of reflecting circular structures to the common latent space. To address this issue, mCDGP uses the common latent space and the cyclic-label dequantization by maximizing the probability function utilizing the cyclic-label feature as one of the observed features. The likelihood maximization problem provides limits to preserve the emotions’ circular structures. Then mCDGP increases the number of dimensions of the common latent space by translating the rough label to the detailed one by label dequantization, with a focus on emotion continuity. Furthermore, label dequantization improves the ability to express label features by retaining circular structures, making accurate visual emotion recognition possible. The main contribution of this paper is the implementation of feature integration through the use of cyclic-label dequantization.

Model Of Rational And Emotional Decision Making In The First Purchase Of Cosmetics

Background: Consumers face both positive and negative impacts when buying a product for the first time. Existing studies emphasize the rational aspects in making decisions to buy cosmetics. Buying cosmetics will include liking the product (emotional aspect) and the good effects obtained from buying the product, in addition to rational considerations such as product safety (rational aspect). This study aims to determine the effect of cognitive and emotional orientation models in making decisions to buy cosmetics for the first time. Objective: This study examines the rational and emotional aspects of buying a cosmetic product for the first time. Method: This research involved 131 consumers who either bought cosmetics for the first time or a new product. Consumer age ranges were from 18 to 22 years. Respondents have used cosmetics for up to 3 years. Respondents were selected using accidental sampling. Results: showed that rational and emotional considerations significantly contribute to purchasing decisions. Consumers not only consider the benefits of these cosmetics, but also pay attention to their feelings towards these products. Conclusion: Consideration of emotional and rational aspects will influence purchasing decisions. The preferences and positive expectations consumers feel will be a more dominant factor than fear of risk. Producers need to communicate information that can make consumers happy in addition to product functions. Keywords: Ratio Considerations, Emotional Considerations, Purchasing Decision-making

The Emotional Intelligence (E.I.) and “Perrotta Human Emotions - Questionnaire - 1” (PHE-Q-1): Development, regulation and validation of a new psychometric instrument

Background: Based on the Perrotta Human Emotions Model (PHEM-2), a psychometric tool was modelled that could help the therapist gain a deep understanding of the patient's emotional capacity, and thus his emotional intelligence, also concerning follow-up and achievements during the psychotherapeutic course. Aim: To check whether the PHEM-2 can be structured into a psychometric tool that assesses the subject's emotional intelligence to calibrate the most appropriate psychotherapeutic intervention for the specific case. Methods: Clinical interview, based on narrative-anamnestic and documentary evidence, and battery of psychometric tests. Results: The PHE-Q-1 demonstrated its clinical usefulness during psychotherapy sessions to improve the patient's emotional capacity and confirmed its validity relative to statistical comparison, reporting an R = 0.999 and p ≤ 0.001. Conclusion: The validity of PHE-Q-1 in investigating the emotional component of human intelligence and its clinical utility concerning one's level of cognitive-emotional dissonance and PHEM-2, during psychotherapeutic encounters conducted according to the brief or otherwise integrated strategic approach, is confirmed.

A SENTIMENT ANALYSIS MODEL FOR ELECTROENCEPHALOGRAM SIGNALS OF STUDENTS IN UNIVERSITIES USING A CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE MODELS

Sentiment analysis in teaching evaluation has significant implications. By analyzing students’ sentiments toward instructors, educational institutions can gain valuable insights into teaching effectiveness. These data can guide curriculum development, instructional improvements, and faculty training initiatives. Positive sentiment indicates effective teaching methods, engagement, and student satisfaction; negative sentiment flags areas that need attention. Sentiment analysis can help identify patterns, trends, and outliers, aiding in targeted interventions and personalized support. It also enables comparisons across different courses, instructors, and departments. However, it is crucial to ensure the accuracy and fairness of sentiment analysis algorithms, considering potential biases and the contextual nature of the feedback. This study proposes a sentiment classification model CNN–SVM that combines a convolutional neural network (CNN) and a support vector machine (SVM). Taking students majoring in art in comprehensive colleges and universities as the research object, by collecting the electroencephalogram (EEG) signals of students during teaching evaluation. CNN–SVM is used as the emotional analysis model to obtain the emotional analysis of teaching evaluation results. EEG is a typical physiological signal, and data based on this signal can more truly reflect student emotions. The adaptive CNN feature extraction function and the super generalization classification performance of SVM can reduce the individual differences and data noise between data, thereby improving sentiment classification performance. The experimental results demonstrate that using technology to analyze sentiment can assist educational institutions in more properly comprehending the feedback and opinions of students on instruction. With regard to sentiment analysis, the CNN–SVM method that is derived to produce the fusion algorithm has solid performance.

Enhancing Mental Health and Medication Adherence Among Men Who Have Sex With Men Recently Diagnosed With HIV With a Dialectical Behavior Therapy-Informed Intervention Incorporating mHealth, Online Skills Training, and Phone Coaching: Development Study Using Human-Centered Design Approach.

Mental health problems are common among men who have sex with men (MSM) living with HIV and may negatively affect medication adherence. Psychosocial interventions designed to address these urgent needs are scarce in China. Incorporating behavioral health theories into intervention development strengthens the effectiveness of these interventions. The absence of a robust theoretical basis for interventions may also present challenges to identify active intervention ingredients. This study aims to systematically describe the development of a mobile health-based intervention for MSM recently diagnosed with HIV in China, including the theoretical basis for the content and the considerations for its technological delivery. We used intervention mapping (IM) to guide overall intervention development, a behavioral intervention technology model for technological delivery design, and a human-centered design and cultural adaptation model for intervention tailoring throughout all steps of IM. The dialectical behavior therapy (DBT)-informed intervention, Turning to Sunshine, comprised 3 components: app-based individual skills learning, group-based skills training, and on-demand phone coaching. The theoretical basis for the intervention content is based on the DBT model of emotions, which fits our conceptualization of the intervention user's mental health needs. The intervention aims to help MSM recently diagnosed with HIV (1) survive moments of high emotional intensity and strong action urges, (2) change emotional expression to regulate emotions, and (3) reduce emotional vulnerability, as well as (4) augment community resources for mental health services. Technological delivery considerations included rationale of the medium, complexity, and esthetics of information delivery; data logs; data visualization; notifications; and passive data collection. This study laid out the steps for the development of a DBT-informed mobile health intervention that integrated app-based individual learning, group-based skills training, and phone coaching. This intervention, Turning to Sunshine, aims to improve mental health outcomes for MSM newly diagnosed with HIV in China. The IM framework informed by human-centered design principles and cultural adaptation considerations offered a systematic approach to develop the current intervention and tailor it to the target intervention users. The behavioral intervention technology model facilitated the translation of behavioral intervention strategies into technological delivery components. The systematic development and reporting of the current intervention can serve as a guide for similar intervention studies. The content of the current intervention could be adapted for a broader population with similar emotional struggles to improve their mental health outcomes.

Innovation of fresh agricultural products e-commerce operation mode based on artificial intelligence and machine learning model

Abstract In recent years, there has been an increasing interest in emotional interaction models based on artificial intelligence technology, and researchers want to deal with real-life problems in daily life through artificial intelligence and emotional interaction technology. In this paper, based on artificial intelligence and the emotional interaction model, we analyze the model architecture of emotional interaction technology and the theory related to the e-commerce operation platform. Combining the two, we propose a collaborative filtering recommendation algorithm, define system performance indexes, and recommend fresh produce to target customers with similar users’ evaluation of fresh produce through artificial intelligence technology. Using the experiments of three different recommendation systems and user behavior data of different months in the past three years as reference, it is concluded that the e-commerce operation of fresh produce based on AI and emotional interaction model can effectively improve the browsing and transaction volume of the e-commerce platform. This shows that artificial intelligence and emotional interaction model can be applied to create a new e-commerce operation mode, which provides research directions and methods for the diversified development of future e-commerce operation modes.

A Study of the Emotional Impact of Interior Lighting Color in Rural Bed and Breakfast Space Design

In architectural spaces, the ambiance created by lighting plays a significant role in influencing people’s emotions, often relying on the use of color and light. However, previous research has primarily focused on urban architectural spaces, with relatively less attention given to rural environments, especially the interior spaces of rural accommodation. The objective of this study is to investigate the impact of colored lighting within rural accommodation interiors on people’s emotions and visual perceptions. Additionally, the study aims to establish an emotional dimension model to map the experimental results onto eight basic emotions, utilizing the PAD model for quantitative analysis. To achieve this goal, the researchers recruited 90 participants and divided them into six groups, each experiencing different combinations of indoor lighting scenes with varying colors. Participants evaluated their emotions and visual perceptions. The study results indicate that the combination of cool and warm white light with colored light significantly influences the arousal level of participants but has a relatively lower impact on pleasure levels, dominance and relaxation levels. In comparing the emotions experienced after exposure to cool and warm white light followed by colored light, warm white light elicited more positive emotions. Furthermore, colored light following cool white light diminished the sense of warmth in light, while yellow–blue light enhanced visual comfort and blue–red light increased attraction. Overall, the effect of colored lighting in rural accommodation interior spaces on improving negative emotions was relatively weak. Conversely, the combination of warm white light with blue–yellow or green–yellow light had a more significant effect on enhancing visual perceptions and reducing participants’ anxiety. This research provides valuable insights and references for designing the lighting ambiance in rural accommodation interiors and lighting design, with the potential to enhance the spatial experience of rural accommodations.

Becoming a physical education teacher: Preservice teachers’ meaning-making process in the transition between a subject didactics course and the practicum

Research has shown that a critical teaching perspective is at risk if preservice teachers perceive a gap between university- and field-based courses. This study explores how preservice physical education (PE) teachers make meaning out of their participation in a subject didactics course and the practicum. Interviews conducted across 1 year revealed that preservice teachers experienced PE teaching as practical skills and the advocacy of movement and health during the university-based course but not during the practicum. In both contexts, teaching as didactic and embodied knowledge was perceived as being meaningful. The study suggests that emotional learning occasions, role models, and clear purposes are supportive for a critical teaching perspective.

FlexComb: A Facial Landmark-Based Model for Expression Combination Generation

Facial expressions are a crucial but challenging aspect of animating in-game characters. They provide vital nonverbal communication cues, but given the high complexity and variability of human faces, the task of capturing the natural diversity and affective complexity of human faces can be a labour-intensive process for animators. This motivates the need for more accurate, realistic and lightweight methods for generating emotional expressions for in-game characters. In this work, we introduce FlexComb, a Facial Landmark-based Expression Combination model, designed to generate a real-time space of realistic facial expression combinations. FlexComb leverages the highly varied CelebV-HQ dataset containing emotions in the wild, and a transformer-based architecture. The central component of the FlexComb system is an emotion recognition model that is trained on the facial dataset, and used to generate a larger dataset of tagged faces. The resulting system generates in-game facial expressions by sampling from this tagged dataset, including expressions that combine emotions in specified amounts. This allows in-game characters to take on variety of realistic facial expressions for a single emotion, which addresses this primary challenge of facial emotion modeling. FlexComb shows potential for expressive facial emotion simulation with applications that include animation, video game development, virtual reality, and human-computer interaction.

Workplace Ostracism and Instigated Workplace Incivility: A Moderated Mediation Model of Narcissism and Negative Emotions

Workplace Ostracism and Instigated Workplace Incivility: A Moderated Mediation Model of Narcissism and Negative Emotions

Evaluating a social and emotional well-being model of service piloted in Aboriginal Community Controlled Health Services in Western Australia: an Aboriginal Participatory Action Research approach

IntroductionThe delivery of services to improve Aboriginal health and well-being must centre culture and integrate a social and emotional well-being understanding and approaches. These approaches are essential in increasing access...

A Neural Predictive Model of Negative Emotions for COVID-19

A Neural Predictive Model of Negative Emotions for COVID-19

Brain-Inspired Modeling and Decision-Making for Human-Like Autonomous Driving in Mixed Traffic Environment

In this paper, a human-like driving system is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the human transportation systems and mitigate misunderstanding and conflicts when interacting with human-driven vehicles. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function with multi-constraint, a dynamic game algorithm is developed to model the interactions and decision making between AV and human-driven vehicles. Additionally, to guarantee the safety during lane-change of AVs, an artificial potential field model is built for collision risk assessment. Further, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with a two-point preview model. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments, and the results demonstrated the feasibility and effectiveness of the proposed method.

Understanding the Impact of Schwartz Rounds on Pediatric Clinicians' Well-Being Using the Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment (PERMA) Model for Flourishing: A Qualitative Analysis.

Objective Schwartz Rounds (SR) is an interdisciplinary, case-based forum that augments compassionate care at the bedside and promotes discussion of the psycho-emotional aspects of patient care. This pilot study analyzed the perspectives of pediatric SR attendees through the lens of human flourishing, as defined by "PERMA" (positive emotion, engagement, relationships, meaning, and accomplishment). Methods This qualitative study was conducted at our Children's Hospital from September 2023 to October 2023. Focus group questions were developed from the Secure Flourishing Index and Community Workplace Flourishing validated tools. Clinicians who attended at least one SR in our Children's Hospital from January 2019 through September 2021 were recruited to participate in focus groups. Transcripts were analyzed using a direct content analysis based on the PERMA framework. Coded content from participants who attended >2 SR since 2019 was considered frequent (FR), whereas that of participants who attended ≤2 SR since 2019 was considered non-frequent (NFR). Results Sixteen clinicians (14 pediatric and two non-pediatric) participated in focus groups, including seven FR and nine NFR participants. There were nine emerging themes, eight of which were characterized among frequent SR attendees: SR serves as a safe and trusted space, promotes validation and support, facilitates introspective thinking, stimulates perspective shifts, augments compassion, reaffirms purpose, positively impacts one's professional identity, and no impact on resilience. In comparison, five of these themes and another theme (humanizes medicine) were identified among non-frequent attendees. All themes reflected one or more PERMA categories. Conclusion SR has the potential to augment human flourishing and holds a vital role in promoting a supportive environment in the workplace. SR thereby offers institutions an effective interventional tool to promote elevated well-being in the workforce.

Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). Firstly, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations.

Emotion Design for Video Games: A Framework for Affective Interactivity

Emotionally evocative games hold great potential for both industry and our basic understanding about human affect. The question of how best games elicit and respond to player emotion brings together research from psychology, design, and human-computer interaction (HCI). However, there is much confusion regarding the nature of emotions, emotional game-player interaction, and the process of creating emotional games. This article reviews important work from relevant fields to provide an interdisciplinary model that integrates current theories and highlights conceptual gaps for relevant fields. Based on this model, we propose a methodological framework that provides a step-by-step process of developing an emotion-adaptive game. This framework is open to different models of emotion, and its modular nature allows games to adapt to developments in the science of emotion and technological innovations in emotion detection. Together, the theoretical overview and framework can help reduce the confusion that surrounds creating and researching emotions in games.

Emotional content and sharing on Facebook: A theory cage match.

While emotional content predicts social media post sharing, competing theories of emotion imply different predictions about how emotional content will influence the virality of social media posts. We tested and compared these theoretical frameworks. Teams of annotators assessed more than 4000 multimedia posts from Polish and Lithuanian Facebook for more than 20 emotions. We found that, drawing on semantic space theory, modeling discrete emotions independently was superior to models examining valence (positive or negative), activation/arousal (high or low), or clusters of emotions and was on par with but had more explanatory power than a seven basic emotion model. Certain discrete emotions were associated with post sharing, including both positive and negative and relatively lower and higher activation/arousal emotions (e.g., amusement, cute/kama muta, anger, and sadness) even when controlling for number of followers, time up, topic, and Facebook angry reactions. These results provide key insights into better understanding of social media post virality.

Responsible Making-Decision Competency Based on the Social Emotional Learning Model for Vietnamese High School Students

Purpose: Social Emotional Learning (SEL) is a model that positively affects adolescents, particularly high school students. Adolescents experience physical and psychological changes that result in emotional and social-behavior difficulties. SEL improves academic performance, mental health problems, peer relationships, and family relationships. Responsible decision-making is one of the six fundamental skills of the SEL model and plays a crucial role. Methods: This study was founded on a literature review to examine the significance of responsible decision-making. Results: The study then provides a model of activities based on identifying and solving problems to assist students in developing their decision-making skills. Conclusion: The approach presented in this study is comprised of two primary activities: basic activity and practical activity; the study is also providing a seven-step procedure for problem-solving. Students are required to perform the content of the exercises following the instructions, and the implementation of these activities should be performed regularly to increase their effectiveness and competence in making decisions.

The perceptions of Turkish students against scientists and the emotions of scientists in the post-pandemic

Today’s students are expected to think like scientists. Every student has a different life and they learn in different ways. We need to understand how students view scientists and what their perceptions of them are in order to make these expectations a reality. This study aims to investigate the perceptions that Turkish secondary school students have of scientists and show how these perceptions have changed in the wake of the pandemic particularly with regard to the emotions of scientists. This research is survey research. The Draw a Scientist Test Checklist (DAST-C) was used as the data collection and analysis tool for the research. In the emotion dimension of this study, emotions in the Plutchik Emotion Model were based on the data collection tool. Descriptive statistics were used to analyse the data. Students’ perceptions of scientists in this study show a fall in the rates of the standards that constitute the stereotypical image. In the findings of the study, it was seen that the scientist was generally young and female. This shows that recent developments have changed students' perceptions. It is clearly seen that the pandemic has changed the minds of scientists and students.

Self-aware face emotion accelerated recognition algorithm: a novel neural network acceleration algorithm of emotion recognition for international students.

With an increasing number of human-computer interaction application scenarios, researchers are looking for computers to recognize human emotions more accurately and efficiently. Such applications are desperately needed at universities, where people want to understand the students' psychology in real time to avoid catastrophes. This research proposed a self-aware face emotion accelerated recognition algorithm (SFEARA) that improves the efficiency of convolutional neural networks (CNNs) in the recognition of facial emotions. SFEARA will recognize that critical and non-critical regions of input data perform high-precision computation and convolutive low-precision computation during the inference process, and finally combine the results, which can help us get the emotional recognition model for international students. Based on a comparison of experimental data, the SFEARA algorithm has 1.3× to 1.6× higher computational efficiency and 30% to 40% lower energy consumption than conventional CNNs in emotion recognition applications, is better suited to the real-time scenario with more background information.