Emotional Model Research Articles

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.

Personality Enhanced Emotion Generation Modeling for Dialogue Systems

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user’s unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

Multi-view emotional expressions dataset using 2D pose estimation

Human body expressions convey emotional shifts and intentions of action and, in some cases, are even more effective than other emotion models. Despite many datasets of body expressions incorporating motion capture available, there is a lack of more widely distributed datasets regarding naturalized body expressions based on the 2D video. In this paper, therefore, we report the multi-view emotional expressions dataset (MEED) using 2D pose estimation. Twenty-two actors presented six emotional (anger, disgust, fear, happiness, sadness, surprise) and neutral body movements from three viewpoints (left, front, right). A total of 4102 videos were captured. The MEED consists of the corresponding pose estimation results (i.e., 397,809 PNG files and 397,809 JSON files). The size of MEED exceeds 150 GB. We believe this dataset will benefit the research in various fields, including affective computing, human-computer interaction, social neuroscience, and psychiatry.

Is it true that negative emotions cause more utilitarian judgements? from the influence of emotion and cognition

ABSTRACT The affect-as-information (AAI) model proposes that emotions influence the accessibility and value of information (Avramova & Inbar, 2013). Furthermore, according to the dual-process model of moral judgement, emotions and cognition influence moral judgement (Greene, 2007; Greene et al., 2001, 2008); however, there is no direct evidence of a causal chain to support this model’s proposition. By using a 3 (emotions: positive vs. neutral vs. negative) × 2 (primed rule: save lives vs. do not kill) between-participants design, we examined two hypotheses in two experiments: supraliminal (Experiment 1) and subliminal (Experiment 2) priming. Our results partially supported the AAI model and confirmed that emotions and cognition independently influence moral judgement. Specifically, the positive emotions group made more utilitarian decisions after being primed with the “save lives” rule and more deontological decisions after being primed with the do not kill rule. However, priming did not affect moral judgement in the negative emotion condition. Further, irrespective of whether priming was done, the negative emotion group mostly made utilitarian decisions. Accordingly, we propose a dynamic dual-process model of moral judgement, that can help clarify how emotion and cognition influence moral judgement.

Examining the integrated broaden-and-build model of positive emotions involving hope in a community sample of Argentinian adults: A selective test among victims of interpersonal aggression

The present study sought to provide a test of the integrated broaden-and-build model of positive emotions that incorporates components of hope (agency & pathways) as hypothesized predictors of life satisfaction in a community sample of 171 (137 female & 34 male) Argentinian adults who have been victims of interpersonal aggression. Results of testing for mediation, controlling for negative mood, indicated that positive mood had indirect effects on life satisfaction through hope agency, but not through hope pathways. Importantly, the direct effect of positive mood on life satisfaction became nonsignificant after accounting for hope. Thus, we found evidence for full mediation involving hope agency. Taken together, these findings provide further evidence for the importance of hope agency, but not hope pathways, as an important mechanism that might account for how positive mood fosters well-being in diverse adult populations.

Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations

Emotion recognition is the ability to precisely infer human emotions from numerous sources and modalities using questionnaires, physical signals, and physiological signals. Recently, emotion recognition has gained attention because of its diverse application areas, like affective computing, healthcare, human–robot interactions, and market research. This paper provides a comprehensive and systematic review of emotion recognition techniques of the current decade. The paper includes emotion recognition using physical and physiological signals. Physical signals involve speech and facial expression, while physiological signals include electroencephalogram, electrocardiogram, galvanic skin response, and eye tracking. The paper provides an introduction to various emotion models, stimuli used for emotion elicitation, and the background of existing automated emotion recognition systems. This paper covers comprehensive searching and scanning of well-known datasets followed by design criteria for review. After a thorough analysis and discussion, we selected 142 journal articles using PRISMA guidelines. The review provides a detailed analysis of existing studies and available datasets of emotion recognition. Our review analysis also presented potential challenges in the existing literature and directions for future research.

Research on Emotion Recognition Method of Flight Training Based on Multimodal Fusion

The emotional activities of the human body are mainly regulated by the autonomic nervous system, the central nervous system, and the advanced cognition of the human brain. This paper proposes an emotional state recognition method in pilot training tasks based on multimodal information fusion. A set of emotion perception recognition systems, a two-dimensional valence-arousal emotional model, and a multimodal information intelligent perception model were established based on the human-computer interaction mode during flight training; an intelligent perception system was designed to collect and intelligently perceive four kinds of peripheral physiological signals of pilots in real-time. And based on traditional machine learning models, a binary tree support vector machine was designed to optimize and improve the multimodal information co-integration decision model, which increased the accuracy of emotional state recognition in flight training by 37.58% on average. The experimental result showed that it realizes accurate monitoring and identification of real-time emotional state, helps to improve the effect of flight training and flight safety, maintains the efficiency of operation, and has important research significance and application prospects in the field of pilot training.

Memristor-Based Circuit Design of PAD Emotional Space and Its Application in Mood Congruity

The one-dimensional and two-dimensional emotion models realized by hardware circuits have been studied. However, three-dimensional emotional model which is most suitable for human emotion has not been considered. In this paper, a bionic circuit of three-dimensional emotional space model is proposed, which can generate brain-like emotions according to the information of visual, speech and text. The designed memristor circuits are based on the brain emotion theory of limbic system, including thalamus, sensory cortex, orbitofrontal cortex, cingulate gyrus, amygdala and other circuit modules. Moreover, the perceptual brain and rational brain in the memristor circuits are also considered. The three-dimensional model in this paper is a PAD emotional space model composed of three dimensions: pleasure (P), arousal (A) and dominance (D). Many emotions can be expressed by using the PAD emotional model. In addition, the PAD emotional model composed of memristor circuits is applied to mood congruity, which considers the relationship between emotion and learning. The PAD emotion designed by memristor circuits may provide a reference for bionic robot to realize human-computer emotional companionship.

Textual Emotion Analysis-based Disabled People Talking Using Improved Metaheuristics with Deep Learning Techniques for Intelligent Systems

Due to the complexity of generalizing and modeling the series of brain signals, detecting emotions in people with sensory disabilities still continues to be challenging. Hence, brain–computer interface technology was used to study the emotions and behavior of people based on brain signals. Emotion analysis is a widely used and robust data mining analysis method. It provides an excellent opportunity to monitor, evaluate, determine, and understand the sentiments of consumers with respect to a product or a service. Yet, a recognition model of emotions in people with visual disabilities has not been evaluated, even though previous studies have already proposed the classification of emotions in people with sensory disabilities using machine learning approaches. Therefore, this study introduces a new salp swarm algorithm with deep recurrent neural network-based textual emotion analysis (SSADRNN-TEA) technique for disabled persons. The major intention of the SSADRNN-TEA technique was to focus on the detection and classification of emotions that exist in social media content. In this work, the SSADRNN-TEA technique undergoes preprocessing to make the input data compatible with the latter stages of processing and BERT word embedding process is applied. Moreover, deep recurrent neural network (DRNN) model is exploited. Finally, SSA is exploited for the optimal adjustment of the DRNN hyperparameters. A widespread experiment is involved in simulating the real-time performance of the SSADRNN-TEA method. The experimental values revealed the improved performance of the SSADRNN-TEA technique in terms of several evaluation metrics.

Understanding Sentiment and Emotion through ChatGPT to Support Emotion-based Management Decision Making

Effective management decision-making is a key element in organisational success. In the ever-evolving digital age, human interaction with computer systems is increasingly complex and diverse. One increasingly popular form of interaction is the use of Chatbots that are based on artificial intelligence technology. Chatbots trained using language models, such as ChatGPT, have demonstrated their ability to communicate with humans naturally and can be used in a variety of applications, including in management contexts. This research aims to fill this knowledge gap by proposing and implementing a customised ChatGPT model for understanding sentiment and emotion in the context of management decision-making. This research is a literature review that adopts a qualitative method approach, which means it will analyse and interpret data by relying on information and text from various sources. The study results show that understanding sentiment and emotion through ChatGPT is an important innovation that can support emotion-based management decision-making in an increasingly complex and rapidly changing business environment. By analysing and recognising feelings, views and attitudes in text, ChatGPT provides valuable insights for managers to respond to market changes, understand customer and employee views, enhance brand and reputation, and create a healthier and more productive work environment.

Negative emotionality, stress, and depressive symptoms in the transition through of college

The purpose of this study was to evaluate the prospective associations between negative emotionality, perceived stress, and depressive symptomology in a longitudinal sample of first-year college students (N = 209, ~62 % female, ~90 % white, Southeastern USA). Data assessment points were in the first and second years of college as well as ~four years after baseline (retention rates ≥80 %). Results from our random-intercept cross-lagged panel model (RI-CLPM) showed robust evidence that negative emotionality, perceived stress, and depressive symptoms were strongly influenced by stable, trait-like influences over time. These latent factors were also moderately to strongly correlated. After accounting for these between-person effects, there was comparatively weak evidence of any within-person change in one variable leading to meaningful change in another. Even the within-person (residual) correlations increased with time, suggesting these constructs became increasingly interdependent. Results best support a co-development model of negative emotionality, perceived stress, and depressive symptom development concerning the population of emerging adults transitioning through college.