Emotion Generation Research Articles

Analysis of the use of pop singing in musical theater singing based on data analysis

Abstract In this paper, we first systematically sorted out the characteristics of pop singing and musical theater singing and explored the generation of emotion and melody application of pop singing in musical theater singing. Then, the gray GM(1,1) model is analyzed, and a gray data mining model based on data analysis is constructed to predict the application of pop singing in combination with the data mining model under data analysis. Finally, the prediction analysis was conducted to use characterization, timbre and style in pop singing, respectively. The results showed that the score of character building was 90, and the errors between the predicted and actual values of timbre and style were between ±0.1 and ±1, respectively, which were within an acceptable range.

Research on the strategy of improving mental health well-being in universities based on game theory

Abstract In this paper, the participants' interaction input emotion is assessed to analyze the current student interaction relationship, and two influencing factors, friendliness and empathy, are extracted. Secondly, the psychological game process of interpersonal interaction is simulated to model the emotion generation process of participants and students, and the sub-game perfect equilibrium strategy of the embedded game is used as the optimal emotion selection strategy. Finally, the student’s emotional states are updated according to the optimal emotional strategy, and the spatial coordinates of students’ emotional states after being stimulated by emotions are labeled with the spatial coordinates of six basic emotions. The results show that the happiness enhancement strategy based on the game model increases from 0 to 0.9, and the effectiveness increases to 1.0.

К ВОПРОСУ КВАНТОВОЙ ТЕОРИИ ФИЗИКИ МОЗГА И ТЕОРИИ КВАНТОВО-ПОДОБНОГО СОЗНАНИЯ (ОБЗОР ЗАРУБЕЖНОЙ ЛИТЕРАТУРЫ)

The article provides a review of foreign scientific literature on existing approaches in scientists' ideas about the quantum theory of brain physics and the theory of quantum-like consciousness. It is shown that in recent years, physics and psychology have made important scientific advances; the emergence of new tools has led to the development of neuroscience, which makes it possible to study the state of the brain when known mental functions are preserved or impaired. The use of mathematical methods has made it possible to develop computational models that describe the fundamental functions of the brain and implement them in technological applications. The most pressing issues of modern research in the field of mental processes of the brain, such as perception, cognition, emotions, consciousness and learning, researchers call the problems of the universal properties of human behavior in various conditions and cultures; the potential of computational psychiatry to predict, understand and treat mental disorders; neural mechanisms of influence on mental content at higher levels of organization of the hierarchical brain; mechanisms of the emergence of abstract ideas from sensorimotor accidents, etc. Currently, in understanding the cognitive processes of the brain, perception, understanding, cognition, mental lexicon, understanding the generation and nature of emotions, memory, learning, etc. A number of researchers use the theory of quantum or quantum-like consciousness. Research in this area is both empirical and theoretical in nature, with the postulates of the theory being considered mainly related to quantum physics due to the lack of other areas in which quantum-like properties are supposedly manifested. It is believed that despite modern advances in artificial intelligence models, neural networks cannot reach the level of the human brain, partly due to differences in the way information is encoded and processed. In order to develop the potential of brain-based artificial intelligence, quantum neural networks are proposed that are similar to the mechanisms and phenomena in the brain. Interactions between biologically important molecules in the brain may underlie the quantum metalanguage that governs the behavior of humans and animals.

ИЗЫСКАНИЕ ОРИГИНАЛЬНОЙ МОДЕЛИ ДЛЯ ИЗУЧЕНИЯ ВЛИЯНИЯ ФАРМАКОЛОГИЧЕСКИ АКТИВНЫХ ВЕЩЕСТВ НА СКОРОСТЬ ПОТРЕБЛЕНИЯ КИСЛОРОДА ТКАНЬЮ ПРЕДСТАТЕЛЬНОЙ ЖЕЛЕЗЫ

The article provides a review of foreign scientific literature on existing approaches in scientists' ideas about the quantum theory of brain physics and the theory of quantum-like consciousness. It is shown that in recent years, physics and psychology have made important scientific advances; the emergence of new tools has led to the development of neuroscience, which makes it possible to study the state of the brain when known mental functions are preserved or impaired. The use of mathematical methods has made it possible to develop computational models that describe the fundamental functions of the brain and implement them in technological applications. The most pressing issues of modern research in the field of mental processes of the brain, such as perception, cognition, emotions, consciousness and learning, researchers call the problems of the universal properties of human behavior in various conditions and cultures; the potential of computational psychiatry to predict, understand and treat mental disorders; neural mechanisms of influence on mental content at higher levels of organization of the hierarchical brain; mechanisms of the emergence of abstract ideas from sensorimotor accidents, etc. Currently, in understanding the cognitive processes of the brain, perception, understanding, cognition, mental lexicon, understanding the generation and nature of emotions, memory, learning, etc. A number of researchers use the theory of quantum or quantum-like consciousness. Research in this area is both empirical and theoretical in nature, with the postulates of the theory being considered mainly related to quantum physics due to the lack of other areas in which quantum-like properties are supposedly manifested. It is believed that despite modern advances in artificial intelligence models, neural networks cannot reach the level of the human brain, partly due to differences in the way information is encoded and processed. In order to develop the potential of brain-based artificial intelligence, quantum neural networks are proposed that are similar to the mechanisms and phenomena in the brain. Interactions between biologically important molecules in the brain may underlie the quantum metalanguage that governs the behavior of humans and animals.

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.

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Computer vision has made significant advancements in emotional design. Designers can now utilize computer vision to create emotionally captivating designs that deeply resonate with people. This article aims at enhancing emotional design selection by separating appearance and color. A two-stage emotional design method is proposed, which yields significantly better results compared to classical single-stage methods.. In the Radboud face dataset (RaFD), facial expressions primarily rely on appearance, while color plays a relatively smaller role. Therefore, the two-stage model presented in this article can focus on shape design. By utilizing the SSIM image quality evaluation index, our model demonstrates a 31.63% improvement in generation performance compared to the CGAN model. Additionally, the PSNR image quality evaluation index shows a 10.78% enhancement in generation performance. The proposed model achieves superior design results and introduces various design elements.This article exhibits certain improvements in design effectiveness and scalability compared to conventional models.

EmoBot: Artificial emotion generation through an emotional chatbot during general-purpose conversations

Emotion modeling has always been intriguing to researchers, where detecting emotion is highly focused and generating emotion is much less focused to date. Therefore, in this paper, we aim to exploring emotion generation, particularly for general-purpose conversations. Based on the Cognitive Appraisal Theory and focusing on audio and textual inputs, we propose a novel method to calculate informative variables to evaluate a particular emotion-generating event and six primary emotions. Incorporating such a method of artificial emotion generation, we implement an emotional chatbot, namely EmoBot. Accordingly, EmoBot analyzes continuous audio and textual inputs, calculates the informative variables to evaluate the current situation, generates appropriate emotions, and responds accordingly. An objective evaluation indicates that EmoBot could generate more accurate emotional and semantic responses than a traditional chatbot that does not consider emotion. Additionally, a subjective evaluation of EmoBot demonstrates the appreciation of users for EmoBot over a traditional chatbot that does not consider emotion.

Bionic Dual-Loop Emotional Learning Circuit and Its Application in Radiation Early Warning Monitoring

Emotional learning is a very important learning mechanism for the human body, and it is also an important part of artificial intelligence. This paper proposes a bionic circuit, which simulates the dual-loop of emotional learning in the human brain. The circuit uses the characteristics of the memristor to simulate the fast loop that quickly generates initial emotions to predict danger and the detour loop which generates a more refined emotion by further processing of the input information. The circuit includes 1) thalamus module, 2) sensory cortex module, 3) amygdala module. The first module sends the strongest input stimulus directly to the amygdala module for rapid processing. The second module processes the input stimuli and sends them to the amygdala module for emotion generation and judgment. The third module is responsible for generating output signals representing the corresponding emotions. The simulation results in PSPICE show that the output signal generated by the circuit can represent the characteristics of the dual-loop of emotional learning, and more realistically simulate the biological process. By configuring the parameters, this circuit can be more flexibly applied to the radiation intensity early warning system to protect the safety of humans when working in a radiation environment.

Emotion regulation research in hospitality and tourism

Purpose This paper aims to develop a comprehensive model on intra- and interpersonal emotion regulation (ER) in hospitality and tourism (H&T) service encounters. Design/methodology/approach A critical review and reflection of ER research from multiple disciplines was conducted. Methodologies appropriate for investigating ER were also reviewed. Findings A comprehensive framework was proposed to outline key influential factors, processes and consequences of intra- and interpersonal ER in service encounters in the H&T industry. Methodologies integrating advanced tools were suggested to measure complex and dynamic emotion generation and regulation processes in social interactions from a multimodal perspective. Research limitations/implications The researchers developed a comprehensive conceptual model on both intra- and interpersonal ER based on a critical review of the most recent psychological research on ER. Various theoretical and methodological considerations are discussed, offering H&T scholars a solid starting point to explore dynamic emotion generation and regulation processes in complex social settings. Moreover, the model provides future directions for the expansion of ER theories, which have been mostly developed and tested based on laboratory research. Originality/value The proposed model addresses two critical issues identified in emotion research in the H&T field: the lack of a dynamic perspective and the neglect of the social nature of emotions. Moreover, the model provides a roadmap for future research.

How Moral Disgust Affects Aggression: The Mediating Roles of Benign and Malicious Envy

ABSTRACT Aggression has been receiving much attention always as it has serious negative effects. Moral disgust may be a protective factor against aggression. This study explores the mediating role of malicious envy and benign envy in the relationship between moral disgust and aggression from the perspective of integrative functional theory of moral disgust. The present study recruited 1052 participants and asked them to complete Three Domains of Disgust Scale (including Moral Disgust Subscale), Benign and Malicious Envy Scale, and Aggression Scale. The results showed that: (1) benign envy acted as a mediating variable between moral disgust and aggression, and played a partial mediating role. Malicious envy acted as a mediating variable between moral disgust and aggression, and played a full mediator role. In other words, both benign envy and malicious envy played an independent mediating role between moral disgust and aggression; (2) Benign envy and malicious envy are the mediator variables at the same time, only malicious envy can play a fully mediating role between moral disgust and aggression. That’s to say, moral disgust can stably and significantly affect aggression through malicious envy. This study provided a theoretical reference to inhibit the generation of negative emotions and aggressive behaviors in social practice.

An Exploratory Attitude and Belief Analysis of Ecotourists’ Destination Image Assessments and Behavioral Intentions

Colombia is noteworthy as a biodiversity hotspot, featuring an extraordinary number of endemic orchids, birds, and butterflies. This exploratory study examined how the perceptions of destination image, considering the cognitive and affective image, can be a predictor or positive influencer of behavioral intentions of ecotourists through symmetric data analysis. The success of a birdwatching destination can be attributed more to the development of a positive overall image, by having a direct and strong influence on behavioral intentions, indirect effects such as affective image can also be identified such as intention to visit and intention to recommend. Using partial least squares (PLS), the author(s) analyzed 64 survey responses collected of rural areas from an invitation made to associations of professional birdwatchers, including a new 15-statement scale specialized on birdwatching as a result of personal interviews with tourism management professionals and entrepreneurs who are experts in birdwatching. The findings supported the reliability of the model, and symmetric analysis presented the higher influence of emotions and affections in increasing intentions of recommendation, considering birdwatching as based on personal relationships. Additionally, the cognitive image for the birders despite representing destination attributes did not have the same impact on behavioral intentions. Therefore, managers should develop positioning strategies based on the generation of emotions.

Deep Learning-Based Machine Color Emotion Generation

This paper investigates generating machine color emotion through deep learning. The grayscale image colorization model's training process resembles human memory color. Sixty images were recolored and quality evaluated to explore machine generated color impressions. Six experimental samples were recolored under D65, A, CWF, and TL84 light sources. Changes in lightness, chroma, and hue angle compared the original and colorized images, exploring light source effects on machine color perception. Analyzing differences in coloring results within the CIEL* a* b* color space for pixels with equal grayscale verified machine color emotion generation. Results show the machine learns to form color impressions from samples. Different light source color temperatures impact color prediction accuracy. The machine accurately colors images based on semantic context, demonstrating spontaneous color emotion generation through deep learning. This research positively contributes to the development of intelligent devices with color emotion.

How do fundamental emotion beliefs affect the rapid emotional processing? An ERP study

Beliefs about whether emotions are good or bad, controllable or uncontrollable are two fundamental emotion beliefs. Studies have confirmed the link between the two beliefs and emotional responses, but how emotion beliefs affect the process from emotional stimulus perception to emotion generation and automatic regulation is unclear. Answering this question helps to understand the role of emotion beliefs in emotional dysfunction and dysregulation and can provide a basis for effective emotion regulation. Therefore, the current study used event-related potential (ERP) measures to examine the time course and neural mechanisms by which emotional beliefs influence the processing of emotional pictures. One hundred participants were divided into four groups (25 per group) according to controllable or uncontrollable beliefs about emotions and good or bad beliefs about negative emotions, and viewed emotional negative and neutral pictures. Results showed that P2 was more positive in participants with emotion controllable vs. uncontrollable belief. The early posterior negativity (EPN) was more negative for unpleasant than neutral images in participants with emotion good and controllable beliefs as well as with bad and uncontrollable beliefs. On late positive potential (LPP), middle LPP (500-1000ms) was more positive in emotion good vs. bad belief holders and the late LPP (1000-2000 ms) was more positive for negative than neutral images in emotion uncontrollable belief holders. The findings suggest that fundamental emotion beliefs can affect individuals' early attention and late meaning evaluation towards unpleasant stimuli. Furthermore, they provide insight into altered beliefs about emotion in people with emotion dysfunction or dysregulation.

Emotion in the mind's eye: Imagination for adaptive cognition.

In a complex world, we are constantly faced with environmental stimuli that shape our moment-to-moment experiences. But just as rich and complex as the external world is the internal milieu-our imagination. Imagination offers a powerful vehicle for playing out hypothetical experiences in the mind's eye. It allows us to mentally time travel to behold what the future might bring, including our greatest desires or fears. Indeed, imagined experiences tend to be emotion-laden. How and why are humans capable of this remarkable feat? Based on psychological findings, we highlight the importance of imagination for emotional aspects of cognition and behavior, namely in the generation and regulation of emotions. Based on recent cognitive neuroscience work, we identify putative neural networks that are most critical for emotional imagination, with a major focus on the default mode network. Finally, we briefly highlight the possible functional implications of individual differences in imagination. Overall, we hope to address why humans have the capacity to simulate hypothetical emotional experiences and how this ability can be harnessed in adaptive (and sometimes maladaptive) ways. We end by discussing open questions.

A psychological flexibility perspective on well-being: Emotional reactivity, adaptive choices, and daily experiences.

According to psychological flexibility theory, fully experiencing one's emotions, even when they involve negative reactions, can enhance psychological well-being. In pursuit of this possibility, procedures capable of disentangling reaction intensities from reaction durations, in response to affective images, were developed and variations of this paradigm were applied in understanding variations in happiness and adaptive behavior. Consistent with psychological flexibility theory, three studies showed that more intense emotional reactions, irrespective of valence, were associated with higher levels of well-being. Two additional studies showed that happy individuals, relative to less happy individuals, exhibited more functional approach/avoidance behavior in behavior-focused tasks. Together, the results are consistent with the idea that adaptive emotion generation systems are those that flexibly adapt emotion output to concurrent emotion-related stimulation. The program of research adds to our understanding of the relationship between emotion reactivity and well-being while highlighting specific processes through which emotion and well-being interact. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

SEC-GAN for robust speaker recognition with emotional state dismatch

Speaker recognition is often dependent on the speaker and susceptible to emotional factors, thus mostly decreasing recognition performance, we propose a framework by combining generative adversarial networks and speaker recognition for generating additional speaker-related emotional training speech feature to enhance robustness under different emotional conditions. In this framework, a new speaker emotion-converted generative adversarial network (SEC-GAN) is developed for speaker recognition. Given the neutral speech of the target speaker, SEC-GAN learns speech information to generate speech feature in other emotions based on neutral speech while retaining speaker identity. In addition, a new loss function is designed to retain the speaker’s internal information during feature reconstruction, and an emotion discriminator is introduced to classify the speech feature’s emotion for better emotion generation quality. Based on the origin neutral and generated training data from native speakers of Mandarin Affective Speech Corpus (MASC), the negative impact of emotion mismatch between speech can be decreased by using our framework. This strategy could solve the common problem in reality that most voice control devices enroll user’s calm speech but fail to recognize user’s identity when they are in other emotion. The experimental results on MASC which is 57.59% show the improvement of 8.27% compared with VGG baseline and 5.62% compared with x-vector in accuracy. Our framework also outperforms the existing state-of-the-art method ECAPA-TDNN and other comparison methods.

A Q-learning approach to generating behavior of emotional persuasion with adaptive time belief in decision-making of agent-based negotiation

There is an increasing research interest in agent-based automated negotiation. However, how to design an agent that can adaptively compete with its human counterpart is still in its early stage. This research makes efforts toward this end by modeling two essential elements of human-led negotiation: emotion and timing, to adapt to negotiation dynamics. In multi-issue-multi-round bilateral negotiation, the Web-Fechner law is applied to model an agent’s emotion generation and the emotion decay law is used to realize a decaying property of emotion. Meanwhile, rather than a fixed time belief, agents are endowed with time beliefs that can vary with selected negotiation tactics and be adjusted by agents’ emotions. A proposal updating algorithm integrating those two elements is consequently proposed based on Q-learning. Then, 135 numerical experiments were conducted with comparative analyses and statistical tests. The results show that the proposed model can significantly increase an individual agent’s utility, the joint utility, and the probability of successful negotiations by an average of 76.14%, 172.22%, and 66.10%, respectively, whereas reducing the utility difference and negotiation rounds by an average of 97.39% and 29.57%, respectively. These results imply that the proposed negotiation model has advantages in improving negotiation utility, efficiency, and outcome fairness.

Neuromorphic Circuit Implementation of Operant Conditioning Based on Emotion Generation and Modulation

In this work, an operant conditioning (OC) model and memristive circuit implementation based on emotion generation and modulation is proposed, which is inspired by neural and psychological mechanisms to simulate human behavioral decisions guided by reinforcers and emotional changes during interaction with dynamically changing environments. The OC model consists of four main modules which are the cue-synapse-action module, the reward and punishment value systems, and the emotion system. Under four conditions of reward appearance or termination, and punishment appearance or termination, the memristive circuit designed according to the OC model can realize the process of exploration, acquisition, extinction, and recovery of OC (external phenomena), and also reflect the process of change of value signals and emotion signals (internal states). In addition, the phenomena of reward fatigue and punishment adaptation are considered so that value and emotion signals gradually diminish with the repetition of invariant reinforcers. The neuromorphic circuit is verified to have good robustness and device tolerance. This work enables multi-timescale OC tasks and is expected to be applied in an emotionally intelligent robot platform to achieve humanoid functions such as real-time dynamic decision-making for interaction with the environment and emotional companionship.

Experience of Playing Sport or Exercising for Women with Pelvic Floor Symptoms: A Qualitative Study

BackgroundWomen participate in sport at lower rates than men, and face unique challenges to participation. One in three women across all sports experience pelvic floor (PF) symptoms such as urinary incontinence during training/competition. There is a dearth of qualitative literature on women’s experiences of playing sport/exercising with PF symptoms. The purpose of this study was to explore the lived experience of symptomatic women within sports/exercise settings and the impact of PF symptoms on sports/exercise participation using in-depth semi-structured interviews.ResultsTwenty-three women (age 26–61 years) who had experienced a breadth of PF symptom type, severity and bother during sport/exercise participated in one–one interviews. Women played a variety of sports and levels of participation. Qualitative content analysis was applied leading to identification of four main themes: (1) I can’t exercise the way I would like to (2) it affects my emotional and social well-being, (3) where I exercise affects my experience and (4) there is so much planning to be able to exercise. Women reported extensive impact on their ability to participate in their preferred type, intensity and frequency of exercise. Women experienced judgement from others, anger, fear of symptoms becoming known and isolation from teams/group exercise settings as a consequence of symptoms. Meticulous and restrictive coping strategies were needed to limit symptom provocation during exercise, including limiting fluid intake and careful consideration of clothing/containment options.ConclusionExperiencing PF symptoms during sport/exercise caused considerable limitation to participation. Generation of negative emotions and pain-staking coping strategies to avoid symptoms, limited the social and mental health benefits typically associated with sport/exercise in symptomatic women. The culture of the sporting environment influenced whether women continued or ceased exercising. In order to promote women’s participation in sport, co-designed strategies for (1) screening and management of PF symptoms and (2) promotion of a supportive and inclusive culture within sports/exercise settings are needed.

Facial expression generation based on variational AutoEncoder network and cloud computing

With the rapid development of portable terminal devices, such as smartphones, the development of expression generation technology makes the emotional communication between people in social networks more rich and diverse. However, there are still great challenges in deploying deep networks on mobile phones with limited resources. Moreover, the muscle movement of the face produces different facial expressions. To this end, this paper proposes an efficient emotion image generation framework based on Transformer and variational auto‐encoders (VAE) on the cloud platform. Specifically, we collect human face which is further transferred to the service. To exploit connections between muscles and expressions, we introduce the multi‐head attention mechanism to construct Transformer. To generate different emotional images, we design a novel emotion generation model named Transformer‐embedded VAE (TEVAE). All the experimental results show that the proposed TEVAE obtains higher performance in emotion image generation tasks compared to VAE.