Emotion Generation Research Articles

The Impacts of Background Music on the Effects of Loving-Kindness Meditation on Positive Emotions.

Loving-kindness meditation (LKM) has been widely used in promoting mental health, with positive emotions as an important mechanism. The current study explored the impact of background music on the effects and difficulties of LKM practice. Two hundred participants were randomly divided into six groups, wherein LKM plus music with harmony only, LKM plus music with harmony and melody, and LKM without music were presented in a different order during the intermediate three days of a five-day LKM intervention. Participants reported three types of positive emotions (pro-social, low-arousal, and medium-arousal positive emotions) and the difficulties during meditation (lack of concentration and lack of pro-social attitudes) after each of three sessions. The results of MANOVA indicated that compared to the session without music, incorporating music could evoke more low-arousal positive emotions and pro-social positive emotions without altering the difficulties. However, the results did not reveal significant differences in the effects of music with harmony and music with harmony and melody on both emotions and difficulties. Additionally, practice effects may have influenced the generation of medium-arousal positive emotions and the difficulty of concentration, but the results were inconsistent across groups. Our findings suggest potential benefits for practitioners of LKM in incorporating music during the meditation process, and the directions for future research were further discussed.

Neural signatures of shared subjective affective engagement and disengagement during movie viewing.

When watching a negative emotional movie, we differ from person to person in the ease with which we engage and the difficulty with which we disengage throughout a temporally evolving narrative. We investigated neural responses of emotional processing, by considering inter-individual synchronization in subjective emotional engagement and disengagement. The neural underpinnings of these shared responses are ideally studied in naturalistic scenarios like movie viewing, wherein individuals emotionally engage and disengage at their own time and pace throughout the course of a narrative. Despite the rich data that naturalistic designs can bring to the study, there is a challenge in determining time-resolved behavioral markers of subjective engagement and disengagement and their underlying neural responses. We used a within-subject cross-over design instructing 22 subjects to watch clips of either neutral or sad content while undergoing functional magnetic resonance imaging (fMRI). Participants watched the same movies a second time while continuously annotating the perceived emotional intensity, thus enabling the mapping of brain activity and emotional experience. Our analyses revealed that between-participant similarity in waxing (engagement) and waning (disengagement) of emotional intensity was directly related to the between-participant similarity in spatiotemporal patterns of brain activation during the movie(s). Similar patterns of engagement reflected common activation in the bilateral ventromedial prefrontal cortex, regions often involved in self-referenced evaluation and generation of negative emotions. Similar patterns of disengagement reflected common activation in central executive and default mode network regions often involved in top-down emotion regulation. Together this work helps to better understand cognitive and neural mechanisms underpinning engagement and disengagement from emotionally evocative narratives.

What do you do or with whom? Understanding happiness with the tourism experience: an AI approach applied to Instagram

More and more tourists are sharing their experiences on their social media through a combination of photos, texts, and hashtags. But there is a scarcity of studies in literature on analyzing tourists’ visual content in relation to tourism destinations. To address this gap in literature, this study explores how and with whom users express the greatest happiness in holiday destinations, and how they share it with their community, through a mixed methods approach composed of analysis of images, text, and metadata. This approach allows us to address the objective of this research, which is the prediction of the happiness felt by tourists during their experience, using innovative techniques that allow the independent variables to be obtained. To predict tourist satisfaction, two sources of data, photos and texts, are analyzed: a novel approach and little explored in the literature, but necessary due to the interaction of both variables. This study applies various artificial intelligence analyses on visual content (deep learning), and textual and metadata content (machine learning) to 39,235 Instagram posts shared by tourists since the emergence of Instagram thirteen years ago, at a cultural and gastronomic tourist destination. The findings of the visual content analysis showed that socialization and company, that is, traveling and interacting with people, was a key aspect of a positive tourism experience. In addition, the gender of the people with whom they traveled, and the tourist’s narcissism were also key aspects in the generation and expression of positive emotions. Regarding the analysis of the textual content, the results showed that when tourists enjoyed a positive experience, they became more involved in the generation of content, that is, they showed their happiness through positive words.

Embodied cognitive restructuring: The impact of posture and movement on changing dysfunctional attitudes

Background and objectivesPrevious theoretical and empirical work has pointed to the important role of the body in emotion generation and emotion regulation. In the present study, we wanted to investigate if the performance of certain body postures and movement could facilitate cognitive restructuring of dysfunctional cognitive attitudes more effectively than traditional, verbal-only methods. MethodsIn total, 130 participants were randomized to one of two groups. One group was subjected to cognitive restructuring (i.e., restructure only group; CR-only), verbally exploring a dysfunctional attitude from a curious, strong, and courageous perspective. The other group received the same verbal instructions but in addition to this, was asked to perform different bodily exercises (i.e., motor-enhanced restructuring group; M-CR) supposed to enhance experience of the different perspectives from which cognitive restructuring was employed. ResultsResults confirmed the primary hypothesis, showing that the M-CR-group showed a larger decline in belief in dysfunctional attitudes compared with the CR-only group (F = 4.2, p = 0.041, d = 0.25). No differences on secondary outcomes were observed between the two groups. LimitationsFuture research should explore the effects of motor-enhanced CR both more long-term (e.g., durability over weeks) and in clinical samples (e.g., anxiety and depression). ConclusionShould the findings be replicated in clinical samples, it is encouraging that simple bodily exercises can enhance the effect of one of the most central skills of cognitive therapy.

Exploring the non-linear relationship and synergistic effect between urban built environment and public sentiment integrating macro- and micro-level perspective: a case study in San Francisco.

Public sentiment can effectively evaluate the public's feelings of well-being in the urban environment and reflect the quality of the spatial environment to a certain extent. Previous studies on the relationship between public sentiment and urban built environmental factors have yielded meaningful results. However, few studies have focused on the effect of micro-built environment on public sentiment at the street level, which directly shapes people's perceptions. In addition, the nonlinear relationship and synergistic effect among urban built environmental factors have been commonly disregarded in previous studies, resulting in an incomplete understanding of the impact of urban built environment on public emotions. Therefore, this paper takes San Francisco as a study case to explore the complex relationship between urban built environmental factors and public emotions. Specifically, this paper measures the polarity of public emotions through sentiment analysis on Twitter data, establishes a comprehensive built environment index system from both macro- and micro- perspectives, and subsequently explores the complex relationship between the urban built environment and public sentiment through the OLS model and Shapley Additive Explanation algorithm. Results show that: (1) micro-built environmental factors have a significant influence on public emotion, although they have been frequently ignored. (2) Public sentiment tends to be more positive in areas with recreation facilities, mixed land use, rich street view visual environment, suitable thermal and acoustic environment, balanced income, and a suitable degree of high population density. (3) A nonlinear relationship and threshold effect exist between the built environmental variables and the semantic orientations of public emotion. Environment improvement strategies based on the synergic effect between variables can effectively promote the generation of positive emotions. Our empirical findings can offer valuable insights to promote feelings of well-being and foster an urban development approach through strategic interventions within the urban built environment.

Memristive PAD three-dimensional emotion generation system based on D–S evidence theory

Memristive PAD three-dimensional emotion generation system based on D–S evidence theory

Emotion generation method in online physical education teaching based on data mining of teacher-student interactions.

Different from conventional educational paradigms, online education lacks the direct interplay between instructors and learners, particularly in the sphere of virtual physical education. Regrettably, extant research seldom directs its focus toward the intricacies of emotional arousal within the teacher-student course dynamic. The formulation of an emotion generation model exhibits constraints necessitating refinement tailored to distinct educational cohorts, disciplines, and instructional contexts. This study proffers an emotion generation model rooted in data mining of teacher-student course interactions to refine emotional discourse and enhance learning outcomes in the realm of online physical education. This model includes techniques for data preprocessing and augmentation, a multimodal dialogue text emotion recognition model, and a topic-expanding emotional dialogue generation model based on joint decoding. The encoder assimilates the input sentence into a fixed-length vector, culminating in the final state, wherein the vector produced by the context recurrent neural network is conjoined with the preceding word's vector and employed as the decoder's input. Leveraging the long-short-term memory neural network facilitates the modeling of emotional fluctuations across multiple rounds of dialogue, thus fulfilling the mandate of emotion prediction. The evaluation of the model against the DailyDialog dataset demonstrates its superiority over the conventional end-to-end model in terms of loss and confusion values. Achieving an accuracy rate of 84.4%, the model substantiates that embedding emotional cues within dialogues augments response generation. The proposed emotion generation model augments emotional discourse and learning efficacy within online physical education, offering fresh avenues for refining and advancing emotion generation models.

Do intangible factors enhance sociocultural productivity and economy in world heritage sites?

Measuring the sociocultural productivity of heritage sites remains an ongoing issue for international organizations concerned with the conservation and promotion of traditional sites. The productivity of these locations is not only affected by tangible elements but also by intangible factors, such as the emotions generated by the experiences. For this purpose, 597 employees of hotels in these historical locations who had visited one of the 14 heritage sites in Spain assessed what role emotions play in this contribution. The methodology used was the application of structural equations. Several conclusions have been drawn utilizing the SmartPLS 4 software. The first is that the generation of positive emotions comes exclusively from cultural and historical dynamization and not from technological advances or an eagerness to learn. The second is that both the application of technological advances and cultural dynamization have a direct impact on productivity.

Research on the effect and guarantee of college sports on college students’ health under the background of ecological pollution

Abstract The rapid development of the social economy has led to the aggravation of ecological pollution and endangered human health, so the idea of promoting health through sports has been put forward. Based on physical health problems such as physical weakness, obesity, insomnia, and eye diseases, as well as psychological problems such as low self-esteem, easy generation of negative emotions, and unstable willpower among contemporary college students, according to college sports can activate brain chemicals and improve basic body functions. It studies the promotion effect of college sports on college students’ health. It puts forward the use of the C4.2 decision tree classification algorithm to ensure that college students play sports to promote health and the information gain rate in the C4.2 decision tree classification algorithm. Improve and optimize its original defects to better use it in the college sports system. Finally, questionnaires and volunteer stratified sampling experiments prove that college sports can better promote the physical and mental health development of college students, which has certain practical significance.

Factors influencing the generation of teachers' emotions.

The teaching profession plays an important role in shaping individuals' lives, with teachers performing complex emotional labour. The management of emotions is an integral part of teachers' professional work, and it is essential to clarify their emotional experiences and the generating of their emotions within a specific cultural context. Based on a phenomenological approach and the use of anecdotal texts, this study examined six common emotional states among teachers, including happiness, guilt, worry, fear, annoyance, and anger, along with the emotional experiences of two specialised categories of teachers, class supervisors, and pre-service teachers. The factors influencing teachers' emotions and their generative mechanisms were analysed. This investigation found that key influences on teachers' emotions stem from factors within the teachers' themselves, the contextual nature of their work, and sociocultural dynamics. Drawing on the analytical frameworks of emotional geography theory, ecological theory of human development, and the ecosystem model of teachers' emotional interactions, the study constructs a model highlighting the generative mechanisms of teachers' emotions, and in which three systems are reflected. Teachers' personal attributes are in the direct area of the model and directly govern the formation of their emotions, while their work context consists of a transitional area in emotion formation and the sociocultural system acts as the latent band influencing emotion development. The mechanism model helps us to understand and recognise teachers' emotions and to explore their pedagogical implications.

Memristor-Based Neural Network Circuit of Associative Memory With Overshadowing and Emotion Congruent Effect.

Most memristor-based neural network circuits consider only a single pattern of overshadowing or emotion, but the relationship between overshadowing and emotion is ignored. In this article, a memristor-based neural network circuit of associative memory with overshadowing and emotion congruent effect is designed, and overshadowing under multiple emotions is taken into account. The designed circuit mainly consists of an emotion module, a memory module, an inhibition module, and a feedback module. The generation and recovery of different emotions are realized by the emotion module. The functions of overshadowing under different emotions and recovery from overshadowing are achieved by the inhibition module and the memory module. Finally, the blocking caused by long-term overshadowing is implemented by the feedback module. The proposed circuit can be applied to bionic emotional robots and offers some references for brain-like systems.

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

Research status and development direction of Child-Friendly Cities: a bibliometric analysis based on VOSviewer

ABSTRACT Since the introduction of The Child-Friendly Cities Initiative in 1996, there has been a growing body of research in this period. Many countries have made some attempts to create Child-Friendly Cities (CFC). However, there is yet to be a clear answer to how to create more child-friendly environments. Therefore, this study aims to analyze the current state of research on CFC through bibliometric analysis and to find future research directions by understanding the inadequacy of the current research. The study searched the database for all literature related to “CFC” from 1996 to 2023. After 166 documents were retrieved, they were analyzed for the overall trends in the number of articles published, main collaborating countries, main publishing journals, and keyword co-occurrence. The findings suggest that existing research has shifted from the broader concept of children’s environments to gradually focusing on the individual child. However, the underlying preferences of “children” as the primary users of space have yet to be explored. Also, the neurological mechanisms in the brain that influence the generation of positive emotions and brain development in children have yet to be mentioned. Accordingly, future research directions are proposed, providing a reference for research on creating CFC.

Real-time emotion generation in human-robot dialogue using large language models.

Affective behaviors enable social robots to not only establish better connections with humans but also serve as a tool for the robots to express their internal states. It has been well established that emotions are important to signal understanding in Human-Robot Interaction (HRI). This work aims to harness the power of Large Language Models (LLM) and proposes an approach to control the affective behavior of robots. By interpreting emotion appraisal as an Emotion Recognition in Conversation (ERC) tasks, we used GPT-3.5 to predict the emotion of a robot's turn in real-time, using the dialogue history of the ongoing conversation. The robot signaled the predicted emotion using facial expressions. The model was evaluated in a within-subjects user study (N = 47) where the model-driven emotion generation was compared against conditions where the robot did not display any emotions and where it displayed incongruent emotions. The participants interacted with the robot by playing a card sorting game that was specifically designed to evoke emotions. The results indicated that the emotions were reliably generated by the LLM and the participants were able to perceive the robot's emotions. It was found that the robot expressing congruent model-driven facial emotion expressions were perceived to be significantly more human-like, emotionally appropriate, and elicit a more positive impression. Participants also scored significantly better in the card sorting game when the robot displayed congruent facial expressions. From a technical perspective, the study shows that LLMs can be used to control the affective behavior of robots reliably in real-time. Additionally, our results could be used in devising novel human-robot interactions, making robots more effective in roles where emotional interaction is important, such as therapy, companionship, or customer service.

Examining the Impact of Crowding Perception on the Generation of Negative Emotions among Users of Small Urban Micro Public Spaces

The improvement of urban congestion and the mood of the populace is vital for achieving the United Nations Sustainable Development Goals. This paper aims to test the hypothesis that crowding in urban micro public spaces (UMPSs) affects emotions, and two different methods were used: a psychological questionnaire to measure whether crowding in such spaces elicits negative emotional responses from users and a portable electroencephalography (EEG) device to explore emotional responses. This study was conducted in Shenyang, China, based on the city’s relevant policies and the type and number of micro spaces and micro public spaces, proving that Shenyang City has paid more attention to planning micro spaces and micro public spaces in its urban development. The results show that 1. UMPS crowding awakens negative emotions in users, which affects their intention to revisit the UMPS, and 2. If the UMPS is more attractive to users, it also improves the negative emotions due to crowding, which implies that the attractiveness of the environment does play an important moderating role. This study may inspire the construction concept of UMPSs in different countries and cities, complementing the planning of urban public spaces to provide more social equity. The creation of UMPS has a positive effect on neighborhood interactions, community network construction, and the reproduction of social capital, which can contribute to the sustainable development of cities.

The impact of emotional valence and stimulus habituation on fMRI signal reliability during emotion generation

BackgroundThe emotional domain is often impaired across many neurological diseases, for this reason it represents a relevant target of rehabilitation interventions. Functional changes in neural activity related to treatment can be assessed with functional MRI (fMRI) using emotion-generation tasks in longitudinal settings. Previous studies demonstrated that within-subject fMRI signal reliability can be affected by several factors such as repetition suppression, type of task and brain anatomy. However, the differential role of repetition suppression and emotional valence of the stimuli on the fMRI signal reliability and reproducibility during an emotion-generation task involving the vision of emotional pictures is yet to be determined. MethodsSixty-two healthy subjects were enrolled and split into two groups: group A (21 subjects, test-retest reliability on same-day and with same-task-form), group B (30 subjects, test-retest reproducibility with 4-month-interval using two equivalent-parallel forms of the task). Test-retest reliability and reproducibility of fMRI responses and patterns were evaluated separately for positive and negative emotional valence conditions in both groups. The analyses were performed voxel-wise, using the general linear model (GLM), and via a region-of-interest (ROI)-based approach, by computing the intra-class correlation coefficient (ICC) on the obtained contrasts. ResultsThe voxel-wise GLM test yielded no significant differences for both conditions in reliability and reproducibility analyses. As to the ROI-based approach, across all areas with significant main effects of the stimuli, the reliability, as measured with ICC, was poor (<0.4) for the positive condition and ranged from poor to excellent (0.4–0.75) for the negative condition. The ICC-based reproducibility analysis, related to the comparison of two different parallel forms, yielded similar results. DiscussionThe voxel-wise GLM analysis failed to capture the poor reliability of fMRI signal which was instead highlighted using the ROI-based ICC analysis. The latter showed higher signal reliability for negative valence stimuli with respect to positive ones. The implementation of two parallel forms allowed to exclude neural suppression as the predominant effect causing low signal reliability, which could be instead ascribed to the employment of different neural strategies to cope with emotional stimuli over time. This is an invaluable information for a better assessment of treatment and rehabilitation effects in longitudinal studies of emotional neural processing.

An explanation framework and method for AI-based text emotion analysis and visualisation

With the rapid development of artificial intelligence, there is an increasing number of industries relying on the accuracy and efficiency of deep learning algorithms. But due to the inexplicability and black box effect of deep neural networks, we can only obtain results without knowing the applied reasoning behind them. That engenders scepticism and resistance from some quarters of deep learning-based technologies. In the context of emotion analysis used in business and public opinion monitoring, it is sometimes difficult for decision-makers to trust the outcome without explanation from the supposedly emotionless machines. There are mathematical-based explanation methods, and they often generalise emotion analysis as a classification task. Still, emotion should be different from other task categories because the generation of emotion involves human-specific factors and logic. This paper proposes an emotion analysis explanation framework that is grounded in psychological theories focusing on the stimulus from classic emotion theories. This proposed framework emphasises considering the cause and trigger of emotions as the explanation for the deep learning-based emotion analysis, and it includes two main components: the extraction of the emotion cause and the visualisation of emotion-triggering words. Compared with the existing approaches, our proposed framework is based on the perspective of human psychology with higher credibility and significant theoretical support. In addition, we purposefully design and implement an intuitive visualisation for the framework, instead of complex numerical representations, to improve the explanation comprehensibility for a broader audience.

Emotion-driven reasoning model for agent-based human–computer negotiation

Human-computer negotiation plays an important role in today's highly developed online transactions. Agents need to have higher emotional intelligence to deal with the random actions of human adversaries. Most of the existing studies focus on deterministic modeling but neglect the role of fuzziness in human–computer negotiation and emotion effects on the interaction between persuasion strategy selection and issue updating. This paper uses emotional rendering to describe the emotional interaction between agents to supplement the emotion generation process in agent-based negotiation. Moreover, this paper constructs emotion-driven reasoning model which integrates an agent’s assessment of emotion and actual utility, and proposes a mechanism for the selection of persuasion strategies and negotiating issue updating driven by the agent’s reasoning ability. A series of agent-agent experiments are conducted, and a human–computer negotiation system is developed for the real human-agent experiment. The agent-agent experimental results show that, compared with the non-emotion model, the proposed model increases the joint utility by 55.1% and compared with the non-reasoning model, the proposed model improves the success rate by 49.2%. Meanwhile, the proposed model is superior to several existing competing models in terms of the success rate, the negotiating rounds, and the success utility. The human-agent experimental results show that the developed negotiation system can achieve satisfactory results with human opponents.

The neutral past: emotional (dys)regulation of autobiographical memory in behavioural variant frontotemporal dementia

ABSTRACT Background: While affective disturbances are a key symptomatic indicator of behavioural variant frontotemporal dementia (bvFTD), little is known about how patients process the emotional load of their autobiographical (i.e. personal) memories. Methods: We assessed the interplay of emotional regulation and autobiographical memory by inviting 18 bvFTD and 20 control participants to remember past personal events. For each memory, participants rated its emotional valence “then” (i.e. when the event has occurred) vs “now” (i.e. when retrieving the event). Results: Patients with bvFTD described their memories as neutral at both times (p = .85), while control participants rated their memories as more positive during “then” than during “now” (p = .013). Autobiographical retrieval triggered fewer emotional words (p < .001) and less specificity (p < .001) in bvFTD patients compared to control participants. Conclusions: The lack of significant differences between the emotional characteristics during “then” than “now” in patients with bvFTD (and the flattening of both) may mirror their hampered ability for emotional generation, which may be associated with difficulties in reframing their past experiences to modify and adapt their meaning. The hampered emotional regulation in bvFTD may also be associated with an avoidance strategy and a passive attitude toward the past.

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.