Emotional Context Research Articles

Swearing and gender dynamics in urban Surabaya: Understanding Gen Z’s swearing in Surabaya’s arek culture

Swearing in society is a multifaceted phenomenon influenced. The purpose of this research is to understand the context of swearing among Generation Z in Surabaya and the differences in swearing between male and female Gen Z individuals in Surabaya. This research uses qualitative research methods involving 45 Generation Z informants from Surabaya. The results of the research show that the decision by Gen Z Surabaya is influenced by emotional context, social norms, culture, and group bonding to strengthen social ties, express emotions, and maintain their unique identity. Male Gen Z in Surabaya use the swear word “jancuk” with a higher pitch and rarely modify it, while female Gen Z use it with a lower pitch and frequently modify it. The conclusions of this research are first, the context of swearing among Generation Z in Surabaya occurs in informal situations among close friends for joking, easing the atmosphere, and insulting to demean the conversation partner, all influenced by Arek culture. Secondly, the difference in swearing between male and female Gen Z in Surabaya is that male Gen Z often use a higher pitch and rarely use modified swear words, while female Gen Z do not always use a high pitch and often use modified swear words to maintain their image. The use of swearing by both male and female Gen Z within the Arek Surabaya culture demonstrates intimacy, openness, egalitarianism, and the absence of social distance.

The impact of the indoor home environment on children's prosocial behaviour

Although the role of the home environment in the development of prosocial behaviour in childhood is well-established, the existing research has focused on the emotional microclimate of the home. We do not know whether the physical home environment has a role to play too. The present study aims to fill this gap. It uses data from a large, nationally representative birth cohort survey in the UK, tracking the trajectories of prosocial behaviour in children across ages 3, 5, 7 and 11 years as a function of three constructs derived from the Home Observation Measurement of the Environment Short Form (HOME-SF), assessed at age 3 years: physical organisation of the home, mother's emotional and verbal responsivity, and mother's use of harsh discipline. Prosocial behaviour was assessed via a parent-reported subscale of the Strengths and Difficulties Questionnaire. The findings, which were robust to adjustment for sex, ethnicity, cognitive ability, special educational needs, neighbourhood environment, family income, family structure, maternal mental health, maternal education and religiosity, showed that all three aspects of the home environment were associated with prosocial behaviour. However, when controlling for all three simultaneously, physical organisation ceased to be a significant predictor of prosocial behaviour although the two measures of the home's emotional environment retained their significance. This indicates that, compared to the physical, the emotional climate of the home has a more substantial impact on children's prosocial behaviour. It also suggests that the physical and the emotional context of the home are strongly interrelated, with aspects of emotional harshness possibly mediating the effects of the home's physical disorganisation.

Finding love in algorithms: deciphering the emotional contexts of close encounters with AI chatbots

Abstract AI chatbots are permeating the socio-emotional realms of human life, presenting both benefits and challenges to interpersonal dynamics and well-being. Despite burgeoning interest in human–AI relationships, the conversational and emotional nuances of real-world, in situ human–AI social interactions remain underexplored. Through computational analysis of a multimodal dataset with over 35,000 screenshots and posts from r/replika, we identified seven prevalent types of human–AI social interactions: intimate behavior, mundane interaction, self-disclosure, play and fantasy, customization, transgression, and communication breakdown, and examined their associations with six basic human emotions. Our findings suggest the paradox of emotional connection with AI, indicated by the bittersweet emotion in intimate encounters with AI chatbots, and the elevated fear in uncanny valley moments when AI exhibits semblances of mind in deep self-disclosure. Customization characterizes the distinctiveness of AI companionship, positively elevating user experiences, whereas transgression and communication breakdown elicit fear or sadness.

Creating Emordle: Animating Word Cloud for Emotion Expression.

We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the extent of the emotion delivered. We also invited general users to create their own emordles based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

Speech emotion recognition method based on time-aware bidirectional multi-scale network

Abstract In response to the difficulty of traditional speech emotion recognition models in capturing long-distance dependencies in speech signals and the impact of changes in speaker pronunciation speed and pause time, this paper proposes a new time emotion modeling method called Time Perceived Bidirectional Multi-scale Network (TIM-Net), which is used to learn Multi-scale contextual emotion expression in different time scales. TIM-Net starts by acquiring temporal emotional representations using time-aware blocks. Subsequently, information from different time points is combined to enhance contextual understanding of emotional expression. Finally, it consolidates various Timescale features to better accommodate emotional fluctuations. The experiment shows that the network can focus useful information on features, and the WAR and UAR of TIM-Net are significantly better than other models on RAVDESS, EMO-DB, and EMOVO datasets.

Atypical attention and saccade vigor in post-traumatic stress disorder

Effective attention control is essential for behavioral adaptation to different environmental contexts. In Post-traumatic Stress Disorder (PTSD) altered attention has been described in trauma-related and other emotional contexts. Nevertheless, atypical attention is also seen with neutral stimuli. The mechanisms of attention alterations in PTSD associated with neutral stimuli are poorly understood.The present study investigates alerting and orienting responses in PTSD participants using emotionally neutral stimuli in a saccade eye movement task incorporating both spatially predictable and temporally unpredictable conditions. We studied 23 PTSD patients and 27 Non-PTSD controls, using repeated-measures mixed modeling to estimate group and task condition differences in behavioral and psychophysiological measures. We explored the relationships among saccade characteristics, pupil size, and PTSD symptoms, including CAPS hypervigilance scores. PTSD, compared to Non-PTSD, participants showed differences in their saccade ‘main sequence’, reflected by higher peak velocities adjusted for amplitude. PTSD participants had smaller primary position errors in the unpredictable saccade condition. They also exhibited greater hyperarousal, reflected by larger pupil size during fixation that was greater in the unpredictable condition.Our results suggest that a heightened state of arousal and hypervigilance in PTSD leads to a state of atypical attention bias, even in emotionally neutral contexts. These differences may reflect higher saccade vigor. The observed differences suggest atypical attention in PTSD, which goes beyond possible distraction associated with emotional or threat-related stimuli.

Emotional contextual effects of face perception: a test of the affective realism hypothesis

Affective feelings naturally infuse individuals’ perceptions, serving as valid windows onto the real world. The affective realism hypothesis further explains how these feelings work: as properties of individuals’ perceptual experiences, these feelings influence perception. Notably, this hypothesis based on affective feelings with different valences has been substantiated, whereas the existing evidence is not compelling enough. Moreover, whether specific affective feelings can be experienced as properties of target perception remains unclear. Addressing these two issues deepens our understanding of the nature of emotional representation. Hence, we investigated the affective realism hypothesis based on affective feelings with different valences and specific emotions, comparing it with the affective misattribution hypothesis. In Experiment 1, we examined the effects of affective feelings with various valences on targets’ perception through the AM (1a) and CFS paradigms (1b). In Experiment 2, we investigated the effects of affective feelings with anger, sadness, and disgust using similar methods. Results from Experiments 1a and 1b consistently indicated significant differences in valence ratings of neutral faces under emotional contexts with varying valences. Experiment 2a revealed significant differences in specific emotion ratings of neutral faces under different specific emotional contexts in the AM paradigm, whereas such differences were not observed in the CFS paradigm in Experiment 2b. We concluded that affective feelings with different valences, rather than specific emotions, can be experienced as inherent properties of target perception, validating the affective realism hypothesis. These findings supported the view that the nature of emotional representation should be described as affective dimensions.

Borderline personality disorder and learning: The influences of emotional state and social versus nonsocial feedback

BackgroundBorderline personality disorder (BPD) has been associated with decision-making deficits, yet such deficits may be context dependent, particularly emotional state and social context. Reinforcement learning models offer an avenue to pinpoint decision-making impairments. The current study used reinforcement learning models to examine whether feedback type (social vs. nonsocial) or emotional state (neutral vs. negative) influence the association between BPD and decision making. MethodAdults (N = 131) with a range of BPD symptoms completed a diagnostic interview and a computerized learning task after neutral and negative emotion inductions. We examined accuracy, learning rate, and exploration. ResultsWe conducted linear models to examine the association between BPD criteria, feedback type, and emotional state on learning parameters and learning accuracy. We found that the negative emotion condition was associated with greater exploration, particularly for those with elevated BPD features. Furthermore, elevated BPD features were associated with impaired accuracy when aiming to avoid loss. A 3-way interaction between BPD, emotion, and feedback indicated that, for people with higher BPD features, learning performance was further impaired when receiving social feedback in the negative emotion condition. LimitationsSeveral limitations warrant mention, including a relatively homogenous sample, possible co-occurring diagnoses, and methodological consideration with the learning task. ConclusionsThe present study underscored the link between BPD and learning impairments. Amplified learning alterations under negative social contexts have important implications for identifying optimal venues to teach new skills (of relevance to treatment) for those with BPD.

Misophonia reactions in the general population are correlated with strong emotional reactions to other everyday sensory-emotional experiences.

Misophonic experiences are common in the general population, and they may shed light on everyday emotional reactions to multi-modal stimuli. We performed an online study of a non-clinical sample to understand the extent to which adults who have misophonic reactions are generally reactive to a range of audio-visual emotion-inducing stimuli. We also hypothesized that musicality might be predictive of one's emotional reactions to these stimuli because music is an activity that involves strong connections between sensory processing and meaningful emotional experiences. Participants completed self-report scales of misophonia and musicality. They also watched videos meant to induce misophonia, autonomous sensory meridian response (ASMR) and musical chills, and were asked to click a button whenever they had any emotional reaction to the video. They also rated the emotional valence and arousal of each video. Reactions to misophonia videos were predicted by reactions to ASMR and chills videos, which could indicate that the frequency with which individuals experience emotional responses varies similarly across both negative and positive emotional contexts. Musicality scores were not correlated with measures of misophonia. These findings could reflect a general phenotype of stronger emotional reactivity to meaningful sensory inputs. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Diverse stimuli induce piloerection and yield varied autonomic responses in humans.

This research provides an in-depth exploration into the triggers and corresponding autonomic responses of piloerection, a phenomenon prevalent across various species. In non-human species, piloerection occurs in reaction to a variety of environmental changes, including social interactions and temperature shifts. However, its understanding in humans has been confined to emotional contexts. This is problematic because it reflects solely upon subjective experience rather than an objective response to the environment. Further, given our shared evolutionary paths, piloerection should function similarly in humans and other animals. I observed 1198 piloerection episodes from eight participants while simultaneously recording multiple autonomic and body temperature indices, finding that piloerection in humans can be elicited by thermal, tactile, and audio-visual stimuli with equal effectiveness. The data also revealed variations in cardiac reactivity measures: audio-visual piloerection was associated with greater sympathetic arousal, while tactile piloerection was linked to greater parasympathetic arousal. Despite prevailing notions of piloerection as a vestigial response in humans, it does respond to decreases in skin temperature and is associated with a rise in skin temperature during episodes. This research underscores that piloerection in humans is not purely vestigial, nor is it solely an affective response to emotional stimuli. Rather, it is best understood as a reflexive response to environmental changes, suggesting a shared functional similarity with other species.

Adversity and error-monitoring: Effects of emotional context.

This study tested whether self-reports of childhood adversity would predict altered error processing under emotional versus non-emotional task conditions. N = 99 undergraduates completed two selective attention tasks, a traditional color-word Stroop task and a modified task using emotional words, while EEG was recorded. Participants also completed self-report measures of adverse and positive childhood experiences, executive functioning, depression, current stress, and emotion regulation. Reports of adversity were robustly correlated with self-reported challenges in executive functioning, even when controlling for self-reported depression and stress, but adversity was not correlated with task performance. With regard to neural markers of error processing, adversity predicted an enhanced error-related negativity and blunted error-positivity, but only during the emotion-word blocks of the task. Moreover, error-related changes in alpha oscillations were predicted by adversity, in a pattern that suggested less error responsiveness in alpha patterns during the emotion block, compared to the color block, among participants with higher adversity. Overall, results indicate alterations in error monitoring associated with adversity, such that in an emotional context, initial error detection is enhanced and sustained error processing is blunted, even in the absence of overt performance changes.

Phase-Transitions in Disgust During Self-Initiated Eating Among Adolescents With Anorexia Nervosa and Related Low-Weight Eating Disorders and Matched Controls.

Understanding the emotional context of feeding behavior may help identify causal mechanisms of food avoidance among individuals with anorexia nervosa. Although predominant food avoidance models assume fear of fat drives feeding behavior, disgust may be more theoretically and proximally relevant to moment-to-moment experiences of feeding. This study, therefore, aimed to examine affect and food avoidance using automated affect analysis from facial response by measuring time-specific transitions in disgust during a laboratory eating paradigm. We hypothesized that phase transitions in disgust would distinguish temporally self-initiated eating from food avoidance. Sixty-three adolescents with anorexia nervosa or another low-weight eating disorder (LWED) and 27 age- and sex-matched controls were recruited as part of a larger study; 45 patients and 22 controls provided data on autonomous eating and facial affect during a laboratory meal. Dynamic structural equation models quantified moment-to-moment relationships between disgust and feeding behavior. Self-initiated eating was associated with greater increases in disgust, but not fear, intensity among those with LWED relative to control participants and greater disgust intensity predicted lower likelihood of self-initiated eating. Phasic transitions in disgust provide moment-to-moment evidence of affective influence on self-initiated eating and lend credibility to the hypothesis that disgust contributes to food avoidance and initiation in individuals with LWED.

Social sharing and expressive suppression in major depressive disorder and borderline personality disorder: An experience sampling study

AbstractMajor depressive disorder (MDD) and borderline personality disorder (BPD) are characterized by disturbed patterns of emotional and interpersonal functioning, which might imply altered use of emotion regulation in interpersonal contexts. In the current study, we examined how individuals with MDD and/or BPD differ from healthy controls in (1) their overall daily life use of expressive suppression and social sharing and (2) their tendency to adjust the use of these strategies to the emotional context (i.e., preceding negative and positive affect). Thirty‐four individuals with MDD, 20 individuals with BPD, 19 individuals with comorbid MDD and BPD, and 40 healthy controls participated in a week of experience sampling during which they reported their use of expressive suppression, social sharing, and experienced negative and positive affect. The results indicated that all clinical groups reported more expressive suppression and social sharing in their daily lives than healthy controls. Group differences remained when controlling for differences in mean experienced affect, except for increased suppression for MDD and increased sharing for BPD and comorbid MDD and BPD, which seemed related to these participants' overall higher levels of negative affect. Additionally, associations between within‐person fluctuations in negative or positive affect and subsequent strategy use were equally strong for clinical and control participants, indicating that clinical groups did not differentially adjust the use of suppression and sharing to the emotional context. In conclusion, individuals with MDD and/or BPD showed increased use of suppression and sharing in daily life, which might contribute to, or follow from their emotional and interpersonal difficulties.

SCIENTIFIC ASPECTS OF MODERN APPROACHES TO MACHINE TRANSLATION FOR SIGN LANGUAGE

Scientific research in the field of automated sign language translation represents a crucial stage in the development of technologies supporting the hearing-impaired and deaf communities. This article presents a comprehensive approach to addressing semantic and technical challenges associated with the uniqueness of sign language. The research goal is to create an innovative system that combines semantic analysis, sign synthesis, and facial mimicry for the most accurate conveyance of emotional context. The study focuses on the unique features of the Kazakh language and cultural contexts that influence sign communication. The research centers on the development of a semantic system capable of adequately interpreting metaphors, idioms, and classifier predicates of sign language. The three-dimensional nature of signs is analyzed, and a solution to the formal description problem is proposed. The article introduces a database, analysis algorithm, and a prototype 3D avatar capable of translating textual data into sign language. Special attention is given to the processing of idioms and variability in expressing emotions in sign language. Utilizing machine learning principles and computational linguistics algorithms, the authors present an integrated approach to sign language translation, considering linguistic, cultural, and emotional aspects. The proposed algorithms and formulas facilitate effective interaction between textual information and sign expression. The research results not only provide an overview of current challenges in automated sign language translation but also offer practical approaches to addressing them. The developed approach could be a key step towards creating more efficient communication systems for the hearing-impaired and deaf. Which in the future may solve numerous issues with Kazakh sign language.

The Role of Social Support in Improving the Well-Being of the Elderly: A Qualitative Review

This research aims to explore the role of social support in improving the well-being of the elderly through a qualitative review. Social support is identified as a crucial factor influencing the quality of life and happiness of older people in social, emotional and instrumental contexts. Qualitative research methods were used to gain in-depth understanding through in-depth interviews with elderly people and content analysis of related literature. The research results show that family, peers, and community play an important role in providing social support to the elderly. This support not only improves their emotional well-being but also provides practical help in living their daily lives. Seniors who feel strong social support tend to have lower levels of depression, better physical health, and a higher quality of life. Policy suggestions include increasing public awareness about the importance of social support for the elderly, developing community programs that support their social interactions, as well as integrating the concept of social support in public policies related to the welfare of the elderly. It is hoped that this research can make a significant contribution in increasing understanding and approaches to the important role of social support in advancing the welfare of the elderly in society.

EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness

The EmoWear dataset provides a bridge to explore Emotion Recognition (ER) via Seismocardiography (SCG), the measurement of small cardio-respiratory induced vibrations on the chest wall through Inertial Measurement Units (IMUs). We recorded Accelerometer (ACC), Gyroscope (GYRO), Electrocardiography (ECG), Blood Volume Pulse (BVP), Respiration (RSP), Electrodermal Activity (EDA), and Skin Temperature (SKT) data from 49 participants who watched validated emotionally stimulating video clips. They self-assessed their emotional valence, arousal, and dominance, as well as extra questions about the video clips. Also, we asked the participants to walk, talk, and drink, so that researchers can detect gait, voice, and swallowing using the same IMU. We demonstrate the effectiveness of emotion stimulation with statistical methods and verify the quality of the collected signals through signal-to-noise ratio and correlation analysis. EmoWear can be used for ER via SCG, ER during gait, multi-modal ER, and the study of IMUs for context-awareness. Targeted contextual information include emotions, gait, voice activity, and drinking, all having the potential to be sensed via a single IMU.

Contextual emotion detection in images using deep learning.

Computerized sentiment detection, based on artificial intelligence and computer vision, has become essential in recent years. Thanks to developments in deep neural networks, this technology can now account for environmental, social, and cultural factors, as well as facial expressions. We aim to create more empathetic systems for various purposes, from medicine to interpreting emotional interactions on social media. To develop this technology, we combined authentic images from various databases, including EMOTIC (ADE20K, MSCOCO), EMODB_SMALL, and FRAMESDB, to train our models. We developed two sophisticated algorithms based on deep learning techniques, DCNN and VGG19. By optimizing the hyperparameters of our models, we analyze context and body language to improve our understanding of human emotions in images. We merge the 26 discrete emotional categories with the three continuous emotional dimensions to identify emotions in context. The proposed pipeline is completed by fusing our models. We adjusted the parameters to outperform previous methods in capturing various emotions in different contexts. Our study showed that the Sentiment_recognition_model and VGG19_contexte increased mAP by 42.81% and 44.12%, respectively, surpassing the results of previous studies. This groundbreaking research could significantly improve contextual emotion recognition in images. The implications of these promising results are far-reaching, extending to diverse fields such as social robotics, affective computing, human-machine interaction, and human-robot communication.

Emotions Effects and Interpretation of Language: Semantic Observation

This paper examines the influence of emotions on language comprehension from a semantic standpoint. It proposes that language has an impact on the regulation and expression of emotions, while emotions also affect language. Specifically, it suggests that emotions primarily have negative effects on the interpretation and importance of language in various contexts among diverse individuals. The paper adopts Ko¨vecses' (2004) approach for linguistic lexical analysis of emotional words and categories in language. It applies this methodology to analyze two songs by male and female singers in the modern era. According to the affect-as-information theory and appraisal theory, it is determined that emotions can change the meaning of any language produced by human cognition. Language can express emotions through a range of words, symbols, and representations that relate to emotional context, gender distinctions, and the natural environment.

Needs assessment and patient-guided development of a video-based diabetic retinopathy patient education tool

Objective: To gain retina physicians’ and diabetic retinopathy (DR) patients’ perspectives on needs and opportunities in DR education, and then develop and pilot test an educational video. Design: This study utilised qualitative interview data for video creation, and interview and survey data for assessment. Setting: This study was conducted in a single large academic medical centre. Method: We conducted semi-structured interviews with attending retina physicians and DR patients (Cohort A) which were coded for themes about needs in DR patient education. Using these interviews, we designed and piloted a 6-minute user-centred animated video among a second patient cohort (Cohort B), who completed post-intervention interviews. Results: Four physicians and 14 DR patients participated in the study. Themes from Cohort A included accessible information, early management, lifestyle factors and emotional context. Physician themes included effective communication, visual information delivery and individual-level diabetes management. Cohort B commented on the subsequently created video’s improved accessibility, engagement and supplementation of their existing DR knowledge. Conclusion: Physicians and patients showed an interest in video education and identified unique educational needs. We used these insights to create a video that demonstrated positive patient uptake. Close attention to retina physicians’ and DR patients’ perspectives can offer a valuable approach in developing materials to increase patients’ health knowledge. Within the context studied, videos may be more accessible and engaging than the use of traditional print-based education materials.

Analyzing principles and applications of machine learning in music: Emotion music generation, and style modeling

The combination of machine learning with music composition and production is proving viable for innovative applications, enabling the creation of novel musical experiences that were once the exclusive domain of human composers. This paper explores the transformative role of machine learning in music, particularly focusing on emotion music generation and style modeling. Through the development and application of models including DNNs, GANs, and Autoencoders, this study delves into how machine learning is being harnessed to not only generate music that embodies specific emotional contexts but also to transfer distinct musical styles onto new compositions. This research discusses the principles of these models, their operational mechanisms, and evaluates their effectiveness through various metrics such as accuracy, precision, and creative authenticity. The outcomes illustrate that these technologies not only enhance the creative possibilities in music but also democratize music production, making it more accessible to non-experts. The implications of these advancements suggest a significant shift in the music industry, where machine learning could become a central component of creative processes. These results pave a path to the understanding of the potential and limitations of machine learning in music and forecasts future trends in this evolving landscape.