Emotional Dynamics Research Articles

To believe or not to believe: leader’s belief about emotion usefulness and its impact on team dynamics

BackgroundThis study aims to investigate the impact of a leader’s beliefs in the usefulness of emotions on the emotional competence and relationship conflicts of their teams.MethodsThe data were collected through an online survey targeting leaders with at least three years of experience in managerial positions in South Korean workplaces, with a total of 326 participants. Latent Profile Analysis (LPA) was conducted to classify leaders into latent groups based on their beliefs regarding the usefulness of positive and negative emotions.ResultsThe latent profile analysis of the leader’s beliefs in the usefulness of positive and negative emotions identified four groups: High Usefulness (HU) Group, High Positive Usefulness (HPU) Group, Reference Group for comparison, and Low Usefulness (LU) Group. It was found that both the HU Group and the HPU Group, who perceive the usefulness of both types of emotions and positive emotions, respectively, showed higher levels of emotional repair and lower levels of relationship conflict within the team compared to the Reference Group and the LU Group.ConclusionsThis study not only fills a gap in organizational research by establishing a clear link between leaders’ emotional beliefs and team dynamics but also emphasizes the social influence of leaders’ emotions within the team setting. The findings of this research advocate for a strategic update to leadership development programs, suggesting the integration of elements specifically designed to augment leaders’ understanding and management of emotional dynamics within their teams.

The Role of Interpersonal Emotion Regulation in Couples' Relationships: Grounded Theory

Background: Interpersonal emotion regulation is a crucial skill that underpins the emotional dynamics in couples' relationships. In regulating interpersonal emotions, couples not only focus on their own emotions but also consider the emotions of their partner. Objectives: The study aimed to explore the role of interpersonal emotion regulation in couples' relationships. Methods: This qualitative study utilized a grounded theory strategy. Married women and men from Yazd (2021) who met the inclusion criteria participated in semi-structured interviews conducted through purposeful sampling until theoretical saturation was achieved. A total of 20 individuals (13 women and 7 men) were included in the study. Data were analyzed using MaxQDA 2020 software. Results: Collecting and coding the data with the Strauss and Corbin method, as open, axial, and selective coding, gave us 229 open codings, 32 axial codings, 7 selective codings, and one nuclear or core category under the title of safe attachment. The major categories included: Personal development, emotional safety, emotional self-awareness, empathic attunement, comprehensive support, comprehensive improvement of quality of life, and promotion of the spousal-safe base. A paradigm model was drawn from the components of the central phenomenon, causal conditions, contextual conditions, intervening conditions, strategies, and consequences. Conclusions: The specification of effective factors in the formation of interpersonal emotion regulation and the research paradigm model enables the accurate identification of strategies for interpersonal emotion regulation and their consequences in couple relationships, facilitating the progression of couples towards safe attachment. It is suggested that family counselors and therapists use these results to help couples.

Community case study: Our Wave, an online platform to facilitate survivor disclosure of sexual assault and trauma recovery

This community case study introduces Our Wave, an online platform that provides a safe, anonymous space for survivors of sexual harm to share their stories, reflect on their healing journeys, and connect with others. Designed to empower survivors, the platform allows users to post anonymous stories or visual media, ask questions, and send messages of hope, all while prioritizing privacy and security. It also aims to create a broader impact by analyzing shared narratives to detect patterns, identify best practices for healing, and inform global approaches to SV recovery. With over 100,000 members and over 1,200 stories and forms of media shared, the platform has become a valuable resource for survivors. Using mixed-methods analysis of platform usage data and anonymous user feedback surveys (n = 288), we examined patterns of engagement and impact. Analysis revealed that 57% of story submissions included “Messages of Healing” and 52% included “Messages of Hope,” with survey responses indicating that anonymity and peer support were crucial factors in facilitating disclosure. Research shows that the social and emotional dynamics of SV disclosure, as well as the responses from others, play a crucial role in a survivor’s healing. By focusing on confidentiality, trauma-informed support, and community-building, Our Wave offers a safe space for survivors to heal, connect, and access resources to support their journey toward recovery.

Measuring dynamic emotional experiences in response to media stimuli

Communication research has long recognized the dynamic nature of most media stimuli and the corresponding dynamic emotional processing implied on the side of the audience. Capturing and analyzing these emotional dynamics during media reception is imperative for advancing our understanding of media processing and effects, but is not common practice in most communication research. This article highlights several methodological approaches to measuring the physiological, behavioral, and experiential components of emotions during media exposure: Electrodermal activity, automated facial expression analysis, continuous response measurement, and self-probed emotional retrospections. We discuss these methods in terms of what they measure, their practical application to different research contexts, and aspects of data-analysis. We further highlight ways to adapt and apply these methods to advance research on hot topics in communication science, psychology, and related fields and provide recommendations for scholars who wish to integrate continuous measures of emotional responses into their research.

The Impact of Emotion Network Density on Psychological Distress in Chinese Parents of Children with Autism: A Daily Diary Study.

Parents of children with autism often experience a wide range of emotions in their daily lives. However, previous research has primarily focused on average levels of emotional challenges, neglecting the significance of daily emotion dynamics that may underlie parental psychological functioning. This study adopted a dynamic network approach to examine the strength of temporal connections within and between various emotions-referred to as emotion network density-and further explore its impacts on parental psychological distress. Participants included 76 Chinese parents (M = 36.36 years, SD = 3.95 years; 58 mothers) of children with autism. Parents reported their psychological distress at baseline and then completed measures of daily emotions over a 14-day period. The densities of overall, positive, and negative emotion networks were estimated using the Multilevel Vector Auto-Regression model. The results indicated that higher densities of the overall and negative emotion networks were associated with increased stress, anxiety, and depressive symptoms in parents. Further analysis of network components showed that the in-strength of fear and guilt (i.e., their likelihood of being affected by other emotions) and the out-strength of anger and guilt (i.e., their capacity to influence other emotions) were positively related to parental psychological distress. However, neither the overall density of the positive emotion network nor its specific components showed a significant relationship with parental psychological distress. These findings highlight the importance of considering the daily dynamics of emotions, particularly negative emotions, from a network perspective to better understand the development of psychological distress in parents of children with autism.

Adverse childhood experiences and emotion dynamics in daily life: a two sample study*

ABSTRACT Research suggests that adverse childhood experiences (ACEs) can have life-long consequences on emotional functioning. However, it is unclear how ACEs shape the dynamic features of everyday emotions. In the current preregistered study with two adult ecological momentary assessment samples (Ns = 122 and 121), we examined the linear and curvilinear associations of ACEs with daily emotion dynamic features. We expected ACEs to show linear associations with a higher baseline level, variability, and inertia of negative emotions, as well as a lower baseline level of positive emotions. Moreover, we expected ACEs to show U-shaped curvilinear associations with the variability of negative and positive emotions. The results did not support our hypotheses. Instead, ACEs showed an inverted U-shaped association with the baseline level and variability of negative emotions. Furthermore, ACEs also showed a U-shaped association with the baseline level of positive emotions and a linear association with higher variability of positive emotions. However, all associations were present in only one of the two samples. Our study underscores the critical need to incorporate a broad spectrum of ACEs in research samples to adequately capture their developmental consequences and the role of ACEs in contributing to the baseline level and variability of daily emotions.

Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise

Nowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting data are often analyzed with moderated autoregressive models to capture the influences of contextual events on the emotion dynamics. The presence of noise (e.g., measurement error) in the measures of the contextual events, however, is commonly ignored in these models. Disregarding noise in these covariates when it is present may result in biased parameter estimates and wrong conclusions drawn about the underlying emotion dynamics. In a simulation study we evaluate the estimation accuracy, assessed in terms of bias and variance, of different moderated autoregressive models in the presence of noise in the covariate. We show that estimation accuracy decreases when the amount of noise in the covariate increases. We also show that this bias is magnified by a larger effect of the covariate, a slower switching frequency of the covariate, a discrete rather than a continuous covariate, and constant rather than occasional noise in the covariate. We also show that the bias that results from a noisy covariate does not decrease when the number of observations increases. We end with a few recommendations for applying moderated autoregressive models based on our simulation.

Exploring Emotional Transitions Triggered by Color Changes: A Multidimensional Analysis

This study investigates the emotional responses triggered by color changes, utilizing both discrete and continuous video stimuli to explore the theory that color influences emotional states. Nineteen participants from the IHHSJF-30201 class were exposed to color pairs derived from the Berkeley Color Project 37 (BCP-37), specifically examining transitions between saturated and medium hues of red, blue, yellow, and purple. Through Spearmans Rho correlation analysis and differential emotion intensity measurements, the study evaluates how sudden versus gradual color changes affect emotional perception. Results demonstrate significant differences in emotional responses between the discrete and continuous color transition groups, highlighting the impact of transition mode on emotional dynamics. The findings support existing theories on the relationship between color and emotion and suggest the potential for personalized color applications to enhance user experience in digital interfaces. Future research directions include integrating machine learning techniques and expanding biometric data collection to further refine the understanding of color-emotion interactions and their practical applications.

Love as a Source of Resilience: A Close Reading of Amitav Ghosh's The Glass Palace

This paper examines the theme of love as a source of resilience in Amitav Ghosh’s The Glass Palace, a novel set against the complex historical backdrop of colonial Southeast Asia. By focusing on the emotional trajectories of key characters, this study explores how love—manifested in its romantic, familial, and platonic forms—becomes a vital force for enduring and transcending adversity. Rajkumar and Dolly’s relationship exemplifies how romantic love fuels ambition and perseverance, even as it is tested by displacement and loss. Familial bonds, such as Uma’s nurturing love and the intergenerational connections between Dinu and Neel, provide a stabilizing force amidst the disruptions of empire and war. Platonic love, as seen in the solidarity between women like Uma and Dolly, further highlights love’s capacity to forge resilience through mutual support. Through a close reading of these emotional dynamics, the paper argues that Ghosh portrays love not merely as a personal sentiment but as a transformative force that intersects with broader themes of historical trauma and identity. However, the fragility of love, especially under the strain of war and colonial upheaval, underscores its limits as well as its enduring significance. Ultimately, the novel affirms love’s role in preserving humanity amidst chaos.

The influence of emotion on memory process

Abstract. This paper shows how emotions, from an intuitive point of view, have an essential and profound impact on the cognitive process across a variety of different environments, such as natural, social, and academic settings. Our skills in learning and memorizing are significantly adjusted and influenced by our emotional reactions to challenges arising from education, including exams, homework assignments, and strict deadlines. The present research is deeply concerned with the specific roles that anxiety, happiness, and sadness play in modulating memory processes, particularly within the context of educational technologies like adaptive learning systems and Massive Open Online Courses. These innovative technologies are currently revolutionizing traditional educational methodologies by introducing new emotional dynamics in learners that affect cognitive engagement, motivation, and memory retention differently than conventional face-to-face learning environments. These findings are essential in optimizing the thoughtful design and effective implementation of educational technologies to better support students affective needs and cognitive development.

EmTract: Extracting emotions from social media

We developed EmTract, an open-source tool designed to extract investor emotions from financial social media data. We contribute a novel dataset of 10,000 financial social media messages annotated with emotion labels and improve the DistilBERT model by incorporating 4861 tokens, including emojis and emoticons. This augmentation improved the model’s accuracy by over 3 percentage points compared to the standard BERT model, while providing faster inference and reduced computational requirements. Our models and datasets are publicly available to promote broader adoption and further research in financial sentiment analysis. We validated EmTract during the 2021 “meme stock” rally, where it accurately captured spikes in emotions such as anger and disgust following trading restrictions, demonstrating the model’s practical applicability in real-world events. Additionally, heterogeneity tests show that emotions have a stronger impact on smaller, more volatile, and heavily shorted stocks, aligning with established behavioral finance theories. These findings underscore the importance of integrating emotional dynamics into market analysis, particularly for speculative assets. Our contributions represent a significant step forward in understanding the psychological drivers of financial markets and offer practical tools for future research and industry applications.

Emotional Intelligence Proficiency in Students Enrolled in Professional Courses

Emotional Intelligence (EI) plays a vital role in both professional and personal success, with research indicating that 80% of a person’s success is attributed to their EI, while only 20% depends on IQ. EI refers to the ability to recognize, understand, and manage emotions effectively, both in oneself and others. This study aims to assess the level of EI among undergraduate and postgraduate students in Dehradun. Data was gathered from 780 students across various institutes in Dehradun. The research is organized into eight sections: introduction, concept of EI, key variables, the importance of EI in success, types and attributes of EI, analysis and discussion of findings, conclusions, and suggestions for improvement. The study highlights the significance of emotional intelligence in shaping student success and offers insights into how educational institutions can foster this skill among students. By understanding the emotional dynamics, students can enhance their personal and professional growth, contributing to their overall well-being and achievement. The conclusions and suggestions from this research will help in developing strategies to nurture EI among students for better outcomes in their academic and future careers.

See, Touch, Feel: Theorizing Twitter/X Images for Diplomacy

Social media images are central to the practice of digital diplomacy. Some of the most contentious political engagements involve digital images designed to challenge or reaffirm the national identity of key political players. Yet the role of touch in conditioning how social media images are seen is curiously absent from International Relations analyses of the visual politics of digital diplomacy. We know far too little about this phenomenon. This article seeks to contribute to the study of images in global politics by developing a tripartite theorization of how the visual, tactile, and emotional intersect to mediate how Twitter images are seen within the practice of digital diplomacy. The article pays attention to the tactile experience of using Twitter, bridging the gap between seeing social media images as visual objects, and the affective resonance these images have as a marker of political power. I argue that touch acts as a conduit between the embodied, material, and visual to produce an affective encounter that contributes to how users feel about Twitter images. The intertextuality of the visual, tactile, and emotional dynamics of Twitter images are important elements of visual social media practices sustaining the strategic representation of identity. This framework allows me to build a theoretically grounded skilful speculation as to how digital diplomacy crisis events such as the China-Australia Twitter spat unfold and resonate beyond the digital.

Psychological Profile of Serial Killers: Analysis of Personality Traits, Motivation and Emotional Dynamics

Psychological Profile of Serial Killers: Analysis of Personality Traits, Motivation and Emotional Dynamics

Deep emotion recognition in textual conversations: a survey

Emotion Recognition in Conversations (ERC) is a key step towards successful human–machine interaction. While the field has seen tremendous advancement in the last few years, new applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker, and emotion dynamics modelling, to interpreting common sense expressions, informal language, and sarcasm, addressing challenges of real-time ERC, recognizing emotion causes, different taxonomies across datasets, multilingual ERC, and interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities of this task. It proceeds with a description of the emotion taxonomies and a variety of ERC benchmark datasets employing such taxonomies. This is followed by descriptions comparing the most prominent works in ERC with explanations of the neural architectures employed. Then, it provides advisable ERC practices towards better frameworks, elaborating on methods to deal with subjectivity in annotations and modelling and methods to deal with the typically unbalanced ERC datasets. Finally, it presents systematic review tables comparing several works regarding the methods used and their performance. Benchmarking these works highlights resorting to pre-trained Transformer Language Models to extract utterance representations, using Gated and Graph Neural Networks to model the interactions between these utterances, and leveraging Generative Large Language Models to tackle ERC within a generative framework. This survey emphasizes the advantage of leveraging techniques to address unbalanced data, the exploration of mixed emotions, and the benefits of incorporating annotation subjectivity in the learning phase.

Toddlers' emotion vocalizations during peer conflicts and contingent teacher interventions in early care and education settings

AbstractThis study used naturalistic audio–visual recordings from early care and education (ECE) settings to examine the associations between toddlers' (76 toddlers, 40 female, Mage = 32.94 months, SD = 4.92 months) multimodal emotion expressions and emotion‐related vocalizations with contingent teacher interventions. Findings indicated a correspondence between multimodal emotion expressions and emotion‐related vocalizations, such that screams and yells corresponded with anger expressions, and cries corresponded with sadness expressions. Time series analysis indicated that toddlers' emotion vocalizations significantly predicted subsequent contingent teacher interventions. Specifically, toddlers' multimodal sadness expressions with vocalizations increased the likelihood of evoking a contingent teacher response seven times more than other emotions and vocalizations. Implications for multimodal emotion correspondences, emotion dynamics, and toddlers' distress expressions are discussed.

An emotional learning process? The role of socially induced and regulated emotions for the development of an entrepreneurial mindset

PurposeAlthough emotional dynamics (EDs) during the entrepreneurial learning (EL) process are acknowledged to promote the growth of an entrepreneurial mindset (EM), while having social causes, empirical research on learning mainly looks at emotions as socially isolated concepts. This study aims to investigate how socially induced and regulated emotions during the EL process affect EM development.Design/methodology/approachWe applied a qualitative, inductive approach related to interpretative phenomenological analysis to get deeply involved into individuals’ experienced emotions. We conducted semi-structured interviews with participants of two EL programs. Open-ended questions stimulated free narratives and detailed descriptions of experiences that were analyzed following a five-stage process.FindingsThere is a correlation between socially induced and regulated emotions and the development of EM elements. We suggest a framework for the EL process based on EDs, which triggers four main mechanisms that help individuals develop an EM: “re-assessment of individual emotions through EDs”, “EDs affected by facilitator intervention,” “sharing and co-creation of emotions,” and “sensemaking of experiences and emotions.”Originality/valueThis study adds to the knowledge on EDs during the EL process and contributes to the literature on EL and emotions in learning. Therewith, it helps to sensitize practitioners toward the complexity of emotions in the entrepreneurial process, allows to moderate individual emotional reactions and social Eds, and improves existing EE programs. Future research could investigate the interplay of specific personality traits, learning environments, and socioemotional team dynamics in EL.

Emotion coupling across socialization contexts in adolescence: Differences in parent-child and peer interactions.

People spontaneously adjust their emotions to others when they interact. This temporal coupling of emotions is an adaptive process facilitating social bonding. The present study examined differences in coupling patterns during parent-child versus peer interactions in adolescence, a developmental period marked by evolving parent-child dynamics and bond formation with peers. Because adolescents prioritize peer bonding while gradually asserting their autonomy from parental influence, we hypothesized that peer dyads showed stronger coupling than parent-adolescent dyads. Adolescents (age 16) with diverse ethnic backgrounds (N = 615; 50.2% female; 46.8% European American, 31.2% African American, 5.0% Hispanic, 3.0% Asian or Pacific Islander, 2.0% Native American, and 12.0% multiple ethnic backgrounds) participated in two videotaped interaction tasks: one with a parent and one with a self-nominated peer. Parent and peer interactions included discussions on positive and negative topics. Both dyad members' emotions were coded in real time. Cross-recurrence quantification analyses and growth-curve modeling revealed concurrent emotion coupling patterns, with peer dyads showing stronger coupling than parent-adolescent dyads. Moreover, peer dyads showed the most pronounced coupling patterns when they discussed personal problems, while parent-adolescent dyads showed the most pronounced coupling patterns when they discussed the planning of a fun activity. Our findings emphasize the importance of microlevel emotion dynamics in understanding larger scale developmental shifts in relationships during adolescence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Physical Engagement in Face-to-Face Interaction Is Associated with Depressive Symptoms of Interaction Partners in the Workplace.

Individuals develop interdependence through interactions. The process of physical coordination during face-to-face interactions facilitates relationship formation, emotional experiences, and emotional contagion. Workplaces inherently involve organic and continuous face-to-face interactions. Examining the interpersonal association between physical movement and emotional state among employees in the workplace is crucial for understanding an organization's emotional dynamics. This study investigated the association between patterns of physical engagement during face-to-face interactions and depressive symptoms with those of interaction partners in a real-world workplace setting. The analysis utilized face-to-face interaction data from 449 employees across ten organizations, measured using wearable devices, along with a self-reported depression scale. The results showed that employees' average physical engagement negatively correlated with the average depressive symptoms of their interaction partners, rather than their own. The association remained robust regardless of the number of relationships or workplace type. Furthermore, the depressive symptoms of individuals who had a significant influence on interpersonal relationships within organizations negatively correlated with their and their partner's physical engagement. Our findings have important implications for the epidemiological understanding of organizational mental health in relation to face-to-face interactions among employees.

Integrating Diverse Data Streams for Enhanced Emotional Intelligence in Mental Health Care

This research presents a comprehensive system for real-time emotion recognition and analysis using multimodal data, including image, video, audio, and text. The system employs deep learning models to extract features and classify emotions from each modality. By integrating these predictions, we aim to provide a holistic understanding of a user's emotional state and assess potential risks. The system further analyzes the collected emotion data to identify trends, patterns, and indicators of emotional well-being and suicide risk. Visualizations such as time-series plots, distribution charts, and stacked area charts are employed to represent the emotional dynamics over time.Based on the analysis; the system generates personalized recommendations and alerts for users exhibiting signs of distress or emotional instability, including potential suicide risk factors. The goal is to promote proactive well-being management and early intervention. It is crucial to emphasize that this system is a tool for early detection and should be used in conjunction with professional mental health care.