Emotion Research Research Articles

The Impact of Positivity in Multi Business Model Innovation in a World of VUCA – How Can Positive Emotions Stimulate the MBMI Process?

The importance of right speed, effectiveness, efficiency and learning in Multi Business Model Innovation (MBMI) is vital to meet today’s Volatile, Uncertainty, Complex and Ambiguous (VUCA) challenges to survive as a Business in Business Model EcosystemS (BMES). Coronavirus pandemic, war in Ukraine, volatile global material and resource prices and deliveries, cyberattacks make big external VUCA events to Businesses. VUCA can also come from the inside of the Business as management style change, interventions or a burn down of the Business. How can Businesses under these VUCA conditions still do high quality MBMI? How can MBMI participants and teams in these processes increase their MBMI output? This paper investigates the impact of positive emotions, relatedness and creativity on the outcome of three different MBMI processes in three different Business Model Ecosystems influenced by VUCA. Positivity, relatedness and creativity are hypothesized as components to strengthen the individuals’ search for meaning in VUCA and for their motivation to act in MBMI processes influenced by VUCA. The study is conducted as a mix method research project in the view of MBMI and psychological theory – particularly the concept of positive emotions and relatedness as mental states for learning and knowledge creation. Interviews and observations show that individuals in networks and teams with a mood of positivity strengthen MBMI and the MBMI learning and creation process. Thus, our intention for further research is twofold: Training in positivity in teams can stimulate the courage to make disruptive solutions sufficient for VUCA. Secondly, in searching for the MBMI mindset psychological traits will be studied by the Big Five profile test. This opens for recruitment of participants to optimizing MBMI teams. Main contributions are: • Positive emotions and relatedness strengthen the urge to learn and to create in VUCA – searching for meaning and motivation to act is related to this concept. • Learning and creative processes increase, since the basis of positivity and relatedness in individuals, teams and networks are strengthened. • Innovative outcome seems of a higher quality when based on positive emotions. Research in positive emotions in Business Model Innovation is rare. • Positive emotion is the core concept and by training of positivity, it may strengthen the MBMI processes and the VUCA competences. • The outcomes and solution of the MBMI processes in the case study show mainly incremental and radical MBMI – only one is disruptive.

What do you meme? – Meme-Making as a research method

ABSTRACT Memes have emerged as a prevalent and influential aspect of contemporary culture and their use is a significant feature of social and relational communications. Yet scant research exists within Higher Education on the use of memes to assist in the research of reactions, emotions, and perceptions. The use of meme-making as a qualitative research tool is grounded in visual research methods. This paper explores a framework for using meme-making as a research tool within Higher Education and considers two case studies that demonstrate how meme-making can been used to capture and communicate the experiences of students. Through these case studies, meme-making was revealed to introduce an element of levity and humour whilst being versatile, flexible, and easily dovetailed to other qualitative approaches. Familiarity with the medium and construction process drew upon an instilled sense of ownership revealing the method to be accessible across a diverse participant base.

Foreign language anxiety, enjoyment, and boredom among Chinese secondary students: a control-value theory approach

For decades, studies on learner emotions in foreign language learning have been predominantly focused on foreign language anxiety, and recent years have witnessed a surge in the exploration of other positive and negative emotions such as enjoyment and boredom. The current study aimed to extend this line of inquiry and explored the bidirectional relations between foreign language achievements and learner emotions through a questionnaire study among English learners (n = 756) in Chinese senior secondary schools, an understudied population in foreign language emotion research. Results indicated that the three emotions were significantly related to each other. Further analyses suggested that anxiety and boredom were significant predictors of achievements, and that language achievements predicated all the three emotions. The study confirms the reciprocal relationships between language achievements and emotions and provides pedagogical implications for language teaching.

Agent Ali: Exploring Emotional Elements in Story Development with Artificial Intelligence

Emotional research in film and animation has long been implemented in the West. This is because Elements of emotion are essential in an animated film to generate an affective impression and emotions in the audience. Malaysia's animation industry has increased recently, and Malaysian animators have produced many animation products. However, previous studies stated that the current situation of local animated films needed a more potent storytelling technique. There is a West scholar who argues that good storytelling can evoke the emotions of the audience. Applying emotions in animation through computer technology is complicated compared to live-action films that can control emotions through the actors' acting techniques. Thus, this study identified the elements of emotion in developing the case study of storytelling Agent Ali in the movie. The mixed method was used to identify aspects of emotion in Agent Ali the Movie, such as Freytag's Pyramid model and Hume AI. As a result, this study found that aspects of emotion exist in the story development process in Agent Ali the Movie, such as happiness, sadness, and anger in every three acts of structure (exposition, conflict, and resolution). The existence of emotion has proved that animated films in Malaysia need to be focused to overcome the weak storytelling technique. At the end of the discussion, this study also found that Artificial intelligence (AI) technology like Hume AI could speed up the animation production process, especially in identifying the facial expressions and body language of characters in the process of storytelling development.

Determining a subset of discrete emotion-evoking images from the international affective picture system in a Turkish sample

The International Affective Picture System (IAPS), widely utilized in emotion research, is based on a dimensional approach. It includes 1196 colored static images depicting different semantic categories, with valence, arousal, and dominance ratings determined for each. However, the specific discrete emotions evoked by these images within the IAPS has remained unclear. Therefore, numerous investigations have been conducted in different cultural contexts to address this matter. This study aimed to determine a subset of images from the IAPS that elicit discrete emotions. To achieve this goal, an image was selected for each semantic category within the IAPS, employing specific criteria, and the elicited discrete emotions were subsequently examined in a Turkish sample. Additionally, valence ratings for these images were obtained within the Turkish culture, facilitating cross-cultural comparisons. Sixty Turkish students (43 female) aged between 19-25 participated in this study. The participants rated the valence, discrete emotion category, and the intensity of the determined discrete emotion (1 = none; 9 = very strongly) for 231 selected images from the IAPS, respectively. Valence ratings were obtained using the paper-pencil version of the Self-Assessment Manikin. Accordingly, 76 images, of which intensity ratings above six were classified into a single discrete emotion with the agreement of 70% and above among the participants. Furthermore, the obtained data were compared with the results of studies conducted in different cultural settings to explore potential cultural differences. Overall, the results highlighted the importance of selecting culture-specific stimuli in emotion studies.

Robust multi-frequency band joint dictionary learning with low-rank representation

Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/inter-class local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets.

Hiding in plain sight: The distinct importance of low-arousal positive affect

Most past research on positive affect and emotion has focused exclusively on high-arousal positive affect (HAPA: e.g., excited), however, low-arousal positive affect (LAPA: e.g., calm) increasingly is included in emotion research. As such, there is a need to synthesize knowledge about the similarities and differences between LAPA and HAPA, the operationalization of LAPA and HAPA, and the distinct characteristics and importance of LAPA within emotional life. A systematic search identified 226 research papers comparing LAPA with HAPA from a broad spectrum of research topics; this review provides a narrative summary of their findings. Indications of differences between LAPA and HAPA were found in 89% of comparisons, with LAPA having a consistently distinguishable relationship to variables such as brain activity, cardiovascular health, decision-making, memory, mindfulness, personality, and solitude, among others. Other notable aspects of LAPA were found, including its role in stress, work, positive sociality, and well-being, as well as its importance in older adults and women. An analysis of items used to measure LAPA and HAPA revealed nuanced differences in conceptualizations, as well as emerging consensus around specific item usage. While considering item use in light of approach-avoidance motivation, we identified three possible LAPA subtypes: calm (a steady state of neither approach nor avoidance), satisfaction (having successfully approached), and relief (having successfully avoided). This review clarifies LAPA’s role in affective life, underscoring that LAPA’s differences from HAPA should be considered in research involving positive affect.

Advancing language teacher emotion research: a nuanced, dialectical, and empowering stance

Abstract This commentary presents a nuanced, dialectical, and empowering perspective on research pertaining to language teacher emotions, drawing upon empirical research papers featured in this special issue, as well as other relevant literature in the field of language teacher education. Recognizing the embodied, dynamic, and potentially contested nature of teacher emotional labor, the paper emphasizes the importance of presenting a nuanced portrayal, adopting a dialectical approach, and embracing an empowering mindset when investigating language teacher emotions. It also highlights important directions for future research, advocating for a conceptual shift toward examining teacher emotional labor as a distributed practice with a participatory and transformative orientation. Furthermore, the significance of both “looking inward” to delve into specific types of emotions and their interactions as well as “looking outward” to explore how emotions relate to other crucial dimensions of language teachers’ professional practices such as identity, mindfulness, and resilience, is emphasized.

Sleep loss and emotion: A systematic review and meta-analysis of over 50 years of experimental research.

In a largely sleep-deprived society, quantifying the effects of sleep loss on emotion is critical for promoting psychological health. This preregistered systematic review and meta-analysis quantified the effects of various forms of sleep loss on multiple aspects of emotional experiences. Eligible studies used experimental reductions of sleep via total sleep deprivation, partial sleep restriction, or sleep fragmentation in healthy populations to examine effects on positive affect, negative affect, general mood disturbances, emotional reactivity, anxiety symptoms, and/or depressive symptoms. In total, 1,338 effect sizes across 154 studies were included (N = 5,717; participant age range = 7-79 years). Random effects models were conducted, and all forms of sleep loss resulted in reduced positive affect (standardized mean difference [SMD] = -0.27 to -1.14), increased anxiety symptoms (SMD = 0.57-0.63), and blunted arousal in response to emotional stimuli (SMD = -0.20 to -0.53). Findings for negative affect, reports of emotional valence in response to emotional stimuli, and depressive symptoms were mixed and depended on the type of sleep loss. Nonlinear effects for the amount of sleep loss as well as differences based on the stage of sleep restricted (i.e., rapid eye movement sleep or slow-wave sleep) were also detected. This study represents the most comprehensive quantitative synthesis of experimental sleep and emotion research to date and provides strong evidence that periods of extended wakefulness, shortened sleep duration, and/or nighttime awakenings adversely influence human emotional functioning. Findings provide an integrative foundation for future research on sleep and emotion and elucidate the precise ways that inadequate sleep may impact our daytime emotional lives. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment.

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

THE INFLUENCE OF PERSONAL BRAND COMMUNICATION ON CONSUMERS

The idea of this research is to combine the influence of personal brand with business needs. This article is a personal brand’s analysis on how personal brand influences consumer interest in products/services. An experiment is carried out on the content viewed by consumers using eye-tracking technology “Neuromarketing & AI LAB” to reveal emotional and structural sensations in the social network Instagram. The aim of this research is to determine the influence of a personal brand on the consumer. Research methodology: analysis of scientific literature, logical definition and theory construction, an experiment: research of human emotions and chosen content. Data analysis method: descriptive statistics and comparative analysis. AOI (heat map) was selected to illustrate the research results. Influencers with up to 100 000 followers were selected – stimuli that trigger or enhance physiological and psychological reactions in the body. It was found that the body and face of a personal brand had the higher influence on consumers. Personal brand attracts consumers’ attention and promotes engagement. The originality and value of the paper: content relevance research of personal brand (identity) influence on consumers in social network Instagram. Further we suggest carrying out similar personal brand’s research choosing other social networks.

Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram

Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.

Material Affective Engagements: Examples from Ancient Mesopotamia

This article applies approaches from current emotion research on material affective scaffolds—objects made and used to enhance, and more generally transform, affective states—to the emerging field of study focusing on emotions in ancient Near Eastern societies. Its main goal is to extend the framework of 4E cognition—with its central notion that human cognition is embodied, embedded, enactive, and extended—to the realm of affective states, emphasizing that through our bodily interactions with material objects we transform not just our cognitive processes but also our emotions, moods, and so forth. Thereby, the present study seeks to contribute to the exploration of the relationships between sensory experiences, emotions, moods, and the material world by investigating the affective meanings that material things acquire through people’s entanglements with them. The study focuses on one particular class of objects—Mesopotamian amulets from the first millennium BC, which served as bodily adornments but were also understood to have the power to evoke affective responses through their activation in ritual performances. Referring to scholarly compendia in Mesopotamian cuneiform texts, this study demonstrates that these objects were recommended by healing experts to influence different affective states, both in oneself and others. It examines the connection between affective states and specific material features of the amulet components (consisting of minerals, metals, and plant and animal substances). Finally, Mesopotamian views of affective states and their management are compared with those of contemporary cognitive-affective science. This comparison shows that although there are some analogies, there are also important differences that depend mainly on different understandings of the human mind and agency. byUlrike SteinertJohannes Gutenberg University Mainzusteiner@unimainz.deandGiovanna ColombettiUniversity of ExeterG.Colombetti@exeter.ac.uk

Introspection in Emotion Research: Challenges and Insights

Introspection, or looking inward to observe one's experience, is inherent in many methods used to study feelings, the experiential component of emotion. Challenges of introspection make faithful, high-fidelity descriptions of feelings difficult to attain. A method that (1) cleaves to a specific moment, (2) cleaves to pristine inner experience, (3) brackets presuppositions, and (4) utilizes an iterative process may be particularly well suited to this task. We review some contemporary introspective methods from the perspective of these four methodological constraints, finding that Descriptive Experience Sampling (DES) addresses the constraints most fully. We present DES findings on feelings to highlight the unique contributions careful introspective methods make to emotion science. High-fidelity descriptions of feelings are necessary for a complete understanding of emotion.

MVIndEmo: a dataset for micro video public-induced emotion prediction on social media

Distinct from the realm of perceived emotion research, induced emotion pertains to the emotional responses engendered within content consumers. This facet has garnered considerable attention and finds extensive application in the analysis of public social media. However, the advent of micro videos presents unique challenges when attempting to discern the induced emotional patterns exhibited by content consumers, owing to their free-style representation and other factors. Consequently, we have put forth two novel tasks concerning the recognition of public-induced emotion on micro videos: emotion polarity and emotion classification. Additionally, we have introduced a accessible dataset specifically tailored for the analysis of public-induced emotion on micro videos. The data corpus has been meticulously collected from Tiktok, a burgeoning social media platform renowned for its trendsetting content. To construct the dataset, we have selected eight captivating topics that elicit vibrant social discussions. In devising our label generation strategy, we have employed an automated approach characterized by the fusion of multiple expert models. This strategy incorporates a confidence measure method that relies on three distinct models for effectively aggregating user comments. To accommodate adaptable benchmark configurations, we provide both binary classification labels and probability distribution labels. The dataset encompasses a vast collection of 7,153 labeled micro videos. We have undertaken an extensive statistical analysis of the dataset to provide a comprehensive overview composition. It is our earnest aspiration that this dataset will serve as a catalyst for pioneering research avenues in the analysis of emotional patterns and the understanding of multi-modal information.

The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts

Selecting appropriate musical stimuli to induce specific emotions represents a recurring challenge in music and emotion research. Most existing stimuli have been categorized according to taxonomies derived from general emotion models (e.g., basic emotions, affective circumplex), have been rated for perceived emotions, and are rarely defined in terms of interrater agreement. To redress these limitations, we present research that served in the development of a new interactive online database, including an initial set of 364 music excerpts from three different genres (classical, pop, and hip/hop) that were rated for felt emotion using the Geneva Emotion Music Scale (GEMS), a music-specific emotion scale. The sample comprised 517 English- and German-speaking participants and each excerpt was rated by an average of 28.76 participants (SD = 7.99). Data analyses focused on research questions that are of particular relevance for musical database development, notably the number of raters required to obtain stable estimates of emotional effects of music and the adequacy of the GEMS as a tool for describing music-evoked emotions across three prominent music genres. Overall, our findings suggest that 10–20 raters are sufficient to obtain stable estimates of emotional effects of music excerpts in most cases, and that the GEMS shows promise as a valid and comprehensive annotation tool for music databases.

‘Making it possible’: the complex dynamics of university foreign language teacher agency for research in funding applications

Teacher agency for research, which refers to teachers’ decision-making and initiative acts in the context of academic engagement, plays a pivotal role for teacher learning, teacher research, and thus teachers’ professional development. Despite the burgeoning number of studies that have examined teachers’ research and publishing experiences, it is unclear how university teachers exercise their agency for research in funding applications. This study examines how foreign language teachers at a university in China practice agency in the application of the National Social Science Fund of China from a Complex Dynamic Systems Theory perspective. Narrative frames and semi-structured interviews were used to collect the data, and thematic analysis was adopted to elucidate the complexity and dynamics of teacher agency for research. Revealing that there are subsystems of teacher agency for research in funding applications, i.e., agency beliefs, agency practice, and agency emotions and that the developmental trajectories of their agency for research are situated and relational, the findings highlight the need to view teacher agency as complex systems and dynamic entities. This study not only offers a conceptual framework as to unravel teacher agency for research in funding applications but also provides a tentative pathway for teachers exercising agency in applying for external funding both in the context of China and beyond.

Exploring urban-rural differences in foreign language enjoyment, anxiety, boredom, and burnout among Chinese secondary students

ABSTRACT Building on situated emotion framework, this study examined the variations in foreign language (FL) enjoyment, anxiety, boredom, and burnout between urban and rural students in China. The sample included 1,455 urban (55.6% male) and 546 rural students (53% male) (Mage = 15.41). Data were gathered through self-administered questionnaires measuring FL enjoyment, anxiety, boredom, and burnout, as well as FL achievement test. Independent sample t-tests found that urban students exhibited higher enjoyment and anxiety compared to their rural peers, while experiencing lower boredom and burnout. Analysis of covariance revealed that the observed differences remained significant after controlling for FL achievement, except for anxiety. Pearson correlation analysis showed that FL achievement for both urban and rural students was positively associated with enjoyment and negatively associated with boredom and burnout. Only a weak positive correlation was observed between anxiety and FL achievement among rural students. Multiple regression analysis indicated that enjoyment was a positive predictor of FL achievement for both groups, whereas burnout negatively influenced their achievement. Additionally, boredom negatively impacted urban students’ achievement, while anxiety emerged as a positive predictor. These results offer valuable insights for FL instruction in both urban and rural contexts and contribute empirical evidence to situated emotion research.

Mini review: Challenges in EEG emotion recognition.

Electroencephalography (EEG) stands as a pioneering tool at the intersection of neuroscience and technology, offering unprecedented insights into human emotions. Through this comprehensive review, we explore the challenges and opportunities associated with EEG-based emotion recognition. While recent literature suggests promising high accuracy rates, these claims necessitate critical scrutiny for their authenticity and applicability. The article highlights the significant challenges in generalizing findings from a multitude of EEG devices and data sources, as well as the difficulties in data collection. Furthermore, the disparity between controlled laboratory settings and genuine emotional experiences presents a paradox within the paradigm of emotion research. We advocate for a balanced approach, emphasizing the importance of critical evaluation, methodological standardization, and acknowledging the dynamism of emotions for a more holistic understanding of the human emotional landscape.

Realities of comfort and discomfort in the heritage language classroom: Looking to transformative positive psychology for juggling a double‐edged sword

AbstractAs emotions research in the field of second language acquisition continues to evolve, it is equally important to explore the impact of social–emotional variables that are specifically relevant to heritage language (HL) contexts. Anchoring on foundations in critical heritage language education (HLE), this study examines the discomforts of the HL classroom from a diverse heritage speaker (HS) perspective. Additionally, comforts that support the HL classroom as a safe space for emotional security and well‐being for HSs across HLs are explored. Examining the HL classroom from the perspective of HL practices and knowledge systems, this study ultimately aims to: (a) outline the emotional complexity of HL pedagogical spaces, and (b) provide concrete and meaningful recommendations for supporting HS well‐being and HL development from a transformative positive psychology lens. Data for the current qualitative study were provided through two separate methodologies. First, 64 HSs of Spanish responded to a qualitative questionnaire probing the emotional reactions and memories instigated by authentic HL classroom reading material on sensitive topics of racism, bilingualism, and immigration. The themes identified in written narrative data through an inductive thematic approach were then used as a foundation for semistructured interviews with language learners (n = 6) and educators (n = 8) from eight different HL backgrounds. Findings revealed feelings of comfort and discomfort, and even trauma and healing, in HLE spaces rooted in (a) language learning experiences, (b) social memories of (dis)comfort, and (c) intergenerational histories. Together, the data suggest how the HL classroom can act both as a trigger of social injustice, linguistic insecurity, and family conflict and, at the same time, as a space instigating affective reactions associated with social rebellion, linguistic confidence, intergenerational healing, and emotional refuge. Specific pedagogical recommendations are made to equip educators with a concrete toolkit for the HL classroom.