Thematische Bibliographien / Emotion

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Auswahl der wissenschaftlichen Literatur zum Thema „Emotion“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 31. Juli 2024

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Zeitschriftenartikel zum Thema "Emotion"

Brito, Pedro Quelhas, Sandra Torres und Jéssica Fernandes. „What kind of emotions do emoticons communicate?“ Asia Pacific Journal of Marketing and Logistics 32, Nr. 7 (10.12.2019): 1495–517. http://dx.doi.org/10.1108/apjml-03-2019-0136.

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Purpose The purpose of this paper is to study the nature and concept of emoticons/emojis. Instead of taking for granted that these user-generated formats are necessarily emotional, we empirically assessed in what extent are they and the specificity of each one. Drawing on congruent mood state, valence core and emotion appraisal theories we expected a compatible statistical association between positive/negative/neutral emotional valence expressions and emoticons of similar valence. The positive emoticons were consistently associated with positive valence posts. Added to that analysis, 21 emotional categories were identified in posts and correlated with eight emoticons. Design/methodology/approach Two studies were used to address this question. The first study defined emoticon concept and interpreted their meaning highlighting their communication goals and anticipated effects. The link between emojis and emoticons was also obtained. Some emoticons types present more ambiguity than others. In the second study, three years of real and private (Facebook) posts from 82 adolescents were content analyzed and coded. Findings Only the neutral emoticons always matched neutral emotional categories found in the written interaction. Although the emoticon valence and emotional category congruence pattern was the rule, we also detected a combination of different valence emoticons types and emotion categories valence expressions. Apparently the connection between emoticon and emotion are not so obviously straightforward as the literature used to assume. The created objects designed to communicate emotions (emoticons) have their specific corresponding logic with the emotional tone of the message. Originality/value Theoretically, we discussed the emotional content of emoticons/emojis. Although this king of signals have an Asian origin and later borrowed from the western countries, their ambiguity and differing specificity have never been analyzed.

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MacCann, Carolyn, Yasemin Erbas, Egon Dejonckheere, Amirali Minbashian, Peter Kuppens und Kirill Fayn. „Emotional Intelligence Relates to Emotions, Emotion Dynamics, and Emotion Complexity“. European Journal of Psychological Assessment 36, Nr. 3 (Mai 2020): 460–70. http://dx.doi.org/10.1027/1015-5759/a000588.

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Abstract. Emotional intelligence (EI) should relate to people’s emotional experiences. We meta-analytically summarize associations of felt affect with ability EI branches (perception, facilitation, understanding, and management) and total scores ( k = 7–14; N = 1,584–2,813). We then use experience sampling ( N = 122 undergraduates over 5 days, 24 beeps) to test whether EI predicts emotion dynamics and complexity. Meta-analyses show that EI correlates significantly with lower negative affect (NA; ρ = −.21) but not higher positive affect (PA; ρ = .05). PA (but not NA) shows a significantly stronger relationship with emotion management (ρ = .23) versus other EI branches (ρ = −.01 to .07). In the experience sampling study, only management significantly related to higher PA, whereas lower NA was significantly related to total EI, perception, facilitation, and management. After controlling for mean affect: (a) only understanding significantly predicted NA dynamics whereas only management and facilitation significantly predicted PA dynamics; (b) management and facilitation predicted lower PA differentiation (EI was unrelated to NA differentiation); and (c) perception and facilitation predicted greater bipolarity. Results show that EI predicts affect, emotion dynamics, and emotion complexity. We discuss the importance of distinguishing between different branches of ability EI.

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Liao, Songyang, Katsuaki Sakata und Galina V. Paramei. „Color Affects Recognition of Emoticon Expressions“. i-Perception 13, Nr. 1 (Januar 2022): 204166952210807. http://dx.doi.org/10.1177/20416695221080778.

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In computer-mediated communication, emoticons are conventionally rendered in yellow. Previous studies demonstrated that colors evoke certain affective meanings, and face color modulates perceived emotion. We investigated whether color variation affects the recognition of emoticon expressions. Japanese participants were presented with emoticons depicting four basic emotions (Happy, Sad, Angry, Surprised) and a Neutral expression, each rendered in eight colors. Four conditions (E1–E4) were employed in the lab-based experiment; E5, with an additional participant sample, was an online replication of the critical E4. In E1, colored emoticons were categorized in a 5AFC task. In E2–E5, stimulus affective meaning was assessed using visual scales with anchors corresponding to each emotion. The conditions varied in stimulus arrays: E2: light gray emoticons; E3: colored circles; E4 and E5: colored emoticons. The affective meaning of Angry and Sad emoticons was found to be stronger when conferred in warm and cool colors, respectively, the pattern highly consistent between E4 and E5. The affective meaning of colored emoticons is regressed to that of achromatic expression counterparts and decontextualized color. The findings provide evidence that affective congruency of the emoticon expression and the color it is rendered in facilitates recognition of the depicted emotion, augmenting the conveyed emotional message.

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Dhaka, Suman, und Naveen Kashyap. „Explicit emotion regulation: Comparing emotion inducing stimuli“. Psychological Thought 10, Nr. 2 (20.10.2017): 303–14. http://dx.doi.org/10.5964/psyct.v10i2.240.

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Emotions are a major part of our subjective experiences of the world. At times, our emotions are not appropriate and require active management. Emotion regulation refers to the various ways of managing or controlling emotional responses. External stimuli play specific role in electing emotions. Pictures and movies elicit emotions and emotional effects of films are believed to exceed that of pictures. The aim of the present study is to compare the effectiveness of emotion regulation strategies across emotion induction method (picture and films). Forty participants rated their emotion on Self-Assessment Manikin (SAM) ratings for each pictorial and video stimuli while following the emotion regulation instructions. Repeated-measures ANOVAs revealed that the pictures were more effective in modulating emotions. Cognitive reappraisal and distraction strategies downregulated emotions.

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Merlina, Tina, Lia Maulia und Rosaria Mita Amalia. „Verbal and Visual Expression of Emotions on Kaskus: a Semiotic Study“. MIMBAR, Jurnal Sosial dan Pembangunan 29, Nr. 1 (20.06.2013): 85. http://dx.doi.org/10.29313/mimbar.v29i1.373.

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This research investigates the types of emoticons which occured on Kaskus. This is a qualitative research. The writer take the data from Kaskus, therefore it is a forum in the internet which has grown to be one of the most popular websites in Indonesia. To identify the types of Emotions on Kaskus, the writer analyzed the data using Ekman (2003) . From the discussions, there are verbal and non verbal sign in the emoticons that appear on Kaskus. The meaning of verbal sign and nonverbal sign in emoticon “marah” represents anger emotion. Emoticon “Ngakak” and “thumbup” represent enjoyable emotion. Emoticon “Sorry” and “Cool” represent sadness emotion. For future studies need to be conducted with an increased sample by using another media such as Whatsapp, YM, etc.

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Lee, Mikyoung, und Keum-Seong Jang. „Nurses’ emotions, emotion regulation and emotional exhaustion“. International Journal of Organizational Analysis 27, Nr. 5 (04.11.2019): 1409–21. http://dx.doi.org/10.1108/ijoa-06-2018-1452.

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Purpose The purpose of this study is to investigate the relations between emotion regulation (reappraisal and suppression), discrete emotions and emotional exhaustion among nurses. Design/methodology/approach A cross-sectional design was used with 168 nurses in South Korea. Structural equation modeling and path analysis were conducted for analysis. Findings Reappraisal correlated positively with enjoyment and pride and negatively with anxiety, anger and frustration, whereas suppression correlated negatively with enjoyment and positively with anxiety and frustration. Moreover, reappraisal was negatively associated with emotional exhaustion, whereas suppression was positively associated with it. Enjoyment was negatively related to emotional exhaustion, and anger and frustration were positively related to it. Enjoyment and frustration mediated the relation between emotion regulation and emotional exhaustion. Findings demonstrate the potentially beneficial influences of reappraisal as well as harmful impacts of suppression in the nursing context. Research limitations/implications This paper expands research on nurses’ emotion management by applying Gross’s emotion regulation framework rather than Hochschild’s emotional labor framework. The mediating result suggests that not only nurses but also hospital administrators and nurse managers should pay attention to nurses’ emotional experiences to improve nurses’ well-being and ultimately better nursing practice. This research can provide the basis for developing practical interventions to efficiently regulate nurses’ emotions. Originality/value This is the first study exploring the mediating role of emotions in the link between nurses’ emotion regulation and emotional exhaustion. It contributes to interdisciplinary research by integrating perspectives from psychological emotion and emotion regulation research into the nursing field.

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Thonhauser, Gerhard. „Towards a Taxonomy of Collective Emotions“. Emotion Review 14, Nr. 1 (Januar 2022): 31–42. http://dx.doi.org/10.1177/17540739211072469.

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This paper distinguishes collective emotions from other phenomena pertaining to the social and interactive nature of emotion and proposes a taxonomy of different types of collective emotion. First, it emphasizes the distinction between collective emotions as affective experiences and underpinning mechanisms. Second, it elaborates on other types of affective experience, namely the social sharing of emotion, group-based emotions, and joint emotions. Then, it proposes a working definition of collective emotion via a minimal threshold and four structural features. Finally, it develops a taxonomy of five types of collective emotion: emotional sharing, emotional contagion, emotional matching, emotional segregation, and emotional fusion.

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Gabrielsson, Alf. „Emotion perceived and emotion felt: Same or different?“ Musicae Scientiae 5, Nr. 1_suppl (September 2001): 123–47. http://dx.doi.org/10.1177/10298649020050s105.

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A distinction is made between emotion perception, that is, to perceive emotional expression in music without necessarily being affected oneself, and emotion induction, that is, listeners’ emotional response to music. This distinction is not always observed, neither in everyday conversation about emotions, nor in scientific papers. Empirical studies of emotion perception are briefly reviewed with regard to listener agreement concerning expressed emotions, followed by a selective review of empirical studies on emotional response to music. Possible relationships between emotion perception and emotional response are discussed and exemplified: positive relationship, negative relationship, no systematic relationship and no relationship. It is emphasised that both emotion perception and, especially, emotional response are dependent on an interplay between musical, personal, and situational factors. Some methodological questions and suggestions for further research are discussed.

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Ptaszynski, Michal, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka und Kenji Araki. „CAO: A Fully Automatic Emoticon Analysis System“. Proceedings of the AAAI Conference on Artificial Intelligence 24, Nr. 1 (04.07.2010): 1026–32. http://dx.doi.org/10.1609/aaai.v24i1.7715.

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This paper presents CAO, a system for affect analysis of emoticons. Emoticons are strings of symbols widely used in text-based online communication to convey emotions. It extracts emoticons from input and determines specific emotions they express. Firstly, by matching the extracted emoticons to a raw emoticon database, containing over ten thousand emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing "mouths" or "eyes," based on the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is firstly based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.

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Mallikarjuna, Basetty, M. Sethu Ram und Supriya Addanke. „An Improved Face-Emotion Recognition to Automatically Generate Human Expression With Emoticons“. International Journal of Reliable and Quality E-Healthcare 11, Nr. 1 (01.01.2022): 1–18. http://dx.doi.org/10.4018/ijrqeh.314945.

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Any human face image expression naturally identifies expressions of happy, sad etc.; sometimes human facial image expression recognition is complex, and it is a combination of two emotions. The existing literature provides face emotion classification and image recognition, and the study on deep learning using convolutional neural networks (CNN), provides face emotion recognition most useful for healthcare and with the most complex of the existing algorithms. This paper improves the human face emotion recognition and provides feelings of interest for others to generate emoticons on their smartphone. Face emotion recognition plays a major role by using convolutional neural networks in the area of deep learning and artificial intelligence for healthcare services. Automatic facial emotion recognition consists of two methods, such as face detection with Ada boost classifier algorithm and emotional classification, which consists of feature extraction by using deep learning methods such as CNN to identify the seven emotions to generate emoticons.

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Dissertationen zum Thema "Emotion"

Kneeland, Elizabeth Tepe. „Emotion Malleability Beliefs| Implications for Emotional Experiences, Emotion Regulation, Psychopathology, and Treatment“. Thesis, Yale University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13851894.

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Emotion dysregulation, or difficulties regulating one's emotions, has been increasingly identified as a transdiagnostic factor that can increase risk for and maintain a wide range of psychiatric disorders (Fernandez, Jazaieri, & Gross, 2016). Emotion malleability beliefs represent a potent psychological factor that relates to emotion regulation and, relatedly, holds important clinical implications for our conceptualizations of and treatment for a range of psychiatric disorders. The current dissertation includes one literature review and four empirical investigations that used an array of methodological approaches and possess different clinical and theoretical emphases to deepen and broaden the established influence of emotion malleability beliefs on emotion regulation and mental health.

Chapter 2 provides a theoretical model and discussion of how emotion malleability beliefs could aid in our understanding of several clinical disorders and could be harnessed to enhance motivation for and engagement in psychological treatment. The study described in Chapter 3 is an experimental investigation in which we demonstrate that emotion malleability beliefs can be experimentally manipulated and such an induction can exert a significant influence on individuals' state emotion regulation when coping with unwanted negative affect. The study presented in Chapter 4 consists of a longitudinal investigation that examines how emotion malleability beliefs relate to emotional experiences, emotion regulation, and clinical symptoms in first-year college students. The investigation included as Chapter 5 is of a daily diary study that elucidates how emotion malleability beliefs are tied to daily emotional experiences and emotion regulation as currently depressed individuals. Finally, the experimental study described in Chapter 6 clarifies how beliefs about emotion's malleability relate to treatment willingness, perceptions of agency over depression, and beliefs regarding prognosis. Overall, the work presented in this dissertation complement and extend past work on the role of emotion malleability beliefs in how individuals navigate their emotional worlds and cope with unwanted emotional experiences with the ultimate goal of promoting psychological adjustment and attenuating psychological distress. Future directions for theoretical and empirical work in this domain are presented in Chapter 7.

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Nicholls, Tanja. „Emotion lexicon in the Sepedi, Xitsonga and Tshivenda language groups in South Africa : the impact of culture on emotion / T. Nicholls“. Thesis, North-West University, 2008. http://hdl.handle.net/10394/2141.

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Araya, Jose Manuel. „Emotion and predictive processing : emotions as perceptions?“ Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33156.

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In this Thesis, I systematize, clarify, and expand the current theory of emotion based on the principles of predictive processing-the interoceptive inference view of emotion-so as to show the following: (1) as it stands, this view is problematic. (2) Once expanded, the view in question can deal with its more pressing problems, and it compares favourably to competing accounts. Thus, the interoceptive inference view of emotion stands out as a plausible theory of emotion. According to the predictive processing (PP) framework, all what the brain does, in all its functions, is to minimize its precision-weighted prediction error (PE) (Clark, 2013, 2016; Hohwy, 2013). Roughly, PE consist in the difference between the sensory signals expected (and generated) from the top-down and the actual, incoming sensory signals. Now, in the PP framework, visual percepts are formed by minimizing visual PE in a specific manner: via visual perceptual inference. That is, the brain forms visual percepts in a top-down fashion by predicting its incoming lower-level sensory signals from higher-level models of the likely (hidden) causes of those visual signals. Such models can be seen as putting forward content-specifying hypotheses about the object or event responsible for triggering incoming sensory activity. A contentful percept is formed once a certain hypothesis achieves to successfully match, and thus supress, current lower-level sensory signals. In the interoceptive inference approach to interoception (Seth, 2013, 2015), the principles of PP have been extended to account for interoception, i.e., the perception of our homeostatic, physiological condition. Just as perception in the visual domain arises via visual perceptual inference, the interoceptive inference approach holds that perception of the inner, physiological milieu arises via interoceptive perceptual inference. Now, what might be called the interoceptive inference theory of valence (ITV) holds that the interoceptive inference approach can be used so as to account for subjective feeling states in general, i.e., mental states that feel good or bad-i.e., valenced mental states. According to ITV, affective valence arises by way of interoceptive perceptual inference. On the other hand, what might be called the interoceptive inference view of emotion (IIE) holds that the interoceptive inference approach can be used so as to account for emotions per se (e.g., fear, anger, joy). More precisely, IIE holds that, in direct analogy to the way in which visual percepts are formed, emotions arise from interoceptive predictions of the causes of current interoceptive afferents. In other words, emotions per se amount to interceptive percepts formed via higher-level, content-specifying emotion hypotheses. In this Thesis, I aim to systematize, clarify, and expand the interoceptive inference approach to interoception, in order to show that: (1) contrary to non-sensory theories of affective valence, valence is indeed constituted by interoceptive perceptions, and that interoceptive percepts do arise via interoceptive perceptual inference. Therefore, ITV holds. (2) Considering that IIE exhibits problematic assumptions, it should be amended. In this respect, I will argue that emotions do not arise via interoceptive perceptual inference (as IIE claims), since this assumes that there must be regularities pertaining to emotion in the physiological domain. I will suggest that emotions arise instead by minimizing interoceptive PE in another fashion. That is, emotions arise via external interoceptive active inference: by sampling and modifying the external environment in order to change an already formed interoceptive percept (which has been formed via interoceptive perceptual inference). That is, emotions are specific strategies for regulating affective valence. More precisely, I will defend the view that a certain emotion E amounts to a specific strategy for minimizing interoceptive PE by way of a specific set of stored knowledge of the counterfactual relations that obtain between (possible) actions and its prospective interoceptive, sensory consequences ("if I act in this manner, interoceptive signals should evolve in such-and-such way"). An emotion arises when such knowledge is applied in order to regulate valence.

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Friesen, Andrew P. „"Catching" emotions : emotion regulation in sport dyads“. Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/621877.

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The purpose of the present research programme was to inform the development and subsequent delivery of an intervention to enhance interpersonal emotion regulation. Although emotion regulation has been emphasised due to its importance in explaining performance and well-being, the focus of research has predominantly been on intrapersonal emotion regulation. The present study addressed the dual-gap in research by extending research in interpersonal emotion regulation in general and developing and testing theory-led interventions for use in sport. A three-stage programme of research was set up with stage one reviewing the extant literature before proposing a social-functional approach to emotions, and in particular the Emotions As Social Information (EASI) model, as possible theoretical frameworks for use in sport. Qualitative methods were emphasised as these are particularly useful in studies seeking to identify mechanisms underlying the effectiveness of interventions. Stage two began with a narrative analysis to outline the potential social functions and consequences of emotional expressions, verbalisations, and actions in ice hockey. Two ice hockey players, each captain of their respective team, participated in semi-structured interviews. Participants described how emotions informed them of important circumstances in their environment that required attention and prepared them for such challenges at the individual level. At a dyadic level, emotions helped participants understand the emotional states and intentions of their teammates contributing toward an assessment of the extent to which they were prepared to face their challenges. At a group level, emotions helped participants lead their teammates in meeting team goals. Finally, at the cultural level, emotions helped participants maintain culture-related identities. Stage two continued with examining the processes, strategies used, and potential moderating factors in interpersonal emotion regulation among 16 ice hockey players from an English professional league. An inductive and deductive analysis revealed 22 distinct strategies used to regulate teammates' emotions. These were distinguished between strategies that were verbal or behavioural in nature. They were further distinguished between strategies employed to initiate interpersonal emotion regulation through affective and cognitive channels. Moderating factors in the interpersonal emotion regulation process were consistent with the EASI model. Stage three involved the development, delivery and assessment of the intervention. A British ice hockey team was recruited and the intervention was delivered over the course of three competitive seasons. The primary intervention goal was to improve interpersonal emotion regulation as evidenced by being able to accurately identify when an emotion regulation strategy was needed, and select and use a strategy that changed emotions in the direction and strength intended (Webb, Miles, & Sheeran, 2012). Given the link between emotion and performance, it was expected that the intervention would bring about improvements in individual and team performance. Techniques to bring about change comprised of brief contact interventions, dressing room debriefs, feedback from emotional intelligence assessments, and the practitioner managing himself as an intervention tool. The merit of the intervention was judged through practitioner reflections, social validity assessments, pre- and post-intervention measures of emotional intelligence and performance. Collectively, the present research programme contributes to the emotion regulation literature not only in sport, but also in psychology in general. A key achievement of the programme has been the development of a theoretically sound but ecologically valid intervention designed to improve the interpersonal emotion regulation skills of athletes. Although the intervention primarily catered to the needs of the current team and utilised the professional philosophy of the researcher-practitioner, the intervention provides support for enhanced performance derived from theory explaining a social-functional account of emotions. Future research might use the theory and approach to testing the theory in different sports to examine the role of each sport sub-culture on interpersonal emotion regulation.

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Stanley, Jennifer Tehan. „Emotion recognition in context“. Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24617.

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Thesis (Ph.D.)--Psychology, Georgia Institute of Technology, 2008.
Committee Chair: Blanchard-Fields, Fredda; Committee Member: Corballis, Paul; Committee Member: Hertzog, Christopher; Committee Member: Isaacowitz, Derek; Committee Member: Kanfer, Ruth

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Perez-Rivera, Marie Belle. „Mothers' beliefs about emotions, mother-child emotion discourse, and children's emotion understanding in Latino families“. Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32229.

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The purpose of this study was to better understand associations between acculturation, parental beliefs, mother-child emotion talk, and emotion understanding in Latino preschool-aged children. Research on Latino families may prove to be important given the little research that has focused on emotion understanding strictly in Latino cultures. Forty Latino mother-child dyads were observed throughout a series of naturalistic observations. Mothers self-reported their acculturation and their beliefs about the value and danger of childrenâ s emotions, childrenâ s emotional development processes, and their role in guiding their childrenâ s emotions. Mother-child emotion talk and framing was measured during a 15 minute story-telling task using a Lego house and through a wordless picture book. Childrenâ s emotion understanding was measured using two standard tasks. Results showed that mothersâ acculturation was related to their beliefs about the danger of emotions, their role in guiding their childâ s emotions, and their childâ s readiness to learn about emotions. Mothersâ acculturation was also related to childrenâ s emotion understanding. Mothersâ beliefs about guiding childrenâ s emotions were related to mothersâ labeling of emotions and to childrenâ s emotion understanding. This study confirms and expands several previous findings relating to emotion socialization of children. Overall, results highlight the importance of acculturation for parentsâ beliefs about emotions and childrenâ s emotion understanding.
Master of Science

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Glisenti, Kevin. „Emotion focused therapy for binge-eating disorder“. Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213834/1/Kevin_Glisenti_Thesis.pdf.

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This thesis explored the feasibility and efficacy of individual emotion-focused therapy for binge-eating disorder, and the role of beliefs about emotions and emotional expressivity as potential mechanisms of change. The unique findings of this research will assist clinicians and patients by providing another treatment option for binge-eating disorder, which is important given mounting evidence of the comparatively high prevalence and clinical significance, and the paucity of proven effective treatment approaches for this condition compared to other eating disorders such as anorexia nervosa and bulimia nervosa.

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Sison, Jo Ann G. „Memory interference and emotion : does remembering emotional items impair recall of other same-emotion items? /“. Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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Laukka, Petri. „Vocal Expression of Emotion : Discrete-emotions and Dimensional Accounts“. Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, Uppsala universitet, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4666.

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CARRETERO, MIGUEL RAMOS. „Expression of Emotion in Virtual Crowds:Investigating Emotion Contagion and Perception of Emotional Behaviour in Crowd Simulation“. Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153966.

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Emotional behaviour in the context of crowd simulationis a topic that is gaining particular interest in the area of artificial intelligence. Recent efforts in this domain havelooked for the modelling of emotional emergence and socialinteraction inside a crowd of virtual agents, but further investigation is still needed in aspects such as simulation of emotional awareness and emotion contagion. Also, in relation to perception of emotions, many questions remain about perception of emotional behaviour in the context of virtual crowds.This thesis investigates the current state-of-the-art of emotional characters in virtual crowds and presents the implementation of a computational model able to generate expressive full-body motion behaviour and emotion contagion in a crowd of virtual agents. Also, as a second part of the thesis, this project presents a perceptual study in which the perception of emotional behaviour is investigated in the context of virtual crowds. The results of this thesis reveal some interesting findings in relation to the perception and modelling of virtual crowds, including some relevant effectsin relation to the influence of emotional crowd behaviourin viewers, specially when virtual crowds are not the mainfocus of a particular scene. These results aim to contribute for the further development of this interdisciplinary area of computer graphics, artificial intelligence and psychology.
Emotionellt beteende i simulerade folkmassor är ett ämne med ökande intresse, inom området för artificiell intelligens. Nya studier har tittat på modellen för social interaktion inuti en grupp av virtuella agenter, men fortsatt utredning behövs fortfarande inom aspekter så som simulation av emotionell medvetenhet och emotionell smitta. Också, när det gäller synen på känslor, kvarstår många frågor kring synen på känslomässigt beteende i samband med virtuella folkmassor. Denna studie undersöker de nuvarande "state-of-theart" emotionella egenskaperna i virtuella folksamlingar och presenterar implementationen av en datormodell som kan generera smittsamma känslor i en grupp av virtuella agenter. Också, när det gäller synen på känslor, kvarstår många frågor kring synen på känslomässigt beteende i samband med virtuella folksamlingar. Som en andra del av denna avhandlingen presenteras, i detta projekt, en perceptuell studie där uppfattningen av emotionella beteenden undersöks i samband med virtuella folksamlingar.

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Bücher zum Thema "Emotion"

Arnold, Magda B. Emotion and personality. New York: Columbia University Press, 1987.

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Goozen, Stephanie H. M. van, Poll, Nanne E. van de und Sergeant Joseph A, Hrsg. Emotions: Essays on emotion theory. Hillsdale, N.J: L. Erlbaum, 1994.

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Sclater, Shelley Day, David W. Jones, Heather Price und Candida Yates, Hrsg. Emotion. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230245136.

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Brady, Michael S. Emotion. 1 [edition]. | New York : Routledge, 2019. | Series: The basics: Routledge, 2018. http://dx.doi.org/10.4324/9780429025068.

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N, Shiota Michelle, Hrsg. Emotion. 2. Aufl. Belmont, Calif: Wadsworth, 2011.

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W, Kalat James, Hrsg. Emotion. 2. Aufl. Australia: Wadsworth, 2012.

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Association, American Psychological, Hrsg. Emotion-focused therapy. Washington, DC: American Psychological Association, 2011.

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Cinque, Kristen. Emotion in Emotions. Divine Alliance LLC, 2017.

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Corrigan, John. Emotion. Herausgegeben von Michael Stausberg und Steven Engler. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198729570.013.37.

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Religion often has attended to emotion as a part of religious experience. While that has enhanced theological depth and precision, it also has framed a highly psychologized view of emotion in religion. Historical scholarship of religious practice has prompted other views of emotion, shifting attention from religious experience to culturally derived emotional frameworks. Researchers have also emphasized the cognitive element in emotion, and the biological superstructures of emotional life. Research has translated to a range of investigative projects, some blending discussion of cognition, feeling, and biology with analysis of social life and the role of culture in emotional performances in religious settings. Current research on emotion has revolved around a fairly standard listing of familiar emotions. It may be, however, that emotions or emotional clusters in religion are not easily placed on such lists. The subfield should not shy from studying emotions that seem strange.

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Meyer, Susan Sauvé, und Adrienne M. Martin. Emotion and the Emotions. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199545971.013.0030.

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Buchteile zum Thema "Emotion"

Frijda, Nico H., Suprapti Markam, Kaori Sato und Reinout Wiers. „Emotions and Emotion Words“. In Everyday Conceptions of Emotion, 121–43. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8484-5_7.

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Porter, Louise. „Emotions and emotion regulation“. In Counselling Young People, 129–48. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003323938-12.

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von Tetzchner, Stephen. „Emotions and Emotion Regulation“. In Child and Adolescent Psychology, 339–67. 1 Edition. | New York : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781315742113-17.

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Frenzel, Anne C., Thomas Goetz und Kristina Stockinger. „Emotions and Emotion Regulation“. In Handbook of Educational Psychology, 219–44. 4. Aufl. New York: Routledge, 2023. http://dx.doi.org/10.4324/9780429433726-13.

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Hesse, Colin, und Alan C. Mikkelson. „Emotion and Emotional Communication“. In The Handbook of Communication Science and Biology, 295–307. New York, NY : Routledge, 2020.: Routledge, 2020. http://dx.doi.org/10.4324/9781351235587-25.

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Bozinovski, Stevo. „Artificial Emotion and Emotion Learning: Emotions as Value Judgements“. In Affective Interactions, 138–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720296_10.

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Ann Wilkinson, Rebecca, und Gioia Chilton. „Positive Emotions and Emotion Regulation“. In Positive Art Therapy Theory and Practice, 64–81. First edition. | New York : Routledge, 2018.: Routledge, 2017. http://dx.doi.org/10.4324/9781315694245-6.

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Sclater, Shelley Day, Candida Yates, Heather Price und David W. Jones. „Introducing Psychosocial Studies of Emotion“. In Emotion, 1–16. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230245136_1.

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Hollway, Wendy, und Tony Jefferson. „Panic and Perjury: A Psychosocial Exploration of Agency“. In Emotion, 123–38. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230245136_10.

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Urwin, Cathy. „Separation and Changing Identity in Becoming a Mother“. In Emotion, 139–52. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230245136_11.

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Konferenzberichte zum Thema "Emotion"

Fan, Xiaoli, Hua Guo, Feng Wu und Shuyu Shao. „Effectiveness Evaluation of Video Evoked Target-Emotion Method“. In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004294.

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This paper mainly evaluates and studies the effectiveness of video to induce five target emotions: happiness, sadness, relaxation, fear and neutrality, providing theoretical basis and guiding significance for better application of such methods in emotion-related research in the future. This study adopted the methods of desktop research, subjective self-evaluation and expert evaluation to collect and screen out 20 emotion-inducing video materials from major mainstream video websites. On this basis, a reasonable emotion-inducing experiment paradigm was designed: First, the guidance was presented, and then the video clips were played to induce the target emotions. After each video, the subjects were required to complete the evaluation of the perception and experience. In order to avoid the mutual influence of the emotional states of different video clips, the subjects were required to rest and recover during the interval before starting the next trial. As for the rules of subjective self-evaluation of emotions, referring to the report questionnaire containing multiple emotional dimensions used by Gross to evaluate movie clips, this paper selects 10 relevant dimensions to form a questionnaire, including happiness, arousal, fear, happiness, interest, pain, relaxation, sadness, surprise and tension, and finally collects the emotional dimensions induced by videos and the corresponding emotional intensity. The emotional intensity was scored by Likert 9-point scoring method. The subjective evaluation data of 68 subjects were collected in the formal experiment. The intensity and differentiation of target emotion were used as the evaluation indexes of the effectiveness of emotion induced by video materials in the paper. Emotional intensity refers to the score of emotional intensity of subjects in the subjective evaluation process, and the hit rate is used to represent the differentiation degree of emotional induction, specifically referring to the ratio of the number of subjects whose score difference of target emotion is higher than that of non-target emotion by ≥1 point to the total number of subjects. Taking the video clip with the most successful target emotion induction as an example, it is found that the target emotion evoked hit rate of these videos is greater than 50% through calculation, indicating that the target emotion evoked is relatively concentrated. In order to further test the effectiveness of emotional induction of video materials, the emotional dimension in the subjective evaluation data was taken as the independent variable, and the corresponding score value was taken as the dependent variable. One-way ANOVA and LSD post test were performed on the subjective score value of each target emotion-induced video material. The results showed that different video materials would produce higher intensity in several emotional dimensions (P<0.05) after emotional induction, but in contrast, the score of target emotion dimension was significantly higher than that of other emotional dimensions, indicating the effectiveness of video in inducing target emotion. This study has a certain reference value for the establishment of emotion database and the effective induction of laboratory emotion in the future emotional research.

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Yang, Jufeng, Dongyu She und Ming Sun. „Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network“. In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/456.

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Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in convolutional neural networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction. While the proposed method prefers to the distribution dataset with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e., Emotion6, Flickr_LDL, Twitter_LDL, and the largest single emotion dataset, i.e., Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.

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Esau, Natascha, Lisa Kleinjohann und Bernd Kleinjohann. „Emotional Competence in Human-Robot Communication“. In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49409.

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Since emotional competence is an important factor in human communication, it will certainly also improve communication between humans and robots or other machines. Emotional competence is defined by the aspects emotion recognition, emotion representation, emotion regulation and emotional behavior. In this paper we present how these aspects are intergrated into the architecture of the robot head MEXI. MEXI is able to recognize emotions from facial expressions and prosody of natural speech and represents its internal state made up of emotions and drives by according facial expressions, head movements and speech utterances. For its emotions and drives internal and external regulation mechanisms are realized. Furthermore, this internal state and its perceptions, including the emotions recognized at its human counterpart, are used by MEXI to control its actions. Thereby MEXI can react adequately in an emotional communication.

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Lorenzo Bautista, John, Yun Kyung Lee, Seungyoon Nam, Chanki Park und Hyun Soon Shin. „Utilizing Dimensional Emotion Representations in Speech Emotion Recognition“. In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004283.

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Speech is a natural way of communication amongst humans and advancements in speech emotion recognition (SER) technology allow further improvement of human-computer interactions (HCI) with speech by understanding human emotions. SER systems are traditionally focused on categorizing emotions into discrete classes. However, discrete classes often overlook some subtleties between each emotion as they are prone to individual differences and cultures. In this study, we focused on the use of dimensional emotional values: valence, arousal, and dominance as outputs for an SER instead of the traditional categorical classification. An SER model is developed using largely pre-trained models Wav2Vec 2.0 and HuBERT as feature encoders as a feature extraction technique from raw audio input. The model’s performance is assessed using a mean concordance coefficient (CCC) score for models trained on an English language-based dataset called Interactive Emotional Dyadic Motion Capture (IEMOCAP) and a Korean language-based dataset called Korean Emotion Multimodal Database (KEMDy19). For the experiments done on the IEMOCAP dataset, we reported a mean CCC of 0.3673 on the Wav2Vec 2.0-based model with CCC values of 0.3004, 0.4585, and 0.3431 for the valence, arousal, and dominance values respectively trained on the “anger”, “happy”, “sad”, and “neutral” emotion classes. Meanwhile, a mean CCC of 0.3573 on the HuBERT-based model with CCC values of 0.2789, 0.3295, and 0.3361 for the respectively on the same set of emotional classes. For the experiments done on the KEMDy19 dataset, a mean CCC of 0.5473 on the Wav2Vec 2.0-based model with CCC values of 0.5804 and 0.5142 for the valence and arousal were achieved using all available emotional classes on the dataset, while a mean CCC of 0.5580 from CCC values of 0.5941 and 0.5219 on four emotional classes “anger”, “happy”, “sad”, and “neutral” were observed. For the HuBERT-based model, a mean CCC of 0.5271 with CCC values of 0.5429 and 0.5113 for the valence and arousal were recorded using all available emotional classes, while a mean CCC of 0.5392 from CCC values of 0.5765 and 0.5019 for the valence and arousal values on the four selected emotional classes. The proposed approach outperforms traditional machine learning methods and previously reported CCC values from other literature. Moreover, the use of dimensional emotional values provides a more fine-grained insight into the user’s emotional states allowing for a much deeper understanding of one’s affective state with reduced dimensionality. By applying such SER technologies to other areas such as HCI, affective computing, and psychological research, more personalized and adaptable user interfaces can be developed to suit the emotional needs of each individual. This could also contribute to the advancement of our understanding of human factors by developing emotion recognition systems.

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Sinha, Arryan, und G. Suseela. „Deep Learning-Based Speech Emotion Recognition“. In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-0892re.

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Speech Emotion Recognition, as described in this study, uses Neural Networks to classify the emotions expressed in each speech (SER). It’s centered upon concept where voice tone and pitch frequently reflect underlying emotion. Speech Emotion Recognition aids in the classification of elicited emotions. The MLP-Classifier is a tool for classifying emotions in a circumstance. As wave signal, allowing for flexible learning rate selection. RAVDESS (Ryerson Audio-Visual Dataset Emotional Speech and Song Database data) will be used. To extract the characteristics from particular audio input, Contrast, MFCC, Mel Spectrograph Frequency, & Chroma are some of factors that may be employed. To facilitate extraction of features from audio script, dataset will be labelled using decimal encoding. Utilizing input audio sample, precision was found to be 80.28%. Additional testing confirmed this result.

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Schmid, Ramona, Sophia Maria Saat, Knut Möller und Verena Wagner-Hartl. „Induction method influence on emotion recognition based on psychophysiological parameters“. In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002851.

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Recognizing emotions is an essential ability in our daily social interactions. However, there are individuals who have difficulties interpreting emotions, such as patients with autism spectrum disorders (ASD). In order to cope better with everyday life, emotion training can be a supporting factor for them. However, studies show that emotion training is not only helpful for patients with ASD, but also in the working environment, for example in trainings for managers or teams. In recent research, there are already approaches to use new technologies such as virtual reality to train emotional and social skills. For the evaluation of these new concepts, it is important to make the emotional state of a person measurable. Therefore, a measurement environment has already been developed at Furtwangen University. This is based on a multidimensional approach combining subjective and objective psychophysiological measures. Moreover, the development of facial emotion recognition (FER) systems based on machine learning techniques are also increasing for measuring a person's emotional state. Often, they focus on the recognition of Ekman’s basic emotions. To train and evaluate such FER systems, these basic emotions have to be induced in an individual. Therefore, a number of methods for emotion induction can be found in research, e.g. visual stimuli or mental methods. However, in most studies, only a few selected emotions, such as anger and happiness, were induced. Thus, there is a lack of studies that examined the induction of all six basic emotions.For that reason, the aim of the presented experimental study was to investigate two different methods of emotion induction for the six basic emotions anger, disgust, fear, happiness, sadness, surprise, and a neutral category. Overall, 14 women and 10 men (N = 24) aged between 19 and 59 years (M = 29.25, SD = 11.46) participated in the study. For the first induction method, affective visual stimuli from common emotional picture databases (EmoPicS, OASIS and IAPS) were used. For the second induction method, emotions were induced by a so-called autobiographical recall. Therefore, the participants had to imagine autobiographical situations that evoked the required emotion in them in the past. After each different induction of one of the six emotions or the neutral category, the participants’ emotional state was assessed using the two dimensions valence and arousal of the Self-Assessment Manikin (SAM). Furthermore, cardiovascular (ECG) and electrodermal (EDA) activity were recorded. The results show a significant interaction induction method x emotional category for both subjective assessments valence and arousal. Furthermore, based on the results of the psychophysiological responses of the participants (ECG and EDA), it is shown that the second method to induce emotions (autobiographical recall) was significantly more arousing than the first induction method using visual stimuli. To sum it up, the results of the experimental study show an influence of the induction method that is evident in both the subjective and the psychophysiological parameters.

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Tivatansakula, Somchanok, Gantaphon Chalumpornb und Supadchaya Puangpontipb. „Healthcare System Focusing on Emotional Aspect Using Augmented Reality: Emotion Detection by Facial Expression“. In Applied Human Factors and Ergonomics Conference. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe100521.

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Current research includes many proposals of systems that provide assistances and services to people in the healthcare fields; however, these systems emphasize the support physical rather than emotional aspects. Emotional health is as important as physical health. Negative emotional health can lead to social or mental health problems. To cope with negative emotional health in daily life, we propose a healthcare system that focuses on emotional aspects. This system provides services to improve user emotion. To improve user emotion, we need to recognize users’ current emotional state. Therefore, our system integrates emotion detection to suggest the appropriate service. This system is designed as a web-based system. While users use the system, facial expression and speech are detected and analyzed and to determine the users’ emotions. When negative emotions are detected, our system suggests that the users take a break by providing services (designed to provide relaxation, amusement and excitement services) with augmented reality and Kinect to improve their emotional state. This paper focuses on feature extraction and classification of emotion detection by facial expression recognition.

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G. Ho, Amic. „Exploring the Role of Emotion in the Design Cycle“. In Applied Human Factors and Ergonomics Conference. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe100583.

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Scholars have focused on “design and emotion” for more than a decade. Various studies, models, and theories have been proposed and adopted to explore the relationship between design and emotion, and to explain how emotion can be applied in the design process. After the theories are categorised according to the respective focuses, three main components that influence or are influenced by emotion can be identified: designers, design outcome and users or consumers in the design cycle. Based on the relationships amongst these components, numerous similar terms categorised under emotion and design that have clear and concrete rationales and definitions have been developed. For instance, “emotionalised design” refers to design in which designers introduce their emotions into the design process. “Emotional design” is viewed as design that can stimulate users’ or consumers’ emotion. “Emotion design” is design in which emotional concerns are involved in the interactions between designers and users.However, few studies have focused on designers’ perceptions of these terms and of the role of emotion in design. By conducting an empirical study, we investigated designers’ perceptions of three terms, emotion design, emotional design, and emotionalised design, and explored designers’ experience regarding emotion and design.

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Huang, Zhaopei, Jinming Zhao und Qin Jin. „ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains“. In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/695.

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Understanding the process of emotion generation is crucial for analyzing the causes behind emotions. Causal Emotion Entailment (CEE), an emotion-understanding task, aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. However, current works in CEE mainly focus on modeling semantic and emotional interactions in conversations, neglecting the exploration of the emotion-generation process. This hinders the models from deeply understanding emotions, restricting their ability to produce explainable predictions. In this work, inspired by the emotion generation process of "stimulus-appraisal-emotion" in the cognitive appraisal theory, we introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations. Specifically, we first introduce the ECR-Chain to ChatGPT via few-shot prompting, which significantly improves its performance on the CEE task. We further propose an automated construction process to utilize ChatGPT in building an ECR-Chain set, which can enhance the reasoning abilities of smaller models through supervised training and assist the Vicuna-7B model in achieving state-of-the-art CEE performance. Moreover, our methods can enable these generative language models to effectively perform emotion-cause reasoning in an explainable manner. Our code, data and more details are at https://github.com/hzp3517/ECR-Chain.

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S. V., Shabin, Reena Benjamin J und Juliet Rose D. B. „Development of Emotion Detector Using Biometric“. In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/nmli5686/ngcesi23p123.

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Emotions play a vital role in people’s everyday life. It is a mental state that does not arise through free will and is often accompanied by physiological changes. Therefore monitoring these changes is important as they are perceptions of emotional changes and can help in identifying matters of concern at an early stage before they become serious. Emotion recognition has become an important subject when it comes to human-machine interaction. Various methods have been used in the past to detect and evaluate human emotions. The most commonly used techniques include the use of textual information, facial expressions, speech, body gestures and physiological signals. In this project we have developed an emotion recognition system based on information provided by the physiological signals. These signals are obtained from a skin temperature sensor, a heart rate sensor, and a blood pressure sensor. The amplified and filtered signals from the sensors are input into the microcontroller where all the processing takes place. The microcontroller wirelessly transmits data to a computer where it is stored for data analyses and feature extraction for emotion recognition. The three basic emotions observed in this project are happy (excited), stressed and neutral (relaxed).

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Berichte der Organisationen zum Thema "Emotion"

Shadel, Doug, Alicia Williams, Karla Pak und Lona Choi-Allum. Emotion: Scammers Use Our Emotions Against Us - Infographic. Washington, DC: AARP Research, Oktober 2021. http://dx.doi.org/10.26419/res.00484.002.

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Yücel Koç, Melike. Emotion Language and Emotion Narratives of Turkish-English Late Bilinguals. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.208.

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Shadel, Doug, Alicia Williams, Karla Pak und Lona Choi-Allum. Emotion: Scammers Use Our Emotions Against Us – Infographic. Spotlight on Black Americans. Washington, DC: AARP Research, Oktober 2021. http://dx.doi.org/10.26419/res.00484.006.

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Shadel, Doug, Alicia Williams, Karla Pak und Lona Choi-Allum. Emotion: Scammers Use Our Emotions Against Us – Infographic. Spotlight on Hispanic Americans. Washington, DC: AARP Research, Oktober 2021. http://dx.doi.org/10.26419/res.00484.010.

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Mills, Carolyn. Synergism of substance and emotion. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.5339.

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Ivanova, S. E. Verbalization emotion: age and gender differences. LJournal, 2016. http://dx.doi.org/10.18411/a-2017-001.

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Gratch, Jonathan, und Stacy Marsella. Evaluating a Computational Model of Emotion. Fort Belvoir, VA: Defense Technical Information Center, Januar 2006. http://dx.doi.org/10.21236/ada459183.

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Deng, Yan. Development of Emotion-Based Education Software. Ames (Iowa): Iowa State University, Januar 2019. http://dx.doi.org/10.31274/cc-20240624-543.

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Fernandez, Katya, und Cathleen Clerkin. The Stories We Tell: Why Cognitive Distortions Matter for Leaders. Center for Creative Leadership, Juni 2021. http://dx.doi.org/10.35613/ccl.2021.2045.

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"This study explored whether leaders’ thought patterns (specifically cognitive distortions) and emotion regulation strategies (specifically cognitive reappraisal, cognitive defusion, and expressive suppression) relate to their work experiences. Findings suggest that leaders’ cognitive distortions are related to their work experiences and that emotion regulation strategies can help leaders mitigate the effects of cognitive distortions. More specifically, the results of this study offer the following insights: • Leaders’ cognitive distortions related to all examined workplace topics (role ambiguity, role conflict, social support, perceived organizational support, job satisfaction, and burnout). • Leaders’ use of emotion regulation strategies (cognitive reappraisal and cognitive defusion) mitigated the impact their cognitive distortions had on burnout, specifically. • Attempting to suppress emotional responses was relatively ineffective compared to the other two emotion regulation strategies (cognitive reappraisal and cognitive defusion). These insights suggest that certain emotion regulation strategies may be helpful in ameliorating the deleterious effects of cognitive distortions on leaders’ burnout. The current paper provides an overview of the different cognitive distortions and emotion regulation strategies explored and includes advice on what leaders can do to more effectively notice and manage cognitive distortions that emerge during distressing situations. "

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Zumalde-Arregi, Imanol. The filmic emotion. A comparative analysis of film theories. Revista Latina de Comunicación Social, 2011. http://dx.doi.org/10.4185/rlcs-66-2011-936-326-349-en.

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