Emotion Perception in Schizophrenia: Context Matters
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Improving Emotion Perception and Emotion Regulation Through a Web-Based Emotional Intelligence Training (WEIT) Program for Future Leaders
Volume 11, Number 2, November 2019 pp 17 - 32 www.um.edu.mt/ijee Improving Emotion Perception and Emotion Regulation Through a Web-Based Emotional Intelligence Training (WEIT) Program for Future Leaders 1 Christina Köppe, Marco Jürgen Held and Astrid Schütz University of Bamberg, Bamberg, Germany We evaluated a Web-Based Emotional Intelligence Training (WEIT) program that was based on the four-branch model of emotional intelligence (EI) and which aimed at improving emotion perception (EP) and emotion regulation (ER) in future leaders. Using a controlled experimental design, we evaluated the short-term (directly after the WEIT program) and long-term (6 weeks later) effects in a sample of 134 (59 training group [TG], 75 wait list control group [CG]) business students, and additionally tested whether WEIT helped to reduce perceived stress. For EP, WEIT led to a significant increase in the TG directly after training (whereas the wait list CG showed no change). Changes remained stable after 6 weeks in the TG, but there were no significant differences between the TG and CG at follow-up. By contrast, ER did not show an increase directly after WEIT, but 6 weeks later, the TG had larger improvements than the CG. The results mostly confirmed that emotional abilities can be increased through web-based training. Participants’ perceived stress did not decrease after the training program. Further refinement and validation of WEIT is needed. Keywords: emotional intelligence, web-based training, stress, future leaders First submission 15th May 2019; Accepted for publication 4th September 2019. Introduction Emotional intelligence (EI) has attracted considerable attention in recent years (see Côté, 2014). -
DISGUST: Features and SAWCHUK and Clinical Implications
Journal of Social and Clinical Psychology, Vol. 24, No. 7, 2005, pp. 932-962 OLATUNJIDISGUST: Features AND SAWCHUK and Clinical Implications DISGUST: CHARACTERISTIC FEATURES, SOCIAL MANIFESTATIONS, AND CLINICAL IMPLICATIONS BUNMI O. OLATUNJI University of Massachusetts CRAIG N. SAWCHUK University of Washington School of Medicine Emotions have been a long–standing cornerstone of research in social and clinical psychology. Although the systematic examination of emotional processes has yielded a rather comprehensive theoretical and scientific literature, dramatically less empirical attention has been devoted to disgust. In the present article, the na- ture, experience, and other associated features of disgust are outlined. We also re- view the domains of disgust and highlight how these domains have expanded over time. The function of disgust in various social constructions, such as cigarette smoking, vegetarianism, and homophobia, is highlighted. Disgust is also becoming increasingly recognized as an influential emotion in the onset, maintenance, and treatment of various phobic states, Obsessive–Compulsive Disorder, and eating disorders. In comparison to the other emotions, disgust offers great promise for fu- ture social and clinical research efforts, and prospective studies designed to improve our understanding of disgust are outlined. The nature, structure, and function of emotions have a rich tradition in the social and clinical psychology literature (Cacioppo & Gardner, 1999). Although emotion theorists have contested over the number of discrete emotional states and their operational definitions (Plutchik, 2001), most agree that emotions are highly influential in organizing thought processes and behavioral tendencies (Izard, 1993; John- Preparation of this manuscript was supported in part by NIMH NRSA grant 1F31MH067519–1A1 awarded to Bunmi O. -
About Emotions There Are 8 Primary Emotions. You Are Born with These
About Emotions There are 8 primary emotions. You are born with these emotions wired into your brain. That wiring causes your body to react in certain ways and for you to have certain urges when the emotion arises. Here is a list of primary emotions: Eight Primary Emotions Anger: fury, outrage, wrath, irritability, hostility, resentment and violence. Sadness: grief, sorrow, gloom, melancholy, despair, loneliness, and depression. Fear: anxiety, apprehension, nervousness, dread, fright, and panic. Joy: enjoyment, happiness, relief, bliss, delight, pride, thrill, and ecstasy. Interest: acceptance, friendliness, trust, kindness, affection, love, and devotion. Surprise: shock, astonishment, amazement, astound, and wonder. Disgust: contempt, disdain, scorn, aversion, distaste, and revulsion. Shame: guilt, embarrassment, chagrin, remorse, regret, and contrition. All other emotions are made up by combining these basic 8 emotions. Sometimes we have secondary emotions, an emotional reaction to an emotion. We learn these. Some examples of these are: o Feeling shame when you get angry. o Feeling angry when you have a shame response (e.g., hurt feelings). o Feeling fear when you get angry (maybe you’ve been punished for anger). There are many more. These are NOT wired into our bodies and brains, but are learned from our families, our culture, and others. When you have a secondary emotion, the key is to figure out what the primary emotion, the feeling at the root of your reaction is, so that you can take an action that is most helpful. . -
Emotion Perception in Habitual Players of Action Video Games Swann Pichon, Benoit Bediou, Lia Antico, Rachael Jack, Oliver Garrod, Chris Sims, C
Emotion Emotion Perception in Habitual Players of Action Video Games Swann Pichon, Benoit Bediou, Lia Antico, Rachael Jack, Oliver Garrod, Chris Sims, C. Shawn Green, Philippe Schyns, and Daphne Bavelier Online First Publication, July 6, 2020. http://dx.doi.org/10.1037/emo0000740 CITATION Pichon, S., Bediou, B., Antico, L., Jack, R., Garrod, O., Sims, C., Green, C. S., Schyns, P., & Bavelier, D. (2020, July 6). Emotion Perception in Habitual Players of Action Video Games. Emotion. Advance online publication. http://dx.doi.org/10.1037/emo0000740 Emotion © 2020 American Psychological Association 2020, Vol. 2, No. 999, 000 ISSN: 1528-3542 http://dx.doi.org/10.1037/emo0000740 Emotion Perception in Habitual Players of Action Video Games Swann Pichon, Benoit Bediou, and Lia Antico Rachael Jack and Oliver Garrod University of Geneva, Campus Biotech Glasgow University Chris Sims C. Shawn Green Rensselaer Polytechnic Institute University of Wisconsin–Madison Philippe Schyns Daphne Bavelier Glasgow University University of Geneva, Campus Biotech Action video game players (AVGPs) display superior performance in various aspects of cognition, especially in perception and top-down attention. The existing literature has examined these performance almost exclusively with stimuli and tasks devoid of any emotional content. Thus, whether the superior performance documented in the cognitive domain extend to the emotional domain remains unknown. We present 2 cross-sectional studies contrasting AVGPs and nonvideo game players (NVGPs) in their ability to perceive facial emotions. Under an enhanced perception account, AVGPs should outperform NVGPs when processing facial emotion. Yet, alternative accounts exist. For instance, under some social accounts, exposure to action video games, which often contain violence, may lower sensitivity for empathy-related expressions such as sadness, happiness, and pain while increasing sensitivity to aggression signals. -
1 Automated Face Analysis for Affective Computing Jeffrey F. Cohn & Fernando De La Torre Abstract Facial Expression
Please do not quote. In press, Handbook of affective computing. New York, NY: Oxford Automated Face Analysis for Affective Computing Jeffrey F. Cohn & Fernando De la Torre Abstract Facial expression communicates emotion, intention, and physical state, and regulates interpersonal behavior. Automated Face Analysis (AFA) for detection, synthesis, and understanding of facial expression is a vital focus of basic research. While open research questions remain, the field has become sufficiently mature to support initial applications in a variety of areas. We review 1) human-observer based approaches to measurement that inform AFA; 2) advances in face detection and tracking, feature extraction, registration, and supervised learning; and 3) applications in action unit and intensity detection, physical pain, psychological distress and depression, detection of deception, interpersonal coordination, expression transfer, and other applications. We consider user-in-the-loop as well as fully automated systems and discuss open questions in basic and applied research. Keywords Automated Face Analysis and Synthesis, Facial Action Coding System (FACS), Continuous Measurement, Emotion, Nonverbal Communication, Synchrony 1. Introduction The face conveys information about a person’s age, sex, background, and identity, what they are feeling, or thinking (Darwin, 1872/1998; Ekman & Rosenberg, 2005). Facial expression regulates face-to-face interactions, indicates reciprocity and interpersonal attraction or repulsion, and enables inter-subjectivity between members of different cultures (Bråten, 2006; Fridlund, 1994; Tronick, 1989). Facial expression reveals comparative evolution, social and emotional development, neurological and psychiatric functioning, and personality processes (Burrows & Cohn, In press; Campos, Barrett, Lamb, Goldsmith, & Stenberg, 1983; Girard, Cohn, Mahoor, Mavadati, & Rosenwald, 2013; Schmidt & Cohn, 2001). Not surprisingly, the face has been of keen interest to behavioral scientists. -
A Review of Alexithymia and Emotion Perception in Music, Odor, Taste, and Touch
MINI REVIEW published: 30 July 2021 doi: 10.3389/fpsyg.2021.707599 Beyond Face and Voice: A Review of Alexithymia and Emotion Perception in Music, Odor, Taste, and Touch Thomas Suslow* and Anette Kersting Department of Psychosomatic Medicine and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany Alexithymia is a clinically relevant personality trait characterized by deficits in recognizing and verbalizing one’s emotions. It has been shown that alexithymia is related to an impaired perception of external emotional stimuli, but previous research focused on emotion perception from faces and voices. Since sensory modalities represent rather distinct input channels it is important to know whether alexithymia also affects emotion perception in other modalities and expressive domains. The objective of our review was to summarize and systematically assess the literature on the impact of alexithymia on the perception of emotional (or hedonic) stimuli in music, odor, taste, and touch. Eleven relevant studies were identified. On the basis of the reviewed research, it can be preliminary concluded that alexithymia might be associated with deficits Edited by: in the perception of primarily negative but also positive emotions in music and a Mathias Weymar, University of Potsdam, Germany reduced perception of aversive taste. The data available on olfaction and touch are Reviewed by: inconsistent or ambiguous and do not allow to draw conclusions. Future investigations Khatereh Borhani, would benefit from a multimethod assessment of alexithymia and control of negative Shahid Beheshti University, Iran Kristen Paula Morie, affect. Multimodal research seems necessary to advance our understanding of emotion Yale University, United States perception deficits in alexithymia and clarify the contribution of modality-specific and Jan Terock, supramodal processing impairments. -
Emotion Classification Based on Biophysical Signals and Machine Learning Techniques
S S symmetry Article Emotion Classification Based on Biophysical Signals and Machine Learning Techniques Oana Bălan 1,* , Gabriela Moise 2 , Livia Petrescu 3 , Alin Moldoveanu 1 , Marius Leordeanu 1 and Florica Moldoveanu 1 1 Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Bucharest 060042, Romania; [email protected] (A.M.); [email protected] (M.L.); fl[email protected] (F.M.) 2 Department of Computer Science, Information Technology, Mathematics and Physics (ITIMF), Petroleum-Gas University of Ploiesti, Ploiesti 100680, Romania; [email protected] 3 Faculty of Biology, University of Bucharest, Bucharest 030014, Romania; [email protected] * Correspondence: [email protected]; Tel.: +40722276571 Received: 12 November 2019; Accepted: 18 December 2019; Published: 20 December 2019 Abstract: Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. -
Emotion Classification Based on Brain Wave: a Survey
Li et al. Hum. Cent. Comput. Inf. Sci. (2019) 9:42 https://doi.org/10.1186/s13673-019-0201-x REVIEW Open Access Emotion classifcation based on brain wave: a survey Ting‑Mei Li1, Han‑Chieh Chao1,2* and Jianming Zhang3 *Correspondence: [email protected] Abstract 1 Department of Electrical Brain wave emotion analysis is the most novel method of emotion analysis at present. Engineering, National Dong Hwa University, Hualien, With the progress of brain science, it is found that human emotions are produced by Taiwan the brain. As a result, many brain‑wave emotion related applications appear. However, Full list of author information the analysis of brain wave emotion improves the difculty of analysis because of the is available at the end of the article complexity of human emotion. Many researchers used diferent classifcation methods and proposed methods for the classifcation of brain wave emotions. In this paper, we investigate the existing methods of brain wave emotion classifcation and describe various classifcation methods. Keywords: EEG, Emotion, Classifcation Introduction Brain science has shown that human emotions are controlled by the brain [1]. Te brain produces brain waves as it transmits messages. Brain wave data is one of the biological messages, and biological messages usually have emotion features. Te feature of emo- tion can be extracted through the analysis of brain wave messages. But because of the environmental background and cultural diferences of human growth, the complexity of human emotion is caused. Terefore, in the analysis of brain wave emotion, classifcation algorithm is very important. In this article, we will focus on the classifcation of brain wave emotions. -
Emotion Autonomic Arousal
PSYC 100 Emotions 2/23/2005 Emotion ■ Emotions reflect a “stirred up’ state ■ Emotions have valence: positive or negative ■ Emotions are thought to have 3 components: ß Physiological arousal ß Subjective experience ß Behavioral expression Autonomic Arousal ■ Increased heart rate ■ Rapid breathing ■ Dilated pupils ■ Sweating ■ Decreased salivation ■ Galvanic Skin Response 2/23/2005 1 Theories of Emotion James-Lange ■ Each emotion has an autonomic signature 2/23/2005 Assessment of James-Lange Theory of Emotion ■ Cannon’s arguments against the theory: ß Visceral response are slower than emotions ß The same visceral responses are associated with many emotions (Î heart rate with anger and joy). ■ Subsequent research provides support: ß Different emotions are associated with different patterns of visceral activity ß Accidental transection of the spinal cord greatly diminishes emotional reactivity (prevents visceral signals from reaching brain) 2 Cannon-Bard Criticisms ■ Arousal without emotion (exercise) 2/23/2005 Facial Feedback Model ■ Similar to James-Lange, but not autonomic signature; facial signature associated with each emotion. 2/23/2005 Facial Expression of Emotion ■ There is an evolutionary link between the experience of emotion and facial expression of emotion: ß Facial expressions serve to inform others of our emotional state ■ Different facial expressions are associated with different emotions ß Ekman’s research ■ Facial expression can alter emotional experience ß Engaging in different facial expressions can alter heart rate and skin temperature 3 Emotions and Darwin ■ Adaptive value ■ Facial expression in infants ■ Facial expression x-cultures ■ Understanding of expressions x-cultures 2/23/2005 Facial Expressions of Emotion ■ Right cortex—left side of face 2/23/2005 Emotions and Learning ■ But experience matters: research with isolated monkeys 2/23/2005 4 Culture and Display Rules ■ Public vs. -
Conceptual Framework for Quantum Affective Computing and Its Use in Fusion of Multi-Robot Emotions
electronics Article Conceptual Framework for Quantum Affective Computing and Its Use in Fusion of Multi-Robot Emotions Fei Yan 1 , Abdullah M. Iliyasu 2,3,∗ and Kaoru Hirota 3,4 1 School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China; [email protected] 2 College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia 3 School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan; [email protected] 4 School of Automation, Beijing Institute of Technology, Beijing 100081, China * Correspondence: [email protected] Abstract: This study presents a modest attempt to interpret, formulate, and manipulate the emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes emotion information as a superposition state, whilst unitary operators are used to manipulate the transition of emotion states which are subsequently recovered via appropriate quantum measurement operations. The framework described provides essential steps towards exploiting the potency of quantum mechanics in a quantum affective computing paradigm. Further, the emotions of multi-robots in a specified communication scenario are fused using quantum entanglement, thereby reducing the number of qubits required to capture the emotion states of all the robots in the environment, and therefore fewer quantum gates are needed to transform the emotion of all or part of the robots from one state to another. In addition to the mathematical rigours expected of the proposed framework, we present a few simulation-based demonstrations to illustrate its feasibility and effectiveness. This exposition is an important step in the transition of formulations of emotional intelligence to the quantum era. -
EMOTION CLASSIFICATION of COVERED FACES 1 WHO CAN READ YOUR FACIAL EXPRESSION? a Comparison of Humans and Machine Learning Class
journal name manuscript No. (will be inserted by the editor) 1 A Comparison of Humans and Machine Learning 2 Classifiers Detecting Emotion from Faces of People 3 with Different Coverings 4 Harisu Abdullahi Shehu · Will N. 5 Browne · Hedwig Eisenbarth 6 7 Received: 9th August 2021 / Accepted: date 8 Abstract Partial face coverings such as sunglasses and face masks uninten- 9 tionally obscure facial expressions, causing a loss of accuracy when humans 10 and computer systems attempt to categorise emotion. Experimental Psychol- 11 ogy would benefit from understanding how computational systems differ from 12 humans when categorising emotions as automated recognition systems become 13 available. We investigated the impact of sunglasses and different face masks on 14 the ability to categorize emotional facial expressions in humans and computer 15 systems to directly compare accuracy in a naturalistic context. Computer sys- 16 tems, represented by the VGG19 deep learning algorithm, and humans assessed 17 images of people with varying emotional facial expressions and with four dif- 18 ferent types of coverings, i.e. unmasked, with a mask covering lower-face, a 19 partial mask with transparent mouth window, and with sunglasses. Further- Harisu Abdullahi Shehu (Corresponding author) School of Engineering and Computer Science, Victoria University of Wellington, 6012 Wellington E-mail: [email protected] ORCID: 0000-0002-9689-3290 Will N. Browne School of Electrical Engineering and Robotics, Queensland University of Technology, Bris- bane QLD 4000, Australia Tel: +61 45 2468 148 E-mail: [email protected] ORCID: 0000-0001-8979-2224 Hedwig Eisenbarth School of Psychology, Victoria University of Wellington, 6012 Wellington Tel: +64 4 463 9541 E-mail: [email protected] ORCID: 0000-0002-0521-2630 2 Shehu H. -
On the Mutual Effects of Pain and Emotion: Facial Pain Expressions
PAINÒ 154 (2013) 793–800 www.elsevier.com/locate/pain On the mutual effects of pain and emotion: Facial pain expressions enhance pain perception and vice versa are perceived as more arousing when feeling pain ⇑ Philipp Reicherts a,1, Antje B.M. Gerdes b,1, Paul Pauli a, Matthias J. Wieser a, a Department of Psychology, Clinical Psychology, Biological Psychology, and Psychotherapy, University of Würzburg, Germany b Department of Psychology, Biological and Clinical Psychology, University of Mannheim, Germany Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. article info abstract Article history: Perception of emotional stimuli alters the perception of pain. Although facial expressions are powerful Received 27 August 2012 emotional cues – the expression of pain especially plays a crucial role for the experience and communi- Received in revised form 15 December 2012 cation of pain – research on their influence on pain perception is scarce. In addition, the opposite effect of Accepted 5 February 2013 pain on the processing of emotion has been elucidated even less. To further scrutinize mutual influences of emotion and pain, 22 participants were administered painful and nonpainful thermal stimuli while watching dynamic facial expressions depicting joy, fear, pain, and a neutral expression. As a control con- Keywords: dition of low visual complexity, a central fixation cross was presented. Participants rated the intensity of Pain expression the thermal stimuli and evaluated valence and arousal of the facial expressions. In addition, facial elec- EMG Heat pain tromyography was recorded as an index of emotion and pain perception. Results show that faces per se, Mutual influences compared to the low-level control condition, decreased pain, suggesting a general attention modulation of pain by complex (social) stimuli.