Constructing Emotion: the Experience of Fear As a Conceptual
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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. . -
Fear, Anger, and Risk
Journal of Personality and Social Psychology Copyright 2001 by the American Psychological Association, Inc. 2001. Vol. 81. No. 1, 146-159 O022-3514/01/$5.O0 DOI. 10.1037//O022-3514.81.1.146 Fear, Anger, and Risk Jennifer S. Lemer Dacher Keltner Carnegie Mellon University University of California, Berkeley Drawing on an appraisal-tendency framework (J. S. Lerner & D. Keltner, 2000), the authors predicted and found that fear and anger have opposite effects on risk perception. Whereas fearful people expressed pessimistic risk estimates and risk-averse choices, angry people expressed optimistic risk estimates and risk-seeking choices. These opposing patterns emerged for naturally occurring and experimentally induced fear and anger. Moreover, estimates of angry people more closely resembled those of happy people than those of fearful people. Consistent with predictions, appraisal tendencies accounted for these effects: Appraisals of certainty and control moderated and (in the case of control) mediated the emotion effects. As a complement to studies that link affective valence to judgment outcomes, the present studies highlight multiple benefits of studying specific emotions. Judgment and decision research has begun to incorporate affect In the present studies we follow the valence tradition by exam- into what was once an almost exclusively cognitive field (for ining the striking influence that feelings can have on normatively discussion, see Lerner & Keltner, 2000; Loewenstein & Lerner, in unrelated judgments and choices. We diverge in an important way, press; Loewenstein, Weber, Hsee, & Welch, 2001; Lopes, 1987; however, by focusing on the influences of specific emotions rather Mellers, Schwartz, Ho, & Ritov, 1997). To date, most judgment than on global negative and positive affect (see also Bodenhausen, and decision researchers have taken a valence-based approach to Sheppard, & Kramer, 1994; DeSteno et al, 2000; Keltner, Ells- affect, contrasting the influences of positive-affect traits and states worth, & Edwards, 1993; Lerner & Keltner, 2000). -
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. -
Neuroticism and Fear of COVID-19. the Interplay Between Boredom
ORIGINAL RESEARCH published: 13 October 2020 doi: 10.3389/fpsyg.2020.574393 Neuroticism and Fear of COVID-19. The Interplay Between Boredom, Fantasy Engagement, and Perceived Control Over Time Barbara Caci1*, Silvana Miceli1, Fabrizio Scrima2 and Maurizio Cardaci1 1Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy, 2Département de Psychologie, Université de Rouen, Moint Saint-Aignan, France The Italian government adopted measures to prevent the spread of coronavirus 2019 (COVID-19) infection from March 9, 2020, to May 4, 2020 and imposed a phase of social Edited by: distancing and self-isolation to all adult citizens. Although justified and necessary, Joanna Sokolowska, University of Social Sciences and psychologists question the impact of this process of COVID-19 isolation on the mental Humanities, Poland health of the population. Hence, this paper investigated the relationship between Reviewed by: neuroticism, boredom, fantasy engagement, perceived control over time, and the fear of Cinzia Guarnaccia, University of Rennes 2 – Upper COVID-19. Specifically, we performed a cross-sectional study aimed at testing an Brittany, France integrative moderated mediation model. Our model assigned the boredom to the mediation Martin Reuter, role and both the fantasy engagement and perceived control of time to the role of University of Bonn, Germany moderators in the relationship between neuroticism and the fear of COVID-19. A sample *Correspondence: Barbara Caci of 301 subjects, mainly women (68.8%), aged between 18 and 57 years (Mage = 22.12 years; [email protected] SD = 6.29), participated in a survey conducted in the 1st-week lockdown phase 2 in Italy from May 7 to 18, 2020. -
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. -
The Influence of Fear on Emotional Brand Attachment
Journal of Consumer Research, Inc. The Impact of Fear on Emotional Brand Attachment Author(s): Lea Dunn and JoAndrea Hoegg Source: Journal of Consumer Research, Vol. 41, No. 1 (June 2014), pp. 152-168 Published by: The University of Chicago Press Stable URL: http://www.jstor.org/stable/10.1086/675377 . Accessed: 07/07/2014 15:03 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. The University of Chicago Press and Journal of Consumer Research, Inc. are collaborating with JSTOR to digitize, preserve and extend access to Journal of Consumer Research. http://www.jstor.org This content downloaded from 128.189.82.95 on Mon, 7 Jul 2014 15:03:06 PM All use subject to JSTOR Terms and Conditions The Impact of Fear on Emotional Brand Attachment LEA DUNN JOANDREA HOEGG The current research investigates the role of fear in the creation of emotional attachment to a brand. Previous research examining the influence of incidental negative emotions on brand evaluations has generally found that negative emotions lead to negative evaluations. The current research suggests that for fear, the relationship may be more positive. Since people cope with fear through affiliation with others, in the absence of other individuals, consumers may seek affiliation with an available brand. -
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. -
A Review Essay of Christine Tappolet's Emotions, Values, and Agency
Philosophical Psychology ISSN: 0951-5089 (Print) 1465-394X (Online) Journal homepage: http://www.tandfonline.com/loi/cphp20 Are emotions perceptions of value (and why this matters)? A review essay of Christine Tappolet’s Emotions, Values, and Agency Charlie Kurth, Haley Crosby & Jack Basse To cite this article: Charlie Kurth, Haley Crosby & Jack Basse (2018): Are emotions perceptions of value (and why this matters)? A review essay of Christine Tappolet’s Emotions, Values, and Agency, Philosophical Psychology, DOI: 10.1080/09515089.2018.1435861 To link to this article: https://doi.org/10.1080/09515089.2018.1435861 Published online: 22 Feb 2018. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=cphp20 PHILOSOPHICAL PSYCHOLOGY, 2018 https://doi.org/10.1080/09515089.2018.1435861 Are emotions perceptions of value (and why this matters)? A review essay of Christine Tappolet’s Emotions, Values, and Agency Charlie Kurth, Haley Crosby and Jack Basse Department of Philosophy, Washington University in St. Louis, St. Louis, USA ABSTRACT ARTICLE HISTORY In Emotions, Values, and Agency, Christine Tappolet develops a Received 14 August 2017 sophisticated, perceptual theory of emotions and their role in Accepted 23 August 2017 wide range of issues in value theory and epistemology. In this KEYWORDS paper, we raise three worries about Tappolet’s proposal. Emotion; perceptual analogy; motivation; sentimentalism Introduction Christine Tappolet’s Emotions, Values, and Agency (Tappolet, 2017) provides a rich, provocative, and highly accessible defense of a perceptual theory of emotion. On her account, emotions are perceptual experiences of evaluative properties: to be disgusted by the maggot infested meat is, quite literally, to perceive the meat as disgusting—to see it as something to be rejected or avoided. -
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.