On the Mutual Effects of Pain and Emotion: Facial Pain Expressions
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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. . -
Damages for Pain and Suffering
DAMAGES FOR PAIN AND SUFFERING MARCUS L. PLANT* THE SHAPE OF THE LAw GENERALLY It does not require any lengthy exposition to set forth the basic principles relating to the recovery of damages for pain and suffering in personal injury actions in tort. Such damages are a recognized element of the successful plaintiff's award.1 The pain and suffering for which recovery may be had includes that incidental to the injury itself and also 2 such as may be attributable to subsequent surgical or medical treatment. It is not essential that plaintiff specifically allege that he endured pain and suffering as a result of the injuries specified in the pleading, if his injuries stated are of such nature that pain and suffering would normally be a consequence of them.' It would be a rare case, however, in which plaintiff's counsel failed to allege pain and suffering and claim damages therefor. Difficult pleading problems do not seem to be involved. The recovery for pain and suffering is a peculiarly personal element of plaintiff's damages. For this reason it was held in the older cases, in which the husband recovered much of the damages for injury to his wife, that the injured married woman could recover for her own pain and suffering.4 Similarly a minor is permitted to recover for his pain 5 and suffering. No particular amount of pain and suffering or term of duration is required as a basis for recovery. It is only necessary that the sufferer be conscious.6 Accordingly, recovery for pain and suffering is not usually permitted in cases involving instantaneous death." Aside from this, how- ever,. -
Common Myths About the Joint Commission Pain Standards
T H S Y M Common myths about The Joint Commission pain standards Myth No. 1: The Joint Commission endorses pain as a vital sign. The Joint Commission never endorsed pain as a vital sign. Joint Commission standards never stated that pain needs to be treated like a vital sign. The roots of this misconception go back to 1990 (more than a decade before Joint Commission pain standards were released), when pain experts called for pain to be “made visible.” Some organizations tried to achieve this by making pain a vital sign. The only time the standards referenced the fifth vital sign was when examples were provided of how some organizations were assessing patient pain. In 2002, The Joint Commission addressed the problems of the fifth vital sign concept by describing the unintended consequences of this approach to pain management, and described how organizations subsequently modified their processes. Myth No. 2: The Joint Commission requires pain assessment for all patients. The original pain standards, which were applicable to all accreditation programs, stated “Pain is assessed in all patients.” This requirement was eliminated in 2009 from all programs except Behavioral Health Care. It was The Joint Commission thought that these patients were less able to bring up the fact that they were in pain and, therefore, required a more pain standards aggressive approach. The current Behavioral Health Care standard states, “The organization screens all patients • The hospital educates for physical pain.” The current standard for the hospital and other programs states, “The organization assesses and all licensed independent practitioners on assessing manages the patient’s pain.” This allows organizations to set their own policies regarding which patients should and managing pain. -
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. -
Psychosexual Characteristics of Vestibulodynia Couples: Partner Solicitousness and Hostility Are Associated with Pain
418 ORIGINAL RESEARCH—PAIN Psychosexual Characteristics of Vestibulodynia Couples: Partner Solicitousness and Hostility are Associated with Pain Mylène Desrosiers, MA,* Sophie Bergeron, PhD,* Marta Meana, PhD,† Bianca Leclerc, B.Sc.,* Yitzchak M. Binik, PhD,‡ and Samir Khalifé, MD§ *Department of Sexology, Université du Québec à Montréal, Montreal, Quebec, Canada; †Department of Psychology, University of Nevada Las Vegas, Las Vegas, NV, USA; ‡Department of Psychology, McGill University, Montreal, Quebec, Canada; §Jewish General Hospital—Department of Obstetrics and Gynecology, Montreal, Quebec, Canada DOI: 10.1111/j.1743-6109.2007.00705.x ABSTRACT Introduction. Provoked vestibulodynia is a prevalent yet misunderstood women’s sexual health issue. In particular, data concerning relationship characteristics and psychosexual functioning of partners of these women are scarce. Moreover, no research to date has examined the role of the partner in vestibulodynia. Aims. This study aimed to characterize and compare the psychosexual profiles of women with vestibulodynia and their partners, in addition to exploring whether partner-related variables correlated with women’s pain and associ- ated psychosexual functioning. Methods. Forty-three couples in which the woman suffered from vestibulodynia completed self-report question- naires focusing on their sexual functioning, dyadic adjustment, and psychological adjustment. Women were diagnosed using the cotton-swab test during a standardized gynecological examination. They also took part in a structured interview during which they were asked about their pain during intercourse and frequency of intercourse. They also completed a questionnaire about their perceptions of their partners’ responses to the pain. Main Outcome Measures. Dependent measures for both members of the couple included the Sexual History Form, the Locke-Wallace Marital Adjustment Scale and the Brief Symptom Inventory. -
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. -
Pain, Sadness, Aggression, and Joy: an Evolutionary Approach to Film Emotions
Pain, Sadness, Aggression, and Joy: An Evolutionary Approach to Film Emotions TORBEN GRODAL Abstract: Based on film examples and evolutionary psychology, this article dis- cusses why viewers are fascinated not only with funny and pleasure-evoking films, but also with sad and disgust-evoking ones. This article argues that al- though the basic emotional mechanisms are made to avoid negative experi- ences and approach pleasant ones, a series of adaptations modify such mechanisms. Goal-setting in narratives implies that a certain amount of neg- ative experiences are gratifying challenges, and comic mechanisms make it possible to deal with negative social emotions such as shame. Innate adapta- tions make negative events fascinating because of the clear survival value, as when children are fascinated by stories about loss of parental attachment. Furthermore, it seems that the interest in tragic stories ending in death is an innate adaptation to reaffirm social attachment by the shared ritual of sad- ness, often linked to acceptance of group living and a tribal identity. Keywords: attachment, cognitive film theory, coping and emotions, evolution- ary psychology, hedonic valence, melodrama, sadness, tragedy Why are viewers attracted to films that contain many negative experiences and endings? It seems counterintuitive that tragic stories can compete for our attention with stories that have a more upbeat content. The basic mechanisms underlying our experience with pleasure and displeasure, which psychologists call positive and negative hedonic valence, would seem to lead us to prefer beneficial and fitness-enhancing experiences and to avoid violent confronta- tion with harmful experiences. We are drawn to tasty food and attractive part- ners and retreat from enemies or bad tasting food. -
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. -
Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 46 ( 2015 ) 635 – 643 International Conference on Information and Communication Technologies (ICICT 2014) Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining a, a b Jasmine Bhaskar * ,Sruthi K , Prema Nedungadi aDepartment of Compute science , Amrita University, Amrita School of Engineering, Amritapuri, 690525, India bAmrita Create, Amrita University, Amrita School of Engineering, Amritapuri, 690525, India Abstract One of the greatest challenges in speech technology is estimating the speaker’s emotion. Most of the existing approaches concentrate either on audio or text features. In this work, we propose a novel approach for emotion classification of audio conversation based on both speech and text. The novelty in this approach is in the choice of features and the generation of a single feature vector for classification. Our main intention is to increase the accuracy of emotion classification of speech by considering both audio and text features. In this work we use standard methods such as Natural Language Processing, Support Vector Machines, WordNet Affect and SentiWordNet. The dataset for this work have been taken from Semval -2007 and eNTERFACE'05 EMOTION Database. © 20152014 PublishedThe Authors. by ElsevierPublished B.V. by ElsevierThis is an B.V. open access article under the CC BY-NC-ND license (Peer-reviewhttp://creativecommons.org/licenses/by-nc-nd/4.0/ under responsibility of organizing committee). of the International Conference on Information and Communication PeerTechnologies-review under (ICICT responsibility 2014). of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014) Keywords: Emotion classification;text mining;speech mining;hybrid approach 1.