Chapter 8SEXUAL MOTIVATION, AROUSAL, and ATTRACTION
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Robust Evidence for Bisexual Orientation Among Men
Robust evidence for bisexual orientation among men Jeremy Jabboura, Luke Holmesb, David Sylvac, Kevin J. Hsud, Theodore L. Semona, A. M. Rosenthala, Adam Safrone, Erlend Slettevoldb, Tuesday M. Watts-Overallf, Ritch C. Savin-Williamsg, John Syllah,i, Gerulf Riegerb,1, and J. Michael Baileya,1,2 aDepartment of Psychology, Northwestern University, Evanston, IL 60208; bDepartment of Psychology, Essex University, Colchester CO4 3SQ, United Kingdom; cDepartment of Psychiatry, Kaiser Permanente, Los Angeles, CA 90056; dDepartment of Psychological and Social Sciences, Pennsylvania State University Abington, Abington, PA 19001; eKinsey Institute, Indiana University, Bloomington, IN 47405; fSchool of Psychology, University of East London, Stratford E15 4LZ, United Kingdom; gDepartment of Psychology, Cornell University, Ithaca, NY 14853-4401; hAmerican Institute of Bisexuality, Los Angeles, CA 90014; and iUniversity of Chicago Law School, University of Chicago, Chicago, IL 60637 Edited by Steven Pinker, Harvard University, Cambridge, MA, and approved June 16, 2020 (received for review February 25, 2020) The question whether some men have a bisexual orientation— emotional biases of the questioners. Some heterosexual and ho- that is, whether they are substantially sexually aroused and mosexual men may find it relatively easy to understand each attracted to both sexes—has remained controversial among both other’s monosexuality because both have strong sexual attraction scientists and laypersons. Skeptics believe that male sexual orien- to one sex and virtually none to the other. For this reason, these tation can only be homosexual or heterosexual, and that bisexual men may have more difficulty accepting bisexuality as it challenges identification reflects nonsexual concerns, such as a desire to their binary conceptualizations of sexual orientation (7). -
Why Feelings Stray: Sources of Affective Misforecasting in Consumer Behavior Vanessa M
Why Feelings Stray: Sources of Affective Misforecasting in Consumer Behavior Vanessa M. Patrick, University of Georgia Deborah J. MacInnis, University of Southern California ABSTRACT drivers of AMF has considerable import for consumer behavior, Affective misforecasting (AMF) is defined as the gap between particularly in the area of consumer satisfaction, brand loyalty and predicted and experienced affect. Based on prior research that positive word-of-mouth. examines AMF, the current study uses qualitative and quantitative Figure 1 depicts the process by which affective misforecasting data to examine the sources of AMF (i.e., why it occurs) in the occurs (for greater detail see MacInnis, Patrick and Park 2005). As consumption domain. The authors find evidence supporting some Figure 1 suggests, affective forecasts are based on a representation sources of AMF identified in the psychology literature, develop a of a future event and an assessment of the possible affective fuller understanding of others, and, find evidence for novel sources reactions to this event. AMF occurs when experienced affect of AMF not previously explored. Importantly, they find consider- deviates from the forecasted affect on one or more of the following able differences in the sources of AMF depending on whether dimensions: valence, intensity and duration. feelings are worse than or better than forecast. Since forecasts can be made regarding the valence of the feelings, the specific emotions expected to be experienced, the INTRODUCTION intensity of feelings or the duration of a projected affective re- Before purchase: “I can’t wait to use this all the time, it is sponse, consequently affective misforecasting can occur along any going to be so much fun, I’m going to go out with my buddies of these dimensions. -
Sacred Shame: Integrating Spirituality and Sexuality Alyssa J
University of St. Thomas, Minnesota St. Catherine University Social Work Master’s Clinical Research Papers School of Social Work 2017 Sacred Shame: Integrating Spirituality and Sexuality Alyssa J. Haggerty University of St. Thomas, Minnesota, [email protected] Follow this and additional works at: https://ir.stthomas.edu/ssw_mstrp Part of the Clinical and Medical Social Work Commons, and the Social Work Commons Recommended Citation Haggerty, Alyssa J., "Sacred Shame: Integrating Spirituality and Sexuality" (2017). Social Work Master’s Clinical Research Papers. 740. https://ir.stthomas.edu/ssw_mstrp/740 This Clinical research paper is brought to you for free and open access by the School of Social Work at UST Research Online. It has been accepted for inclusion in Social Work Master’s Clinical Research Papers by an authorized administrator of UST Research Online. For more information, please contact [email protected]. RUNNING HEAD: SACRED SHAME: INTEGRATING SPIRITUALITY AND SEXUALITY Sacred Shame: Integrating Spirituality and Sexuality By Alyssa Haggerty, B.A. Committee Members: Rachel Murr MSW, LMSW Mary Hoffman MSW, LGSW Mari Ann Graham, PhD, LISW (Committee Chair) The Clinical Research Project is a graduation requirement for the MSW students at St. Catherine University/University of St. Thomas School of Social Work in St. Paul, Minnesota and is conducted within a nine-month time frame to demonstrate facility with basic research methods. Students must independently conceptualize a research problem, formulate a research design that is approved by a research committee and the university Institutional Review Board, implement the project, and publicly present the findings of the study. This project is neither a Master’s thesis nor a dissertation. -
Asexuality 101
BY THE NUMBERS Asexual people (or aces) experience little or no 28% sexual attraction. While most asexual people desire emotionally intimate relationships, they are not drawn to sex as a way to express that intimacy. of the community is 18 or younger ASEXUALITY ISN’T ACES MIGHT 32% Abstinence because of Want friendship, a bad relationship understanding, and Abstinence because of empathy religious reasons Fall in love of the community are between 19 and 21 Celibacy Experience arousal and Sexual repression, orgasm aversion, or Masturbate 19% dysfunction Have sex Loss of libido due to Not have sex age or circumstance Be of any gender, age, Fear of intimacy or background of the community are currently Inability to find a Have a spouse and/or in high school partner children 40% of the community are in college Aromantic – people who experience little or no romantic 20% attraction and are content with close friendships and other non-romantic relationships. Demisexual – people who only experience sexual attraction of the community identify as once they form a strong emotional connection with the person. transgender or are questioning Grey-A – people who identify somewhere between sexual and their gender identity asexual on the sexuality spectrum. 41% Queerplatonic – One type of non-romantic relationship where there is an intense emotional connection going beyond what is traditionally thought of as friendship. Romantic orientations – Aces commonly use hetero-, homo-, of the community identify as part of the LGBT community bi-, and pan- in front of the word romantic to describe who they experience romantic attraction to. Source: Asexy Community Census http://www.tinyurl.com/AsexyCensusResults Asexual Awareness Week Community Engagement Series – Trevor Project | Last Updated April 2012 ACE SPECIFIC Feeling e mpty, isolated, Some aces voice a fear of ISSUES and/or alone. -
Classification of Human Emotions from Electroencephalogram (EEG) Signal Using Deep Neural Network
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 9, 2017 Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network Abeer Al-Nafjan Areej Al-Wabil College of Computer and Information Sciences Center for Complex Engineering Systems Imam Muhammad bin Saud University King Abdulaziz City for Science and Technology Riyadh, Saudi Arabia Riyadh, Saudi Arabia Manar Hosny Yousef Al-Ohali College of Computer and Information Sciences College of Computer and Information Sciences King Saud University King Saud University Riyadh, Saudi Arabia Riyadh, Saudi Arabia Abstract—Estimation of human emotions from [1]. Recognizing a user‘s affective state can be used to Electroencephalogram (EEG) signals plays a vital role in optimize training and enhancement of the BCI operations [2]. developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG is often used in BCI research experimentation because EEG-based emotion recognition. This was motivated by the the process is non-invasive to the research subject and minimal recent advances in accuracy and efficiency from applying deep risk is involved. The devices‘ usability, reliability, cost- learning techniques in pattern recognition and classification effectiveness, and the relative convenience of conducting applications. We adapted DNN to identify human emotions of a studies and recruiting participants due to their portability have given EEG signal (DEAP dataset) from power spectral density been cited as factors influencing the increased adoption of this (PSD) and frontal asymmetry features. The proposed approach is method in applied research contexts [3], [4]. These advantages compared to state-of-the-art emotion detection systems on the are often accompanied by challenges such as low spatial same dataset. -
EMPOWERMENT a Group Therapy Curriculum
EMPOWERMENT A Group Therapy Curriculum Treating Young Adult Women Experiencing Female Sexual Interest/Arousal Disorder Ana K. Jaimes Aragón Contents Introduction .............................................................................................................................. 1 Participant Criteria ................................................................................................................ 3 Treatment Format .................................................................................................................. 7 Treatment Evaluation Questionnaire ........................................................................... 11 Self of the Therapist ............................................................................................................. 14 Session 1: Meet and Greet .................................................................................................................. 15 Handout 1 .................................................................................................................................. 27 Handout 2 .................................................................................................................................. 29 Handout 3 .................................................................................................................................. 30 Session 2: What is Shame? ................................................................................................................. 32 Handout 4 ................................................................................................................................. -
What We Mean When We Talk About Suffering—And Why Eric Cassell Should Not Have the Last Word
What We Mean When We Talk About Suffering—and Why Eric Cassell Should Not Have the Last Word Tyler Tate, Robert Pearlman Perspectives in Biology and Medicine, Volume 62, Number 1, Winter 2019, pp. 95-110 (Article) Published by Johns Hopkins University Press For additional information about this article https://muse.jhu.edu/article/722412 Access provided at 26 Apr 2019 00:52 GMT from University of Washington @ Seattle What We Mean When We Talk About Suffering—and Why Eric Cassell Should Not Have the Last Word Tyler Tate* and Robert Pearlman† ABSTRACT This paper analyzes the phenomenon of suffering and its relation- ship to medical practice by focusing on the paradigmatic work of Eric Cassell. First, it explains Cassell’s influential model of suffering. Second, it surveys various critiques of Cassell. Next it outlines the authors’ concerns with Cassell’s model: it is aggressive, obscure, and fails to capture important features of the suffering experience. Finally, the authors propose a conceptual framework to help clarify the distinctive nature of sub- jective patient suffering. This framework contains two necessary conditions: (1) a loss of a person’s sense of self, and (2) a negative affective experience. The authors suggest how this framework can be used in the medical encounter to promote clinician-patient communication and the relief of suffering. *Center for Ethics in Health Care and School of Medicine, Oregon Health and Science University, Portland. †National Center for Ethics in Health Care, Washington, DC, and School of Medicine, University of Washington, Seattle. Correspondence: Tyler Tate, Oregon Health and Science University, School of Medicine, Depart- ment of Pediatrics, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098. -
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 Classification Using Physiological Signals
Emotion Classification Using Physiological Signals Byoung-Jun Park1, Eun-Hye Jang1, Myoung Ae Chung1, Sang-Hyeob Kim1*, Jin Hun Sohn2* 1IT Convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, 305-700 2Department of Psychology/Brain Research Institute, Chungnam National University, Daejeon, 305-765 ABSTRACT Objective: The aim of this study is to discriminate negative emotions, such as sadness, fear, surprise, and stress using physiological signals. Background: Recently, the main topic of emotion classification research is to recognize human’s feeling or emotion using various physiological signals. It is one of the core processes to implement emotional intelligence in human computer interaction (HCI) research. Method: Electrodermal activity (EDA), electrocardiogram (ECG), skin temperature (SKT), and photoplethysmography (PPG) are recorded and analyzed as physiological signals. And emotional stimuli are audio-visual film clips which have examined for their appropriateness and effectiveness through preliminary experiment. For classification of negative emotions, five machine learning algorithms, i.e., LDF, CART, SOM, and Naïve Bayes are used. Results: Result of emotion classification shows that an accuracy of emotion classification by CART (84.0%) was the highest and by LDA (50.7%) was the lowest. SOM showed emotion classification accuracy of 51.2% and Naïve Bayes was 76.2%. Conclusion: We could identify that CART was the optimal emotion classification algorithm for classifying 4 negative emotions (sadness, fear, surprise, and stress). Application: This result can be helpful to provide the basis for the emotion recognition technique in HCI. Keywords: Emotion classification, Negative emotion, Machine learning algorithm, Physiological signal 1. Introduction Nasoz, Alvarez, Lisetti, and Finkelstein, 2003). -
OWNING YOUR FEELINGS Tips for Success
OWNING YOUR FEELINGS It can be easy to get caught up in your emotions as you’re feeling them. Most people don’t think about what emotions they are dealing with, but taking the time to really identify what you’re feeling can help you to better cope with challenging situations. The English language has over 3,000 words for Tips for success emotions. Allow yourself to feel. Sometimes there are societal pressures that encourage people to shut down their emotions, often expressed through People who are good at statements like, “Big girls don’t cry,” or “Man up.” These outdated ideas are being specific about harmful, not helpful. Everyone has emotionsthey are part of the human identifying and labeling experienceand you have every right to feel them, regardless of gender, their emotions are less sexual orientation, ethnicity, socio-economic status, race, political likely to binge drink, be affiliation or religion. physically aggressive, or selfinjure when Don’t ignore how you’re feeling. Most of us have heard the term “bottling up distressed. your feelings” before. When we try to push feelings aside without addressing them, they build strength and make us more likely to “explode” at some point in When schoolaged kids are the future. It may not always be appropriate to process your emotions at the taught about emotions for very moment you are feeling them, but try to do so as soon as you can. 20-30 minutes per week their social behavior and Talk it out. Find someone you trust that you can talk to about how you’re school performance feeling. -
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. -
Sexual Fantasy and Masturbation Among Asexual Individuals: an In-Depth Exploration
Arch Sex Behav (2017) 46:311–328 DOI 10.1007/s10508-016-0870-8 SPECIAL SECTION: THE PUZZLE OF SEXUAL ORIENTATION Sexual Fantasy and Masturbation Among Asexual Individuals: An In-Depth Exploration 1 1 2 Morag A. Yule • Lori A. Brotto • Boris B. Gorzalka Received: 4 January 2016 / Revised: 8 August 2016 / Accepted: 20 September 2016 / Published online: 23 November 2016 Ó Springer Science+Business Media New York 2016 Abstract Human asexuality is generally defined as a lack of pants(bothmenandwomen)wereequallylikelytofantasizeabout sexual attraction. We used online questionnaires to investigate topics such as fetishes and BDSM. reasons for masturbation, and explored and compared the con- tentsofsexualfantasiesofasexualindividuals(identifiedusing Keywords Asexuality Á Sexual orientation Á Masturbation Á the Asexual Identification Scale) with those of sexual individ- Sexual fantasy uals. A total of 351 asexual participants (292 women, 59 men) and 388sexualparticipants(221women,167men)participated.Asex- ual women were significantly less likely to masturbate than sexual Introduction women, sexual men, and asexual men. Asexual women were less likely to report masturbating for sexual pleasure or fun than their Although the definition of asexuality varies somewhat, the gen- sexualcounterparts, and asexualmen were less likely to reportmas- erallyaccepteddefinitionisthedefinitionforwardedbythelargest turbating forsexualpleasure than sexualmen. Both asexualwomen online web-community of asexual individuals (Asexuality Visi- andmen weresignificantlymorelikelythansexualwomenand