Affective Working Memory Research-Article8375972019
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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. -
Tor Wager Diana L
Tor Wager Diana L. Taylor Distinguished Professor of Psychological and Brain Sciences Dartmouth College Email: [email protected] https://wagerlab.colorado.edu Last Updated: July, 2019 Executive summary ● Appointments: Faculty since 2004, starting as Assistant Professor at Columbia University. Associate Professor in 2009, moved to University of Colorado, Boulder in 2010; Professor since 2014. 2019-Present: Diana L. Taylor Distinguished Professor of Psychological and Brain Sciences at Dartmouth College. ● Publications: 240 publications with >50,000 total citations (Google Scholar), 11 papers cited over 1000 times. H-index = 79. Journals include Science, Nature, New England Journal of Medicine, Nature Neuroscience, Neuron, Nature Methods, PNAS, Psychological Science, PLoS Biology, Trends in Cognitive Sciences, Nature Reviews Neuroscience, Nature Reviews Neurology, Nature Medicine, Journal of Neuroscience. ● Funding: Currently principal investigator on 3 NIH R01s, and co-investigator on other collaborative grants. Past funding sources include NIH, NSF, Army Research Institute, Templeton Foundation, DoD. P.I. on 4 R01s, 1 R21, 1 RC1, 1 NSF. ● Awards: Awards include NSF Graduate Fellowship, MacLean Award from American Psychosomatic Society, Colorado Faculty Research Award, “Rising Star” from American Psychological Society, Cognitive Neuroscience Society Young Investigator Award, Web of Science “Highly Cited Researcher”, Fellow of American Psychological Society. Two patents on research products. ● Outreach: >300 invited talks at universities/international conferences since 2005. Invited talks in Psychology, Neuroscience, Cognitive Science, Psychiatry, Neurology, Anesthesiology, Radiology, Medical Anthropology, Marketing, and others. Media outreach: Featured in New York Times, The Economist, NPR (Science Friday and Radiolab), CBS Evening News, PBS special on healing, BBC, BBC Horizons, Fox News, 60 Minutes, others. -
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. -
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. . -
Daily Emotional Labor, Negative Affect State, and Emotional Exhaustion: Cross-Level Moderators of Affective Commitment
sustainability Article Daily Emotional Labor, Negative Affect State, and Emotional Exhaustion: Cross-Level Moderators of Affective Commitment Hyewon Kong 1 and Joo-Eon Jeon 2,* 1 College of Liberal Arts and Interdisciplinary Studies, Kyonggi University, Suwon 16227, Korea; [email protected] 2 Department of Business Administration, Anyang University, Anyang 14028, Korea * Correspondence: [email protected] Received: 9 May 2018; Accepted: 8 June 2018; Published: 12 June 2018 Abstract: Employees’ emotional-labor strategies, experienced affects, and emotional exhaustion in the workplace may vary over time within individuals, even within the same day. However, previous studies on these relationships have not highlighted their dynamic properties of these relationships. In addition, although the effects of surface and deep acting on emotional exhaustion have been investigated in emotional-labor research, empirical studies on these relationships still report mixed results. Thus, we suggest that moderators may affect the relationship between emotional labor and emotional exhaustion. Also, this study examines the relationship between emotional labor and emotional exhaustion within individuals by repeated measurements, and verifies the mediating effect of a negative affect state. Finally, our study confirms the moderating effects that affective commitment has on the relationship between emotional labor and emotional exhaustion. Data was collected from tellers who had a high degree of interaction with clients at banks based in South Korea. A total of 56 tellers participated in the survey and responded for five working days. A total of 616 data entries were collected from the 56 respondents. We used a hierarchical linear model (HLM) to examine our hypothesis. The results showed that surface-acting emotional labor increases emotional exhaustion; furthermore, the relationship between surface acting emotional labor and emotional exhaustion is mediated by a negative affect state within individuals. -
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). -
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. -
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. -
Dispositional Affect As a Moderator in the Relationship Between Role Conflict and Exposure to Bullying Behaviors
fpsyg-10-00044 January 22, 2019 Time: 17:17 # 1 ORIGINAL RESEARCH published: 24 January 2019 doi: 10.3389/fpsyg.2019.00044 Dispositional Affect as a Moderator in the Relationship Between Role Conflict and Exposure to Bullying Behaviors Iselin Reknes1*, Ståle Valvatne Einarsen1, Johannes Gjerstad1,2 and Morten Birkeland Nielsen1,2 1 Department of Psychosocial Science, University of Bergen, Bergen, Norway, 2 National Institute of Occupational Health, Oslo, Norway Stressors in the work environment and individual dispositions among targets have been established separately as antecedents and risk factors of workplace bullying. However, few studies have examined these stressors in conjunction in order to determine personal dispositions among targets as possible moderators in the work stressor–bullying relationship. The aim of the present study was to examine multiple types of dispositional affect among targets as potential moderators in the relationship between role conflict and exposure to bullying behaviors, employing two independent cross-sectional Edited by: samples. The first sample comprised 462 employees from a Norwegian sea transport Gabriela Topa, organization, where trait anger and trait anxiety were included moderators. The second Universidad Nacional de Educación sample was a nationwide probability sample of the Norwegian working population and a Distancia (UNED), Spain comprised 1,608 employees randomly drawn from The Norwegian Central Employee Reviewed by: Miriam Lacey, Register, where positive and negative affect were included moderators. The results Pepperdine University, United States showed that trait anger, trait anxiety, and negative affect strengthened the positive Javier Bardon, Universidad Rey Juan Carlos, Spain relationship between role conflict and reports of bullying behaviors. Positive affect did *Correspondence: not moderate this relationship. -
The Future of Emotion Research in the Study of Psychopathology
Emotion Review Vol. 2, No. 3 (July 2010) 225–228 © 2010 SAGE Publications and The International Society for Research on Emotion The Future of Emotion Research in the Study ISSN 1754-0739 DOI: 10.1177/1754073910361986 of Psychopathology er.sagepub.com Ann M. Kring Department of Psychology, University of California, Berkeley, USA Abstract Research on emotion and psychopathology has blossomed due in part to the translation of affective science theory and methods to the study of diverse disorders. This translational approach has helped the field to hone in more precisely on the nature of emo- tion deficits to identify antecedent causes and maintaining processes, and to develop promising new interventions. The future of emotion research in psychopathology will benefit from three inter-related areas, including an emphasis on emotion difficulties that cut across traditional diagnostic boundaries (i.e., a transdiagnostic approach), the explicit linking of emotion and cognition in behavioral and neuroimaging studies in psychopathology, and continued translation of the latest conceptualizations of emotion to the study of psychopathology. Keywords cognition, emotion, mental illness, psychopathology, transdiagnostic In the past two decades, the number of studies on emotion and Where We Have Been psychopathology has expanded greatly. A search in PsychInfo for peer-reviewed articles containing the most generic of key- Given the ubiquity of emotion-related symptoms in psychopa- words, “emotion” and “mental illness” or “psychopathology”, thology (e.g., anger, anhedonia, anxiety, excited mood, fear, yields 613 peer reviewed articles in just the past 20 years. By flat affect, guilt, irritability, sad mood), most of the early contrast, 441 articles with these keywords are found for the 80 research sought to describe emotion-related disturbances. -
Expert Meeting on Empathy and Compassion
Expert Meeting on Empathy and Compassion Leveraging Basic Research to Inform Intervention Development September 16–17, 2019 Washington, DC Final March 17, 2020 This meeting summary was prepared by Frances McFarland, Rose Li and Associates, Inc., under contract to the National Institute on Aging of the National Institutes of Health. The views expressed in this document reflect both individual and collective opinions of the meeting participants and not necessarily those of the National Institute on Aging. Review of earlier versions of this meeting summary by the following individuals is gratefully acknowledged: Arielle Baskin- Sommers, Sona Dimidjian, Rebecca Lazeration, Robert Levenson, Rose Li, Abigail Marsh, Joan Monin, Lis Nielsen, Lisa Onken, Nancy Tuvesson, Greg Siegle, Lucas Smalldon, Bethany Stokes Expert Meeting on Empathy and Compassion September 2019 Acronym Definitions AD Alzheimer’s disease ADRD AD and related dementias BBCSS NASEM Board of Behavioral, Cognitive, and Sensory Sciences BSR NIA Division of Behavioral and Social Research bvFTD behavioral variant frontotemporal dementia CBT cognitive behavioral therapy CM compassion meditation LKM lovingkindness meditation NASEM National Academies of Science, Engineering, and Medicine NIA National Institute on Aging RCT randomized controlled trial SOBC Science of Behavior Change Acronym Definitions Page i Expert Meeting on Empathy and Compassion September 2019 Table of Contents Executive Summary ..............................................................................................................