Emotional Regulation: What Is It and How Does It Develop? by Danielle Boaden Speech-Language Pathologist and Clinical Program Assistant, the Hanen Centre
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The Influence of Emotional States on Short-Term Memory Retention by Using Electroencephalography (EEG) Measurements: a Case Study
The Influence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study Ioana A. Badara1, Shobhitha Sarab2, Abhilash Medisetty2, Allen P. Cook1, Joyce Cook1 and Buket D. Barkana2 1School of Education, University of Bridgeport, 221 University Ave., Bridgeport, Connecticut, 06604, U.S.A. 2Department of Electrical Engineering, University of Bridgeport, 221 University Ave., Bridgeport, Connecticut, 06604, U.S.A. Keywords: Memory, Learning, Emotions, EEG, ERP, Neuroscience, Education. Abstract: This study explored how emotions can impact short-term memory retention, and thus the process of learning, by analyzing five mental tasks. EEG measurements were used to explore the effects of three emotional states (e.g., neutral, positive, and negative states) on memory retention. The ANT Neuro system with 625Hz sampling frequency was used for EEG recordings. A public-domain library with emotion-annotated images was used to evoke the three emotional states in study participants. EEG recordings were performed while each participant was asked to memorize a list of words and numbers, followed by exposure to images from the library corresponding to each of the three emotional states, and recall of the words and numbers from the list. The ASA software and EEGLab were utilized for the analysis of the data in five EEG bands, which were Alpha, Beta, Delta, Gamma, and Theta. The frequency of recalled event-related words and numbers after emotion arousal were found to be significantly different when compared to those following exposure to neutral emotions. The highest average energy for all tasks was observed in the Delta activity. Alpha, Beta, and Gamma activities were found to be slightly higher during the recall after positive emotion arousal. -
Anger in Negotiations: a Review of Causes, Effects, and Unanswered Questions David A
Negotiation and Conflict Management Research Anger in Negotiations: A Review of Causes, Effects, and Unanswered Questions David A. Hunsaker Department of Management, University of Utah, Salt Lake City, UT, U.S.A. Keywords Abstract anger, negotiation, emotion, attribution. This article reviews the literature on the emotion of anger in the negotia- tion context. I discuss the known antecedents of anger in negotiation, as Correspondence well as its positive and negative inter- and intrapersonal effects. I pay par- David Hunsaker, David Eccles ticular attention to the apparent disagreements within the literature con- School of Business, Spencer Fox cerning the benefits and drawbacks of using anger to gain advantage in Eccles Business Building, negotiations and employ Attribution Theory as a unifying mechanism to University of Utah, 1655 East Campus Center Drive, Salt Lake help explain these diverse findings. I call attention to the weaknesses evi- City, UT 84112, U.S.A.; e-mail: dent in current research questions and methodologies and end with sug- david.hunsaker@eccles. gestions for future research in this important area. utah.edu. What role does anger play in the negotiation context? Can this emotion be harnessed and manipulated to increase negotiating power and improve negotiation outcomes, or does it have inevitable downsides? The purpose of this article is to review the work of scholars that have focused on the emotion of anger within the negotiation context. I begin by offering some background on the field of research to be reviewed. I then explain why Attri- bution Theory is particularly helpful in making sense of diverse findings in the field. -
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. -
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. . -
1 the Development of Empathy: How, When, and Why Nicole M. Mcdonald & Daniel S. Messinger University of Miami Department Of
1 The Development of Empathy: How, When, and Why Nicole M. McDonald & Daniel S. Messinger University of Miami Department of Psychology 5665 Ponce de Leon Dr. Coral Gables, FL 33146, USA 2 Empathy is a potential psychological motivator for helping others in distress. Empathy can be defined as the ability to feel or imagine another person’s emotional experience. The ability to empathize is an important part of social and emotional development, affecting an individual’s behavior toward others and the quality of social relationships. In this chapter, we begin by describing the development of empathy in children as they move toward becoming empathic adults. We then discuss biological and environmental processes that facilitate the development of empathy. Next, we discuss important social outcomes associated with empathic ability. Finally, we describe atypical empathy development, exploring the disorders of autism and psychopathy in an attempt to learn about the consequences of not having an intact ability to empathize. Development of Empathy in Children Early theorists suggested that young children were too egocentric or otherwise not cognitively able to experience empathy (Freud 1958; Piaget 1965). However, a multitude of studies have provided evidence that very young children are, in fact, capable of displaying a variety of rather sophisticated empathy related behaviors (Zahn-Waxler et al. 1979; Zahn-Waxler et al. 1992a; Zahn-Waxler et al. 1992b). Measuring constructs such as empathy in very young children does involve special challenges because of their limited verbal expressiveness. Nevertheless, young children also present a special opportunity to measure constructs such as empathy behaviorally, with less interference from concepts such as social desirability or skepticism. -
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. -
The Somatic Marker Hypothesis: a Neural Theory of Economic Decision
Games and Economic Behavior 52 (2005) 336–372 www.elsevier.com/locate/geb The somatic marker hypothesis: A neural theory of economic decision Antoine Bechara ∗, Antonio R. Damasio Department of Neurology, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA Received 8 June 2004 Available online 23 September 2004 Abstract Modern economic theory ignores the influence of emotions on decision-making. Emerging neuro- science evidence suggests that sound and rational decision making, in fact, depends on prior accurate emotional processing. The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision-making and its influence by emotion. The key idea of this hypothe- sis is that decision-making is a process that is influenced by marker signals that arise in bioregulatory processes, including those that express themselves in emotions and feelings. This influence can oc- cur at multiple levels of operation, some of which occur consciously, and some of which occur non-consciously. Here we review studies that confirm various predictions from the hypothesis, and propose a neural model for economic decision, in which emotions are a major factor in the interac- tion between environmental conditions and human decision processes, with these emotional systems providing valuable implicit or explicit knowledge for making fast and advantageous decisions. 2004 Elsevier Inc. All rights reserved. Introduction Modern economic theory assumes that human decision-making involves rational Bayesian maximization of expected utility, as if humans were equipped with unlim- ited knowledge, time, and information-processing power. The influence of emotions on * Corresponding author. E-mail address: [email protected] (A. -
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. -
On Emotion Specificity in Decision Making
Judgment and Decision Making, Vol. 3, No. 1, January 2008, pp. 18–27. On emotion specificity in decision making: Why feeling is for doing Marcel Zeelenberg∗1, Rob M. A. Nelissen1, Seger M. Breugelmans2, & Rik Pieters3 1 Department of Social Psychology and TIBER, Tilburg University 2 Department of Developmental, Clinical and Cross-cultural Psychology, Tilburg University 3 Department of Marketing and TIBER, Tilburg University Abstract We present a motivational account of the impact of emotion on decision making, termed the feeling-is-for-doing approach. We first describe the psychology of emotion and argue for a need to be specific when studying emotion’s impact on decision making. Next we describe what our approach entails and how it relates emotion, via motivation to behavior. Then we offer two illustrations of our own research that provide support for two important elements in our reasoning. We end with specifying four criteria that we consider to be important when studying how feeling guides our everyday doing. Keywords: emotion, decision making, motivation, action. 1 Introduction 1988). Bounded rationality may be helped by the exis- tence of emotion, because emotions restrict the size of the Most theories of rational decision making are descrip- consideration set and focus the decision maker on certain, tively implausible, especially if taken as process models. relevant aspects of the options (Hanoch, 2001). Emotions The idea that we take the time and invest the effort to pro- assign value to objects, aid the learning of how to ob- duce a list of advantages and disadvantages, or costs and tain those objects, and provide the motivation for doing benefits for all alternatives in each single decision and so (Gifford, 2002).