How Persistent Fear and Anxiety Can Affect Young Children's Learning
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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. -
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. -
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. -
Somatic Markers, Working Memory, and Decision Making
Cognitive, Affective, & Behavioral Neuroscience 2002, 2 (4), 341-353 Somatic markers, working memory, and decision making JOHN M. HINSON, TINA L. JAMESON, and PAUL WHITNEY Washington State University, Pullman, Washington The somatic marker hypothesis formulated by Damasio (e.g., 1994; Damasio, Tranel, & Damasio, 1991)argues that affectivereactions ordinarily guide and simplify decision making. Although originally intended to explain decision-making deficits in people with specific frontal lobe damage, the hypothe- sis also applies to decision-making problems in populations without brain injury. Subsequently, the gambling task was developed by Bechara (Bechara, Damasio, Damasio, & Anderson, 1994) as a diag- nostic test of decision-making deficit in neurological populations. More recently, the gambling task has been used to explore implications of the somatic marker hypothesis, as well as to study suboptimal de- cision making in a variety of domains. We examined relations among gambling task decision making, working memory (WM) load, and somatic markers in a modified version of the gambling task. In- creased WM load produced by secondary tasks led to poorer gambling performance. Declines in gam- bling performance were associated with the absence of the affective reactions that anticipate choice outcomes and guide future decision making. Our experiments provide evidence that WM processes contribute to the development of somatic markers. If WM functioning is taxed, somatic markers may not develop, and decision making may thereby suffer. One of the most consistent challenges of daily life is & Damasio, 2000). That is, decision making is guided by the management of information in order to continually the immediate outcomes of actions, without regard to what make decisions about courses of action. -
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. -
It's Complicated: a Literature Review of Happiness and the Big Five
Bengtson 1 Lilly Bengtson PSYC330 It’s Complicated: A Review of Literature on Happiness and the Big Five One of the great quests of an individual’s life is often to find happiness. But what does “happiness” mean? Can it even be “found?” These questions and more have been addressed with the growth of the positive psychology movement, a modern attempt to examine happiness from a scientific perspective. A natural first step in the study of happiness is evaluating exactly who is happy, and why. While a great many factors influence one’s satisfaction with life, personality is an especially relevant contributor to consider. Personality factors influence how people see the world, how they behave, and how they move through life, so it follows that these same factors would strongly influence one’s ultimate failure or success in achieving happiness. The Five Factor model of personality is a tried-and-true trait model which breaks personality down into five basic components: extraversion, openness, conscientiousness, agreeableness, and neuroticism. This empirically-validated model has been combined with the relatively recent positive psychology movement to study how personality traits affect individuals’ overall happiness. The Big Five traits of neuroticism and extraversion have been shown to correlate strongly with measures of individual happiness, but this effect is moderated by both internal and external factors of an individual’s life circumstances. In order to study the relationship between personality traits and happiness, one must first establish exactly how to evaluate this concept. A common measure for happiness is subjective well-being, which can be broken down into individual scales of life satisfaction, positive affect, and negative affect. -
Joint Emotion Label Space Modelling for Affect Lexica
Joint Emotion Label Space Modelling for Affect Lexica Luna De Bruynea,∗, Pepa Atanasovab, Isabelle Augensteinb aGhent University, Ghent, Belgium bUniversity of Copenhagen, Copenhagen, Denmark Abstract Emotion lexica are commonly used resources to combat data poverty in au- tomatic emotion detection. However, vocabulary coverage issues, differences in construction method and discrepancies in emotion framework and representa- tion result in a heterogeneous landscape of emotion detection resources, calling for a unified approach to utilising them. To combat this, we present an ex- tended emotion lexicon of 30,273 unique entries, which is a result of merging eight existing emotion lexica by means of a multi-view variational autoencoder (VAE). We showed that a VAE is a valid approach for combining lexica with different label spaces into a joint emotion label space with a chosen number of dimensions, and that these dimensions are still interpretable. We tested the utility of the unified VAE lexicon by employing the lexicon values as features in an emotion detection model. We found that the VAE lexicon outperformed in- dividual lexica, but contrary to our expectations, it did not outperform a naive concatenation of lexica, although it did contribute to the naive concatenation when added as an extra lexicon. Furthermore, using lexicon information as ad- ditional features on top of state-of-the-art language models usually resulted in arXiv:1911.08782v3 [cs.CL] 18 Jun 2021 a better performance than when no lexicon information was used. Keywords: NLP, Emotion detection, Emotion lexica, VAE ∗Corresponding author. Email address: [email protected] (Luna De Bruyne) Preprint submitted to Computer Speech & Language June 21, 2021 1. -
Scientific Evaluation of Animal Emotions: Brief History and Recent New Zealand Contributions
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Open Journal Systems at the Victoria University of Wellington Library Scientific evaluation of animal emotions: Brief history and recent New Zealand contributions Beausoleil, N.J., Stratton, R.B., Guesgen, M.J., Sutherland, M.A., Johnson, C.B. Abstract The idea of animals having emotions was once rejected as being anthropomorphic and unscientific. However, with society’s changing views and advances in scientific knowledge and technology, the idea of animal emotions is becoming more accepted. Emotions are subjective internal experiences that can’t be measured directly. Animal welfare scientists must infer emotions by measuring the behavioural, physiological and neurobiological components of emotional experience. In this paper, we describe innovative ways in which these indicators have been used by New Zealand scientists to facilitate a more holistic understanding of the emotions and welfare of animals. Introduction From a scientific perspective, emotion is defined as an innate response to an event or situation (internal or external) that comprises behavioural, physiological, subjective (the feeling) and cognitive (subsequent decision-making) components.1 Emotions are the result of complex processing, by the nervous system, of sensory information gathered from within and outside the animal’s body. The capture and processing of this sensory information are influenced by the biology of the animal species, as well as by individual factors such as the animal’s genetic predispositions, life stage, sex, previous experience, learning and memory.2 The emotion or emotions resulting from these processes of mental evaluation are thus uniquely personal to the individual, but can be broadly characterized by their valence (pleasant or unpleasant) and the degree of arousal generated.3 While the behavioural, physiological and, in some cases, cognitive components of an emotional response can be scientifically evaluated using observable indicators, the subjective component cannot. -
Seq2emo: a Sequence to Multi-Label Emotion Classification Model
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model Chenyang Huang, Amine Trabelsi, Xuebin Qin, Nawshad Farruque, Lili Mou, Osmar Zaïane Alberta Machine Intelligence Institute Department of Computing Science, University of Alberta {chenyangh,atrabels,xuebin,nawshad,zaiane}@ualberta.ca [email protected] Abstract suggests strong correlation among different emo- tions (Plutchik, 1980). For example, “hate” may Multi-label emotion classification is an impor- tant task in NLP and is essential to many co-occur more often with “disgust” than “joy.” applications. In this work, we propose An alternative approach to multi-label emotion a sequence-to-emotion (Seq2Emo) approach, classification is the classifier chain (CC, Read et al., which implicitly models emotion correlations 2009). CC predicts the label(s) of an input in an in a bi-directional decoder. Experiments on autoregressive manner, for example, by a sequence- SemEval’18 and GoEmotions datasets show to-sequence (Seq2Seq) model (Yang et al., 2018). that our approach outperforms state-of-the-art However, Seq2Seq models are known to have the methods (without using external data). In problem of exposure bias (Bengio et al., 2015), i.e., particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) ap- an error at early steps may affect future predictions. proaches in a fair setting.1 In this work, we propose a sequence-to-emotion (Seq2Emo) approach, where we consider emotion 1 Introduction correlations implicitly. Similar to CC, we also build Emotion classification from text (Yadollahi et al., a Seq2Seq-like model, but predict a binary indica- 2017; Sailunaz et al., 2018) plays an important role tor of an emotion at each decoding step of Seq2Seq. -
Arousal, Positive and Negative Affect, and Preference for Renaissance and 20Th-Century Paintings ~
Motivation and Emotion, Volume 1, Number l, 1977 Arousal, Positive and Negative Affect, and Preference for Renaissance and 20th-Century Paintings ~ Viadimir J. Kone6ni~ and Dianne Sargent-Pollock University of California, San Diego Treatments expected to raise the level of arousal and induce different de- grees of positive and negative affect were paired on some trials with Renais- sance or 20th-century paintings, whereas no paintings were shown on other trials. The design was within-subjects (24 females); the dependent measures were skin conductance (SC) and the preference for paintings. All treat- ments, including the paintings/no paintings factor, raised SC over the base- line, and the pattern was essentially additive. The perceived failure to attain control over aversive auditory stimulation (resulting in negative affect) raised SC to a higher level than did the unavoidable aversive stimulation (minimal affect) or monetary gains (pos#ive affect). Paintings paired with affect (positive or negative) were rated as more pleasing than were those paired with no affect. The Renaissance works were preferred to the 20th- century works when negative affect was induced, whereas the opposite was true in the case of positive affect. The collative-motivation model couM not account for the fact that pleasingness of paintings was not related to SC by an inverted-U function. The contiguity model couM not explain the con- siderable rated pleasingness of paintings paired with negative affect. In con- trast, there was considerable support for a more comprehensive hypothesis ("distraction~soothingness") based on attentional and affective considera- tions, and the differential cognitive labeling of fluctuations of arousal.