Emotion Recognition: Patterns, Applications and Challenges 9781536197990, 1536197998

Table of contents :
Contents
Preface
Chapter 1
Assessment of the Emotion Recognition in Schizophrenia
Abstract
Introduction
Social Cognition
Social Perception
Theory of Mind
Social Knowledge
Attributional Bias/Style
Emotional Processing
Neuronal Mechanisms
Psychometry
Social Cognitive Deficits in Schizophrenia
Social Perception
Theory of Mind
Attributional Style
Alterations in Emotional Recognition in Schizophrenia
Emotional Processing
Methods
Results
Ekman 60 Faces
Facial Expressions of Emotions: Stimuli and Test (FEEST)
Emotion Hexagon Test
Face Emotion Discrimination Test (FEDT)
Face Emotion Identification Test (FEIT)
Voice Emotion Identification Test (VEIT)
Voice Emotion Discrimination Test (VEDT)
Japanese and Caucasian Brief Affect Recognition Test (JACBART)
Emotion Recognition Index (ERI)
Pen Emotion Recognition Test (ER-40)
Bell Lysaker Emotion Recognition Test (BLERT 15)
Diagnostic Analysis of Nonverbal Accuracy (DANVA 2)
Emotion Recognition Evaluation Test (ERET)
Reading the Mind in the Eyes (Test de Baron-Cohen)
The Karolinska Directed Emotional Faces (KDEF)
Emotion Labeling Task
Odd-Man-Out Matching Task
Vocal Emotion Labeling Task
NimStim Set of Facial Expressions
The Multimodal Emotion Recognition Test (MERT)
A Tool for Recognition of Emotions in Neuropyschiatric Disorders (TRENDS)
Chinese Facial Expressions of Emotion
Montreal Set of Facial Displays of Emotion (MSFDE)
Reading the Mind in the Voice
Discussion
Conclusion
References
Chapter 2
Emotion Recognition Failure in Psychiatric Disorders: A Potential Pitfall for Psychological Well-Being
Abstract
Emotion Recognition Definition
Physiological/Neurological Correlates and Mechanism
Assessment Methods
The Ekman 60 Faces Test
The Brief Affect Recognition Test
The Japanese and Caucasian Brief Affective Recognition Test
The Diagnostic Analysis of Nonverbal Accuracy
The Profile of Nonverbal Sensitivity
The Multimodal Emotion Recognition Test
The Reading the Mind in the Eye Test
Emotion Recognition in Mood Disorders
Bipolar Disorder
Major Depressive Disorder
Emotion Recognition in Anxiety Disorders
Social Anxiety Disorder
Generalized Anxiety Disorder
Panic Disorder
Emotion Recognition in Schizophrenia
Emotion Recognition in Addictive Disorders
Substance Use Disorder
Internet Gaming Disorder
Conclusion
References
Chapter 3
Emotional Intelligence for Futuristic Machines: Techniques, Applications, and Challenges
Abstract
1. Introduction
2. An Overview of Diverse FER Methods
3. Facial Expressions and Optical Flow
4. FER Datasets and Challenges
5. Real-World Applications of FER
6. Discussion
References
Index
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PSYCHOLOGY OF EMOTIONS, MOTIVATIONS AND ACTIONS

EMOTION RECOGNITION PATTERNS, APPLICATIONS AND CHALLENGES

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

PSYCHOLOGY OF EMOTIONS, MOTIVATIONS AND ACTIONS Additional books and e-books in this series can be found on Nova’s website under the Series tab.

PSYCHOLOGY OF EMOTIONS, MOTIVATIONS AND ACTIONS

EMOTION RECOGNITION PATTERNS, APPLICATIONS AND CHALLENGES

ROBERT D. CAMPBELL EDITOR

Copyright © 2021 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: [email protected].

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data ISBN: 978-1-53619-H%RRN

Published by Nova Science Publishers, Inc. † New York

CONTENTS Preface Chapter 1

Chapter 2

Chapter 3

Index

vii Assessment of the Emotion Recognition in Schizophrenia Paula Saiz and Guillermo Lahera Emotion Recognition Failure in Psychiatric Disorders: A Potential Pitfall for Psychological Well-Being Faruk Obuća, Pınar Ünal Aydın and Orkun Aydın Emotional Intelligence for Futuristic Machines: Techniques, Applications, and Challenges Shivangi Anthwal and Dinesh Ganotra

1

55

93 113

PREFACE The experience of emotion is a core element of human psychology, and the capacity to recognize emotions in others is an essential component of human social life. In some individuals, the ability to understand emotion in oneself and in others is compromised due to psychiatric disorders, which can have a substantial deleterious effect on one’s sense of well-being. This field has also been explored in the context of computer science, as artificial intelligence centered around emotion recognition has the potential to transform the nature of human-machine interaction. This book consists of three chapters that explore these concepts, granting students and researchers new insight on the nature of this necessary element of human life. Chapter 1 - It has been reported that individuals with schizophrenia have difficulties with emotion recognition. Emotion recognition is an element of emotion processing, one of the five domains of social cognition. In recent decades, social cognition has acquired vital importance in investigating this disorder, is considered a therapeutic target because it is directly related to individual functioning. There are many assessment tasks for the different social cognition areas, but the knowledge of their psychometric characteristics is limited. This work consists of a review of the available instruments, analyzing the psychometric properties of 25 tasks that assess emotional recognition in patients with schizophrenia. Subsequently, its differential characteristics and suitability for the different studies and sub-

viii

Robert D. Campbell

populations in the study of social cognition of people with schizophrenia are discussed. Chapter 2 - Emotion recognition (ER) is the capacity to perceive and identify emotions accurately. ER is considered essential for improving social interactions, and therefore it progresses in a relatively early phase of brain maturation. Multiple brain regions are deemed to mediate the ER, e.g., the inferior frontal cortex, the amygdala, the middle temporal gyrus, and the fusiform gyrus. The authors examined strengths and weaknesses of some of the most used ER assessment tools, including The Ekman 60 Faces Test, The Brief Affect Recognition Test (BART), The Japanese and Caucasian Brief Affective Recognition Test (JACBART), The Diagnostic Analysis of Nonverbal Accuracy (DANVA), The Profile of Nonverbal Sensitivity (PONS), The Multimodal Emotion Recognition Test (MERT), and The Reading the Mind in the Eye Test (RMET). Moreover, the authors analyzed the nature of ER in a number of prevalent mood (major depressive disorder and bipolar disorder), anxiety (social anxiety disorder, generalized anxiety disorder, and panic disorder), and addictive disorders (substance use disorder and internet gaming disorder), as well as in schizophrenia. While the literature review presented several inconsistent findings, the largest part of the research papers indicated the ER failure in these psychiatric disorders. Chapter 3 - Artificial Intelligence is a general appellation employed to describe the principles and development of systems aimed at emulating human intelligence for performing tasks requiring cogent reasoning, visual perception, and decision making related to the environment. Emotional intelligence, the ability to comprehend, use, and regulate emotions is often reckoned as a critical component of human intelligence, and is useful for optimizing human-human interaction. A recent influx of proactive devices and environments has made human-machine interfaces ubiquitous. With the interaction of humans amongst themselves as the blueprint for interaction between humans and machines, there is a growing need to induce emotional intelligence in the latter to regulate the interaction and enhance user experience. Communication among humans is supplemented by their innate capacity to infer the emotional state of the interlocutor with affective signals manifested with physical correlates of emotion such as facial expressions

Preface

ix

(FEs), speech, and voice inflections. Rigorous experiments in face-to-face multimodal cognition suggested FEs to be more predominant as compared to other modalities in conveying the underlying emotional state. Therefore, conventional human-machine interfaces that ignore or marginalize the user’s FEs fail to procure and access a relevant segment of information present in the conversation signals. This has necessitated a paradigm shift in humanmachine interaction with incorporation of FEs as a communication channel. A proactive affect-sensitive interface, able to regulate human-machine interaction in accordance with affective state of the user, has multitudinous prospective applications in a wide array of domains. This has lent a powerful impetus to assessment of emotions by FEs, an integral component of nonverbal paralinguistic communication. Motivated with the need of inducing emotional intelligence in machines, several models for representing affective facial displays have been introduced in the past. This chapter presents a systematic overview of diverse characteristic patterns presented for reliable analysis of emotional facial displays. The manifestation of emotions via FEs entails a non-rigid motion of facial features that can be embodied by a dense optical flow field, which is the apparent image motion in a time-progressing visual. The dearth of a detailed corpora pertaining specifically to the theme of visual information-based recognition of facial expressions with optical flow has motivated us to articulate various studies concerning this subject. Lastly, this chapter delineates the multifaceted concomitant challenges, outlines the strengths and limitations of different methods for emotion recognition with analysis of facial patterns and cites fascinating real-world instances apposite to the discipline.

In: Emotion Recognition Editor: Robert D. Campbell

ISBN: 978-1-53619-766-2 © 2021 Nova Science Publishers, Inc.

Chapter 1

ASSESSMENT OF THE EMOTION RECOGNITION IN SCHIZOPHRENIA Paula Saiz and Guillermo Lahera* University of Alcalá. Principe de Asturias University Hospital, Alcalá de Henares, Madrid, Spain

ABSTRACT It has been reported that individuals with schizophrenia have difficulties with emotion recognition. Emotion recognition is an element of emotion processing, one of the five domains of social cognition. In recent decades, social cognition has acquired vital importance in investigating this disorder, is considered a therapeutic target because it is directly related to individual functioning. There are many assessment tasks for the different social cognition areas, but the knowledge of their psychometric characteristics is limited. This work consists of a review of the available instruments, analyzing the psychometric properties of 25 tasks that assess emotional recognition in patients with schizophrenia. Subsequently, its differential characteristics and suitability for the different studies and subpopulations in the study of social cognition of people with schizophrenia are discussed.

*

Corresponding Author’s E-mail: [email protected].

2

Paula Saiz and Guillermo Lahera

Keywords: emotional recognition, schizophrenia, assessment instruments, validity, accuracy, social cognition, facial recognition

INTRODUCTION Schizophrenia is a psychiatric illness that greatly impacts an individual’s life and society, affecting the cognitive, affective and social spheres. It constitutes a complex and heterogeneous [1] cognitive behavioral syndrome, characterized by psychotic and non-psychotic symptoms [2]. The prevalence is 1% of the world’s population [2]. It originates from a genetic or environmental disorder of brain development, affecting genes and proteins responsible for the formation and functioning of neural networks [3]. It is currently believed that an interaction between genetic predisposition and environmental factors is necessary to develop it. The hypothesis of neurological development, in which there is an exposure to a risk factor during pregnancy, is accepted but, many other factors have been seen. Some examples are maternal stress, infections, nutritional deficiencies, CIR, or complications. There has been an appreciation of socioeconomic factors such as childhood adversity, immigration, and growth in cities. It also correlates with cannabis abuse in adolescence, head injuries, epilepsy, autoimmune diseases, severe infections, being born in late winter or early spring and older (>43 years) or younger (