A Computational Basis for the Emotions

2011 ◽  
pp. 94-109 ◽  
Author(s):  
N Korsten ◽  
JG Taylor
Keyword(s):  
Affective Computing ◽  
Affective State ◽  
Human Being ◽  
Human Emotions ◽  
Computational Basis

In order to achieve ‘affective computing’ it is necessary to know what is being computed. That is, in order to compute with what would pass for human emotions, it is necessary to have a computational basis for the emotions themselves. What does it mean quantitatively if a human is sad or angry? How is this affective state computed in their brain? It is this question, on the very core of the computational nature of the human emotions, which is addressed in this chapter. A proposal will be made as to this computational basis based on the well established approach to emotions as arising from an appraisal of a given situation or event by a specific human being.

scholarly journals The Application of Affective Computing Technology to E-Learning

2014 ◽  
pp. 109-128
Author(s):  
Nik Thompson ◽  
Tanya Jane McGill
Keyword(s):  
Affective Computing ◽  
Physiological State ◽  
Affective State ◽  
Significant Proportion ◽  
Human Interaction ◽  
Human Communication ◽  
Tutoring Systems ◽  
User Input ◽  
E Learning ◽  
New Generation

This chapter discusses the domain of affective computing and reviews the area of affective tutoring systems: e-learning applications that possess the ability to detect and appropriately respond to the affective state of the learner. A significant proportion of human communication is non-verbal or implicit, and the communication of affective state provides valuable context and insights. Computers are for all intents and purposes blind to this form of communication, creating what has been described as an “affective gap.” Affective computing aims to eliminate this gap and to foster the development of a new generation of computer interfaces that emulate a more natural human-human interaction paradigm. The domain of learning is considered to be of particular note due to the complex interplay between emotions and learning. This is discussed in this chapter along with the need for new theories of learning that incorporate affect. Next, the more commonly applicable means for inferring affective state are identified and discussed. These can be broadly categorized into methods that involve the user’s input and methods that acquire the information independent of any user input. This latter category is of interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions, and physiological state. The chapter concludes with a review of prominent affective tutoring systems in current research and promotes future directions for e-learning that capitalize on the strengths of affective computing.

Affective Tutoring Systems

2012 ◽  
Vol 8 (4) ◽  
pp. 75-89 ◽  
Author(s):  
Nik Thompson ◽  
Tanya Jane McGill
Keyword(s):  
Affective Computing ◽  
Physiological State ◽  
Affective State ◽  
Complex Interplay ◽  
Future Directions ◽  
Tutoring Systems ◽  
User Input ◽  
Face To Face ◽  
E Learning ◽  
Potential Benefits

This paper introduces the field of affective computing, and the benefits that can be realized by enhancing e-learning applications with the ability to detect and respond to emotions experienced by the learner. Affective computing has potential benefits for all areas of computing where the computer replaces or mediates face to face communication. The particular relevance of affective computing to e-learning, due to the complex interplay between emotions and the learning process, is considered along with the need for new theories of learning that incorporate affect. Some of the potential means for inferring users’ affective state are also reviewed. These can be broadly categorized into methods that involve the user’s input, and methods that acquire the information independent of any user input. This latter category is of particular interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions or physiological state. The paper concludes with a review of prominent affective tutoring systems and promotes future directions for e-learning that capitalize on the strengths of affective computing.

Analyzing Emotional Oscillatory Brain Network for Valence and Arousal-Based Emotion Recognition Using EEG Data

2019 ◽  
Vol 18 (04) ◽  
pp. 1359-1378
Author(s):  
Jianzhuo Yan ◽  
Hongzhi Kuai ◽  
Jianhui Chen ◽  
Ning Zhong
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Affective State ◽  
Feature Selection Method ◽  
Emotional Valence ◽  
Brain Network ◽  
Previous Method ◽  
Emotional States ◽  
Eeg Data ◽  
Valence And Arousal

Emotion recognition is a highly noteworthy and challenging work in both cognitive science and affective computing. Currently, neurobiology studies have revealed the partially synchronous oscillating phenomenon within brain, which needs to be analyzed from oscillatory synchronization. This combination of oscillations and synchronism is worthy of further exploration to achieve inspiring learning of the emotion recognition models. In this paper, we propose a novel approach of valence and arousal-based emotion recognition using EEG data. First, we construct the emotional oscillatory brain network (EOBN) inspired by the partially synchronous oscillating phenomenon for emotional valence and arousal. And then, a coefficient of variation and Welch’s [Formula: see text]-test based feature selection method is used to identify the core pattern (cEOBN) within EOBN for different emotional dimensions. Finally, an emotional recognition model (ERM) is built by combining cEOBN-inspired information obtained in the above process and different classifiers. The proposed approach can combine oscillation and synchronization characteristics of multi-channel EEG signals for recognizing different emotional states under the valence and arousal dimensions. The cEOBN-based inspired information can effectively reduce the dimensionality of the data. The experimental results show that the previous method can be used to detect affective state at a reasonable level of accuracy.

A Learning Object Recommendation System

2014 ◽  
pp. 254-269
Author(s):  
Adriano Pereira ◽  
Iara Augustin
Keyword(s):  
Learning Styles ◽  
Affective Computing ◽  
Recommendation System ◽  
Affective State ◽  
Large Data ◽  
Learning Objects ◽  
Data Availability ◽  
Learning Object ◽  
Affect Detection ◽  
Data Objects

Emotions play a very important role in the learning process. Affective computing studies try to identify users’ affective state, as emotion, using affect models and affect detection techniques, in order to improve human-computer interactions, as in a learning environment. The Internet explosion makes a huge volume of information, including learning objects data, available. In this scenario, recommendation systems help users by selecting and suggesting probable interesting items, dealing with large data availability and decision making problems, and customizing users’ interaction. In u-learning context, students could learn anywhere and anytime, having different options of data objects available. Since different students have different preferences and learning styles, personalization becomes an important feature in u-learning systems. Considering all this, the authors propose the Affective-Recommender, a learning object recommendation system. In this chapter, they describe the system’s requirements and architecture, focusing on affect detection and the recommendation algorithm, an example of use case, and results of system implementation over Moodle LMS.

“Affective Computing” Is Not an Oxymoron

2017 ◽  
Author(s):  
Rosalind W. Picard ◽  
Adolfo Plasencia
Keyword(s):  
Information Systems ◽  
Affective Computing ◽  
Emotional Experience ◽  
Normal Functioning ◽  
Human Emotion ◽  
The Disabled ◽  
Human Emotions ◽  
Media Lab

In this dialogue, the scientist Rosalind W. Picard from MIT Media Lab begins by explaining why the expression "Affective computing" is not an oxymoron, and describes how they are trying to bridge the gap between information systems and human emotions in her laboratory. She details  how they are attempting to give computers and digital machines better abilities so that they can “see” the emotions of their users, and outlines what a machine would have to be like to pass the Turing ‘emotions’ test. Rosalind goes on to describe why emotion is part of all communication, even when the communication itself might not explicitly have emotion in it, arguing that consciousness also involves feelings that cannot be expressed and why emotional experience is an essential part of the normal functioning of the conscious system. Later she outlines her research in affective computing, where they managed to measure signals using a sensor that responds to some human emotion or feelings, and explains how technology can become a sort of ‘affective prosthesis’ to help the disabled, and people with difficulties, in understanding and handling emotions.

scholarly journals Affective Medicine

2010 ◽  
Vol 49 (03) ◽  
pp. 207-218 ◽  
Author(s):  
A. Luneski ◽  
E. Konstantinidis ◽  
P. D. Bamidis
Keyword(s):  
Medical Informatics ◽  
Affective Computing ◽  
Virtual Communities ◽  
State Of The Art ◽  
Affective State ◽  
Home Healthcare ◽  
Medical Community ◽  
Affective Communication ◽  
E Learning ◽  
Application Fields

Summary Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”.AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, Am I, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field.

5. The computer that cried

2019 ◽  
pp. 79-95
Author(s):  
Dylan Evans
Keyword(s):  
Artificial Intelligence ◽  
Artificial Life ◽  
Affective Computing ◽  
Life Forms ◽  
Virtual Agents ◽  
Building Systems ◽  
Computer Scientists ◽  
Recent Developments ◽  
Human Emotions

The most recent discipline to have entered the debate on emotion is artificial intelligence. Since the early 1990s, computer scientists have become increasingly interested in building systems and devices that can recognize and simulate human emotions, and workers in robotics are already making some progress in this area. ‘The computer that cried’ discusses recent developments in affective computing and speculates on where it will lead. Will we succeed in building robots that have feelings just like we do? What might be the consequences of such technology? We may find that building artificial life forms with emotions—either virtual agents in a simulated world or real physical robots—helps us to understand more about our own emotions.

scholarly journals On the Emotive Phraseologisms with Kinesthetic Basis Taking Place in the Phraseological System of the English Language

2016 ◽  
Vol 6 (2) ◽  
pp. 150
Author(s):  
Jamila Ramiz Abdullayeva
Keyword(s):  
English Language ◽  
Emotional State ◽  
Point Of View ◽  
Stable Set ◽  
Human Being ◽  
Direct Observations ◽  
Human Emotions

<p>In the article emotive phraseologisms with kinesthetic basis, taking place in the phraseological system of the English language are analyzed. As the object of investigation, a group of chosen phraseological units expressing emotions and feelings of human being have been subjected to investigation. Study of this phraseosemantical field is one of the most discussed and complicated types of phraseological expressions, being linked with human emotions, and feelings which are unavailable for the direct observations. From this point of view, issues of means of conceptual-emotional state of human being acquire great importance. In our article we keep to the wide understanding of phraseology, object of which is all the stable set expressions with the complication of meaning possessing the signs of unchangeability.</p>

scholarly journals AUTOMATIC HUMAN STRESS DETECTION BASED ON WEBCAM PHOTOPLETHYSMOGRAPHIC SIGNALS

2016 ◽  
Vol 16 (04) ◽  
pp. 1650039 ◽  
Author(s):  
CHOUBEILA MAAOUI ◽  
FREDERIC BOUSEFSAF ◽  
ALAIN PRUSKI
Keyword(s):  
Affective Computing ◽  
Affective State ◽  
Computer Application ◽  
Support Vector ◽  
Linear Discriminant ◽  
Stress Classification ◽  
Pattern Recognition Systems ◽  
Human Stress ◽  
Stroop Color Word Test ◽  
Machine Communication

One of the goals of affective computing field is to provide to computers the ability to recognize automatically the affective state of the user in order to have more intuitive human–machine communication. This paper aims to detect automatically the stress user when he is interacting with computer. The developed system is based on instantaneous pulse rate (PR) signal extracted from imaging photoplethysmography (PPG). Seven features from time and frequency domain are extracted from PR signal and processed by learning pattern recognition systems. Two methods based on Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are used and compared to classify the user’s emotional state. A computer application based on “Stroop color word Test” is developed to elicit emotional stress in the subject. The proposed method can achieve the overall average classification accuracy of 94.42% and 91.10% with SVM and LDA, respectively. Current results indicate that our approach is effective for stress classification.

scholarly journals Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1738 ◽  
Author(s):  
Oana Bălan ◽  
Gabriela Moise ◽  
Alin Moldoveanu ◽  
Marius Leordeanu ◽  
Florica Moldoveanu
Keyword(s):  
Random Forest ◽  
Affective Computing ◽  
Computational Models ◽  
Affective State ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Neural Network Models ◽  
Linear Discriminant ◽  
Fear Level ◽  
Learning Techniques

There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user’s current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation—the two-level (0—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) paradigms. Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier—89.96% and 85.33% for the two-level and four-level fear evaluation modality.

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