Emotion-affected decision making in human simulation

Human modelling is an interdisciplinary research field. The topic, emotion-affected decision making, was originally a cognitive psychology issue, but is now recognized as an important research direction for both computer science and biomedical modelling. The main aim of this paper is to attempt to bridge the gap between psychology and bioengineering in emotion-affected decision making. The work is based on Ortony's theory of emotions and bounded rationality theory, and attempts to connect the emotion process with decision making. A computational emotion model is proposed, and the initial framework of this model in virtual human simulation within the platform of VirtoolsTm is presented

Evaluating the Emotional State of a User Using a Webcam

In online learning is more difficult for teachers identify to see how individual students behave. Student’s emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student’s performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student’s learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner’s facial expressions and verbalizations. The majority of current software does not work in real time – scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states

Configuration et exploitation d'une machine émotionnelle

Dans ce travail, nous explorons la faisabilité de doter les machines de la capacité de prédire, dans un contexte d'interaction homme-machine (IHM), l'émotion d'un utilisateur, ainsi que son intensité, de manière instantanée pour une grande variété de situations. Plus spécifiquement, une application a été développée, appelée machine émotionnelle, capable de «comprendre» la signification d'une situation en se basant sur le modèle théorique d'évaluation de l'émotion Ortony, Clore et Collins (OCC). Cette machine est apte, également, à prédire les réactions émotionnelles des utilisateurs, en combinant des versions améliorées des k plus proches voisins et des réseaux de neurones. Une procédure empirique a été réalisée pour l'acquisition des données. Ces dernières ont fourni une connaissance consistante aux algorithmes d'apprentissage choisis et ont permis de tester la performance de la machine. Les résultats obtenus montrent que la machine émotionnelle proposée est capable de produire de bonnes prédictions. Une telle réalisation pourrait encourager son utilisation future dans des domaines exploitant la reconnaissance automatique de l'émotion.This work explores the feasibility of equipping computers with the ability to predict, in a context of a human computer interaction, the probable user’s emotion and its intensity for a wide variety of emotion-eliciting situations. More specifically, an online framework, the Emotional Machine, is developed enabling computers to «understand» situations using OCC model of emotion and to predict user’s reaction by combining refined versions of Artificial Neural Network and k Nearest Neighbours algorithms. An empirical procedure including a web-based anonymous questionnaire for data acquisition was designed to provide the chosen machine learning algorithms with a consistent knowledge and to test the application’s recognition performance. Results from the empirical investigation show that the proposed Emotional Machine is capable of producing accurate predictions. Such an achievement may encourage future using of our framework for automated emotion recognition in various application fields

Affective interface – toward a machine that exhibits its state emotionally

In the field of human-machine interaction, it is generally agreed that emotion may affect the users’ response behavior. With this consensus, interfaces of the machine are developed, among which a kind of interface that shows the machine state with human emotions is less studied. This kind of machine interface is called emotional or affective interface. This thesis studied affective interfaces. Two research questions were identified in the study: (1) how an affective interface is built and (2) whether an affective interface of a consumer device (e.g., personal digit assistant, personal computer, cellular phone, etc.) would significantly affect human’s emotion, judgment and decision making behavior? Corresponding to the two research questions, this research consisted of three works along with their respective objectives: (1) developed a general design approach to affective interfaces; (2) constructed an affective interface test-bed with high fidelity, which is a laptop computer with an attention to its internal temperature state, by using the developed approach in (1); (3) conducted experiments to show that the affective interface of the test-bed has a significant effect on human emotion, judgment and decision making. This research concluded (1) the general interface design approach, as developed, is valid to all interfaces including the affective element in human-machine interactions, (2) the human judgment and decision making behaviour will be significantly affected by the affective interface (with the confidence interval being 95%), and (3) affective interfaces significantly change human emotions

Mind, Computational Thinking & Neural Network

Mind, Computational Thinking & Neural Networ

Francois Delsarte, Prince Sergei Volkonsky and Mikhail Chekhov

In 1913 Prince Sergei Volkonsky, a director of the Russian Imperial Ballet, published two books based on Delsarte’s principles which had considerable impact on the Russian modernist theatre. Stanislavsky, Meyerhold and Vakhtangov, all explored the semiology of emotional expression, as did the Russian physiologists Sechenov and Pavlov. Mikhail Chekhov, in particular, developed principles of physical acting, and the article concludes with a comparison between his work and that of Delsarte

Introduction

This is the introduction to the anthology Game Love:Essays on Play and Affection. The first 10 pages were written by Jessica Enevold and the ensuing chapter presentations by Esther MacCallum-Stewart. The whole book was edited together by Enevold & MacCallum-Stewart based on an idea by Jessica Enevold. The Introduction explains the background of the anthology, including an introduction to the ontological model for analyzing game love in games, first drawn up by Jessica Enevold in 2008. It places the book within a context of computer games research, digital culture and HCI-research, making clear some of the connections and selections between previous research of games and emotions and affect and the human component, the player and other roles, of playing games