A Palette of Deepened Emotions: Exploring Emotional Challenge in Virtual Reality Games

Recent work introduced the notion of ‘emotional challenge’promising for understanding more unique and diverse player experiences (PX). Although emotional challenge has immediately attracted HCI researchers’ attention, the concept has not been experimentally explored, especially in virtual reality (VR), one of the latest gaming environments. We conducted two experiments to investigate how emotional challenge affects PX when separately from or jointly with conventional challenge in VR and PC conditions. We found that relatively exclusive emotional challenge induced a wider range of different emotions in both conditions, while the adding of emotional challenge broadened emotional responses only in VR. In both experiments, VR significantly enhanced the measured PX of emotional responses, appreciation, immersion and presence. Our findings indicate that VR may be an ideal medium to present emotional challenge and also extend the understanding of emotional (and conventional) challenge in video games

Fibonacci Level Adjustment for Optimizing Player’s Performance and Engagement

Players’ engagement intensity in computer games is influenced by the level of difficulty the game offers. Traditional game-level plots adopt linear increases that sometimes do not match the users’ skill growth, causing boredom and hampering the users’ further skill growth. In this study, a nonlinear level adjustment scenario was proposed based on the Fibonacci sequence that provides gradual increases in the early stages of the games but more drastic changes in later phases. Here, the game’s difficulty level was automatically decided by a machine learning method. To test the proposed method, comparisons between four level adjustments in computer games: traditional plots, self-selected plots, linear adaptive plots, and the proposed nonlinear adaptive plots were run. The experiment was carried out with 40 testers. The experiment results show that the best player’s peak level in the proposed nonlinear adjustment was twice as high as that of linear adjustment. Also, the number of stages required to reach the peak under the proposed scenario was half that of linear games. This high playing performance goes hand in hand with deep playing engagement. The results demonstrate the efficiency of the proposed level adjustment algorithm

Système intelligent pour le suivi et l’optimisation de l’état cognitif

Les émotions des êtres humains changent régulièrement et parfois de manière brusque entrainant un changement de l’état mental c’est-à-dire de l’aptitude cérébrale à fonctionner normalement. Il en résulte une capacité cognitive (ou état cognitif) de l’individu à pouvoir raisonner, accéder à la mémoire, ou effectuer des déductions, variable selon l’état mental. Ceci affecte, en conséquence, les performances des utilisateurs qui varient en fonction de leurs état cognitifs. Cette thèse vise à optimiser l’état cognitif d’un utilisateur lors de ses interactions avec un environnement virtuel. Comme cet état dépend des émotions, l’optimisation de l’état cognitif peut être réalisée à travers l’optimisation des émotions et en particulier la réduction des émotions négatives. Une première partie concerne les moyens de mesurer en temps réel (par un Module de mesures) l’état émotionnel et mental d’un utilisateur lors de ses interactions avec un environnement virtuel. Nous avons réalisé pour cela quatre études expérimentales avec quatre environnements différents. Nous avons montré que ces mesures peuvent être réalisées en utilisant différents capteurs physiologiques. Nous avons aussi montré qu’il est possible de prédire la tendance de l’excitation (un état mental) à partir d’un traceur de regard. Dans une deuxième partie, nous présentons l’Agent Neural qui modifie les environnements virtuels afin de provoquer une modification de l’état émotionnel d’un utilisateur pour améliorer son état cognitif. Nous avons réalisé quatre études expérimentales avec quatre environnements virtuels, où l’Agent Neural intervient dans ces environnements afin de changer l’état émotionnel de l’utilisateur. Nous avons montré que l’agent est capable d’intervenir dans plusieurs types d’environnements et de modifier les émotions de l’utilisateur. Dans une troisième partie, présentons l’Agent Limbique, qui personnalise et améliore les adaptations faites par l’Agent Neural à travers l’observation et l’apprentissage des impacts des changements des environnements virtuels et des réactions émotionnelles des utilisateurs. Nous avons montré que cet agent est capable d’analyser les interventions de l’Agent Neural et de les modifier. Nous avons montré aussi que l’Agent Limbique est capable de générer une nouvelle règle d’intervention et de prédire son impact sur l’utilisateur. La combinaison du Module de mesures, de l’Agent Neural, et de l’Agent Limbique, nous a permis de créer un système de contrôle cognitif intelligent que nous avons appelé Système Limbique Digital.The human’s emotions change regularly and sometimes suddenly leading to changes in their mental state which is the brain’s ability to function normally. This mental state’s changes affect the users’ cognitive ability (or cognitive state) to reason, access memory, or make inferences, which varies depending on the mental state. Consequently, this affects the users’ performances which varies according to their cognitive states. This thesis aims to optimize the users’ cognitive state during their interactions with a virtual environment. Since this state depends on emotions, optimization of cognitive state can be achieved through the optimization of emotions and in particular the reduction of negative emotions. In a first part, we present the means of measuring in real time (using a Measuring module) the users’ emotional and mental state during their interactions with a virtual environment. We performed four experimental studies with four different environments. We have shown that these measurements can be performed using different physiological sensors. We have also shown that it is possible to predict the tendency of excitement (a mental state) using an eye tracker. In a second part, we present the Neural Agent which modifies virtual environments to provoke a modification on the users’ emotional state in order to improve their cognitive state. We performed four experimental studies with four virtual environments, in which the Neural Agent intervenes in these environments to change the users’ emotional state. We have shown that the agent is able to intervene in several types of environments and able to modify the users’ emotions. In a third part, we present the Limbic Agent, which personalizes and improves the adaptations performed by the Neural Agent through the observation and the learning from the virtual environments changes’ impacts and the users’ emotional reactions. We have shown that this agent is able to analyze the Neural Agent’s interventions and able to modify them. We have also shown that the Limbic Agent is able to generate a new intervention rule and predict its impact on the user. The combination of the Measuring Module, the Neural Agent, and the Limbic Agent, allowed us to create an intelligent cognitive control system that we called the Digital Limbic System

Dynamic Personalization of Gameful Interactive Systems

Gameful design, the process of creating a system with affordances for gameful experiences, can be used to increase user engagement and enjoyment of digital interactive systems. It can also be used to create applications for behaviour change in areas such as health, wellness, education, customer loyalty, and employee management. However, existing research suggests that the qualities of users, such as their personality traits, preferences, or identification with a task, can influence gamification outcomes. It is important to understand how to personalize gameful systems, given how user qualities shape the gameful experience. Current evidence suggests that personalized gameful systems can lead to increased user engagement and be more effective in helping users achieve their goals than generic ones. However, to create these kinds of systems, designers need a specific method to guide them in personalizing the gameful experience to their target audience. To address this need, this thesis proposes a novel method for personalized gameful design divided into three steps: (1) classification of user preferences, (2) classification and selection of gameful design elements, and (3) heuristic evaluation of the design. Regarding the classification of user preferences, this thesis evaluates and validates the Hexad Gamification User Types Scale, which scores a person in six user types: philanthropist, socialiser, free spirit, achiever, player, and disruptor. Results show that the scale’s structural validity is acceptable for gamification studies through reliability analysis and factor analysis. For classification and selection of gameful design elements, this thesis presents a conceptual framework based on participants’ self-reported preferences, which classifies elements in eight groups organized into three categories: individual motivations (immersion and progression), external motivations (risk/reward, customization, and incentives), and social motivations (socialization, altruism, and assistance). And to evaluate the design of gameful applications, this thesis introduces a set of 28 gameful design heuristics, which are based on motivational theories and gameful design methods and enable user experience professionals to conduct a heuristic evaluation of a gameful application. Furthermore, this thesis describes the design, implementation, and pilot evaluation of a software platform for the study of personalized gameful design. It integrates nine gameful design elements built around a main instrumental task, enabling researchers to observe and study the gameful experience of participants. The platform is flexible so the instrumental task can be changed, game elements can be added or removed, and the level and type of personalization or customization can be controlled. This allows researchers to generate different experimental conditions to study a broad range of research questions. Our personalized gameful design method provides practical tools and clear guidelines to help designers effectively build personalized gameful systems