Cognitive Architectures for Serious Games

This dissertation summarises a research path aimed at fostering the use of Cognitive Architectures in Serious Games research field. Cognitive Architectures are an embodiment of scientific hypotheses and theories aimed at capturing the mechanisms of cognition that are considered consistent over time and independent of specific tasks or domains. The theoretical approaches provided by the research in computational cognitive modelling have been used to formalise a methodological framework to guide researchers and experts in the game-based education sector in designing, implementing, and evaluating Serious Games. The investigation of cognitive processes involved during the game experience represents the fundamental step of the pro- posed approach. Two different case studies are described to discuss the possible use of the suggested framework. In the first case study, the aim was to design a modified version of the Tetris game with the intention of making the game more effective in training the visual-spatial skill called mental rotation. In the second scenario, the frame- work was used as a basis for creating an innovative persuasive game. This case study provides an example of adopting cognitive architectures for implementing a non-player character with human-like behaviour developed using targeted cognitive theories

Hybridizing 3-dimensional multiple object tracking with neurofeedback to enhance preparation, performance, and learning

Le vaste domaine de l’amélioration cognitive traverse les applications comportementales, biochimiques et physiques. Aussi nombreuses sont les techniques que les limites de ces premières : des études de pauvre méthodologie, des pratiques éthiquement ambiguës, de faibles effets positifs, des effets secondaires significatifs, des couts financiers importants, un investissement de temps significatif, une accessibilité inégale, et encore un manque de transfert. L’objectif de cette thèse est de proposer une méthode novatrice d’intégration de l’une de ces techniques, le neurofeedback, directement dans un paradigme d’apprentissage afin d’améliorer la performance cognitive et l’apprentissage. Cette thèse propose les modalités, les fondements empiriques et des données à l’appui de ce paradigme efficace d’apprentissage ‘bouclé’. En manipulant la difficulté dans une tâche en fonction de l’activité cérébrale en temps réel, il est démontré que dans un paradigme d’apprentissage traditionnel (3-dimentional multiple object tracking), la vitesse et le degré d’apprentissage peuvent être améliorés de manière significative lorsque comparés au paradigme traditionnel ou encore à un groupe de contrôle actif. La performance améliorée demeure observée même avec un retrait du signal de rétroaction, ce qui suggère que les effets de l’entrainement amélioré sont consolidés et ne dépendent pas d’une rétroaction continue. Ensuite, cette thèse révèle comment de tels effets se produisent, en examinant les corrélés neuronaux des états de préparation et de performance à travers les conditions d’état de base et pendant la tâche, de plus qu’en fonction du résultat (réussite/échec) et de la difficulté (basse/moyenne/haute vitesse). La préparation, la performance et la charge cognitive sont mesurées via des liens robustement établis dans un contexte d’activité cérébrale fonctionnelle mesurée par l’électroencéphalographie quantitative. Il est démontré que l’ajout d’une assistance- à-la-tâche apportée par la fréquence alpha dominante est non seulement appropriée aux conditions de ce paradigme, mais influence la charge cognitive afin de favoriser un maintien du sujet dans sa zone de développement proximale, ce qui facilite l’apprentissage et améliore la performance. Ce type de paradigme d’apprentissage peut contribuer à surmonter, au minimum, un des limites fondamentales du neurofeedback et des autres techniques d’amélioration cognitive : le manque de transfert, en utilisant une méthode pouvant être intégrée directement dans le contexte dans lequel l’amélioration de la performance est souhaitée.The domain of cognitive enhancement is vast, spanning behavioral, biochemical and physical applications. The techniques are as numerous as are the limitations: poorly conducted studies, ethically ambiguous practices, limited positive effects, significant side-effects, high financial costs, significant time investment, unequal accessibility, and lack of transfer. The purpose of this thesis is to propose a novel way of integrating one of these techniques, neurofeedback, directly into a learning context in order to enhance cognitive performance and learning. This thesis provides the framework, empirical foundations, and supporting evidence for a highly efficient ‘closed-loop’ learning paradigm. By manipulating task difficulty based on a measure of cognitive load within a classic learning scenario (3-dimentional multiple object tracking) using real-time brain activity, results demonstrate that over 10 sessions, speed and degree of learning can be substantially improved compared with a classic learning system or an active sham-control group. Superior performance persists even once the feedback signal is removed, which suggests that the effects of enhanced training are consolidated and do not rely on continued feedback. Next, this thesis examines how these effects occur, exploring the neural correlates of the states of preparedness and performance across baseline and task conditions, further examining correlates related to trial results (correct/incorrect) and task difficulty (slow/medium/fast speeds). Cognitive preparedness, performance and load are measured using well-established relationships between real-time quantified brain activity as measured by quantitative electroencephalography. It is shown that the addition of neurofeedback-based task assistance based on peak alpha frequency is appropriate to task conditions and manages to influence cognitive load, keeping the subject in the zone of proximal development more often, facilitating learning and improving performance. This type of learning paradigm could contribute to overcoming at least one of the fundamental limitations of neurofeedback and other cognitive enhancement techniques : a lack of observable transfer effects, by utilizing a method that can be directly integrated into the context in which improved performance is sought

A Culturally Informed Treatment for the Black Community: Using Rap Therapy and Belief Systems Analysis Together

Rap therapy and Belief Systems Analysis are both culturally sensitive treatment approaches that were developed to treat African Americans. Both of these treatment approaches were developed out of strategies that African Americans have used as means of being resilient in the face of oppressive circumstances. These treatment approaches can be used conjointly to help enhance the well-being of African American clients. Both approaches help clients develop more positive, healthier outlooks and perspectives. Rap therapy can be very helpful in establishing rapport, and helping clients have a comfortable means of expressing their thoughts and feelings. Belief Systems Analysis can provide a framework within which to redirect and reframe perspectives and outlooks. Although literature has not examined the conjoint usages of these therapeutic approaches, they both have been found to have positive impacts independently, and can potentially be enhanced by being used conjointl

Behavioral design in video games: a roadmap for ethical and responsible games that contribute to long-term consumer health and well-being

Commissioned by the Dutch Ministry of the Interior and Kingdom RelationsEffective Protection of Fundamental Rights in a pluralist worl

The Effects of Narrative and Achievements on Learning in a 2D Platformer Video Game

Game design is a rigorous practice rife with complexity. The design of learning games is similarly complex to the design of their entertainment-based relatives. This complexity is partially due to the many interacting components that comprise games. The impacts of these individual components are not well understood. Advancing the understanding of how such component parts contribute to the formed game will inform decisions related to their inclusion and subsequent design within games. Achievements and narrative are two such components. They have been examined within gamified systems, but little research has studied them within the context of a serious game. The interactions between such elements and other game elements could produce results that diverge from the results of their use in isolation of a complete gaming framework. This dissertation selectively incorporates or excludes narrative and achievements within a two-dimensional platformer serious game to understand their impact on learning, flow, engagement, narrative transportation, and intrinsic motivation. Conditions are examined individually as well as in a combined condition. A control condition is maintained for comparison. Results indicate that narrative and achievements were not effective in improving the effectiveness of the game. Potential causes are discussed in tandem with the implications for the design and integration within a gaming framework. While the manipulations did not improve effectiveness, the game was responsible for substantially increased knowledge acquisition, as determined by pre and posttest results

Evaluating Copyright Protection in the Data-Driven Era: Centering on Motion Picture\u27s Past and Future

Since the 1910s, Hollywood has measured audience preferences with rough industry-created methods. In the 1940s, scientific audience research led by George Gallup started to conduct film audience surveys with traditional statistical and psychological methods. However, the quantity, quality, and speed were limited. Things dramatically changed in the internet age. The prevalence of digital data increases the instantaneousness, convenience, width, and depth of collecting audience and content data. Advanced data and AI technologies have also allowed machines to provide filmmakers with ideas or even make human-like expressions. This brings new copyright challenges in the data-driven era. Massive amounts of text and data are the premise of text and data mining (TDM), as well as the admission ticket to access machine learning technologies. Given the high and uncertain copyright violation risks in the data-driven creation process, whoever controls the copyrighted film materials can monopolize the data and AI technologies to create motion pictures in the data-driven era. Considering that copyright shall not be the gatekeeper to new technological uses that do not impair the original uses of copyrighted works in the existing markets, this study proposes to create a TDM and model training limitations or exceptions to copyrights and recommends the Singapore legislative model. Motion pictures, as public entertainment media, have inherently limited creative choices. Identifying data-driven works’ human original expression components is also challenging. This study proposes establishing a voluntarily negotiated license institution backed up by a compulsory license to enable other filmmakers to reuse film materials in new motion pictures. The film material’s degree of human original authorship certified by film artists’ guilds shall be a crucial factor in deciding the compulsory license’s royalty rate and terms to encourage retaining human artists. This study argues that international and domestic policymakers should enjoy broad discretion to qualify data-driven work’s copyright protection because data-driven work is a new category of work. It would be too late to wait until ubiquitous data-driven works block human creative freedom and floods of data-driven work copyright litigations overwhelm the judicial systems

Automated iterative game design

Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and iteratively altering a game design. Explicit models of the actions in games as planning operators allowed an intelligent system to reason about how actions and action sequences affect gameplay and to create new mechanics. Metrics to analyze differences in player strategies were presented and were able to identify flaws in game designs. An intelligent system learned design knowledge about gameplay and was able to reduce the number of design iterations needed during playtesting a game to achieve a design goal. Implications for how intelligent systems augment and automate human game design practices are discussed.Ph.D

An adaptive model for digital game based learning

Digital Game-based Learning (DGBL) has the potential to be a more effective means of instruction than traditional methods. However meta-analyses of studies on the effectiveness of DGBL have yielded mixed results. One of the challenges faced in the design and development of effective and motivating DGBL is the integration of learning and gameplay. A game that is effective at learning transfer, yet is no fun to play, is not going to engage learners for very long. This served as the motivation to devise a systematic approach to the design, development and evaluation of effective and engaging DGBL. A comprehensive literature review examined: how games can be made engaging and how the mechanics of learning can be mapped to the mechanics of gameplay; how learning can be designed to be universal to all; how learning analytics can empower learners and educators; and how an agile approach to the development of instructional materials leads to continuous improvement. These and other considerations led to the development of the Adaptive Model for Digital Game Based Learning (AMDGBL). To test how successful the model would be in developing effective, motivating and universal DGBL, a Virtual Reality (VR) game that teaches graph theory was designed, built and evaluated using the AMDGBL. An accompanying platform featuring an Application Programming Interface (API) for storing learner interaction data and a web-based learning analytics dashboard (LAD) were developed. A mixed methods approach was taken for a study of learners (N=20) who playtested the game and viewed visualizations in the dashboard. Observational and think aloud notes were recorded as they played and gameplay data was stored via the API. The participants also filled out a questionnaire. The notes taken were thematically analysed, and the gameplay data and questionnaire responses were statistically analysed. Triangulation of data improved confidence in findings and yielded new insights. The learner study became a case study for a second, qualitative study of DGBL practitioners (N=12). The VR game was demonstrated and a series of visualizations presented to the participants. They then completed a questionnaire featuring open questions about: the need for the model; the benefits of VR; and the embedding of learning analytics, universal design for learning, iteration with formative evaluation, and triangulation at the heart of the model. The responses were thematically analysed. The results of both studies supported the following assertions: that the AMDGBL would allow for iterative improvement of a DGBL prototype; that employing the AMDGBL would lead to an effective DGBL solution; that the inclusion of UDL would lead to a more universally-designed game; that the LAD would help learners with executive functions; and that VR would foster learner autonomy