Abstract
The chapter's goal is to present a novel computational cognitive neuroscience framework. This framework is proposed by the author to conceptually facilitate the integration of such technology features as customization of engaging game attributes, computational simulation of real-life actions in virtual reality environments, and computational modelling of accurate evaluation and intervention techniques with demonstrated effectiveness for autistic adults. The autistic adults-centered design is proposed as the core method to implement this framework throughout phases and stages used in the presented virtual-reality-game-based technology development pipeline. This method allows them to co-design and choose in-game learning preferred strategies. This computational cognitive neuroscience framework is oriented to stakeholders in the educational technology field interested in promoting equity and inclusion by playing a role as co-designers of effective, comprehensive, and engaging assessment and intervention technology-based support to empower underrepresented autistic communities.
TopIntroduction
The main chapter objective is to present a computational cognitive neuroscience framework to co-design accessible, affordable, and effective virtual-reality (VR) game-based technologies with and for autistic adults. Equity and inclusion are addressed through two approaches. In terms of equity, the author presents a framework through which educational technology stakeholders can contribute to making more accessible VR game based-supports to meet autistic adults’ learning needs. As an inclusion goal, this framework focuses on the user-centred design method to dignify and empower autistic adults as co-designers of technology-based support through which they can choose in-game learning mechanisms of what to learn, how to master it, and how best to do it.
This framework is oriented to educational technology stakeholders who want actively to participate as co-designers of technology-based supports and facilitators of autistic adults' empowerment through user-centred design approaches. The computational cognitive neuroscience approach will allow educational technology stakeholders to learn how to integrate and implement such features as individual customization, simulation of real-life settings, player engagement and empowerment, computational modelling of evidence-based practices, and real-time neurofeedback during goal-directed in-game actions. Such a framework also is oriented to stakeholders in the educational technology field who want to actively co-design comprehensive technology-based supports for teaching and learning by integrating effective assessment and intervention techniques for autistic adults. Such evidence-based assessment and interventions in autistic adults used to build learning mechanics and integrated into in-game goal-directed actions address the theoretical and pedagogical discussion regarding the gamification of learning. Throughout the chapter, varied implementation examples from different research approaches are used to illustrate potential integrations of evidence-based practices for autistic adults into computational models to simulate, customize, and predict their in-game actions. This framework is a high-level and detailed description of the co-development pipeline stages and essential game-based technology features to integrate interdisciplinary languages, concepts and methods. This chapter presents a general overview of integration practices and implementation techniques. Hence, the readers will have a comprehensive approach and a starting point to dive into as co-designers or facilitators with any learner in its diversity. A more detailed discussion of best practices exceeds the purpose of this chapter, but the reader can find best practices and implementation techniques in the literature cited. For instance, guidelines about the game design basics (Brathwaite, & Schreiber, 2017) or about how to situate learning, maximize cognitive dispositions for learning, engage learners through experience, facilitate learning tasks, or take advantage of flexibility, reusability and exploitability of game-based learning products (Catalano et al., 2014). In particular, in the additional reading section, the readers can find guidelines for any specific concept or method discussed here.
In the background section, it is discussed the importance of taking advantage of the most recent technology advancements and the user-centred game-based technology design to fulfill the unmet need for technology-based support for autistic adults. The author defines such core concepts as serious videogames and game mechanics under the emergent computational cognitive neuroscience approach. This approach as a technology development framework is oriented to integrate conceptually and methodologically such technology features as customization of engaging game attributes, computational simulation of real-life actions in VR environments, and computational modelling of accurate evaluation and intervention techniques with demonstrated effectiveness for autistic adults.
Key Terms in this Chapter
Players Engagement: It is shown when players experience a flow state while learning complex skills if their current ability matches the game difficulty level and if the game mechanics provide a sense of immersion, presence, and arousal to complete complex goal-directed actions.
Serious Videogames: Computer games designed and applied to utility purposes further than pure entertainment.
Game Architecture: It refers to the programming code organized into functions that allow the programmed Artificial Intelligence game agents to tailor the game mechanics, call evaluation functions during game action performance, provide timely in-game feedback and scaffold players for learning complex skills.
Reinforcement Learning (RL): In the Artificial Intelligence area, these algorithmic techniques have been developed as a subarea of Machine Learning to assess, reinforce, and predict future players' actions in a particular game environment.
Autism Spectrum Disorder (ASD): Life-long neurodevelopmental disorder characterized by deficits in social-communication and restricted or repetitive behaviours.
Function: In a programming language, functions are blocks of programming code that work as a list of instructions to computationally perform tasks such as calling other functions, collecting and retrieving data, and setting user environment changes according to user inputs.
Learning Mechanics: In goal-directed actions, learning mechanisms are a system to identify target behaviours, select teaching strategies, evaluate the current game action performance considering neurocognitive processes, tailor the learning scaffolding, provide timely feedback, and continuously update learning goals.
Computational Modelling: In computational cognitive neuroscience, one goal is to explore the implementation of neurocognitive and behaviour algorithms to model, explain and predict human decision-making mechanisms and procedural steps to perform actions in real-life environments simulated computationally.
Game Mechanics: System of playing mechanisms setting the aesthetics, dynamics and attributes of the pieces a gamer can control, actions a player can take, and rules that govern the game environment.
Game Action: Human players' action is defined and measured computationally as an iterative procedural-step neurocognitive map for learning social-communication, emotional, adaptive, and soft skills in a new interactive game environment by strategically using their cognitive, sensory, and motor processes at the current point in development, and learned models of such environment they already explored.