A novel multidimensional dynamic difficulty adjustment algorithm: Use case in a cognitive training video game

Area of Science:

• Cognitive Psychology
• Neuroscience
• Human-Computer Interaction

Background:

• Cognitive control training is crucial for various cognitive functions.
• Existing training methods often lack personalization and engagement.
• Action video games show potential for enhancing brain plasticity.

Purpose of the Study:

• To introduce a novel methodological framework for cognitive control training.
• To develop a video game with a multidimensional dynamic difficulty adjustment (DDA) system.
• To provide a blueprint for designing effective computerized cognitive training tools.

Main Methods:

• A modular video game architecture with configurable cognitive training modules.
• An action-based central hub for seamless module transitions.
• A multidimensional DDA system adapting in real-time to player performance.

Main Results:

• Players reached their zone of proximal development within 30-45 minutes per module.
• The DDA system maintained gameplay variability for skill transfer.
• The system accommodated a wide range of skill levels, ensuring smooth onboarding.

Conclusions:

• The proposed framework offers a rigorous methodology for cognitive training tool design.
• The DDA system enhances personalized learning and engagement.
• This approach supports the development of effective computerized cognitive interventions.