The N-back task is widely recognized as a paradigm for cognitive training due to its adaptability and effectiveness in mitigating working memory decline. However, its repetitive and monotonous design often leads to reduced engagement and poor long-term adherence, lowering intervention effectiveness. To address these limitations, researchers have turned to game-based interventions and immersive technologies to enhance user motivation and sustain training participation. In this study, we propose VRN-back, a music rhythm-based gamified cognitive training system in virtual reality (VR). The system combines the adaptive N-back paradigm with rhythm-game mechanics to increase enjoyment and motivation, while immersive interaction fosters engagement and enhances training outcomes. We conducted a seven-day study with 15 young adults to evaluate the system's impact on their working memory, transfer to related cognitive domains, and user experience. Results showed significant improvements in N-back accuracy, reaction time, and maximum N level, as well as transfer effects on attention and inhibitory control tasks. Subjective evaluations indicated good usability and user experience, with the System Usability Scale scoring 80.83/100, the short User Experience Questionnaire rating pragmatic quality as "Good" and hedonic quality as "Above Average", and the Pleasure-Arousal-Dominance scale confirming sustained positive emotional states. Simulator sickness remained minimal before and after training, ensuring usability. These findings suggest the feasibility and effectiveness of immersive rhythm-based gamified VR systems for cognitive training, laying a foundation for future research on long-term cognitive intervention technologies.

National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), China Retrieving out-of-reach objects is a crucial task in virtual reality (VR). One of the most commonly used approaches for this task is the gesture-based approach, which allows for bare-hand, eyes-free, and direct retrieval. However, previous work has primarily focused on assigned gesture design, neglecting the context. This can make it challenging to accurately retrieve an object from a large number of objects due to the one-to-one mapping metaphor, limitations of finger poses, and memory burdens. There is a general consensus that objects and contexts are related, which suggests that the object expected to be retrieved is related to the context, including the scene and the objects with which users interact. As such, we propose a commonsense knowledge-driven joint reasoning approach for object retrieval, where human grasping gestures and context are modeled using an And-Or graph (AOG). This approach enables users to accurately retrieve objects from a large number of candidate objects by using natural grasping gestures based on their experience of grasping physical objects. Experimental results demonstrate that our proposed approach improves retrieval accuracy. We also propose an object retrieval system based on the proposed approach. Two user studies show that our system enables efficient object retrieval in virtual environments (VEs).