Cognitive training for sustained attention and working memory is vital across domains relying on robust mental capacity such as education or rehabilitation. Adaptive systems are essential, dynamically matching difficulty to user ability to maintain engagement and accelerate learning. Current adaptive systems often rely on simple performance heuristics or predict visual complexity and affect instead of cognitive load. This study presents the first implementation of real-time adaptive cognitive load control in Virtual Reality cognitive training based on eye-tracking and physiological data. We developed a bidirectional LSTM model with a self-attention mechanism, trained on eye-tracking and physiological (PPG, GSR) data from 74 participants. We deployed it in real-time with 54 participants across single-task (sustained attention) and dual-task (sustained attention + mental arithmetic) paradigms. Difficulty was adjusted dynamically based on participant self-assessment or model's real-time cognitive load predictions. Participants showed a tendency to estimate the task as too difficult, even though they were objectively performing at their best. Over the course of a 10-minute session, both adaptation methods converged at equivalent difficulty in single-task scenarios, with no significant differences in subjective workload or game performance. However, in the dual-task conditions, the model successfully pushed users to higher difficulty levels without performance penalties or increased frustration, highlighting a user tendency to underestimate capacity under high cognitive load. Findings indicate that machine learning models may provide more objective cognitive capacity assessments than self-directed approaches, mitigating subjective performance biases and enabling more effective training by pushing users beyond subjective comfort zones toward physiologically-determined optimal challenge levels.

翻译：针对持续性注意与工作记忆的认知训练，在教育或康复等依赖强大心智能力的领域中至关重要。自适应系统通过动态调整难度以匹配用户能力，对于维持参与度和加速学习进程必不可少。当前的自适应系统通常依赖于简单的性能启发式方法，或预测视觉复杂度与情感状态而非认知负荷。本研究首次实现了基于眼动追踪与生理数据的虚拟现实认知训练中实时自适应认知负荷控制。我们开发了一种结合自注意力机制的双向LSTM模型，该模型使用来自74名参与者的眼动追踪与生理（PPG、GSR）数据进行训练。我们在54名参与者中实时部署了该系统，涵盖单任务（持续性注意）与双任务（持续性注意+心算）范式。难度根据参与者自我评估或模型实时认知负荷预测进行动态调整。参与者倾向于将任务评估为过于困难，尽管他们客观上表现最佳。在10分钟的训练过程中，两种自适应方法在单任务场景中收敛于同等难度水平，主观工作量与游戏表现均无显著差异。然而在双任务条件下，该模型成功将用户推向更高难度水平，且未导致表现下降或挫败感增加，这揭示了用户在高认知负荷下倾向于低估自身能力。研究结果表明，机器学习模型可能比自我导向方法提供更客观的认知能力评估，通过将用户推离主观舒适区、迈向生理学确定的最佳挑战水平，从而减轻主观表现偏差并实现更有效的训练。