用AI动态调节时间压力，提升人类算术表现。
Cognitive Exoskeleton: Augmenting Human Cognition with an AI-Mediated Intelligent Visual Feedback
- 双DRL框架：一个代理模拟用户认知，另一个调节反馈
- 实验证明比基线组性能提升，且适应性更强
- 适合人机协同、认知增强领域的研究者参考
本文提出一种AI中介框架，通过深度强化学习（DRL）提供自适应时间压力反馈，以提升用户在算术任务中的表现。时间压力可影响注意力与焦虑，进而正负影响表现。我们设计双DRL框架：一个调控代理根据实时表现调整反馈，另一个仿真代理基于现有数据集模拟用户认知行为，避免大规模用户实验。用户研究表明，该框架有效提升了用户性能，优于基线组。
原文摘要 · Abstract (English)
In this paper, we introduce an AI-mediated framework that can provide intelligent feedback to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) to provide adaptive time pressure feedback to improve user performance in a math arithmetic task. Time pressure feedback could either improve or deteriorate user performance by regulating user attention and anxiety. Adaptive time pressure feedback controlled by a DRL policy according to users' real-time performance could potentially solve this trade-off problem. However, the DRL training and hyperparameter tuning may require large amounts of data and iterative user studies. Therefore, we propose a dual-DRL framework that trains a regulation DRL agent to regulate user performance by interacting with another simulation DRL agent that mimics user cognition behaviors from an existing dataset. Our user study demonstrates the feasibility and effectiveness of the dual-DRL framework in augmenting user performance, in comparison to the baseline group.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。