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Cognitive Exoskeleton: Augmenting Human Cognition with an AI-Mediated Intelligent Visual Feedback

arXiv 2508.00846 ↗First posted 9 Jul 2025cs.HC cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 71 references that are in the map. First posted on arXiv 9 Jul 2025.

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References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spacescited by 123
  3. 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
  4. 2019Deep Reinforcement Learning from Policy-Dependent Human Feedbackcited by 56
  5. 2019Cognitive Model Priors for Predicting Human Decisionscited by 25
  6. 2020End-to-end Deep Prototype and Exemplar Models for Predicting Human Behaviorcited by 6
  7. 2016Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Makingcited by 5
  8. 2018An Intermittent Click Planning Modelcited by 2

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  4. 2024A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback
  5. 2026Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents
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  7. 2026CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution
  8. 2025An Introduction to Deep Reinforcement and Imitation Learning