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

2025/07/09 by Songlin Xu, Xinyu Zhang, Xu, Songlin +1
Computer Science · Engineering · Medicine · #Artificial Intelligence (cs.AI) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Prosthetics and Rehabilitation Robotics #Stroke Rehabilitation and Recovery

paper · pdf · doi:10.48550/arxiv.2508.00846

openalex publication_date 2025/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior
• Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity
• Deep Reinforcement Learning from Policy-Dependent Human Feedback
• Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
• Proximal Policy Optimization Algorithms
• Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making
• The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

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