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

TL;DR

This paper presents a dual-DRL framework that uses AI-mediated visual feedback to enhance human cognition during math tasks by adaptively managing time pressure, demonstrating improved user performance.

Contribution

The paper introduces a novel dual-DRL approach that trains a regulation agent via simulation to optimize real-time feedback for cognitive augmentation.

Findings

Dual-DRL framework effectively improves user performance.

Adaptive feedback regulates attention and anxiety.

Feasibility demonstrated through user study.

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.