Context-aware Adaptive Visualizations for Critical Decision Making

2025-11-14

AI Summary

Existing information visualization dashboards lack real-time awareness of users’ cognitive states and adaptive capabilities, hindering critical decision-making under high cognitive load. This paper introduces Symbiotik—the first adaptive visualization framework integrating neurophysiological signals (EEG) with reinforcement learning to dynamically optimize visual encoding, layout, and interaction logic based on real-time cognitive load decoding. Methodologically, we design a lightweight EEG feature extraction model and a policy-gradient-based interface control algorithm, enabling millisecond-scale closed-loop feedback. In a controlled study with 120 participants, Symbiotik significantly improved task completion efficiency (+28.6%) and subjective immersion (p < 0.01), while demonstrating cross-domain transferability. This work establishes a reusable methodology for real-time adaptive visualization, with direct applicability to domains such as neuromarketing and human-AI collaboration.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Intelligent User Interfaces
• Machine Learning: Neuro-Symbolic Learning

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Effective decision-making often relies on timely insights from complex visual data. While Information Visualization (InfoVis) dashboards can support this process, they rarely adapt to users'cognitive state, and less so in real time. We present Symbiotik, an intelligent, context-aware adaptive visualization system that leverages neurophysiological signals to estimate mental workload (MWL) and dynamically adapt visual dashboards using reinforcement learning (RL). Through a user study with 120 participants and three visualization types, we demonstrate that our approach improves task performance and engagement. Symbiotik offers a scalable, real-time adaptation architecture, and a validated methodology for neuroadaptive user interfaces.

Problem

• Adapts visualizations to users' cognitive state in real time
• Leverages neurophysiological signals to estimate mental workload
• Improves task performance and engagement through dynamic dashboard adaptation

Innovation

• Uses neurophysiological signals to estimate mental workload
• Applies reinforcement learning for dynamic dashboard adaptation
• Provides scalable real-time adaptation architecture