Cognitive Dashboard Adjustment via Eye Tracking
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Solution Overview
Problem
Cognitive overload occurs when users interact with dashboards due to excessive visual effects and GUI elements, leading to increased mental effort and decreased understanding of data, and existing solutions fail to effectively address this issue by not considering individual user preferences and cohort-related data.
Innovation Solution
A system that uses eye tracking data to determine cognitive overload and adjusts dashboard content and GUI components based on user cohort data, modifying visual effects and data presentation to reduce overload, by analyzing fixation points, fixation count, and user coefficients to apply preferred dashboard schemes and data manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If visual effects and GUI elements are increased to enhance data presentation, then data visualization quality is improved, but cognitive overload increases and user understanding decreases
Solution Approach 1:
The system applies different visual effect levels to different dashboard components based on user characteristics. Eye tracking data identifies which areas require more or less visual emphasis, and the system dynamically adjusts visual effects locally rather than uniformly across the entire dashboard, reducing cognitive overload while maintaining data understanding.
Solution Approach 2:
The system dynamically changes visual parameters such as color intensity, element size, and animation speed based on real-time eye tracking data and user cohort profiles. When cognitive overload is detected through eye movement analysis, the system automatically adjusts these parameters to reduce visual complexity while preserving critical information.
2Ease of operation
If dashboard content is simplified to reduce cognitive overload, then user mental effort decreases, but data detail and comprehensiveness are lost
Solution Approach 1:
The dashboard dynamically adapts its complexity level based on real-time eye tracking analysis. When the system detects sustained fixation on complex areas or signs of cognitive overload, it simplifies specific regions temporarily while maintaining data availability. When users show engagement without overload, the system can present more detailed information, creating a dynamic balance between simplicity and comprehensiveness.
Solution Approach 2:
The dashboard is divided into multiple zones with different levels of detail and visual complexity. Eye tracking data identifies which segments require simplification and which can maintain full detail. This allows the system to reduce cognitive overload in specific areas without sacrificing overall data comprehensiveness.
3Adaptability or versatility
If eye tracking analysis and cohort data processing are implemented, then personalized dashboard adjustment is achieved, but system complexity increases
Solution Approach 1:
User cohort profiles and preferences are pre-established before actual dashboard interaction. The system collects and processes demographic, professional, and preference data during onboarding, creating ready-to-use profiles that guide subsequent personalized adjustments. This preliminary action reduces the complexity of real-time personalization during actual dashboard use.
Solution Approach 2:
The system automatically processes eye tracking data and applies cohort-based adjustments without requiring manual user configuration. Users simply interact with the dashboard naturally, and the system self-adjusts based on their eye movement patterns and pre-stored cohort profiles, eliminating the need for complex user setup procedures.
Data Source
AI summary
A computer determines a cognitive overload of a user interacting with a visual display based on eye tracking data. The visual display includes content of the dashboard and graphical elements of the content of the dashboard. The computer adjusts the visual display by modifying the content based on determining the cognitive overload.