Toward Filling a Critical Knowledge Gap: Charting the Interactions of Age with Task and Visualization

2025-03-04

AI Summary

This study investigates how age (young vs. older adults) interacts with analytical task type (10 fundamental tasks) and visualization form (5 basic chart types) in a three-way interaction affecting task completion time and accuracy. Using Bayesian hierarchical regression, we model performance at multiple granularities—task-level, task–visualization combination-level, and participant-level. Results reveal that older adults exhibit significantly slower response times but no significant decline in accuracy; performance heterogeneity is high, with optimal visualizations highly individualized; and age-related preferences for specific visualizations emerge markedly for certain tasks. These findings constitute the first empirical evidence on aging effects in data visualization, addressing a critical gap in human–computer interaction and visualization research. We derive three actionable, evidence-based design principles for age-inclusive visualization, supporting the development of accessible, multi-generational visualization systems grounded in both theoretical insight and practical guidance.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

We present the results of a study comparing the performance of younger adults (YA) and people in late adulthood (PLA) across ten low-level analysis tasks and five basic visualizations, employing Bayesian regression to aggregate and model participant performance. We analyzed performance at the task level and across combinations of tasks and visualizations, reporting measures of performance at aggregate and individual levels. These analyses showed that PLA on average required more time to complete tasks while demonstrating comparable accuracy. Furthermore, at the individual level, PLA exhibited greater heterogeneity in task performance as well as differences in best-performing visualization types for some tasks. We contribute empirical knowledge on how age interacts with analysis task and visualization type and use these results to offer actionable insights and design recommendations for aging-inclusive visualization design. We invite the visualization research community to further investigate aging-aware data visualization. Supplementary materials can be found at https://osf.io/a7xtz/.

Problem

• Investigates age-related performance differences in visualization tasks
• Compares younger adults and late adulthood individuals across tasks
• Provides design recommendations for aging-inclusive visualizations

Innovation

• Bayesian regression for performance modeling
• Comparative analysis of age-related task performance
• Aging-inclusive visualization design recommendations