Measuring Decline in Fluid Intelligence More Accurately: Evidence from Cam-CAN and BrainTagger Cognitive Assessments Across the Lifespan

Abstract

In recent decades, healthcare systems have increasingly prioritized the prevention of adverse events, yet cognitive safety has received comparatively less attention. Cognitive assessment games hold great promise as sensitive, user-friendly tools for repeated evaluations, offering distinct advantages over traditional paper-and-pencil tests. These games, which have demonstrated psychometric validity in measuring targeted cognitive functions such as working memory and executive functioning, now require application and rigorous validation in clinical settings. In this paper, we examine some of the cognitive harms encountered in healthcare and present novel findings showing that cognitive assessment tools designed to evaluate various aspects of fluid intelligence display the expected performance changes across the lifespan.

Ruiheng Yu 余睿恒

Phd student of Biomedical Engineering

My research interests include Computer vision, explainable AI, LLM in health area.

Lu Wang

Assistant Professor of Biomedical Engineering

My research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.