Behavioral and Cognitive Signals of Aging in Real-World Contexts

Post Date: June 24, 2026

Expiration Date: June 24, 2028

Recent advances in computational approaches for human-based behavioral analysis highlight the value of identifying reproducible, interpretable units of behavior that generalize across contexts. These approaches emphasize temporally structured patterns of human behavior—such as recurring sequences, transitions, or rhythms of activity—rather than isolated events or aggregate summary measures. Pattern-based representations of behavior have demonstrated utility in capturing complex functional processes while supporting reproducibility and cross-study comparison.

In parallel, growing interest in continuous, real-world cognitive assessment has motivated the development of cognitive vital signs: longitudinal indicators derived from everyday behavior, speech, interaction patterns, or brief tasks that reflect underlying cognitive and functional processes. Such indicators offer the potential to characterize change over time in ways that complement traditional clinic-based assessments.

Together, these developments create an opportunity to translate insights from computational models of human behavior into human life span developmental and aging research. Realizing this opportunity requires rigorous methods to ensure that behavioral and cognitive signals are interpretable, robust, and valid across a myriad of real-world settings, populations, and contexts.

Areas of Scientific Interest (Examples)

Illustrative areas of interest may include, but are not limited to:

• Characterization of recurring behavioral patterns in daily life—such as regularity of sleep–wake cycles, sequences of goal-directed activities, navigation routines, social interaction rhythms, or transitions between activities—as indicators of functional change with life span development and aging.
• Development and validation of cognitive vital signs derived from real-world data streams, including patterns in speech and language use, response timing, error correction, multitasking behavior, or performance on brief, repeated cognitive tasks embedded in daily life.
• Computational methods to extract, represent, and compare temporally structured behavioral and cognitive patterns (e.g., sequences, bouts, transitions, or variability over time) rather than relying solely on aggregate or cross-sectional measures.
• Approaches to improve the reproducibility, transferability, and comparability of behavioral and cognitive signals across studies, platforms, populations, and contexts.
• Studies examining sources of variability in behavioral and cognitive signal extraction, including differences arising from context, interpretation, or measurement approaches, and strategies to account for this variability in analysis and validation.
• Methods that link computationally derived behavioral or cognitive representations to interpretable functional constructs, supporting transparent validation and appropriate use in aging research.