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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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Related Experiment Video

Updated: Oct 11, 2025

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
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Proportional Changes in Cognitive Subdomains During Normal Brain Aging.

Yauhen Statsenko1,2, Tetiana Habuza2,3, Klaus Neidl-Van Gorkom1

  • 1Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.

Frontiers in Aging Neuroscience
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Summary

Healthy aging shows proportional changes in cognitive functions like processing speed and inhibitory control. These findings could help detect dementia by identifying deviations from normal aging patterns.

Keywords:
agingclinical psychologycognitive declinecognitive domainsmachine learningneurodegenerationproportionalitypsychophysiological test

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Biostatistics

Background:

  • Current neuroscience lacks reliable methods for early dementia screening.
  • Diagnosing age-related cognitive decline requires understanding proportional changes in cognitive subdomains.

Purpose of the Study:

  • To enhance diagnostics of age-related cognitive functions.
  • To investigate the proportionality of age-related changes in cognitive subdomains.

Main Methods:

  • Developed novel psychophysiological indices: index of simple reaction time to decision-making time (ISD), ISD with accuracy (ISDA), and index of visual-motor reaction with accuracy (ISCA).
  • Utilized an open-access dataset (POBA) of individuals without dementia.
  • Employed unsupervised machine learning clustering to define age groups and analyzed index distributions across age.

Main Results:

  • Unsupervised clustering identified four homogeneous age groups: Adolescents (0-19), Young adults (20-39), Midlife adults (40-59), and Older adults (60+).
  • ISD, ISDA, and ISCA showed similar distributions across Young, Midlife, and Older adults, differing only from Adolescents, indicating proportional cognitive changes post-maturation.
  • Reaction to a moving object (RMO_mean) showed a different pattern, with Midlife adults significantly differing from other groups.

Conclusions:

  • Healthy brain aging is characterized by proportional changes in information processing speed and inhibitory control.
  • The developed indices (ISD, ISDA, ISCA) provide insights into cognitive aging patterns.
  • Future research should explore these indicators in dementia patients to identify potential diagnostic deviations.