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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: Aug 7, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Using Item Response Times in Online Questionnaires to Detect Mild Cognitive Impairment.

Stefan Schneider1,2, Doertes U Junghaenel1,2, Erik Meijer3

  • 1Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, CA, USA.

The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences
|March 7, 2023
PubMed
Summary

Response times (RTs) in online surveys can identify cognitive impairment, no dementia (CIND). Slower average RTs and increased RT variability predict CIND, offering a new tool for early detection in aging research.

Keywords:
Cognitive functionCognitive testingInternetSurveys

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Area of Science:

  • Gerontology
  • Cognitive Science
  • Psychometrics

Background:

  • Web-based data collection yields response times (RTs) as paradata.
  • RTs may serve as passive indicators of cognitive status in aging populations.

Purpose of the Study:

  • To determine if RTs from online surveys can prospectively differentiate cognitively normal individuals from those with cognitive impairment, no dementia (CIND).

Main Methods:

  • Analysis of passively recorded RTs from 1,053 survey items over 6.5 years in 943 older adults (≥50 years).
  • A multilevel location-scale model derived respondent-level RT parameters: average RT, systematic RT adjustments, and unsystematic RT fluctuations.
  • CIND status was assessed at the study's conclusion.

Main Results:

  • All three RT parameters significantly predicted CIND (AUC = 0.74).
  • Slower average RTs, reduced systematic RT adjustments, and increased unsystematic RT fluctuations prospectively indicated higher CIND likelihood.
  • Predictive power extended up to 6.5 years for average RT, 4.5 years for systematic adjustments, and 1.5 years for unsystematic fluctuations.

Conclusions:

  • Online survey RTs are a potential early biomarker for CIND.
  • This finding can enhance the analysis of cognitive impairment predictors and consequences in digital research.