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Optimizing the Color Shapes Task for Ambulatory Assessment and Drift Diffusion Modeling: A Factorial Experiment.

Sharon Haeun Kim1,2, Jonathan G Hakun3,4,5,6, Yanling Li1

  • 1Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA, United States.

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|October 1, 2025
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Summary
This summary is machine-generated.

This study optimized a smartphone visual working memory task for early Alzheimer disease risk detection. Computational modeling revealed age-related cognitive changes, enhancing digital assessment sensitivity.

Keywords:
Bayesian multilevel modelingcomputational cognitive markersdrift diffusion modelmobile phonesmartphone-based cognitive testingsubtle cognitive decline

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Area of Science:

  • Cognitive neuroscience
  • Digital health
  • Computational psychiatry

Background:

  • Digital cognitive assessments, including ambulatory ones, improve detection of subtle cognitive changes.
  • Computational modeling can enhance digital assessment sensitivity by capturing core cognitive processes.

Purpose of the Study:

  • Validate a smartphone visual working memory task for preclinical Alzheimer disease risk detection.
  • Optimize task properties for computational cognitive feature extraction using drift diffusion modeling.

Main Methods:

  • Analyzed data from 68 participants completing 16 variations of a smartphone visual working memory task over 8 days.
  • Fit a drift diffusion model to response time and accuracy data.
  • Manipulated task properties (study time, change probability, urgency, array size) to assess impact on drift diffusion model parameters.

Main Results:

  • Task property manipulations influenced decision-making parameters (bias, caution).
  • Longer study times unexpectedly slowed drift rate in one condition.
  • Individual differences in drift rate and caution correlated with age, with older participants showing slower drift and higher caution.

Conclusions:

  • Identified an optimized smartphone visual working memory task for real-world cognitive assessment.
  • The approach supports data analysis via computational cognitive modeling for early Alzheimer disease risk detection and monitoring.