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

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.