Publication detail

Background: Recent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, have shown promise to improve the detection of subtle cognitive changes. The use of computational modeling approaches may further improve the sensitivity of the digital cognitive assessments to detect subtle cognitive changes by capturing features that reflect core cognitive processes from non-cognitive/non-decision-related processes. Objective: We explored the validity of a brief, smartphone-based adaptation of an associative visual working memory change detection task that has shown sensitivity in the early detection of preclinical AD-related cognitive impairment. We aimed to optimize the task for computational cognitive feature extraction with drift diffusion modeling. Methods: We analyzed data from a sample of 68 participants (69% women; 81% White; age range 24-80 years, mean 49 years, SD 14), who completed 60 trials for each of the 16 variations of a Visual Working Memory Binding task (‘Color Shapes’) on smartphones, over an 8-day period. A drift diffusion model was fit to Color Shapes response time and accuracy data to dissociate features of the decision-making process. We experimentally manipulated three properties of the Color Shapes task (study time, probability of change, choice urgency) to test how these constraints yield differences in key computational features (rate of evidence accumulation process, initial bias towards a response option, caution in decision making). We also evaluated how an additional task property, the test array size (whole display vs. single probe), impacted responses across all conditions. Results: Overall accuracy was high (>80%) and minimal missingness (1.3% of expected data) was observed. Three primary observations were made regarding the task property manipulations and drift diffusion model fit: (1) increasing the probability of change was associated with higher ‘initial bias’ toward a “different” response, (2) increasing the choice urgency during the test phase was associated with decreased caution in decision-making (‘boundary separation’), and (3) contrary to expectation, longer study times did not affect evidence accumulation rate (‘drift rate’). In addition, we observed that individual differences in evidence accumulation rate (drift rate) and caution (boundary separation) were associated with age. Conclusions: We identified a version of the Color Shapes task that is ideal for smartphone-based, repeated cognitive assessments in real-world settings, especially when the resulting data are analyzed through computational cognitive modeling. Our proposed approach can advance the development of tools and programs for an efficient and effective early detection and monitoring of early risk for Alzheimer’s disease.

@article{kim_etal:preprint:computational,
    title   = {{O}ptimizing the {C}olor {S}hapes task for ambulatory assessment and computational cognitive feature extraction via drift diffusion modeling},
    author  = {Kim, Sharon and Hakun, Jonathan and Li, Yanling and Harrington, Karra D. and Elbich, Daniel B. and Sliwinski, Martin and Vandekerckhove, Joachim and Oravecz, Zita},
    year    = {preprint},
    journal = {JMIR Preprints},
    volume  = {2024},
    pages   = {66300}
}