Accounting for retest effects in cognitive testing with the Bayesian double exponential model via intensive measurement burst designs

Zita Oravecz, Karra D. Harrington, Jonathan G. Hakun, Mindy J. Katz, Cuiling Wang, Ruixue Zhaoyang, Martin J. Sliwinski

Research output: Contribution to journalArticlepeer-review

Abstract

Monitoring early changes in cognitive performance is useful for studying cognitive aging as well as for detecting early markers of neurodegenerative diseases. Repeated evaluation of cognition via a measurement burst design can accomplish this goal. In such design participants complete brief evaluations of cognition, multiple times per day for several days, and ideally, repeat the process once or twice a year. However, long-term cognitive change in such repeated assessments can be masked by short-term within-person variability and retest learning (practice) effects. In this paper, we show how a Bayesian double exponential model can account for retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance. We also highlight how this approach allows for the inclusion of person-level predictors and draw intuitive inferences on cognitive change with Bayesian posterior probabilities. We use older adults’ performance on cognitive tasks of processing speed and spatial working memory to demonstrate how individual differences in peak performance and change can be related to predictors of aging such as biological age and mild cognitive impairment status.

Original languageEnglish (US)
Article number897343
JournalFrontiers in Aging Neuroscience
Volume14
DOIs
StatePublished - Sep 26 2022

Keywords

  • Bayesian multilevel modeling
  • double negative exponential model
  • measurement burst design
  • retest learning
  • subtle cognitive decline

ASJC Scopus subject areas

  • Aging
  • Cognitive Neuroscience

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