Comparing individual differences in inconsistency and plasticity as predictors of cognitive function in older adults

ABSTRACT

Introduction: Recent theorizing differentiates key constraints on cognition, including one’s current range of processing efficiency (i.e., flexibility or inconsistency) as well as the capacity to expand flexibility over time (i.e., plasticity). The present study uses intensive assessment of response time data to examine the interplay between markers of intraindividual variability (inconsistency) and gains across biweekly retest sessions (plasticity) in relation to age-related cognitive function. Method: Participants included 304 adults (aged 64 to 92 years: M = 74.02, SD = 5.95) from Project MIND, a longitudinal burst design study assessing performance across micro and macro intervals (response latency trials, weekly bursts, annual retests). For two reaction time (RT) measures (choice RT and one-back choice RT), baseline measures of RT inconsistency (intraindividual standard deviation, ISD, across trials at the first testing session) and plasticity (within-person performance gains in average RT across the 5 biweekly burst sessions) were computed and were then employed in linear mixed models as predictors of individual differences in cognitive function and longitudinal (6-year) rates of cognitive change. Results: Independent of chronological age and years of education, higher RT inconsistency was associated uniformly with poorer cognitive function at baseline and with increased cognitive decline for measures of episodic memory and crystallized verbal ability. In contrast, predictive associations for plasticity were more modest for baseline cognitive function and were absent for 6-year cognitive change. Conclusions: These findings underscore the potential utility of response times for articulating inconsistency and plasticity as dynamic predictors of cognitive function in older adults.

KEYWORDS:

• Inconsistency
• flexibility
• variability
• plasticity
• aging
• cognition
• cross-sectional
• longitudinal
• linear mixed models

Acknowledgements

We thank the volunteer participants of Project MIND for their time and effort and Victoria Longitudinal Study (VLS) staff members for their assistance in data collection and preparation.

Disclosure Statement

No potential confict of interest was reported by the author(s).

Supplementary material

A supplementary table is available via the “Supplementary” tab on the article’s online page (http://dx.doi.org/10.1080/13803395.2015.1136598).

Additional information

Funding

Jacob Grand was supported by a doctoral fellowship from the Canadian Institutes of Health Research. Robert Stawski and Stuart MacDonald acknowledge support from the National Institutes of Health/National Institute on Aging [grant number R21AG045575]; and the Natural Sciences and Engineering Research Council of Canada [grant number 418676-2012].