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A well-known challenge for research in the cognitive neuropsychology of aging is to distinguish between the deficits and changes associated with normal aging and those indicative of early cognitive impairment. In a series of 2 studies, the authors explored whether 2 neurocognitive markers, speed (mean level) and inconsistency (intraindividual variability), distinguished between age groups (64-73 and 74-90+ years) and cognitive status groups (nonimpaired, mildly impaired, and moderately impaired). Study 1 (n = 416) showed that both level and inconsistency distinguished between the age and 2 cognitive status (not impaired, mildly impaired) groups, with a modest tendency for inconsistency to predict group membership over and above mean level. Study 2 (n = 304) replicated these results but extended them because of the qualifying effects associated with the unique moderately impaired oldest group. Specifically, not only were the groups more firmly distinguished by both indicators of speed, but evidence for the differential contribution of performance inconsistency was stronger. Neurocognitive markers of speed and inconsistency may be leading indicators of emerging cognitive impairment.

The authors examined the factor structure of 4 indicators of executive functioning derived from 2 new (i.e., Hayling and Brixton) and 2 traditional (i.e., Stroop and Color Trails) tests. Data were from a cross-sectional sample of 55- to 85-year-old healthy adults (N=427) from the Victoria Longitudinal Study. Confirmatory factor analysis (LISREL 8.52) tested both a 2-factor model of Inhibition (Hayling, Stroop) and Shifting (Brixton, Color Trails) and a single-factor model. The 2-factor model did not fit the data because the covariance matrix of the factors was not positive definite. The single-factor model fit the data well, chi(2)(2, N=427)=0.32, p=.85, root-mean-square error of approximation (RMSEA)=.00, comparative fit index (CFI)=1.00, goodness-of-fit index (GFI)=1.00. Moreover, the single-factor structure of executive functioning was invariant (configural and metric) across gender, and invariant (configural with limited metric) across age. Structural relations showed that poorer executive functioning performance was related to older age and lower fluid intelligence, chi(2)(11, N=418)=23.04, p=.02, RMSEA=.05, CFI=.97, GFI=.98.

Inconsistency in latency across trials of 2-choice reaction time data was analyzed in 273 participants ranging in age from 6 to 81 years. A U-shaped curve defined the relationship between age and inconsistency, with increases in age associated with lower inconsistency throughout childhood and higher inconsistency throughout adulthood. Differences in inconsistency were independent of practice, fatigue, and age-related differences in mean level of performance. Evidence for general and specific variability-producing processes was found in those aged less than 21 years, whereas only a specific process, such as attentional blocks, was evident for those 21 years and older. The findings highlight the importance of considering moment-to-moment changes in performance in psychological research.

Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitive status (i.e., cognitive impairment, no dementia [CIND] classification) and attrition 5 years later.Two hundred twelve community-dwelling older adults, initially aged 64-92 years, remained in the study after 5 years. Inconsistency was calculated from baseline reaction time performance. Participants were assigned to groups on the basis of their fluctuations in CIND classification over time. Logistic and Cox regressions were used.Baseline inconsistency significantly distinguished among those who remained or transitioned into CIND over the 5 years and those who were consistently intact (e.g., stable intact vs. stable CIND, Wald (1) = 7.91, p.01, Exp(β) = 1.49). Average level of inconsistency over time was also predictive of study attrition, for example, Wald (1) = 11.31, p.01, Exp(β) = 1.24.For both outcomes, greater inconsistency was associated with a greater likelihood of being in a maladaptive group 5 years later. Variability based on moderately cognitively challenging tasks appeared to be particularly sensitive to longitudinal changes in cognitive ability. Mean rate of responding was a comparable predictor of change in most instances, but individuals were at greater relative risk of being in a maladaptive outcome group if they were more inconsistent rather than if they were slower in responding. Implications for the potential utility of intraindividual variability in reaction time as an early marker of cognitive decline are discussed.