Coupled cognitive changes in adulthood: A meta-analysis

Abstract

With advancing age, healthy adults typically exhibit decreases in performance across many different cognitive abilities such as memory, processing speed, spatial ability, and abstract reasoning. However, there are marked individual differences in rates of cognitive decline, with some adults declining steeply and others maintaining high levels of functioning. To move toward a comprehensive understanding of cognitive aging, it is critical to know whether individual differences in longitudinal changes interrelate across different cognitive abilities. We identified 89 effect sizes representing shared variance in longitudinal cognitive change from 22 unique datasets composed of more than 30,000 unique individuals, which we meta-analyzed using a series of multilevel metaregression models. An average of 60% of the variation in cognitive changes was shared across cognitive abilities. Shared variation in changes increased with age, from approximately 45% at age 35 years to approximately 70% at age 85 years. There was a moderate-to-strong correspondence (r = .49, congruence coefficient = .98) between the extent to which a variable indicated general intelligence and the extent to which change in that variable indicated a general factor of aging-related change. Shared variation in changes did not differ substantially across cognitive ability domain classifications. In a sensitivity analysis based on studies that carefully controlled for dementia, shared variation in longitudinal cognitive changes remained at upward of 60%, and age-related increases in shared variation in cognitive changes continued to be evident. These results together provide strong evidence for a general factor of cognitive aging that strengthens with advancing adult age. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Figure 1.

Left: Histogram of the within-variable level-slope correlations, weighted by the inverse number of effect sizes contributed by the associated dataset. Weights are scaled to sum to the total number of effect sizes (98). The dashed vertical line represents the weighted meta-analytic estimate (−.042) for the level-slope correlation. To facilitate comparisons with results pertaining to slope communalities, the solid vertical line depicts the weighted meta-analytic estimate (.001) for the level-slope correlations using the slope communality weights. Right: Level-slope correlation plotted as a function of age at level. The overlaid regression line represents the model-implied trend and its 95% confidence interval from a meta-regression model that is weighted by the inverse number of effect sizes contributed by the associated dataset. The positive association between age at level and the level-slope correlation is significant at p=.031, but is not significant when restricting analyses to effect sizes for which age at level is greater than 50 (87 observations, p = .243).

Figure 2.

Histogram of the ratio of the slope communality precision to the level communality precision. The vertical dashed line depicts the median value (.072. It can be seen that the slope communalities tend to be estimated with substantially less precision than the level communalities.

Figure 3.

Histograms of level communalities and slope communalities. Histograms are weighted by the respective precision of the individual estimates and by the inverse number of effect sizes contributed by the associated dataset. In each panel, weights are scaled to sum to the total number of effect sizes (89). The dashed vertical line represents the weighted meta-analytic estimate of the mean communality for the levels (.558) and slopes (.600), respectively. To facilitate comparisons across level and slope communalities, the solid vertical line depicts the weighted meta-analytic estimate for the level communalities using the slope communality weights (.585).

Figure 4.

Funnel plots of level communalities and slope communalities. Effect size estimates are on the x axis and precision of the estimates are on the y axis. In each panel, precision values were scaled such that they sum to the total number of effect sizes (89). It can be seen that both plots are approximately symmetrical. To formally test funnel asymmetry we regressed effect size estimates on precision, with and without weighting by precision. For level communality, the p values for the weighted and unweighted regressions were .298 and .759, respectively. For slope communality, the p values for the weighted and unweighted regressions were .486 and .271, respectively. Thus, there was no evidence that effect size estimates were systematically associated with the precisions at which they were estimated, as might occur under conditions of publication bias or p hacking.

Figure 5.

Scatterplots of the association between level communality estimates and slope communality estimates with (top) and without (bottom) centering estimates within dataset. The area of each point corresponds to the precision of the communality estimate, with larger points representing more precise communalities. Precision weights are scaled to sum to the total number of effect sizes (89).

Figure 6.

Path diagram representing meta-analytic estimates for standardized factor loadings of levels of individual cognitive abilities on a general factor of levels (left) and standardized factors loadings of longitudinal slopes of individual cognitive abilities on a general factor of changes (right). Variances were omitted from the diagram. Standardized factor loadings were calculated by taking the square root of the respective communalities. Reason = Reasoning. Verb Know = Verbal Knowledge. Prosp Memory = Prospective Memory.

Figure 7.

Slope communality plotted as a function of mean age at baseline for full meta-analytic dataset (left) and for dementia-controlled sensitivity analyses (right). The area of each point corresponds to the precision of the slope communality estimate, with larger points representing more precise communalities. The overlaid regression lines represent the meta-regression model-implied linear trends and their 95% confidence intervals. For the full dataset, the positive association between slope communality and mean age at baseline remained (p = 0.013) when restricting analysis to estimates derived from mean ages at baseline that were greater than 50 years. For the dementia-controlled sensitivity analyses, the positive association between slope communality and mean age at baseline also remained (p <.0005) when restricting analysis to estimates derived from mean ages at baseline that were greater than 50 years.