Processing speed, working memory and reasoning ability from childhood to old age
Introduction
This study tested a suggestion by Jensen (2006) that putative changes in brain structure responsible for declining cognitive abilities during adulthood and old age may be a mirror image of structural changes responsible for improving cognitive development during childhood. This proposition derives from theory that identifies individual differences in processing speed as central to an understanding of intelligence differences and also attributes average changes in cognitive abilities during childhood and old age to speed changes. There are well established average changes in speed with age on perceptual speed and elementary cognitive tasks like reaction times (RT) and inspection time (IT), whereby processing becomes faster during childhood and adolescence but slows throughout adulthood. Thus, Kail (1991) established that improvement to processing speed is more rapid during younger years but tends to asymptote close to early adult levels by about 14 years of age. Similarly, Salthouse (1996) summarized evidence that processing speed declines on average across adult years from 20 to 80, the mean trend being approximately linear.
Salthouse (1996) has developed a theory that substantially attributes age-related, reduced effectiveness of higher order cognitive functioning to general slowing in processing speed, which imposes a fundamental constraint on the efficiency of working memory. Thus, because only brief retention is possible without rehearsal, slower processing results in slower execution of ongoing operations and poorer synchronization of task components. In turn, a more limited working memory adversely impacts cognitive decision making.
Salthouse’s theory resembles what Fry and Hale (1996) described as a “cognitive developmental cascade”, that is, a sequence of processing stages within which the effectiveness of processing at the first stage has a flow-on effect for the next stage, which influences the next, and so on. Fry and Hale described childhood cognitive maturation in terms of causal relations between increasing chronological age, processing speed, working memory, and fluid intelligence, by cross-sectional comparisons across years 7–19. Path analysis confirmed a developmental cascade whereby age-related faster processing speed resulted in improved working memory, which linked to higher fluid performance (as demonstrated in Fig. 1). After controlling statistically for age, Fry and Hale found that individual differences in speed influenced memory, and when both age and speed were controlled, working memory influenced fluid performance. Fry and Hale also discounted the possibility that age-related improvement in fluid ability was responsible for faster processing speed, by demonstrating marked, constant age differences across different speeded tasks in age samples matched on raw ability scores. The cascade model therefore provided a good account for average mental age changes and for ability differences within age bands, in processing speed, working memory and reasoning. Kail (2007) confirmed these results, also showing that the cascade explanation held longitudinally for reasoning on a re-test after one year.
One interpretation of these results is that maturing brain structures improve processing speed, with concurrent improvement in working memory and other cognitive functions. These structures achieve optimal capacities by early adulthood but then gradually deteriorate across the adult years. As Jensen (2006, p. 97) has surmised, “the overall picture of mental decline … as shown by chronometric tests is much like a mirror image of the developmental curves from early childhood to maturity ”. However, although this account appears consistent with changes in the psychological constructs outlined here, it is possible that cognitive deterioration during old age involves more than simply the reverse of whatever maturational changes improve capacities during childhood. If changes in old age reflect other than a mirror image of childhood development, it should be possible to identify differences between trends during childhood and old age.
Gregory, Nettelbeck, Howard, and Wilson (2009) reported a more complex cascade for people aged 70–91 years. They found significant paths from age to fluid reasoning via perceptual speed and working memory. However, cross-sectional and longitudinal analyses also found direct paths between age and working memory and between perceptual speed and reasoning. Gregory et al. therefore concluded that relations between age, processing speed, working memory and reasoning ability are more complex among elderly adults than among children, perhaps because of age-related changes other than slower speed that might directly impact working memory. They also proposed that a direct path from processing speed to reasoning could reflect confounding by motor problems.
The present study extends previous research by testing the cascade model cross-sectionally for an age range from 8 to 80 years. The test battery was selected to define latent variables for processing speed, working memory and fluid reasoning ability. Two hypotheses, derived from past results from Fry and Hale (1996) and Salthouse (1996) were:
- 1.
Age-related (i.e. improving) childhood performance in processing speed, working memory and reasoning ability is described by a structural cascade model (see Fig. 1). Specifically, significant coefficients linking age to the latent variables are limited to causal paths from age to processing speed, from processing speed to working memory and from working memory to reasoning ability.
- 2.
The same cascade model describes deteriorating processing speed, working memory and reasoning ability during adulthood and old age. That is, older age causes slower processing speed, which causes poorer working memory, which results in poorer reasoning ability. As with the childhood model, significant coefficients linking age to the latent variables are limited to the causal paths in Fig. 1 from age to processing speed, from processing speed to working memory and from working memory to reasoning ability.
Support for both hypotheses would be consistent with Jensen’s (2006) suggestion that cognitive changes in old age are a mirror image of the developmental trends found from early childhood to adulthood.
Section snippets
Participants
Participants numbered 478 (288 males, 190 females; M = 28.1 years, SD = 21.7), recruited into four non-overlapping age groups. These samples of convenience provided a wide age range from childhood to old age. Youngest children were assumed capable of understanding all test requirements in the common battery. Children were in two groups, both readily accessible within metropolitan schools; 8–10 years (mid-primary education n = 120, M = 9.69 years, SD = 0.76) and 12–14 years (early secondary education n = 120, M =
Results
Table 1 shows descriptive statistics for all variables, for the entire sample and the four age subsamples. Fig. 2 shows the means of standard scores for Digit Symbol, Visual Matching, reflected IT, reflected simple RT, and reflected odd-man-out DT, together representing PS, regressed on age. There is an obvious reversal in direction of the relationship between PS and age beyond 20 years. We therefore initially tested the cascade model on two groups; children 18–14 and adults 18 years and older.
Discussion
Hypothesis 1 was supported, confirming a developmental cascade for children and adolescents, as found by Fry and Hale (1996). PS and WM mediated improved reasoning ability with age, with 70% of variance in PS accounted for by age, with PS accounting for 66% of variance in WM and the latter accounting for 78% of variance in RA.
With adults, however, the simple cascade consistent with Salthouse (1996) was not found. Instead, results confirmed a direct path from age to WM as reported by Gregory et
Acknowledgement
This work was supported by Australian Research Council Discovery Grant DP0211113 to both authors.
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