Localizing age-related individual differences in a hierarchical structure

Salthouse, Timothy A.

doi:10.1016/j.intell.2004.07.003

Cited by 227 publications

(276 citation statements)

References 58 publications

(27 reference statements)

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“…Many variables that were significantly related to age were predominantly influenced by the memory or speed constructs and not Gf. As suggested by Salthouse (2004) and Salthouse and Ferrer-Caja (2003), there is evidence for at least three statistically independent influences of age on cognitive functioning, one on g, one on episodic memory, and one on speed.It is also noteworthy that some variables that might be hypothesized to involve aspects of cognitive control, such as the time to respond to incongruent items in the Stroop Color-Word test, or the number of words recalled after an interference list, were not related to Gf. These findings indicate that the current analytical method is capable of distinguishing among different types of cognitive processing because the Gf construct apparently does not affect conflict monitoring of the type involved in the Stroop test, or maintenance of information in the presence of distraction as operationalized in the contrast of pre-and post-interference recall.…”

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confidence: 90%

“…Deary, 2000), and one of the most replicated results in research on aging and cognition is that a very large number of cognitive variables are negatively related to adult age (e.g., Salthouse, 2001a;2004;Salthouse, Atkinson & Berish, 2003;Salthouse & Davis, 2006;Salthouse & Ferrer-Caja, 2003). Interestingly, these two sets of results are linked because the degree to which a given cognitive variable is related to other cognitive variables (as reflected by the variable's loading on the first principal component in a principal components analysis) has been found to predict the magnitude of the age correlation on the variable (e.g., Salthouse, 2001a,b, c).…”

Section: Introductionmentioning

confidence: 99%

“…The exceptions are the four vocabulary variables, which increased until about age 65 followed by a modest decrease. Although some of the variables in Figure 2 had nonlinear age trends, quadratic and cubic trends accounted for small proportions of the variance compared to the linear term (also see Salthouse, 2004). Only linear age relations were therefore modeled in the analyses, which could result in an underestimation of the absolute magnitude of some of the age-related influences.…”

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Contextual analysis of fluid intelligence

2008

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The nature of fluid intelligence was investigated by identifying variables that were, and were not, significantly related to this construct. Relevant information was obtained from three sources: reanalyses of data from previous studies, a study in which 791 adults performed storage-plusprocessing working memory tasks, and a study in which 236 adults performed a variety of working memory, updating, and cognitive control tasks. The results suggest that fluid intelligence represents a broad individual difference dimension contributing to diverse types of controlled or effortful processing. The analyses also revealed that very few of the age-related effects on the target variables were statistically independent of effects on established cognitive abilities, which suggests most of the age-related influences on a wide variety of cognitive control variables overlap with age-related influences on cognitive abilities such as fluid intelligence, episodic memory, and perceptual speed.The finding that nearly all cognitive variables are positively related to one another has been described as one of the most replicated results in psychology (cf. Deary, 2000), and one of the most replicated results in research on aging and cognition is that a very large number of cognitive variables are negatively related to adult age (e.g., Salthouse, 2001a;2004;Salthouse, Atkinson & Berish, 2003;Salthouse & Davis, 2006;Salthouse & Ferrer-Caja, 2003). Interestingly, these two sets of results are linked because the degree to which a given cognitive variable is related to other cognitive variables (as reflected by the variable's loading on the first principal component in a principal components analysis) has been found to predict the magnitude of the age correlation on the variable (e.g., Salthouse, 2001a,b, c). To illustrate, in an analysis of 30 different data sets, Salthouse (2001a) found a median rank-order correlation of .80 between a variable's loading on the first principal component and the absolute magnitude of the variable's correlation with age. Another intriguing outcome of these analyses was that the variables with the strongest associations with other variables and the strongest associations with age were frequently measures of reasoning or fluid intelligence (Gf).Relations among cognitive variables are often represented in terms of an organizational structure based on the patterns of correlations. There is considerable agreement that a particularly meaningful correlation-based organization is a hierarchical structure, with observed variables at the lowest level, various cognitive abilities at intermediate levels, and a g factor at the highest level (e.g., Carroll, 1993;Gustafsson, 1988;Jensen, 1998). A consistent finding of analyses investigating where in the hierarchical structure age-related influences operate is the discovery of significant negative relations of age on the highest-order factor in the structure (e.g., Salthouse, 2004;2005a;Salthouse & Ferrer-Caja, 2003). Moreover, these © 2007 Elsevier Inc. All rights r...

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Section: Introductionmentioning

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Contextual analysis of fluid intelligence

2008

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“…This aggregate data set has several advantages over data from a single study, including a much larger and more diverse sample than is typically feasible in a single study and a broader variety of target variables (cf. Salthouse, 2004).…”

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Relations Between Cognitive Abilities and Measures of Executive Functioning.

2005

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Although frequently mentioned in contemporary neuropsychology, the term executive functioning has been a source of considerable confusion. One way in which the meaning of a variable can be investigated involves examining its pattern of relations with established cognitive abilities. This method was applied to a variety of variables hypothesized to assess executive functioning in 2 data sets, 1 consisting of 328 adults between 18 and 93 years of age and a 2nd composite data set based on nearly 7,000 healthy adults between 18 and 95 years of age. Most of the hypothesized executive functioning variables were strongly related to reasoning and perceptual speed abilities, and very few had any unique relations with age after taking into consideration the relations of age through the cognitive abilities. These results raise questions about the extent to which neuropsychological tests of executive functioning measure a distinct dimension of variation in normal adults.

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“…Although the computerbased version was based upon standard administration protocols, the current absence of such data necessitates that these results be interpreted with appropriate caution. The measure of fluid cognitive function provided by UK Biobank (which we have called VNR) showed a relatively modest age-related trajectory, in contrast to the steeper and well-replicated age-related decline that would be expected for this construct 66,67 (Supplementary Figure 4). This may partly explain the relatively modest correlation of VNR with TMT performance, when compared to those previously reported 9 and may have also had a bearing on our analyses of genetic overlap within UK Biobank.…”

Section: Discussionmentioning

confidence: 98%

Genetic contributions to Trail Making Test performance in UK Biobank

et al. 2017

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The Trail Making Test (TMT) is a widely used test of executive function and has been thought to be strongly associated with general cognitive function. We examined the genetic architecture of the TMT and its shared genetic aetiology with other tests of cognitive function in 23 821 participants from UK Biobank. The single-nucleotide polymorphism-based heritability estimates for trail-making measures were 7.9% (part A), 22.4% (part B) and 17.6% (part B − part A). Significant genetic correlations were identified between trail-making measures and verbal-numerical reasoning (r g 40.6), general cognitive function (r g 40.6), processing speed (r g 40.7) and memory (r g 40.3). Polygenic profile analysis indicated considerable shared genetic aetiology between trail making, general cognitive function, processing speed and memory (standardized β between 0.03 and 0.08). These results suggest that trail making is both phenotypically and genetically strongly associated with general cognitive function and processing speed.Molecular Psychiatry advance online publication, 19 September 2017; doi:10.1038/mp.2017.189 INTRODUCTIONThe Trail Making Test (TMT) is widely used in both research and clinical settings as a test of some aspects of executive function. [1][2][3] The TMT is usually given as two parts, from which three measures are derived. In TMT Part A (TMT A), participants are required to connect an array of numbers in ascending order, by drawing a continuous line (trail) between them as quickly and accurately as possible. TMT Part B (TMT B) requires participants to connect an array of both numbers and letters in alternating ascending order (1, A, 2, B, 3, C and so on) with the same emphasis on speed and accuracy (Supplementary Figure 1). Subtracting TMT A completion time from that of TMT B (TMT B − A) is thought to allow the relative contributions of visual search and psychomotor speed to be parsed from the more complex executive functions (such as cognitive flexibility) required to alternate between numbers and letters. [4][5][6][7][8] TMT performance has been ascribed to a number of cognitive processes, 'including attention, visual search and scanning, sequencing and shifting, psychomotor speed, abstraction, flexibility, ability to execute and modify a plan of action, and ability to maintain two trains of thought simultaneously '. 9 It is considered a useful tool in research and clinical practice due to the sensitivity of the task (particularly TMT B and B − A) to frontal lobe damage (in some, but not other studies 10 ) and dementia. [11][12][13] There are declines in both TMT A and B performance in ageing. 10,[14][15][16][17][18][19][20] There is also evidence for performance deficits on TMT B in mood disorders 21 and in patients with schizophrenia and their relatives 18,[22][23][24][25][26][27] Family-based and twin-based studies have provided evidence for a genetic contribution to individual differences in trail making, estimating the heritability for trail making part A between 0.23 and 0.38, and between 0.39 and 0.65...

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Localizing age-related individual differences in a hierarchical structure

Cited by 227 publications

References 58 publications

Contextual analysis of fluid intelligence

Contextual analysis of fluid intelligence

Relations Between Cognitive Abilities and Measures of Executive Functioning.

Genetic contributions to Trail Making Test performance in UK Biobank

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