Relations Between Cognitive Abilities and Measures of Executive Functioning.

Salthouse, Timothy A.

doi:10.1037/0894-4105.19.4.532

Cited by 425 publications

(442 citation statements)

References 65 publications

(84 reference statements)

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“…However, the results of this project reveal that Gf is related to many different types of variables, including measures of monitoring the status of changing sequences of information, measures of memory for non-verbal information, and measures from a variety of simple tasks when some type of additional processing is required. Because these measures are quite diverse, it may not be reasonable to assume that one particular subset of them necessarily represents the core or essence of Gf without explaining the relations of Gf to the other measures.The idea that Gf encompasses a broad spectrum of controlled processing is consistent with earlier research which found strong relations between the Gf construct and the variance common to different variables hypothesized to reflect the neuropsychological construct of executive functioning (e.g., Salthouse, 2005b;Salthouse & Davis, 2006;Salthouse, et al, 2005). Although motivated from different research traditions and usually studied separately, the results of this and other recent studies suggest that research with cognitive, psychometric, and neuropsychological variables may have been characterizing essentially the same dimension of individual differences among normal healthy adults.…”

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confidence: 75%

“…Conceptualizing abilities as latent constructs That is, when researchers study isolated variables there is no way to determine whether the age-related influences on those variables are distinct from the age-related influences on other variables. Unique effects associated with age in a contextual analysis are particularly informative in this respect because, by definition, they are statistically independent of agerelated influences on other variables and constructs included in the analyses.The reference cognitive ability variables used in earlier contextual analyses have been found to have good internal consistency reliability (e.g., Salthouse, 2005b;Salthouse, Atkinson & Berish, 2003;Salthouse, Berish & Siedlecki, 2004;, and to have parallel factor structures across studies. Evidence relevant to the robustness of the structures is summarized in Table 1, where it can be seen that the patterns of construct-variable and construct-construct relations were similar in four independent data sets.…”

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confidence: 99%

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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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confidence: 99%

Contextual analysis of fluid intelligence

2008

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“…Even though various cognitive tests are used in the studied populations, different cognitive domains can be compared since they are correlated. Therefore, moderate correlation (the Pearson correlation coefficient of 0.429, P-valueo0.0001) between visuospatial ability and global cognition ability in the ERF, as well as correlation (the Pearson correlation coefficient of 0.460, P-valueo0.0001) between visuospatial ability and executive function which is recognized as a central domain of cognitive functioning 58,59 allow us to compare association of the most interesting overlapping variant with block-design test in the ERF and MMSE test in the RS.…”

Section: Discussionmentioning

confidence: 99%

The dystrophin gene and cognitive function in the general population

et al. 2014

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The aim of our study is to investigate whether single-nucleotide dystrophin gene (DMD) variants associate with variability in cognitive functions in healthy populations. The study included 1240 participants from the Erasmus Rucphen family (ERF) study and 1464 individuals from the Rotterdam Study (RS). The participants whose exomes were sequenced and who were assessed for various cognitive traits were included in the analysis. To determine the association between DMD variants and cognitive ability, linear (mixed) modeling with adjustment for age, sex and education was used. Moreover, Sequence Kernel Association Test (SKAT) was used to test the overall association of the rare genetic variants present in the DMD with cognitive traits. Although no DMD variant surpassed the prespecified significance threshold (Po1 × 10 − 4 ), rs147546024:A4G showed strong association (β = 1.786, P-value = 2.56 × 10 − 4 ) with block-design test in the ERF study, while another variant rs1800273:G4A showed suggestive association (β = − 0.465, P-value = 0.002) with Mini-Mental State Examination test in the RS. Both variants are highly conserved, although rs147546024:A4G is an intronic variant, whereas rs1800273:G4A is a missense variant in the DMD which has a predicted damaging effect on the protein. Further gene-based analysis of DMD revealed suggestive association (P-values = 0.087 and 0.074) with general cognitive ability in both cohorts. In conclusion, both single variant and gene-based analyses suggest the existence of variants in the DMD which may affect cognitive functioning in the general populations.

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“…Salthouse, 2005) implies that a better design would incorporate multiple measures of multiple executive functions. The second aim was to further explore the role of personality as a determinant of self-reports of executive problems.…”

Section: Studymentioning

confidence: 99%

Self-report measures of executive function problems correlate with personality, not performance-based executive function measures, in nonclinical samples.

Buchanan

2016

Psychological Assessment

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Researchers and clinicians often measure executive function in patients and normal samples. In addition to cognitive tests that objectively measure executive function, several instruments have been developed that address individuals' everyday experience of executive problems. Such selfreport measures of executive problems may have value, but there are questions about the extent to which they tap objectively-measurable executive problems or are influenced by variables such as personality. Relationships between self-reported executive problems, personality, and cognitive test performance were assessed in three separate, well-powered, methodologically distinct correlational studies using non-clinical samples. These studies used multiple measures of personality and self-reported executive function problems. Across all three studies, self-reported executive function problems were found to correlate with neuroticism and with low conscientiousness, with medium to large effect sizes. However self-reported problems did not correlate with performance on Trail Making, Phonemic Fluency, Semantic Fluency or Digit Span tests tapping executive function. A key implication of these findings is that in non-clinical samples, self-report questionnaires may not be proxies for executive functioning as measured by neuropsychological tests.

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Relations Between Cognitive Abilities and Measures of Executive Functioning.

Cited by 425 publications

References 65 publications

Contextual analysis of fluid intelligence

Contextual analysis of fluid intelligence

The dystrophin gene and cognitive function in the general population

Self-report measures of executive function problems correlate with personality, not performance-based executive function measures, in nonclinical samples.

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