Working Memory and Intelligence: The Same or Different Constructs?

Ackerman, Phillip L.; Beier, Margaret E.; Boyle, Mary O.

doi:10.1037/0033-2909.131.1.30

Cited by 853 publications

(778 citation statements)

References 156 publications

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“…Furthermore, correlations between a WM composite and score on the Raven's Advanced Progressive Matrices test, which is often considered a prototypical Gf test, were .69, .59, and .61 in three studies reported in Salthouse (1993a Engle, Tuholski, Laughlin and Conway (1999), Kane, Hambrick, Tuholski, Wilhelm, Payne and Engle (2004), and Unsworth, Heitz, Schrock and Engle (2005). After adjusting for unreliability, Ackerman, Beier and Boyle (2004) found a meta-analytic estimate for the Gf-WM correlation of .63. The Gf-WM correlations are generally higher when the relations are examined at the level of factor scores, composite scores, or latent variables.…”

Section: Results Inmentioning

confidence: 91%

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: Results Inmentioning

confidence: 91%

Contextual analysis of fluid intelligence

2008

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“…Once a group-level based connectivity model is available, the method enables to classify new subjects on the basis of their individual connectivity profile. Second, we cannot exclude that the present metabolic and structural relationships with verbal WM are not driven by more global cognitive characteristics such as general intelligence (Ackerman et al, 2005). …”

Section: Discussionmentioning

confidence: 91%

Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults

et al. 2013

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Studies of functional connectivity suggest that the default mode network (DMN) might be relevant for cognitive functions. Here, we examined metabolic and structural connectivity between major DMN nodes, the posterior cingulate (PCC) and medial prefrontal cortex (MPFC), in relation to normal working memory (WM). Metabolic connectivity, a correlation between FDG uptake in PCC and MPFC, was examined in groups of subjects with (relative to median) low (n=18) and high (n=17) performance on digit span backward test as an index of verbal WM. In addition, fiber tractography based on PCC and MPFC nodes as way points was performed in a subset of subjects.FDG uptake in the DMN nodes did not differ between high and low performers. However, significantly (p=0.01) lower metabolic connectivity was found in the group of low performers.Furthermore, as compared to high performers, low performers showed lower density of the left superior cingulate bundle.Verbal WM performance is related to metabolic and structural connectivity within the DMN in young healthy adults. Metabolic connectivity as quantified with FDG-PET might be a sensitive marker of the normal variability in some cognitive functions.

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“…This finding indicates that group differences in WM may be attributable to differences in general intelligence. Relationships between WM capacity and intellectual ability have also been documented by studies of typically -19 -developing children and young adults (Ackerman, Beier, & Boyle, 2005;Cornoldi, Orsini, Cianci, Giofrè, & Pezzuti, 2013).…”

Section: Predictive Effect Of Working Memory On Reading and Spelling mentioning

confidence: 99%

Working Memory in Portuguese Children With Developmental Dyslexia

Moura¹,

Simões²,

Pereira³

2014

Applied Neuropsychology: Child

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A Portuguese sample of 50 children with developmental dyslexia and 50 typical readers who were matched for age (8 to 12 years old) were tested on measures of working memory.Relative to the typical readers, the children with developmental dyslexia performed significantly lower on phonological loop and central executive tasks; however, they exhibited no impairments on visuospatial sketchpad tasks. After controlling for the influence of the phonological loop, the group differences in central executive were no longer significant. The results of a receiver operating characteristics curve analysis and a binary logistic regression analysis suggested that the phonological loop and central executive tasks (but not the visuospatial sketchpad tasks) were relevant variables for identifying children with developmental dyslexia. Hierarchical linear regression analyses showed that the phonological loop and central executive (Backward Digit Span only) tasks were significant predictors of reading and spelling abilities.

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Working Memory and Intelligence: The Same or Different Constructs?

Cited by 853 publications

References 156 publications

Contextual analysis of fluid intelligence

Contextual analysis of fluid intelligence

Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults

Working Memory in Portuguese Children With Developmental Dyslexia

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