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A watershed model of individual differences in fluid intelligence

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integra...

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Autores principales: Kievit, Rogier A., Davis, Simon W., Griffiths, John, Correia, Marta M., Cam-CAN, Henson, Richard N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pergamon Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5081064/
https://www.ncbi.nlm.nih.gov/pubmed/27520470
http://dx.doi.org/10.1016/j.neuropsychologia.2016.08.008
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author Kievit, Rogier A.
Davis, Simon W.
Griffiths, John
Correia, Marta M.
Cam-CAN
Henson, Richard N.
author_facet Kievit, Rogier A.
Davis, Simon W.
Griffiths, John
Correia, Marta M.
Cam-CAN
Henson, Richard N.
author_sort Kievit, Rogier A.
collection PubMed
description Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.
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spelling pubmed-50810642016-10-28 A watershed model of individual differences in fluid intelligence Kievit, Rogier A. Davis, Simon W. Griffiths, John Correia, Marta M. Cam-CAN Henson, Richard N. Neuropsychologia Article Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes. Pergamon Press 2016-10 /pmc/articles/PMC5081064/ /pubmed/27520470 http://dx.doi.org/10.1016/j.neuropsychologia.2016.08.008 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kievit, Rogier A.
Davis, Simon W.
Griffiths, John
Correia, Marta M.
Cam-CAN
Henson, Richard N.
A watershed model of individual differences in fluid intelligence
title A watershed model of individual differences in fluid intelligence
title_full A watershed model of individual differences in fluid intelligence
title_fullStr A watershed model of individual differences in fluid intelligence
title_full_unstemmed A watershed model of individual differences in fluid intelligence
title_short A watershed model of individual differences in fluid intelligence
title_sort watershed model of individual differences in fluid intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5081064/
https://www.ncbi.nlm.nih.gov/pubmed/27520470
http://dx.doi.org/10.1016/j.neuropsychologia.2016.08.008
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