A watershed model of individual differences in fluid intelligence. (October 2016)

• Record Type: Journal Article
• Authors: Kievit, Rogier A.; Davis, Simon W.; Griffiths, John; Correia, Marta M.; Cam-CAN, ; Henson, Richard N.
• Is Part Of: Neuropsychologia. Volume 91(2016)
• Page Start: 186
• Page End: 198
• Publication Date: 2016-10
• Subjects: Cognitive ageing -- White matter -- Processing speed -- Fluid intelligence -- Structural Equation Modelling -- Watershed model; Neuropsychology -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Neuropsychologie -- Périodiques; Neuropsychology; Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.neuropsychologia.2016.08.008
• Languages: English
• ISSNs: 0028-3932

Abstract

Abstract: 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. Highlights: A watershed model is proposed to explain individual differences in fluid intelligence. The model suggests a hierarchical, many-to-one cascade of partially independent effects. White matter affects processing speed, which in turn affects fluid intelligence. A structural equation model implementation of the watershed model is developed. In an N=555 lifespan sample (18–87) the watershed model outperforms competing models.