Brain Advance Access originally published online on December 22, 2003
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Brain, Vol. 127, No. 3, 517-525, 2004
© 2004 Guarantors of Brain
doi: 10.1093/brain/awh060
Neurophysiological factors in human information processing capacity
1 Functional Neuroimaging Section, Rudolf Magnus Institute of Neuroscience, Department of Psychiatry, University Medical Center Utrecht, The Netherlands and 2 Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
Correspondence to: Nick F. Ramsey, PhD, Functional Neuroimaging Section, Rudolf Magnus Institute of Neuroscience, Department of Psychiatry, University Medical Center Utrecht, Room A.01.126, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands E-mail: n.ramsey{at}azu.nl
What determines how well an individual can manage the complexity of information processing demands when several tasks have to be executed simultaneously? Various theoretical frameworks address the mechanisms of information processing and the changes that take place when processes become automated, and brain regions involved in various types of information processing have been identified, as well as sequences of events in the brain. The neurophysiological substrate of human information processing capacity, i.e. the amount that can be processed simultaneously, is, however, unresolved, as is the basis of inter-individual variability in capacity. Automatization of cognitive functions is known to increase capacity to process additional tasks, but behavioural indices of automatization are poor predictors of processing capacity in individuals. Automatization also leads to a decline of brain activity in the working memory system. In this study, we test the hypothesis that processing capacity is closely related to the way that the brain adjusts to practice of a single cognitive task, i.e. to the changes in neuronal activity that accompany automatization as measured with functional MRI (fMRI). Using a task that taxes the working memory system, and is sensitive to automatization, performance improved while activity in the network declined, as expected. The key finding is that the magnitude of automatization-induced reduction of activity in this system was a strong predictor for the ability to perform two different working memory tasks simultaneously (after scanning). It explained 60% of the variation in information processing capacity across individuals. In contrast, the behavioural measures of automatization did not predict this. We postulate that automatization involves at least two partially independent neurophysiological mechanisms, i.e. (i) streamlining of neuronal communication which improves performance on a single task; and (ii) functional trimming of neuronal ensembles which enhances the capacity to accommodate processing of additional tasks, potentially by facilitating rapid switching of instruction sets or contexts. Finally, this study shows that fMRI can provide information that predicts behavioural output, which is not provided by overt behavioural measures.
Key Words: working memory; information processing capacity; fMRI; individual variation; multitasking; automatization
Abbreviations: ACC= anterior cingulate cortex; AUT = automatization; CT = control task; DLPFC = dorsolateral prefrontal cortex; fMRI = functional MRI; NT = novel task; PT = practised task; WM = working memory
Received August 26, 2002. Revised August 15, 2003. Accepted October 14, 2003.
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