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A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (Nā=ā308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
According to a popular stereotype, women are better at multitasking than men, but empirical evidence for gender differences in multitasking performance is mixed. Previous work has focused on specific aspects of multitasking or has not considered gender differences in abilities contributing to multitasking performance. We therefore tested gender differences (N = 96, 50% female) in sequential (i.e., task switching) and concurrent (i.e., dual tasking) multitasking, while controlling for possible gender differences in working memory, processing speed, spatial abilities, and fluid intelligence. Applying two standard experimental paradigms allowed us to test multitasking abilities across five different empirical indices (i.e., performance costs) for both reaction time (RT) and accuracy measures, respectively. Multitasking resulted in substantial performance costs across all experimental conditions without a single significant gender difference in any of these ten measures, even when controlling for gender differences in underlying cognitive abilities. Thus, our results do not confirm the widespread stereotype that women are better at multitasking than men at least in the popular sequential and concurrent multitasking settings used in the present study.