Network models have become a popular alternative to factor models for analysing the phenotypic relationships among cognitive abilities. Studies have begun to compare these models directly to one another using cognitive ability data, although such a comparison has so far not extended to genetics. Our aim with this study was therefore to compare factor and network models of cognitive abilities first at a phenotypic level and then at a genetic level. We analyzed data from the Twins Early Development Study that were collected using 14 cognitive ability measures from 11,290 twins in the UK aged 12 years old. We conducted phenotypic and genetic analyses in which numerous factor and network models were tested, including a novel network twin model. Factor and network models both provided useful representations of the phenotypic and genetic relationships among cognitive abilities. Surprisingly, several relationships among cognitive abilities within the genetic networks were negative, which suggests that these cognitive abilities might share some genetic variants with inverse effects, although more research is currently needed to confirm this. Implications for future genomic research are discussed.

This study investigates neural mechanisms of divergent and convergent thinking in the verbal knowledge domain while taking into account activation related to working memory (WM). Divergent thinking was assessed using the Alternate Uses Task (AUT) and convergent thinking using the Compound Remote Associates task (RAT). We analyzed upper alpha band (10–12 Hz) oscillatory activity, in which we accounted for the temporal dynamics of both thinking processes by investigating three different time points during each trial for both tasks. We subtracted WM-related oscillatory activity measured by a serial recall task within the same knowledge domain and by using highly similar stimulus material as in both divergent and convergent thinking tasks. Our results show a strong upper alpha synchronization during divergent relative to convergent thinking, most pronounced at fronto-parietal electrodes. Moreover, we observed highest synchronization towards the middle (in contrast to the beginning and end) of each trial during both thinking processes. The results of the present study extend previous findings in the visuo-spatial knowledge domain, using a highly similar analytical approach to investigate divergent and convergent thinking. Together, these findings provide theoretical implications on how divergent and convergent thinking interact beyond WM across different knowledge domains by emphasizing their complex interplay.

Evidence is mixed whether fluid intelligence (Gf) is associated with increased or decreased alpha and beta band activity (7–30 Hz). Moreover, the Gf relationship with the delta and theta band activity (1–7 Hz) is unknown. We recorded electroencephalographic (EEG) data in 160 healthy adults solving Raven's Advanced Progressive Matrices with a randomized item order to control for item difficulty unaffected by sequential effects. The participants studied each matrix for 30 s before the response bank onset, so we could track the time course of neural activity during problem solving. We measured Gf using six tests. Gf positively correlated with the delta band power, while there was no correlation with the theta band power. For almost all of the participants, we identified the specific slow rhythm frequency, which varied in power as a function of item difficulty. We observed that the lower this frequency, the higher Gf, but only in men. Finally, the alpha and low-beta activity correlated negatively with Gf after we had filtered out the activity during idle intervals (the latter reflecting waiting for the response bank). Overall, the brain activity in the delta, alpha, and beta bands explained 22.6% of Gf variance.

Children's differences in early life cognitive development are driven by the interplay of genetic and environmental factors, but identifying replicable gene-environment interactions (GxE) has proven difficult. We systematically tested GxE effects in the prediction of cognitive development from 2 to 4 years, using polygenic scores (PGS) for years spent in education and 39 measures of the home and neighborhood environment. Data came from up to 6973 unrelated individuals from the Twins Early Development Study (TEDS), a UK population-representative cohort. The environmental measures accounted together for 20.6% of the variance in cognitive development, while the PGS accounted for 0.5% (p < .001). We observed substantial gene-environment correlations but found no conclusive evidence for GxE effects. While associations between PGS and cognitive development were weak, genetic and environmental factors had direct and additive (i.e., main effects) rather than interactive influences on early life cognitive development.