A neural network model of individual differences in task switching abilities.

We use a biologically grounded neural network model to investigate the brain mechanisms underlying individual differences specific to the selection and instantiation of representations that exert cognitive control in task switching. Existing computational models of task switching do not focus on individual differences and so cannot explain why task switching abilities are separable from other executive function (EF) abilities (such as response inhibition). We explore hypotheses regarding neural mechanisms underlying the "Shifting-Specific" and "Common EF" components of EF proposed in the Unity/Diversity model (Miyake & Friedman, 2012) and similar components in related theoretical frameworks. We do so by adapting a well-developed neural network model of working memory (Prefrontal cortex, Basal ganglia Working Memory or PBWM; Hazy, Frank, & O'Reilly, 2007) to task switching and the Stroop task, and comparing its behavior on those tasks under a variety of individual difference manipulations. Results are consistent with the hypotheses that variation specific to task switching (i.e., Shifting-Specific) may be related to uncontrolled, automatic persistence of goal representations, whereas variation general to multiple EFs (i.e., Common EF) may be related to the strength of PFC representations and their effect on processing in the remainder of the cognitive system. Moreover, increasing signal to noise ratio in PFC, theoretically tied to levels of tonic dopamine and a genetic polymorphism in the COMT gene, reduced Stroop interference but increased switch costs. This stability-flexibility tradeoff provides an explanation for why these two EF components sometimes show opposing correlations with other variables such as attention problems and self-restraint.

The nature and nurture of high IQ: an extended sensitive period for intellectual development.

IQ predicts many measures of life success, as well as trajectories of brain development. Prolonged cortical thickening observed in individuals with high IQ might reflect an extended period of synaptogenesis and high environmental sensitivity or plasticity. We tested this hypothesis by examining the timing of changes in the magnitude of genetic and environmental influences on IQ as a function of IQ score. We found that individuals with high IQ show high environmental influence on IQ into adolescence (resembling younger children), whereas individuals with low IQ show high heritability of IQ in adolescence (resembling adults), a pattern consistent with an extended sensitive period for intellectual development in more-intelligent individuals. The pattern held across a cross-sectional sample of almost 11,000 twin pairs and a longitudinal sample of twins, biological siblings, and adoptive siblings.

From an executive network to executive control: a computational model of the n-back task.

A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal "executive network," and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical framework for executive control. To address this challenge, we developed a biologically constrained model of the n-back task that emergently develops the ability to appropriately gate, bind, and maintain information in working memory in the course of learning to perform the task. Furthermore, the model is sensitive to proactive interference in ways that match findings from neuroimaging and shows a U-shaped performance curve after manipulation of prefrontal dopaminergic mechanisms similar to that observed in studies of genetic polymorphisms and pharmacological manipulations. Our model represents a formal computational link between anatomical, functional neuroimaging, genetic, behavioral, and theoretical levels of analysis in the study of executive control. In addition, the model specifies one way in which the pFC, BG, parietal, and sensory cortices may learn to cooperate and give rise to executive control.

The developmental etiology of high IQ.

The genetic and environmental trends in IQ development were assessed in 483 same-sex twin pairs in the Colorado longitudinal twin study using maximum-likelihood model-fitting analysis. The twins were assessed periodically from ages 1 to 16. Results show a decreasing influence of shared environment and an increasing influence of heritability across development, with large and increasing age to age stability of genetic influences. Non-shared environment contributes almost exclusively to age to age change. Similar analyses were conducted designating the top 15% of the sample as having high IQ at each age. The developmental etiology of high IQ did not significantly differ from that found for the continuous measure in this relatively novel analysis. These results demonstrate early stability in etiological influences on IQ and have potential implications for gene-finding efforts, suggesting that samples selected for high IQ can be used to find genetic variation that will be applicable to the full range of the IQ distribution, although conclusive demonstration that the same genes are indeed involved was beyond the scope of this study.

A twin study of the genetics of high cognitive ability selected from 11,000 twin pairs in six studies from four countries.

Although much genetic research has addressed normal variation in intelligence, little is known about the etiology of high cognitive abilities. Using data from 11,000 twin pairs (age range = 6-71 years) from the genetics of high cognitive abilities consortium, we investigated the genetic and environmental etiologies of high general cognitive ability (g). Age-appropriate psychometric cognitive tests were administered to the twins and used to create g scores standardized within each study. Liability-threshold model fitting was used to estimate genetic and environmental parameters for the top 15% of the distribution of g. Genetic influence for high g was substantial (0.50, with a 95% confidence interval of 0.41-0.60). Shared environmental influences were moderate (0.28, 0.19-0.37). We conclude that genetic variation contributes substantially to high g in Australia, the Netherlands, the United Kingdom and the United States.