A neurocomputational model of developmental trajectories of gifted children under a polygenic model: When are gifted children held back by poor environments?

Associations between Socioeconomic Status, Cognition, and Brain Structure: Evaluating Potential Causal Pathways Through Mechanistic Models of Development

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences.

Measurement of Executive Functioning and High Intellectual Ability in Childhood: A Comparative Meta-Analysis

From a neuroconstructivist approach and a developmental model of high intellectual ability (HIA), it is argued that the management of intellectual resources through executive functioning (EF) is one of the factors influencing the expression of high potential. The main objective is to determine the effectiveness of measures of executive functioning used comparing schoolchildren with HIA and those of average intelligence. A meta-analysis was carried out on a selection of 17 studies for a total sample of 1518 children with either HIA or an average level of intelligence. Pooled estimates of effect size revealed a significant difference favoring the HIA individuals in the two components of EF related with WM verbal (d = 1.015), and WM visual-spatial (d = 0.709). Other components did not show significant differences: inhibition (d = −0.014), flexibility (d = 0.068), and planification (d = −0.038). The empirical heterogeneity was very high. It is concluded that these instruments show a degree of measurement impurity, which condition their validity and reliability, and that schoolchildren with HIA display better executive functioning in the components of verbal and visual-spatial working memory.

Evolving Connectionist Models to Capture Population Variability across Language Development: Modeling Children's Past Tense Formation

Children's acquisition of the English past tense has been widely studied as a testing ground for theories of language development, mostly because it comprises a set of quasi-regular mappings. English verbs are of two types: regular verbs, which form their past tense based on a productive rule, and irregular verbs, which form their past tenses through exceptions to that rule. Although many connectionist models exist for capturing language development, few consider individual differences. In this article, we explore the use of populations of artificial neural networks (ANNs) that evolve according to behavioral genetics principles in order to create computational models capable of capturing the population variability exhibited by children in acquiring English past tense verbs. Literature in the field of behavioral genetics views variability in children's learning in terms of genetic and environmental influences. In our model, the effects of genetic influences are simulated through variations in parameters controlling computational properties of ANNs, and the effects of environmental influences are simulated via a filter applied to the training set. This filter alters the quality of information available to the artificial learning system and creates a unique subsample of the training set for each simulated individual. Our approach uses a population of twins to disentangle genetic and environmental influences on past tense performance and to capture the wide range of variability exhibited by children as they learn English past tenses. We use a novel technique to create the population of ANN twins based on the biological processes of meiosis and fertilization. This approach allows modeling of both individual differences and development (within the lifespan of an individual) in a single framework. Finally, our approach permits the application of selection on developmental performance on the quasi-regular task across generations. Setting individual differences within an evolutionary framework is an important and novel contribution of our work. We present an experimental evaluation of this model, focusing on individual differences in performance. The experiments led to several novel findings, including: divergence of population attributes during selection to favor regular verbs, irregular verbs, or both; evidence of canalization, analogous to Waddington's developmental epigenetic landscape, once selection starts targeting a particular aspect of the task domain; and the limiting effect on the power of selection in the face of stochastic selection (roulette wheel), sexual reproduction, and a variable learning environment for each individual. Most notably, the heritability of traits showed an inverse relationship to optimization. Selected traits show lower heritability as the genetic variation of the population reduces. The simulations demonstrate the viability of linking concepts such as heritability of individual differences, cognitive development, and selection over generations within a single computational framework.