How Many Pathways Underlie Socioeconomic Differences in the Development of Cognition and Achievement?

Parental socioeconomic status weakly predicts specific cognitive and academic skills beyond general cognitive ability

Parental socioeconomic status (SES) is a well-established predictor of children's neurocognitive development. Several theories propose that specific cognitive skills are particularly vulnerable. However, this can be challenging to test, because cognitive assessments are not pure measures of distinct neurocognitive processes, and scores across different tests are often highly correlated. Aside from one previous study by Tucker-Drob, little research has tested if associations between SES and cognition are explained by differences in general cognitive ability rather than specific cognitive skills. Using structural equation modelling (SEM), we tested if parental SES is associated with individual cognitive test scores after controlling for latent general cognitive ability. Data from three large-scale cohorts totalling over 16,360 participants from the UK and USA (ages 6-19) were used. Associations between SES and cognitive test scores are mainly (but not entirely) explained through general cognitive ability. Socioeconomic advantage was associated with particularly strong vocabulary performance, unexplained by general ability. When controlling for general cognitive ability, socioeconomic disadvantage was associated with better executive functions. Better characterizing relationships between cognition and adversity is a crucial first step toward designing interventions to narrow socioeconomic gaps. RESEARCH HIGHLIGHTS: Understanding environmental influences on cognitive development is a crucial goal for developmental science-parental socioeconomic status (SES) is one of the strongest predictors. Several theories have proposed that specific cognitive skills, such as language or certain executive functions, are particularly susceptible to socioeconomic adversity. Using structural equation modelling, we tested whether SES predicts specific cognitive and academic tests after controlling for latent general cognitive ability across three large-scale cohorts. SES moderately predicted latent general cognitive ability, but associations with specific cognitive skills were mainly small, with a few exceptions.

Learning by parenting: How do mothers respond to their children's developmental declines?

Children's developmental processes are not always linear. During the childhood period, children usually experience ups and downs in their skills, and how parents respond to these changes can crucially condition the subsequent process of child development. This paper examines (1) how children's developmental declines impact the level of cognitive stimulation implemented by the mothers, and (2) whether these effects vary by socioeconomic groups. Using longitudinal NLSY79-CYA data from the US, I implement a series of two-way fixed effects and fixed effects counterfactual models. Findings show that mothers respond negatively to the declines in their children's mathematical skills by decreasing their levels of cognitive stimulation, although the effects are relatively small, approximately one-tenth of a standard deviation. This effect is concentrated among mothers with low levels of education or those at the bottom part of the income distribution. Additionally, it's observed that mothers in the sample do not modify their behaviours in response to declines in their children's reading skills. All in all, this evidence suggests that mothers might be reinforcing existing disadvantages by decreasing their cognitive stimulation when their children show developmental declines and that this mechanism could be responsible for broadening the developmental gap between children from low- and high-socioeconomic backgrounds.

Beyond the factor indeterminacy problem using genome-wide association data

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.

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.

Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics

Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9-11). We found large, independent effects of schooling (β ~ 0.15), cogPGS (β ~ 0.10), and SES (β ~ 0.20) on working memory, crystallized (cIQ), and fluid intelligence (fIQ). Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.

General factors of white matter microstructure from DTI and NODDI in the developing brain

Preterm birth is closely associated with diffuse white matter dysmaturation inferred from diffusion MRI and neurocognitive impairment in childhood. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are distinct dMRI modalities, yet metrics derived from these two methods share variance across tracts. This raises the hypothesis that dimensionality reduction approaches may provide efficient whole-brain estimates of white matter microstructure that capture (dys)maturational processes. To investigate the optimal model for accurate classification of generalised white matter dysmaturation in preterm infants we assessed variation in DTI and NODDI metrics across 16 major white matter tracts using principal component analysis and structural equation modelling, in 79 term and 141 preterm infants at term equivalent age. We used logistic regression models to evaluate performances of single-metric and multimodality general factor frameworks for efficient classification of preterm infants based on variation in white matter microstructure. Single-metric general factors from DTI and NODDI capture substantial shared variance (41.8-72.5%) across 16 white matter tracts, and two multimodality factors captured 93.9% of variance shared between DTI and NODDI metrics themselves. General factors associate with preterm birth and a single model that includes all seven DTI and NODDI metrics provides the most accurate prediction of microstructural variations associated with preterm birth. This suggests that despite global covariance of dMRI metrics in neonates, each metric represents information about specific (and additive) aspects of the underlying microstructure that differ in preterm compared to term subjects.

From Genome-Wide to Environment-Wide: Capturing the Environome

Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome.

Psychotic-like experiences, polygenic risk scores for schizophrenia, and structural properties of the salience, default mode, and central-executive networks in healthy participants from UK Biobank

Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank, N = 3875) including information on PLEs (obtained from the Mental Health Questionnaire (MHQ); UKBiobank Category: 144; N auditory hallucinations = 55, N visual hallucinations = 79, N persecutory delusions = 16, N delusions of reference = 13), polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode, and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations, both negative and positive, with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus, and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. Generally, these results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and may predispose to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.

Preschool Verbal and Nonverbal Ability Mediate the Association Between Socioeconomic Status and School Performance

We compared the extent to which the long‐term influence of family socioeconomic status (SES) on children's school performance from age 7 through 16 years was mediated by their preschool verbal and nonverbal ability. In 661 British children, who completed 17 researcher‐administered ability tests at age 4.5 years, SES correlated more strongly with verbal than nonverbal ability (.39 vs. .26). Verbal ability mediated about half of the association between SES and school performance at age 7, while nonverbal ability accounted for a third of the link. Only SES, but not verbal or nonverbal ability, was associated with changes in school performance from age 7 to 16. We found that SES‐related differences in school performance are only partly transmitted through children's preschool verbal abilities.

Stable longitudinal associations of family income with children's hippocampal volume and memory persist after controlling for polygenic scores of educational attainment

Despite common notion that the correlation of socioeconomic status with child cognitive performance may be driven by both environmentally- and genetically-mediated transactional pathways, there is a lack of longitudinal and genetically informed research that examines these postulated associations. The present study addresses whether family income predicts associative memory growth and hippocampal development in middle childhood and tests whether these associations persist when controlling for DNA-based polygenic scores of educational attainment. Participants were 142 6-to-7-year-old children, of which 127 returned when they were 8-to-9 years old. Longitudinal analyses indicated that the association of family income with children's memory performance and hippocampal volume remained stable over this age range and did not predict change. On average, children from economically disadvantaged background showed lower memory performance and had a smaller hippocampal volume. There was no evidence to suggest that differences in memory performance were mediated by differences in hippocampal volume. Further exploratory results suggested that the relationship of income with hippocampal volume and memory in middle childhood is not primarily driven by genetic variance captured by polygenic scores of educational attainment, despite the fact that polygenic scores significantly predicted family income.

The moderating role of SES on genetic differences in educational achievement in the Netherlands

Parental socioeconomic status (SES) is a strong predictor of children's educational achievement (EA), with an increasing effect throughout development. Inequality in educational outcomes between children from different SES backgrounds exists in all Western countries. It has been proposed that a cause of this inequality lies in the interplay between genetic effects and SES on EA, which might depend on society and the equality of the education system. This study adopted two approaches, a classical twin design and polygenic score (PGS) approach, to address the effect of parental SES on EA in a large sample of 12-year-old Dutch twin pairs (2479 MZ and 4450 DZ twin pairs with PGSs for educational attainment available in 2335 children) from the Netherlands Twin Register (NTR). The findings of this study indicated that average EA increased with increasing parental SES. The difference in EA between boys and girls became smaller in the higher SES groups. The classical twin design analyses based on genetic covariance structure modeling pointed to lower genetic, environmental, and thus phenotypic variation in EA at higher SES. Independent from a child's PGS, parental SES predicted EA. However, the strength of the association between PGS and EA did not depend on parental SES. In a within-family design, the twin with a higher PGS scored higher on EA than the co-twin, demonstrating that the effect of the PGS on EA was at least partly independent from parental SES. To conclude, EA depended on SES both directly and indirectly, and SES moderated the additive genetic and environmental components of EA. Adding information from PGS, in addition to parental SES, improved the prediction of children's EA.

Theoretical and Methodological Implications of Associations between Executive Function and Mathematics in Early Childhood

Despite agreement about the importance of executive function (EF) for children's early math achievement, its treatment in correlational studies reflects a lack of agreement about the theoretical connection between the two. It remains unclear whether the association between EF and math operates through a latent EF construct or specific EF components. Specifying the correct measurement model has important theoretical implications for the predicted effects of EF interventions on children's math achievement. In the current study, we tested whether associations between EF and math operate via a latent EF factor, or via specific EF components using data from a large, nationally representative sample. We then replicated these same analyses with a meta-analytic database drawn from ten studies that collected measures of children's EF and math achievement. Our results lend support to explanations that a single EF factor accounts for most of the EF component-specific associations with math achievement. We discuss theoretical and methodological implications of these findings for future work.

Coupled cognitive changes in adulthood: A meta-analysis

With advancing age, healthy adults typically exhibit decreases in performance across many different cognitive abilities such as memory, processing speed, spatial ability, and abstract reasoning. However, there are marked individual differences in rates of cognitive decline, with some adults declining steeply and others maintaining high levels of functioning. To move toward a comprehensive understanding of cognitive aging, it is critical to know whether individual differences in longitudinal changes interrelate across different cognitive abilities. We identified 89 effect sizes representing shared variance in longitudinal cognitive change from 22 unique datasets composed of more than 30,000 unique individuals, which we meta-analyzed using a series of multilevel metaregression models. An average of 60% of the variation in cognitive changes was shared across cognitive abilities. Shared variation in changes increased with age, from approximately 45% at age 35 years to approximately 70% at age 85 years. There was a moderate-to-strong correspondence (r = .49, congruence coefficient = .98) between the extent to which a variable indicated general intelligence and the extent to which change in that variable indicated a general factor of aging-related change. Shared variation in changes did not differ substantially across cognitive ability domain classifications. In a sensitivity analysis based on studies that carefully controlled for dementia, shared variation in longitudinal cognitive changes remained at upward of 60%, and age-related increases in shared variation in cognitive changes continued to be evident. These results together provide strong evidence for a general factor of cognitive aging that strengthens with advancing adult age. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Developmental differences in reward sensitivity and sensation seeking in adolescence: Testing sex-specific associations with gonadal hormones and pubertal development

Sensation seeking has been found to increase, on average, from childhood to adolescence. Developmental scientists have hypothesized that this change could be driven by the rise of gonadal hormones at puberty, which affect reward-related processing in the brain. In a large, age-heterogeneous, population-based sample of adolescents and young adults (N = 810; ages 13-20 years), we tested for sex-specific associations between age, self-reported pubertal development, gonadal hormones (estradiol and testosterone) as measured in saliva, reward sensitivity as measured by a multivariate battery of in-laboratory tasks (including the Iowa gambling task, balloon analogue risk task, and stoplight task), and self-reported sensation seeking. Reward sensitivity was more strongly associated with sensation seeking in males than females. For both males and females, reward sensitivity was unrelated to age but was higher among those who reported more advanced pubertal development. There were significant sex differences in the effects of self-reported pubertal development on sensation seeking, with a positive association evident in males but a negative association in females. Moreover, gonadal hormones also showed diverging associations with sensation seeking-positive with testosterone but negative with estradiol. Overall, the results indicate that sensation seeking among adolescents and young adults depends on a complex constellation of developmental influences that operate via sex-specific mechanisms. (PsycINFO Database Record

Ageing and brain white matter structure in 3,513 UK Biobank participants

Quantifying the microstructural properties of the human brain's connections is necessary for understanding normal ageing and disease. Here we examine brain white matter magnetic resonance imaging (MRI) data in 3,513 generally healthy people aged 44.64–77.12 years from the UK Biobank. Using conventional water diffusion measures and newer, rarely studied indices from neurite orientation dispersion and density imaging, we document large age associations with white matter microstructure. Mean diffusivity is the most age-sensitive measure, with negative age associations strongest in the thalamic radiation and association fibres. White matter microstructure across brain tracts becomes increasingly correlated in older age. This may reflect an age-related aggregation of systemic detrimental effects. We report several other novel results, including age associations with hemisphere and sex, and comparative volumetric MRI analyses. Results from this unusually large, single-scanner sample provide one of the most extensive characterizations of age associations with major white matter tracts in the human brain.

Part of understanding ageing involves knowing how the brain's connecting pathways change in healthy aging. Here, authors provide a detailed characterisation of data from 3513 UK Biobank participants, and show that the microstructure of these pathways becomes more similar with age.

Genetically-mediated associations between measures of childhood character and academic achievement

Researchers and the general public have become increasingly intrigued by the roles that systematic tendencies toward thinking, feeling, and behaving might play in academic achievement. Some measures of constructs belonging to this group have been well studied in genetics and psychometrics, while much less is known about measures of other such constructs. The current study focuses on 7 character traits prominently featured in influential intervention-oriented and/or socialization theories of academic achievement: grit, intellectual curiosity, intellectual self-concept, mastery orientation, educational value, intelligence mindset, and test motivation. In a population-based sample of 811 school-aged twins and triplets from the Texas Twin Project, we tested (a) how each measure relates to indices of the Big Five personality traits, (b) how the measures relate to one another, (c) the extent to which each measure is associated with genetic and environmental influences and whether such influences operate through common dimensions of individual differences, and (d) the extent to which genetic and environmental factors mediate the relations between fluid intelligence, character measures, verbal knowledge, and academic achievement. We find moderate relations among the measures that can be captured by a highly heritable common dimension representing a mixture of Openness and Conscientiousness. Moreover, genetically influenced variance in the character measures is associated with multiple measures of verbal knowledge and academic achievement, even after controlling for fluid intelligence. In contrast, environmentally influenced variance in character is largely unrelated to knowledge and achievement outcomes. We propose that character measures popularly used in education may be best conceptualized as indexing facets of personality that are of particular relevance to academic achievement. (PsycINFO Database Record

Correlated longitudinal changes across linguistic, achievement, and psychomotor domains in early childhood: evidence for a global dimension of development

An important question within developmental psychology concerns the extent to which the maturational gains that children make across multiple diverse domains of functioning can be attributed to global (domain-general) developmental processes. The present study investigated this question by examining the extent to which individual differences in change across children's development in five different domains are correlated. Multivariate growth-curve models were fit to longitudinal data on linguistic, mathematics, reading, gross motor, and fine motor skills in 8950 children ranging in age from 44 to 86 months (3.7 years to 7.2 years). All five rates of change were positively intercorrelated. A common factor accounted for 42% of the individual differences in change. These results suggest that a global dimension underlies substantial proportions of cognitive and psychomotor development.

Learning Motivation Mediates Gene-by-Socioeconomic Status Interaction on Mathematics Achievement in Early Childhood

There is accumulating evidence that genetic influences on achievement are more pronounced among children living in higher socioeconomic status homes, and that these gene-by-environment interactions occur prior to children's entry into formal schooling. We hypothesized that one pathway through which socioeconomic status promotes genetic influences on early achievement is by facilitating the processes by which children select, evoke, and attend to learning experiences that are consistent with genetically influenced individual differences in their motivation to learn. We examined this hypothesis in a nationally representative sample of approximately 650 pairs of four-year old identical and fraternal twins who were administered a measure of math achievement, and rated by their parents on a broad set of items assessing learning motivation. Results indicated a genetic link between learning motivation and math achievement that varied positively with family socioeconomic status: Genetic differences in learning motivation contributed to math achievement more strongly in more advantaged homes. Once this effect of learning motivation was controlled for, gene-by-socioeconomic status interaction on math achievement was reduced from previously significant levels, to nonsignificant levels.

Intellectual interest mediates gene × socioeconomic status interaction on adolescent academic achievement

Recent studies have demonstrated that genetic influences on cognitive ability and academic achievement are larger for children raised in higher socioeconomic status (SES) homes. However, little work has been done to document the psychosocial processes that underlie this Gene × Environment interaction. One process may involve the conversion of intellectual interest into academic achievement. Analyses of data from 777 pairs of 17-year-old twins indicated that Gene × SES effects on achievement scores can be accounted for by stronger influences of genes for intellectual interest on achievement at higher levels of SES. These findings are consistent with the hypothesis that higher SES affords greater opportunity for children to seek out and benefit from learning experiences that are congruent with their genetically influenced intellectual interests.