Although common sense suggests that environmental influences increasingly account for individual differences in behavior as experiences accumulate during the course of life, this hypothesis has not previously been tested, in part because of the large sample sizes needed for an adequately powered analysis. Here we show for general cognitive ability that, to the contrary, genetic influence increases with age. The heritability of general cognitive ability increases significantly and linearly from 41% in childhood (9 years) to 55% in adolescence (12 years) and to 66% in young adulthood (17 years) in a sample of 11 000 pairs of twins from four countries, a larger sample than all previous studies combined. In addition to its far-reaching implications for neuroscience and molecular genetics, this finding suggests new ways of thinking about the interface between nature and nurture during the school years. Why, despite life's 'slings and arrows of outrageous fortune', do genetically driven differences increasingly account for differences in general cognitive ability? We suggest that the answer lies with genotype-environment correlation: as children grow up, they increasingly select, modify and even create their own experiences in part based on their genetic propensities.

Meta-analyses from the 1990s have previously established a significant, small-tomoderate, and negative correlation between math achievement and math anxiety. Since these publications, research has continued to investigate this relation with more diverse samples and measures. Thus, the goal of the present meta-analysis was to provide an update of the math anxiety-math achievement relation and its moderators. Analyzing 747 effect sizes accumulated from research conducted between 1992 and 2018, we found a small-to-moderate, negative, and statistically significant correlation (r =-.28) between math anxiety and math achievement. The relation was significant for all moderator subgroups, with the exception of the relation between math anxiety and assessments measuring the approximate number system. Grade level, math ability level, adolescent/adult math anxiety scales, math topic of anxiety scale, and math assessments were significant moderators of this relation. There is also a tendency for published studies to report significantly stronger correlations than unpublished studies but, overall, large, negative effect sizes are under-reported. Our results are consistent with previous findings of a significant relation between math anxiety and math achievement. This association starts in childhood, remains significant through adulthood, is smaller for students in grades 3 through 5 and postsecondary school, is larger for math anxiety than for statistics anxiety and for certain math anxiety scales, and is smaller for math exam grades and samples selected for low math ability. This work supports future research efforts to determine effective math achievement and math anxiety interventions, which may be most helpful to implement during childhood.

The linear relations between math anxiety and math cognition have been frequently studied. However, the relations between anxiety and performance on complex cognitive tasks have been repeatedly demonstrated to follow a curvilinear fashion. Given the lack of attention to the possibility of such complex interplay between emotion and cognition in the math learning literature, the current study aimed to address this gap via exploring the relations between math anxiety, math motivation, and math cognition. The current study consisted of two samples. One sample included 262 pairs of young adolescent twins and the other included 237 adult college students. Participants self-reported their math anxiety and math motivation. Math cognition was assessed using a comprehensive battery of mathematics tasks. In both samples, results showed inverted-U relations between math anxiety and math performance in students with high intrinsic math motivation, and modest negative associations between math anxiety and math performance in students with low intrinsic math motivation. However, this pattern was not observed in tasks assessing student’s nonsymbolic and symbolic number estimation. These findings may help advance our understanding of mathematics learning processes and may provide important insights for treatment programs that target improving mathematics learning experiences and mathematical skills.

The goal of this first major report from the Western Reserve Reading Project Math component is to explore the etiology of the relationship among tester-administered measures of mathematics ability, reading ability, and general cognitive ability. Data are available on 314 pairs of monozygotic and same-sex dizygotic twins analyzed across 5 waves of assessment. Univariate analyses provide a range of estimates of genetic (h 2 = .00 -.63) and shared (c 2 = .15-.52) environmental influences across math calculation, fluency, and problem solving measures. Multivariate analyses indicate genetic overlap between math problem solving with general cognitive ability and reading decoding, whereas math fluency shares significant genetic overlap with reading fluency and general cognitive ability. Further, math fluency has unique genetic influences. In general, math ability has shared environmental overlap with general cognitive ability and decoding. These results indicate that aspects of math that include problem solving have different genetic and environmental influences than math calculation. Moreover, math fluency, a timed measure of calculation, is the only measured math ability with unique genetic influences. Keywords mathematics; reading; twins; genetics; environmentThe National Assessment of Educational Progress (2005) survey of The Nation's Report Card reported that 64% of fourth-grade students failed to demonstrate a "proficient" level of required math skills. Moreover, the National Research Council (1999) of the National Academy of Sciences reported that very few interventions have been successful in increasing mathematics scores in low-performing children. It was further suggested in this report that the reason behind these failures is a relative lack of scientific research in mathematics ability, as well as the relationship between it and other domains such as reading and general cognitive ability.Correspondence concerning this article should be addressed to Sara A. Hart Emerging research by Geary (2004), Jordan and colleagues (e.g., Jordan, Hanich &Kaplan, 2003), and Fuchs and colleagues (e.g., Fuchs, 2005) has begun to identify a theoretical base for understanding and measuring mathematical ability. Fuchs et al. (2005) found that nonverbal problem solving, working memory, and phonological processing differentially affected math performance depending on how it was measured (i.e., computation, concepts-applications, and fact fluency). Moreover, nonverbal reasoning, concept formation, working memory, and arithmetic number combination skill predicted computational estimation skill in third graders . In another related study of third graders, arithmetic ability was predicted by phonological decoding and processing speed, and arithmetic word problems were uniquely predicted by nonverbal problem solving, concept formation, sight word efficiency, and language . Despite this growing literature, still relatively little is known about the etiology of the relationship between mathematics, reading, and general cognitive...

There is a growing literature concerning the role of the home math environment in children’s math development. In this study, we examined the relation between these constructs by specifically addressing three goals. The first goal was to identify the measurement structure of the home math environment through a series of confirmatory factor analyses. The second goal was to examine the role of the home math environment in predicting parent report of children’s math skills. The third goal was to test a series of potential alternative explanations for the relation between the home math environment and parent report of children’s skills, specifically the direct and indirect role of household income, parent math anxiety, and parent math ability as measured by their approximate number system performance. A final sample of 339 parents of children aged 3 through 8 drawn from Mechanical Turk answered a questionnaire online. The best fitting model of the home math environment was a bifactor model with a general factor representing the general home math environment, and three specific factors representing the direct numeracy environment, the indirect numeracy environment, and the spatial environment. When examining the association of the home math environment factors to parent report of child skills, the general home math environment factor and the spatial environment were the only significant predictors. Parents who reported doing more general math activities in the home reported having children with higher math skills, whereas parents who reported doing more spatial activities reported having children with lower math skills.

Background Emerging work suggests that academic achievement may be influenced by the management of affect as well as through efficient information processing of task demands. In particular, mathematical anxiety has attracted recent attention because of its damaging psychological effects and potential associations with mathematical problem-solving and achievement. The present study investigated the genetic and environmental factors contributing to the observed differences in the anxiety people feel when confronted with mathematical tasks. In addition, the genetic and environmental mechanisms that link mathematical anxiety with math cognition and general anxiety were also explored. Methods Univariate and multivariate quantitative genetic models were conducted in a sample of 514 12-year-old twin siblings. Results Genetic factors accounted for roughly 40% of the variation in mathematical anxiety, with the remaining being accounted for by child-specific environmental factors. Multivariate genetic analyses suggested that mathematical anxiety was influenced by the genetic and non-familial environmental risk factors associated with general anxiety and additional independent genetic influences associated with math-based problem solving. Conclusions The development of mathematical anxiety may involve not only exposure to negative experiences with mathematics, but also likely involves genetic risks related to both anxiety and math cognition. These results suggest that integrating cognitive and affective domains may be particularly important for mathematics, and may extend to other areas of academic achievement.

This study examined shared environmental influences on the longitudinal stability of general cognitive ability, as mediated by socioeconomic status and chaos in the home, using 287 pairs of elementary school-age twins drawn from the Western Reserve Reading Project (WRRP). General cognitive ability was evaluated at two annual assessments using the Stanford-Binet Intelligence Test. SES was examined using the highest level of education achieved by the mother of the twins, and chaos by a 6-item parent-report questionnaire. Results suggest that SES and CHAOS not only account for independent sources of shared environmental influences related to general cognitive ability at a given measurement occasion, but these effects also account for a portion of the longitudinal stability of general cognitive ability in early childhood.

Mathematical thinking is in high demand in the global market, but approximately 6 percent of school-age children across the globe experience math difficulties (Shalev et al., 2000). The home math environment (HME), which includes all math-related activities, attitudes, beliefs, expectations, and utterances in the home, may be associated with children’s math development. To examine the relation between the HME and children’s math abilities, a preregistered meta-analysis was conducted to estimate the average weighted correlation coefficient (r) between the HME and children’s math achievement and how potential moderators (i.e., assessment, study, and sample features) might contribute to study heterogeneity. A multilevel correlated effects model using 631 effect sizes from 64 quantitative studies comprising 68 independent samples found a positive, statistically significant average weighted correlation of r = .13 (SE = .02, p < .001). Our combined sensitivity analyses showed that the present findings were robust and that the sample of studies has evidential value. A number of assessment, study, and sample characteristics contributed to study heterogeneity, showing that no single feature of HME research was driving the large between-study differences found for the association between the HME and children’s math achievement. These findings indicate that children’s environments and interactions related to their learning are supported in the specific context of math learning. Our results also show that the HME represents a setting in which children learn about math through social interactions with their caregivers (Vygotsky, 1978) and what they learn depends on the influence of many levels of environmental input (Bronfenbrenner, 1979) and the specificity of input children receive (Bornstein, 2002).