Mathematics anxiety (MA) is consistently linked to lower engagement, motivation, and poorer mathematics performance. Its prevalence is significant; a study of 15-year-olds showed 30% experienced anxiety or felt incapable while solving math problems. Understanding the etiology of the MA-attitude-performance association is crucial due to its impact on learning outcomes. This study investigates the extent to which overlapping genetic and environmental factors underlie the associations between MA, attitudes towards mathematics, cognition, and achievement. This research lays the groundwork for identifying genetic variants linked to individual differences in MA and mathematical learning. It also informs developmental research on how students select and modify their math experiences based on genetic predispositions. Identifying which aspects of performance and attitudes are most closely associated with anxiety, and the etiologies of these associations, will guide future interventions aimed at reducing MA and fostering mathematics learning. The study will explore the genetic and environmental overlap between MA and aspects of mathematics attitudes and performance, extending previous research which found that the association between MA and performance (measured as mathematics problem-solving ability) was mostly explained by common genetic influences. Finally, the study will examine the extent to which individual differences in general anxiety account for the links between MA and mathematics attitudes, cognition, and achievement, given that MA and general anxiety share some physiological and cognitive overlaps, yet are only moderately correlated.

Research indicates a moderate negative association between MA and mathematics motivation and attitudes, including lower perseverance. Moderate to strong negative associations between mathematics attitudes and anxiety are observed across student populations and pre-service teachers. The tendency to avoid math situations, which covaries with MA, aligns with avoidance behavior associated with general anxiety and negative beliefs about competence. Mathematics self-efficacy, mediating the negative association between MA and performance, has been highlighted. Higher initial grades lead to higher self-efficacy and lower subsequent MA. Self-efficacy also mediates the link between self-regulatory behavior and MA. Expectancy-value theory suggests that subjective task value is crucial in motivated behavior, yet few studies have explored the MA-attitude association beyond self-efficacy. Existing studies show MA relates similarly to self-efficacy, interest, and importance attributed to mathematics. However, it remains unclear whether the same or distinct genetic and environmental influences underlie the relations between MA and mathematics attitudes. Regarding MA and achievement, students experience MA across the ability spectrum, although those with dyscalculia are more likely to have high MA. Studies show a negative association between MA and mathematical performance, significant even after accounting for general cognitive ability. Longitudinal research suggests that MA stability increases during adolescence, partly due to stable low achievement. However, other studies found reciprocal longitudinal links between negative emotions (including MA) and achievement, suggesting a complex interplay. One line of investigation explores the possibility of a deficit in lower-level numerical processing contributing to MA via its negative association with achievement. Studies have yielded mixed results on the association between MA and basic numerosity. Despite many studies on the phenotypic association between MA and mathematics cognition, only one previous study using a genetically informative design explored this association, finding that common genetic influences largely explained the MA-performance link.

Participants were from the Twins Early Development Study (TEDS), a longitudinal study of twins born in England and Wales between 1994 and 1996. The sample was representative of the British population. Informed consent was obtained. Data from two waves (age 16 and 18–21) were used, focusing on mathematics ability and achievement, self-efficacy, interest, MA, and general anxiety. Individuals with major medical, genetic, or neurodevelopmental disorders were excluded. Mathematics anxiety was assessed using a modified Abbreviated Math Anxiety Scale (AMAS). Mathematics attitudes (self-efficacy and interest) were measured using scales adapted from the OECD Programme for International Student Assessment. Mathematics performance was measured using GCSE grades (recoded) and an online test battery assessing understanding numbers, problem verification, and approximate number sense. General anxiety was assessed using the Generalized Anxiety Disorder Scale (GAD-7). Phenotypic analyses involved descriptive statistics and ANOVAs, controlling for sample dependency (twin pairs). Measures were residualized for age and sex and standardized. Genetic analyses used the twin method to decompose individual differences into genetic and environmental sources of variance, comparing monozygotic (MZ) and dizygotic (DZ) twins. ACE and ADE models were fitted, depending on the DZ intraclass correlation relative to MZ. Multivariate genetic analysis, using Cholesky decompositions, explored the genetic and environmental overlap between MA, mathematics motivation, and performance. Sex differences were tested using univariate ANOVAs.

Descriptive statistics showed that males had higher mathematics self-efficacy, interest, and performance, and lower mathematics and general anxiety than females. Univariate analyses showed that, with the exception of GCSE exam scores (which showed significant shared environmental influence), the AE model was the best fit for all traits. Heritability ranged from 36% to 63%, with the remaining variance attributed to non-shared environmental factors. Phenotypic correlations between MA and other mathematics-related variables were moderately negative (−0.31 to −0.45), while genetic correlations were strongly negative (−0.67 to −0.75). The exception was the association between MA and approximate number sense, which showed weaker phenotypic and genetic relations. Multivariate analyses indicated that all mathematics-related measures accounted for ~75% of the genetic variance in MA and ~20% of its environmental variance. Genetic effects were largely shared across all measures except approximate number sense. This genetic overlap was not explained by general anxiety.

The strong negative genetic correlations between MA and other mathematics-related traits support a substantial shared genetic etiology. The results extend previous findings by demonstrating that these shared genetic influences are not accounted for by general anxiety. The substantial genetic overlap highlights the importance of considering genetic factors in understanding individual differences in mathematics-related traits and interventions. The finding that approximate number sense showed a weaker genetic correlation with MA compared to other mathematical abilities suggests the need for further exploration into the specific genetic mechanisms and cognitive pathways involved in the relationship between MA and mathematical skills. The relatively small proportion of environmental variance in MA that was shared across all mathematics related traits suggests that interventions targeting shared environment factors might have limited impact on reducing math anxiety.

This study provides strong evidence for a significant shared genetic influence underlying the association between mathematics anxiety, attitudes, and performance. The findings highlight the need for future genetic research to identify specific genes involved. Further research could investigate the specific mechanisms through which genetic factors influence the development and manifestation of mathematics anxiety and related traits. Furthermore, exploring the interaction between genetic predispositions and environmental factors in shaping mathematics-related experiences is warranted.

The sample consisted primarily of twins from England and Wales, limiting the generalizability to other populations. The cross-sectional nature of some data limits the ability to make strong causal inferences. The reliance on self-reported measures introduces potential bias. The study did not explore potential gene-environment interactions in detail, which may influence the development of mathematics anxiety.