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Objective <p>This study examined the relationship between executive functions (EF) and mathematical skills throughout development using a meta-analysis of longitudinal studies.</p> Method <p>This study included (a) longitudinal studies that (b) reported correlations between EF measures (assessed at Time 1) and mathematics outcomes (assessed at Time 2) in (c) typically developing samples ranging in age from birth to 18&#xa0;years. Studies were excluded if they were (a) not written in English or Portuguese, (b) aggregated data from typical and atypical populations, or (c) combined data from children and adolescents without distinction. A systematic search was conducted in October 2021 and later updated in 2025 using PsycINFO, SciELO, and PubMed. The risk of publication bias was assessed using funnel plot analysis and Egger’s test. A random-effects meta-analysis was performed.</p> Results <p>Twenty-nine studies involving children and adolescents (<i>n</i> = 104,295; M_age at Time 1 = 5.4&#xa0;years; M_age at Time 2 = 8.4&#xa0;years) were included. The overall correlation between EF and mathematics was moderate and statistically significant (<i>r</i> = 0.30, 95% CI [0.24, 0.36]). Among EF components, working memory showed the strongest association with mathematical performance (<i>r</i> = 0.43, 95% CI [0.35, 0.50]), followed by cognitive flexibility (<i>r</i> = 0.34, 95% CI [0.27, 0.42]) and inhibitory control (<i>r</i> = 0.21, 95% CI [0.13, 0.29]). Age and study quality did not significantly moderate the relationship between EF and mathematics.</p> Conclusion <p>The findings suggest that EF, particularly working memory, is a meaningful predictor of mathematical performance across development. These results underscore the importance of early EF assessment in informing interventions designed to prevent math learning difficulties. Despite the low risk of publication bias, the high heterogeneity observed in most analyses suggests the influence of additional moderating variables that warrant further investigation.</p>

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The relationship between executive functions and mathematics: a systematic review with meta-analysis of longitudinal studies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Pedro Paulo Marci Tette,
  • Cláudia Nascimento Guaraldo Justi,
  • Francis Ricardo dos Reis Justi

摘要

Objective

This study examined the relationship between executive functions (EF) and mathematical skills throughout development using a meta-analysis of longitudinal studies.

Method

This study included (a) longitudinal studies that (b) reported correlations between EF measures (assessed at Time 1) and mathematics outcomes (assessed at Time 2) in (c) typically developing samples ranging in age from birth to 18 years. Studies were excluded if they were (a) not written in English or Portuguese, (b) aggregated data from typical and atypical populations, or (c) combined data from children and adolescents without distinction. A systematic search was conducted in October 2021 and later updated in 2025 using PsycINFO, SciELO, and PubMed. The risk of publication bias was assessed using funnel plot analysis and Egger’s test. A random-effects meta-analysis was performed.

Results

Twenty-nine studies involving children and adolescents (n = 104,295; M_age at Time 1 = 5.4 years; M_age at Time 2 = 8.4 years) were included. The overall correlation between EF and mathematics was moderate and statistically significant (r = 0.30, 95% CI [0.24, 0.36]). Among EF components, working memory showed the strongest association with mathematical performance (r = 0.43, 95% CI [0.35, 0.50]), followed by cognitive flexibility (r = 0.34, 95% CI [0.27, 0.42]) and inhibitory control (r = 0.21, 95% CI [0.13, 0.29]). Age and study quality did not significantly moderate the relationship between EF and mathematics.

Conclusion

The findings suggest that EF, particularly working memory, is a meaningful predictor of mathematical performance across development. These results underscore the importance of early EF assessment in informing interventions designed to prevent math learning difficulties. Despite the low risk of publication bias, the high heterogeneity observed in most analyses suggests the influence of additional moderating variables that warrant further investigation.