A large body of evidence suggests that math learning in children is built upon innate mechanisms for representing numerical quantities in the intraparietal sulcus (IPS). Learning math, however, is about more than processing quantitative information. It is also about understanding relations between quantities and making inferences based on these relations. Consistent with this idea, recent behavioral studies suggest that the ability to process transitive relations (A > B, B > C, therefore A > C) may contribute to math skills in children. Here we used fMRI coupled with a longitudinal design to determine whether the neural processing of transitive relations in children could predict their current and future math skills. At baseline (T1), children (n = 31) processed transitive relations in an MRI scanner. Math skills were measured at T1 and again 1.5 years later (T2). Using a machine learning approach with cross-validation, we found that activity associated with the representation of transitive relations in the IPS predicted math calculation skills at both T1 and T2. Our study highlights the potential of neurobiological measures of transitive reasoning for forecasting math skills in children, providing additional evidence for a link between this type of reasoning and math learning.

中文翻译：

---

传递关系的神经表示可预测儿童当前和将来的数学计算技能。

大量证据表明，儿童数学学习是建立在代表顶内沟（IPS）中数值数量的固有机制之上的。但是，学习数学不仅仅是处理定量信息。它还涉及理解数量之间的关系并基于这些关系进行推论。与这个想法一致的是，最近的行为研究表明，处理传递关系的能力（A> B，B> C，因此A> C）可能有助于儿童的数学技能。在这里，我们使用fMRI结合纵向设计来确定儿童中传递关系的神经处理是否可以预测他们当前和将来的数学技能。在基线（T1）时，儿童（n = 31）在MRI扫描仪中处理了传递关系。在T1和1.5年后（T2）再次测量数学技能。使用带有交叉验证的机器学习方法，我们发现与传递关系表示相关的活动预测了T1和T2的数学计算技能。我们的研究强调了传递推理的神经生物学措施在预测儿童数学技能方面的潜力，为这种推理与数学学习之间的联系提供了额外的证据。