Strock Anthony, Liu Ruizhe, Iyer Rishab, Mistry Percy K, Menon Vinod
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Cogsci. 2025;47:1882-1888.
The mapping between nonsymbolic quantities and symbolic numbers lays the foundation for mathematical development in children. However, the neural mechanisms underlying this crucial cognitive bridge remain unclear. Here, we investigate the computational principles governing symbolic-nonsymbolic integration using a biologically inspired neural network trained through developmentally inspired stages. Our investigation reveals that generalization from nonsymbolic to symbolic numerical processing emerges specifically when representational alignment forms between these numerical formats. Notably, this alignment appears to be stronger in cross-format comparison-based mapping compared to direct-label-based mapping. Furthermore, we demonstrate that subsequent symbolic specialization creates a representational divergence that impairs nonsymbolic performance while maintaining the ordinal structure of the mapping. These findings highlight representational alignment as a fundamental mechanism in numerical cognition and suggest that targeted cross-format comparison tasks may be particularly effective in improving mathematical learning in children with numerical processing difficulties.
非符号数量与符号数字之间的映射为儿童的数学发展奠定了基础。然而，这座关键认知桥梁背后的神经机制仍不清楚。在这里，我们使用通过受发育启发的阶段进行训练的生物启发神经网络，研究了控制符号 - 非符号整合的计算原理。我们的研究表明，当这些数字格式之间形成表征对齐时，从非符号到符号数字处理的泛化会特别出现。值得注意的是，与基于直接标签的映射相比，这种对齐在基于跨格式比较的映射中似乎更强。此外，我们证明随后的符号特化会产生表征差异，这会损害非符号性能，同时保持映射的序数结构。这些发现突出了表征对齐作为数字认知中的一种基本机制，并表明有针对性的跨格式比较任务可能在改善有数字处理困难的儿童的数学学习方面特别有效。
