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Nonsymbolic number and cumulative area representations contribute shared and unique variance to symbolic math competence

机译:非符号数和累积面积表示法有助于符号数学能力的共享和唯一方差

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摘要

Humans and nonhuman animals share the capacity to estimate, without counting, the number of objects in a set by relying on an approximate number system (ANS). Only humans, however, learn the concepts and operations of symbolic mathematics. Despite vast differences between these two systems of quantification, neural and behavioral findings suggest functional connections. Another line of research suggests that the ANS is part of a larger, more general system of magnitude representation. Reports of cognitive interactions and common neural coding for number and other magnitudes such as spatial extent led us to ask whether, and how, nonnumerical magnitude interfaces with mathematical competence. On two magnitude comparison tasks, college students estimated (without counting or explicit calculation) which of two arrays was greater in number or cumulative area. They also completed a battery of standardized math tests. Individual differences in both number and cumulative area precision (measured by accuracy on the magnitude comparison tasks) correlated with interindividual variability in math competence, particularly advanced arithmetic and geometry, even after accounting for general aspects of intelligence. Moreover, analyses revealed that whereas number precision contributed unique variance to advanced arithmetic, cumulative area precision contributed unique variance to geometry. Taken together, these results provide evidence for shared and unique contributions of nonsymbolic number and cumulative area representations to formally taught mathematics. More broadly, they suggest that uniquely human branches of mathematics interface with an evolutionarily primitive general magnitude system, which includes partially overlapping representations of numerical and nonnumerical magnitude.
机译:人类和非人类动物具有依靠近似数字系统(ANS)来估计而不计算集合中对象数量的能力。但是,只有人类才能学习符号数学的概念和操作。尽管这两种量化系统之间存在巨大差异,但神经和行为研究结果表明功能上的联系。另一研究表明,ANS是更大,更通用的量值表示系统的一部分。关于数量和其他数量级(例如空间范围)的认知交互作用和常见神经编码的报告,使我们开始质疑非数字数量级是否与数学能力相交。在执行两个幅度比较任务时,大学生估计(未进行计数或显式计算)两个数组中哪个数组的数量或累积面积更大。他们还完成了一系列标准化数学测试。数量和累积面积精度的个体差异(通过幅度比较任务的精度来衡量)与算术能力(尤其是高级算术和几何)的个体间差异相关,即使考虑了智力的一般方面也是如此。此外,分析表明,尽管数字精度为高级算术贡献了唯一的方差,而累积面积精度却为几何带来了独特的方差。综上所述,这些结果为非符号数和累积面积表示形式对正式授课的数学的共同和独特贡献提供了证据。从更广泛的意义上讲,他们认为数学的独特人类分支与进化上原始的一般量级系统接口,该系统包括数字量级和非数字量级的部分重叠表示。

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