The ability to manipulate logical-mathematical symbols (LMS), encompassing tasks such as calculation, reasoning, and programming, is a cognitive skill arguably unique to humans. Considering the relatively recent emergence of this ability in human evolutionary history, it has been suggested that LMS processing may build upon more fundamental cognitive systems, possibly through neuronal recycling. Previous studies have pinpointed two primary candidates, natural language processing and spatial cognition. Existing comparisons between these domains largely relied on task-level comparison, which may be confounded by task idiosyncrasy. The present study instead compared the neural correlates at the domain level with both automated meta-analysis and synthesized maps based on three representative LMS tasks, reasoning, calculation, and mental programming. Our results revealed a more substantial cortical overlap between LMS processing and spatial cognition, in contrast to language processing. Furthermore, in regions activated by both spatial and language processing, the multivariate activation pattern for LMS processing exhibited greater multivariate similarity to spatial cognition than to language processing. A hierarchical clustering analysis further indicated that typical LMS tasks were indistinguishable from spatial cognition tasks at the neural level, suggesting an inherent connection between these two cognitive processes. Taken together, our findings support the hypothesis that spatial cognition is likely the basis of LMS processing, which may shed light on the limitations of large language models in logical reasoning, particularly those trained exclusively on textual data without explicit emphasis on spatial content.

翻译：逻辑-数学符号（LMS）的处理能力——涵盖计算、推理和编程等任务——是一种可论证为人类独有的认知技能。考虑到这种能力在人类进化史上出现相对较晚，有观点认为LMS处理可能建立在更基础的认知系统之上，或许通过神经元再利用机制实现。先前研究已确定两个主要候选系统：自然语言处理和空间认知。现有研究对这些领域的比较主要依赖于任务层面的对比，但可能受到任务特异性的干扰。本研究通过自动化元分析和基于三项典型LMS任务（推理、计算和心智编程）的综合图谱，在领域层面比较了神经关联。结果显示，与语言处理相比，LMS处理与空间认知存在更显著的皮层重叠。此外，在空间与语言处理共同激活的脑区中，LMS处理的多变量激活模式与空间认知的多变量相似度高于语言处理。层次聚类分析进一步表明，典型LMS任务在神经层面与空间认知任务无法区分，提示这两种认知过程存在内在联系。综合而言，我们的发现支持空间认知可能是LMS处理基础的假说，这或许能解释大语言模型在逻辑推理方面的局限性——特别是那些仅基于文本数据训练且未明确强调空间内容的模型。