The neural correlates of logical-mathematical symbol systems processing resemble that of spatial cognition more than natural language processing

TL;DR

This study shows that neural processing of logical-mathematical symbols is more similar to spatial cognition than natural language, suggesting spatial cognition underpins LMS processing and impacting AI reasoning models.

Contribution

It provides domain-level neural evidence that LMS processing aligns more closely with spatial cognition than language, advancing understanding of cognitive and AI mechanisms.

Findings

LMS processing overlaps more with spatial cognition than language in the brain.

Neural activation patterns for LMS are more similar to spatial cognition.

Hierarchical clustering shows LMS tasks are indistinguishable from spatial cognition at the neural level.

Abstract

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.