Cross-domain encoding models reveal shared and domain-specific neural representations across language and mathematics

Abstract

Whether language and mathematics rely on shared or distinct neural representations remains an unresolved question in cognitive neuroscience. Here we combine latent features from a large language model (LLM) with vertex-wise encoding models to examine cross-domain generalization between language and mathematics. Thirty-two participants performed sentence comprehension and calculation tasks during fMRI, and encoding models were trained using features embedded in a common latent space. Cross-domain prediction identified cortical regions associated with partially shared representations, most prominently the left 55b, while control analyses suggested that these effects could not be fully explained by low-level visual processing or simple task-general factors. Task-specificity contrasts revealed stronger language-related prediction in the left anterior superior temporal and angular gyri and math-related prediction in the left precentral and intraparietal sulci. Model-weight analyses further showed that shared and domain-specific prediction patterns were reflected in distinct weight profiles across cortical regions. Connectivity analyses showed task-dependent functional coupling between cross-domain regions and language- or math-related networks. Together, these findings suggest that language and mathematics involve partially shared neural representations alongside domain-specific cortical organization, helping reconcile previous contrasting views on their neural basis.