Combining computational controls with natural text reveals new aspects of meaning composition

Abstract

To study a core component of human intelligence—our ability to combine the meaning of words—neuroscientists have looked to theories from linguistics. However, linguistic theories are insufficient to account for all brain responses that reflect linguistic composition. In contrast, we adopt a data-driven computational approach to study the combined meaning of words beyond their individual meaning. We term this product “supra-word meaning” and investigate its neural bases by devising a computational representation for it and using it to predict brain recordings from two imaging modalities with complementary spatial and temporal resolutions. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical-level meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests the hypothesis that composed meaning might be maintained through a different neural mechanism than the synchronized firing of pyramidal cells. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.