Multifaceted neural representation of words in naturalistic language

TL;DR

This study uncovers the neural basis of how the brain represents complex word properties during natural language understanding by combining psycholinguistic modeling with fMRI analysis.

Contribution

It identifies eight latent dimensions of word properties and maps them onto specific cortical systems during narrative comprehension.

Findings

Eight interpretable latent dimensions of words were identified.

Latent dimensions predict behavioral performance in language tasks.

Neural encoding of these dimensions involves overlapping cortical systems.

Abstract

Understanding how the brain represents the multifaceted properties of words in context is essential for explaining the neural architecture of human language. Here, we combine large-scale psycholinguistic modeling with naturalistic fMRI to uncover the latent structure of word properties and their neural representations during narrative comprehension. By analyzing 106 psycholinguistic variables across 13,850 English words, we identified eight interpretable latent dimensions spanning lexical usage, word form, phonology orthography mapping, sublexical regularity, and semantic organization. These factors robustly predicted behavioral performance across lexical decision, naming, recognition, and semantic judgment tasks, demonstrating their cognitive relevance. Parcel-based and multivariate fMRI analyses of narrative listening revealed that these latent dimensions are encoded in overlapping yet functionally differentiated cortical systems. Multidimensional scaling and hierarchical clustering analyses further identified four interacting subsystems supporting sensorimotor grounding, controlled semantic retrieval, resolution of lexical competition, and contextual episodic integration. Together, these findings provide a unified neurocognitive framework linking fundamental lexical psycholinguistic dimensions to distributed cortical systems engaged during naturalistic language comprehension.