Tracking Neural Correlates of Contextualized Meanings with Representational Similarity Analysis

Although it is uncontroversial that word meanings shift depending on their context, our understanding of contextualized lexical meaning remains poor. How is a contextualized semantic space organized? In this MEG study (27 human participants, 16 women, 10 men, 1 nonbinary), we manipulated the semantic and syntactic contexts of wordforms to query the organization of this space. All wordforms were noun/verb ambiguous and varied in the semantic distance between their noun and verb uses: unambiguous stems, polysemes with distinct but related meanings, and homonyms with completely unrelated meanings. The senses of each stem were disambiguated by a unique discourse sentence and the items were placed in syntactic contexts of varying sizes. Univariate results characterized syntactic context as a bilateral and distributed effect. A multivariate representational similarity analysis correlated one-hot models of the categorical factors and contextualized embedding-based models with MEG activity. Of all models representing ambiguity, only a model differentiating between syntactic categories across contexts correlated with the brain. An All-Embeddings model, where each contextualized word had a distinct representation, explained distributed neural activity across the left hemisphere. Finally, a Syntactic Context model and Within-Context-Stem model were significant in left occipitoparietal regions. While the noun versus verb contrast affected neural signals robustly, we saw no evidence of the homonym–polyseme–unambiguous contrast, over and above the evidence for fully itemized representations. These findings suggest that in contexts devoid of ambiguity, the neural representation of a word is mainly shaped by its syntactic category and its contextually informed, unique semantic representation.