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 non-binary), we manipulated the semantic and syntactic contexts of word forms 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 as well as 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 occipito-parietal regions. While the noun vs. 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.<b>Significance statement</b> A word's context can define its meaning. Context is an integral part of understanding language, yet the organization of the semantic space formed by words in context remains unclear. We used magnetoencephalography (MEG) to investigate the dynamic interaction between contextualized semantic representations, syntactic categories, ambiguity and local syntactic contexts. We find a left-lateralized network encoding a semantic space where each contextualized instance of a word has a distinct neural representation, while syntactic category had a broad bilateral representation. Our study provides a link between naturalistic multivariate studies of item/word-level semantic processing and more traditional controlled factorial investigations of lexical meaning. These findings enrich our understanding of the neural underpinnings of words in context and highlights the role of syntactic context.