Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: Evidence from RSA of BOLD signals

Carota, F., Nili, H., Pulvermüller, F., & Kriegeskorte, N. (2020). Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: Evidence from RSA of BOLD signals. NeuroImage. Advance online publication. doi:10.1016/j.neuroimage.2020.117408.
A class of semantic theories defines concepts in terms of statistical distributions of lexical items, basing meaning on vectors of word co-occurrence frequencies. A different approach emphasizes abstract hierarchical taxonomic relationships among concepts. However, the functional relevance of these different accounts and how they capture information-encoding of meaning in the brain still remains elusive. We investigated to what extent distributional and taxonomic models explained word-elicited neural responses using cross-validated representational similarity analysis (RSA) of functional magnetic resonance imaging (fMRI) and novel model comparisons. Our findings show that the brain encodes both types of semantic similarities, but in distinct cortical regions. Posterior middle temporal regions reflected word links based on hierarchical taxonomies, along with the action-relatedness of the semantic word categories. In contrast, distributional semantics best predicted the representational patterns in left inferior frontal gyrus (LIFG, BA 47). Both representations coexisted in angular gyrus supporting semantic binding and integration. These results reveal that neuronal networks with distinct cortical distributions across higher-order association cortex encode different representational properties of word meanings. Taxonomy may shape long-term lexical-semantic representations in memory consistently with sensorimotor details of semantic categories, whilst distributional knowledge in the LIFG (BA 47) enable semantic combinatorics in the context of language use. Our approach helps to elucidate the nature of semantic representations essential for understanding human language.
Publication type
Journal article
Publication date
2020

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