Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models.

ABSTRACT: To explain how the human brain represents and organizes meaning, many theoretical and computational language models have been proposed over the years, varying in their underlying computational principles and in the language samples based on which they are built. However, how well they capture the neural encoding of lexical semantics remains elusive. We used representational similarity analysis (RSA) to evaluate to what extent three models of different types explained neural responses elicited by word stimuli: an External corpus-based word2vec model, an Internal free word association model, and a Hybrid ConceptNet model. Semantic networks were constructed using word relations computed in the three models and experimental stimuli were selected through a community detection procedure. The sim

SUBMITTER: Yang Y 

PROVIDER: S-EPMC10789206 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature