The neural representation of abstract words may arise through grounding word meaning in language itself.

ABSTRACT: In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statist

SUBMITTER: Hulten A 

PROVIDER: S-EPMC8449102 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature