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

Annika Hulten, Marijn van Vliet, Sasa Kivisaari, Lotta Lammi, Tiina Lindh-Knuutila, Ali Faisal, Riitta Salmelin

Research output: Contribution to journalArticleScientificpeer-review

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 statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co-varies, at 280–420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left-hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.
Original languageEnglish
Article number25593
Number of pages12
JournalHuman Brain Mapping
DOIs
Publication statusE-pub ahead of print - 15 Jul 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • abstract concepts
  • concrete words
  • decoding
  • machine learning
  • MEG
  • RSA
  • semantics
  • word processing

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