Satoshi Nishida, CiNet PI, Antoine Blanc (Nishida Group), Shinji Nishimoto, CiNet PI published a research article in PLOS Computational Biology.

Author summary:

Word vectors, which have been originally developed in the field of engineering (natural language processing), have been extensively leveraged in neuroscience studies to model semantic representations in the human brain. These studies have attempted to model brain semantic representations by associating them with the meanings of thousands of words via a word vector space. However, there has been no study explicitly examining whether the brain semantic representations modeled by word vectors actually capture our perception of semantic information. To address this issue, we compared the semantic representational structure of words in the brain estimated from word vector-based brain models with that evaluated from behavioral data in psychological experiments. The results revealed a significant correlation between these model- and behavior-derived semantic representational structures of words. This indicates that the brain semantic representations modeled using word vectors actually reflect the human perception of word meanings. Our findings contribute to the establishment of word vector-based brain modeling as a useful tool in studying human semantic processing.

Paper information:

Nishida S, Blanc A, Maeda N, Kado M, Nishimoto S (2021) Behavioral correlates of cortical semantic representations modeled by word vectors. PLoS Comput Biol 17(6): e1009138.
https://doi.org/10.1371/journal.pcbi.1009138