Behavioral correlates of cortical semantic representations modeled by word vectors

Author:

Nishida SatoshiORCID,Blanc Antoine,Maeda NaoyaORCID,Kado Masataka,Nishimoto ShinjiORCID

Abstract

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives in the brain estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of brain word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.

Funder

Japan Society for the Promotion of Science

Precursory Research for Embryonic Science and Technology

Exploratory Research for Advanced Technology

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference55 articles.

1. Indexing by latent semantic analysis;S Deerwester;J Am Soc Inf Sci,1990

2. Distributed representations of words and phrases and their compositionality;T Mikolov;Adv Neural Inf Process Syst,2013

3. Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014). 2014. pp. 1532–1543.

4. Enriching word vectors with subword information;P Bojanowski;Trans Assoc Comput Linguist,2017

5. Producing high-dimensional semantic spaces from lexical co-occurrence;K Lund;Behav Res methods, instruments, Comput,1996

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