Distributed representation of semantics in the human brain: Evidence from studies using natural language processing techniques

Abstract

: How semantics are represented in human brain is a central issue in cognitive neuroscience. Previous studies typically address this issue by artificially manipulating the properties of stimuli or task demands. Having brought valuable insights into the neurobiology of language, this psychological experimental approach may still fail to characterize semantic information with high resolution, and have difficulty quantifying context information and high-level concepts. The recently-developed natural language processing (NLP) techniques provide tools to represent the discrete semantics in the form of vectors, enabling automatic extraction of word semantics and even the information of context and syntax. Recent studies have applied NLP techniques to model the semantic of stimuli, and mapped the semantic vectors onto brain activities through representational similarity analyses or linear regression. A consistent finding is that the semantic information is represented by a vastly distributed network across the frontal, temporal and occipital cortices. Future studies may adopt multi-modal neural networks and knowledge graphs to extract richer information of semantics, apply NLP models to automatically assess the language ability of special groups, and improve the interpretability of deep neural network models with neurocognitive findings.