Predicting Human Brain Activity Associated with the Meanings of Nouns

TLDR

The representation of conceptual knowledge in the human brain remains debated, but fMRI studies show distinct spatial activation patterns for different semantic categories such as tools, buildings, and animals. The study aims to develop a computational model that predicts fMRI neural activation for words lacking empirical data. The model is trained on a trillion‑word text corpus together with fMRI data from several dozen concrete nouns. After training, the model accurately predicts fMRI activation for thousands of concrete nouns, achieving highly significant accuracy on the 60 nouns with existing fMRI data.

Abstract

The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.