Abstract
In the brain, the semantic system is thought to store concepts. However, little is known about how it connects different concepts and infers semantic relations. To address this question, we collected hours of functional magnetic resonance imaging (fMRI) data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response, and further applied it to thousands of new words. We found that both semantic categories and relations were represented by spatially overlapping cortical networks, instead of anatomically segregated regions. Importantly, many such semantic relations that reflected conceptual progression from concreteness to abstractness were represented by a similar cortical pattern of anti-correlation between the default mode network and the frontoparietal attention network. Our results suggest that the human brain represents a continuous semantic space and uses distributed networks to encode not only concepts but also relationships between concepts. In particular, the default mode network plays a central role in semantic processing for abstraction of concepts across various domains.
Significance Natural language comprehension requires that brains not only store concepts but also connect them to one another. But how does the brain relate one concept to another? To answer this question, we use a data-driven approach to model cortical responses to natural stories, and to study how the brain represents the semantic relations between thousands of words. Our results show that distributed and anti-correlated cortical networks represent semantic relations. In particular, the anti-correlation between the default mode network and the frontoparietal attention network represents the cortical signature common to semantic relations that reflect abstraction of concepts across various domains. This finding suggests an active role of the default mode network in semantic cognition, instead of being merely “task-negative”.
