Measures of Semantic Distance from Word Embeddings Predict Neural Responses During Inferences about People and Objects
Abstract
Recent advances in Natural Language Processing (NLP) make it possible to quantify relationships among different words extracted from large-scale human text corpora. Using a word embeddings model, we quantified the semantic distance between pairs of adjectives that could describe people or objects (e.g., smart, friendly; round, wooden) and scanned participants using fMRI while they had the opportunity to generalize from one known attribute to an unknown attribute across parametrically varying degrees of semantic distance (e.g., given that this person is smart, how likely are they to be friendly?; given that this furniture is round, how likely is it to be wooden?). Across categories, we observed a positive parametric effect of semantic distance on activation in the dorsomedial prefrontal cortex (DMPFC). Results connect to this region’s role in abstraction and inference under reducible uncertainty, with implications for understanding how people generalize beyond what they know to make inferences about novel individuals, items, or experiences.