Visual Similarity in Adult Word Extension and Artificial Word Learning

Patricio M. Vera

When two objects share a name, how much does their visual similarity explain, and what do the teaching examples add? First, we reanalyzed trial-level data from a large adult word-extension study (Lewis & Frank, 2018), computing each test image's distance to the examples shown in the space of a frozen self-supervised visual encoder (DINOv2). Distance was associated with extension (log odds -3.23 per standard deviation; 520 people), and an exploratory post hoc analysis suggested that this association persisted within the taught domain after controlling for taxonomic level and teaching condition. Example composition also mattered: after three examples, adults extended a word to 1.1% of superordinate items when the examples were subordinate and to 77.5% when they were superordinate. Second, in an artificial learner using the same encoder, words joining two categories were learned less accurately the farther apart the categories were (84.7%, 65.4%, and 53.0% naming accuracy for near, mid, and far pairs). The encoder's distances correlated with human similarity and confusability measures from three datasets (rho = .501-.795), but this representational alignment did not translate into…