Preserved Structure Across Vector Space Representations

Certain concepts, words, and images are intuitively more similar than others (dog vs. cat, dog vs. spoon), though quantifying such similarity is notoriously difficult. Indeed, this kind of computation is likely a critical part of learning the category boundaries for words within a given language. Here, we use a set of 27 items (e.g. 'dog') that are highly common in infants' input, and use both image- and word-based algorithms to independently compute similarity among them. We find three key results. First, the pairwise item similarities derived within image-space and word-space are correlated, suggesting preserved structure among these extremely different representational formats. Second, the closest 'neighbors' for each item, within each space, showed significant overlap (e.g. both found 'egg' as a neighbor of 'apple'). Third, items with the most overlapping neighbors are later-learned by infants and toddlers. We conclude that this approach, which does not rely on human ratings of similarity, may nevertheless reflect stable within-class structure across these two spaces. We speculate that such invariance might aid lexical acquisition, by serving as an informative marker of category boundaries.