Quantifying informativeness of names in visual space

The human lexicon expresses a wide array of concepts with a limited set of words. Previous work has suggested that semantic categories are structured compactly to enable informative communication. Informativeness is typically quantified with respect to an entire semantic domain and not at the level of individual names. We develop a measure of name informativeness using an information-theoretic framework grounded in visual object representations derived from natural images. Our approach uses computer vision models to characterize informativeness of individual names with respect to large-scale data in a naturalistic setting. We show that our informativeness measure predicts degrees of specificity in lexical categories more precisely than alternative measures based on entropy and frequency. We also show that name informativeness jointly captures within-category similarity and distinctiveness across categories. Our analyses suggest how the variability of names from a broad part of the lexicon may be understood through the lens of information theory.