Quantifying the roles of visual, linguistic, and visual-linguistic complexity in noun and verb acquisition.

Children often learn the meanings of nouns before they grasp the meanings of verbs. This discrepancy could arise from differences in the complexity of visual characteristics for categories that language describes, the inherent structure of language, or how these two sources of information align. To explore this question, we analyze visual and linguistic representations derived from large-scale pre-trained artificial neural networks of common nouns and verbs, focusing on these three hypotheses about early verb learning. Our findings reveal that verb representations are more variable and less distinct within their domain compared to nouns. When only one example per category is available, the alignment between visual and linguistic representations is weaker for verbs than for nouns. However, with multiple examples (mirroring human language development), this alignment improves significantly for verbs, approaching that of nouns. This suggests that the difficulty in learning verbs is not primarily due to mapping visual events to verb meanings, but rather in forming accurate representations of each verb category. Regression analysis indicates that visual variability significantly impacts verb learning, followed by the alignment of visual and linguistic elements and linguistic variability. Our study provides a quantitative and integrative framework to account for the challenges children face in early word learning, opening new avenues for resolving the longstanding debate on why verbs are harder to learn than nouns.