Semantic Coherence Facilitates Distributional Learning of Word Meanings

Computational work has suggested that one could, in princi-ple, learn aspects of word meaning simply from patterns of co-occurrence between words. The extent to which humans can do this distributional learning is an open question – artifi-cial language learning experiments have yielded mixed results, prompting suggestions that distributional cues must be corre-lated with other cues, such as phonological regularities, for successful learning. We conducted a large-scale experiment comparing how distributional learning is afforded by two dif-ferent types of cues – phonological regularities and semantic coherence. We found that semantic coherence more strongly facilitates distributional learning than onset-consonant phono-logical regularities