The Learning Bias for Cross-Category Harmony is Sensitive to Semantic Similarity: Evidence from Artificial Language Learning Experiments

Cross-category harmony is one of the most well-known typological universals. It describes a trend of consistent alignment of different syntactic categories across phrases within a language. Explanations for this universal vary as to whether cognitive factors play a role or the tendency is instead due to mechanisms of language change alone. In this article we report a series of artificial language learning experiments that aim to test a hypothesized link between cognition and cross-category harmony. As with the typological tendency itself, we find mixed evidence for harmony across different types of phrases. Specifically, learners are biased in favor of consistent alignment of the verb in the verb phrase and the adposition in the adpositional phrase. However, the bias for consistent alignment of the verb in the verb phrase and the adjective in the noun phrase depends on the semantic similarity between adjectives and verbs. When adjectives are active and therefore more verb-like (e.g. broken ), we find harmony; when they are stative and therefore less verb-like (e.g. blue ), we do not. These results suggest that the bias for cross-category harmony is not purely based on the syntactic notions of head and dependent, but reflects the interaction between a general cognitive bias favoring consistent order and cross-category similarity.