The world’s languages exhibit striking diversity. At the same time, recurring linguistic patterns suggest the possibility that this diversity is shaped by features of human cognition. One well-studied example is word order in complex noun phrases (like 'these two red vases'). While many orders of these elements are possible, a subset appear to be preferred. It has been argued that this order reflects a single underlying representation of noun phrase structure, from which preferred orders are straightforwardly derived (e.g., Cinque, 2005). Building on previous experimental evidence using artificial language learning, we show that these preferred orders arise not only in existing languages, but also in improvised sequences of gestures produced by English speakers. We then use corpus data from a wide range of languages to argue that the hypothesized underlying structure of the noun phrase might be learnable from statistical features relating objects and their properties conceptually. Using an information-theoretic measure of strength of association, we find that adjectival properties (e.g. 'red') are on average more closely related to the objects they modify (e.g. 'wine'), than numerosities (e.g. 'two'), which are in turn more closely related to the objects they modify than demonstratives (e.g., 'this'). It is exactly those orders which transparently reflect this—by placing adjectives closest to the noun, and demonstratives farthest away—which are more common across languages, and preferred in our silent gesture experiments. These results suggest that our experience with objects in the world, combined with a preference for transparent mappings from conceptual structure to linear order, can explain constraints on noun phrase order.
From the world to word order: Deriving biases in
noun phrase order from statistical properties of the world
