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The grammatical structure of human languages is extremely complex, yet children master this complexity with apparent ease. One explanation is that we come to the task of acquisition equipped with knowledge about the possible grammatical structures of human languages—so-called “Universal Grammar”. An alternative is that grammatical patterns are abstracted from the input via a process of identifying reoccurring patterns and using that information to form grammatical generalizations. This statistical learning hypothesis receives support from computational research, which has revealed that even low level statistics based on adjacent word co-occurrences yield grammatically relevant information. Moreover, even as adults, our knowledge and usage of grammatical patterns is often graded and probabilistic, and in ways which directly reflect the statistical makeup of the language we experience. The current chapter explores such evidence and concludes that statistical learning mechanisms play a critical role in acquisition, whilst acknowledging holes in our current knowledge, particularly with respect to the learning of ‘higher level’ syntactic behaviours. Throughout, I emphasize that although a statistical approach is traditionally associated with a strongly empiricist position, specific accounts make specific claims about the nature of the learner, both in terms of learning mechanisms and the information that is primitive to the learning system. In particular, working models which construct grammatical generalizations often assume inbuilt semantic abstractions.

Original publication





Book title

Frontiers Collection

Publication Date



Part F969


65 - 92