Proceedings of the National Academy of Sciences of the United States of America. 2009;106(41):17284.
Theories of grammatical development differ in how much abstract knowledge they attribute
to young children. Here, we report a series of experiments using a computational model
to evaluate the explanatory power of child grammars based not on abstract rules but
on concrete words and phrases and some local abstractions associated with these words
and phrases. We use a Bayesian procedure to extract such item-based grammars from
transcriptions of 28+ h of each of two children's speech at 2 and 3 years of age.
We then use these grammars to parse all of the unique multiword utterances from transcriptions
of separate recordings of these same children at each of the two ages. We found that
at 2 years of age such a model had good coverage and predictive fit, with the children
showing radically limited productivity. Furthermore, adding expert-annotated parts
of speech to the induction procedure had little effect on coverage, with the exception
of the category of noun. At age 3, the children's productivity sharply increased and
the addition of a verb and a noun category markedly improved the model's performance.