Previous research has shown that, for learners to fully acquire a miniature phrase structure language, the language must contain cues to the phrases-for example, prosodic grouping or morphological agreement of the words within a phrase (Morgan, Meier, & Newport, 1987 , 1989 . Research on word segmentation has shown that learners can use transitional probabilities between syllables to segment speech into word-like units . In the present research, we combine and extend these two sets of findings,

more »

... asking whether learners can use transitional probabilities between words (or word classes) to segment sentences into phrases, and use this phrasal information to fully acquire the syntax of a miniature language. Adult subjects were exposed to sentences from a miniature language. A pattern in the transitional probabilities between words-high within phrases, low at phrase boundaries-was created by adding syntactic properties that are widespread in natural languages: optional phrases, repeated phrases, moved phrases, different-sized form classes, or all four properties combined. All conditions outperformed controls in learning the language. The best learning occurred with all properties combined, despite the fact that this language was the most complex. These data address the important question of how language learning is successful in the face of the massive complexity of natural languages. In our experiments, learning got better, not worse, when properly structured complexity was added to a language. The results also show that the same type of statistical computation useful in word segmentation might be used as well in learning syntax, suggesting that the range of statistics needed for acquiring various types of structure in natural languages might be suitably small.