Statistical Learning of Syntax (Among Other Higher-Order Relational Structures)

Fluency in a language requires understanding abstract relationships between types or classes of words - the syntax of language. The learning problem has seemed so overwhelming to some that - for a long time - the dominant view was that much of this structure was not or could not, in fact, be learned (e.g. Crain, 1992; Wexler, 1991). The object of my thesis work is to examine whether and under what conditions we can learn one particular aspect of language often assumed to be innate, namely phrase structure. In three experiments, I examine acquisition of category relationships (i.e. phrases) from distributional information in the context of two miniature artificial language paradigms - one auditory and one visual. In this set of studies, I find that learners are able to generalize on the basis of strong distributional cues to phrase information with the assistance of a non-distributional cue to category membership. While it was possible to learn some aspects of phrase structure from distributional information alone, in a large language the non-distributional cue appears to enable high-order abstract generalizations that depend on category membership and category relatedness. The third experiment creates a visual analogue to the auditory phrase structure learning paradigm. Learning outcomes in the visual system were commensurate with those from the auditory artificial language, suggesting the ability to learn higher-order relationships from distributional information is largely modality independent