
This article explores a learning model for acquiring a variety of null and non-null-subject languages (i.e., consistent, partial, semi and non-null-subject languages). This model builds upon a version of the Null Subject Parameter(s) based on the “Borer-Chomsky Conjecture” (BCC), which assumes that the presence or absence of a D(definiteness)-feature in different functional heads, together with EPP (Extended projection principle) related features, account for the distributions of null subjects in a complex typology of (non-)null-subject languages. This BCC-based learning model assumes the hypothesis that children, in order to learn the pattern of null subjects in their language, need to look at the morphology of functional elements. By reviewing acquisition studies, I examine whether the model is compatible with the data. I argue that there is no evidence of parameter missetting in any of the languages examined, and that children’s early sensitivity to functional elements suggests that the BCC-based learning model is a suitable theory for the acquisition of null subjects.
generic null subjects, Language and Literature, null subjects, P, learning model
generic null subjects, Language and Literature, null subjects, P, learning model
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
