When learners surpass their models: the acquisition of American Sign Language from inconsistent input

Abstract

The present study examines the impact of highly inconsistent input on language acquisition. The American deaf community provides a unique opportunity to observe children exposed to nonnative language models as their only linguistic input. This research is a detailed case study of one child acquiring his native language in such circumstances. It asks whether this child is capable of organizing a natural language out of input data that are not representative of certain natural language principles. Simon is a deaf child whose deaf parents both learned American Sign Language (ASL) after age 15. Simon's only ASL input is provided by his late-learner parents. The study examines Simon's performance at age 7 on an ASL morphology task, compared with eight children who have native signing parents, and also compared with Simon's own parents. The results show that Simon's production of ASL substantially surpasses that of his parents. Simon's parents, like other late learners of ASL, perform below adult native signing criteria, with many inconsistencies and errors in their use of ASL morphology. In contrast, Simon's performance is much more regular, and in fact on most ASL morphemes is equal to that of children exposed to a native signing model. The results thus indicate that Simon is capable of acquiring a regular and orderly morphological rule system for which his input provides only highly inconsistent and noisy data. In addition, the results provide some insight into the mechanisms by which such learning may occur. Although the ASL situation is rare, it reveals clues that may contribute to our understanding of the human capacity for language learning.