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In The Proceedings of the 22nd Annual Conference of the
Cognitive Science Society (pp. 645-650). Mahwah, NJ: Lawrence Erlbaum.
Subjacency Constraints without Universal Grammar:
Evidence from Artificial Language Learning and Connectionist Modeling.
Michelle R. Ellefson, & Morten H. Christiansen
Abstract
The acquisition and processing of language is governed by
a number of universal constraints, many of which undoubtedly
derive from innate properties of the human brain.
However, language researchers disagree about whether
these constraints are linguistic or cognitive in nature. In
this paper, we suggest that the constraints on complex
question formation, traditionally explained in terms of the
linguistic principle of subjacency, may instead derive from
limitations on sequential learning. We present results from
an artificial language learning experiment in which subjects
were trained either on a "natural" language involving
no subjacency violations, or an "unnatural" language that
incorporated a limited number of subjacency violations.
Although two-thirds of the sentence types were the same
across both languages, the natural language was acquired
significantly better than its unnatural counterpart. The
presence of the unnatural subjacency items negatively affected
the learning of the unnatural language as a whole.
Connectionist simulations using simple recurrent networks,
trained on the same stimuli, replicated these results.
This suggests that sequential constraints on learning can
explain why subjacency violations are avoided: they make
language more difficult to learn. Thus, the constraints on
complex question formation may be better explained in
terms of innate cognitive constraints, rather than linguistic
constraints deriving from an innate Universal Grammar.
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