Edited by Inbal Arnon and Eve V. Clark
[Trends in Language Acquisition Research 7] 2011
► pp. 277–294
Bayesian modeling of sources of constraint in language acquisition
Theories of language acquisition must address the role of constraints in children's learning. Are they language-specific or domain-general? Do they come from the learner or are do they result from external factors like the nature of the data? In this chapter we describe how Bayesian modeling may be used to explore this issue. The Bayesian framework has been useful for determining what an ideal learner might be able to learn given a certain set of specific constraints and a certain type of input. It also provides a natural way to compare the effect of different constraints, and to grow towards increasingly cognitively natural models by altering those constraints. Keywords: Learning constraints; Bayesian modeling
Cited by 2 other publications
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