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The learner as statistician: three principles of computational success in language acquisition
Author(s) -
Soderstrom Melanie,
Conwell Erin,
Feldman Naomi,
Morgan James
Publication year - 2009
Publication title -
developmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.801
H-Index - 127
eISSN - 1467-7687
pISSN - 1363-755X
DOI - 10.1111/j.1467-7687.2009.00827.x
Subject(s) - statistician , psychology , cognitive science , medicine , pathology
© 2009 The Authors. Journal compilation © 2009 Blackwell Publishing Ltd. central question is what are the mechanisms of change. And in answering this question there is a corresponding move to focusing on the nature, structure and richness of the input to learners at multiple timescales and very fine-grained levels of detail. This is complemented by a greater understanding of the learner as situated in a rich and supportive context that evolves over time. Thus, when the field previously asked ‘what information do language learners have to work with?’ and ‘what can they learn with that information?’ the answers were ‘too little’ and ‘not much’. More recent work such as that presented in this collection, however, suggests that the answers to these questions are actually, ‘a lot’ and ‘quite a bit’. An important question for future work, then, will be what are the processes by which this evolution from learning to development occurs. That is, how do individual instances of in-the-moment learning coalesce and build on a longer timescale into developmental change (see also McMurray, Toscano, Horst & Samuelson, in press; Samuelson & Horst, 2008). Understanding the core computational principles of language development is an important step in this direction.