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Learning and Long-Term Retention of Large-Scale Artificial Languages

Author(s)
Frank, Michael C.; Tenenbaum, Joshua B.; Gibson, Edward A.
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Abstract
Recovering discrete words from continuous speech is one of the first challenges facing language learners. Infants and adults can make use of the statistical structure of utterances to learn the forms of words from unsegmented input, suggesting that this ability may be useful for bootstrapping language-specific cues to segmentation. It is unknown, however, whether performance shown in small-scale laboratory demonstrations of “statistical learning” can scale up to allow learning of the lexicons of natural languages, which are orders of magnitude larger. Artificial language experiments with adults can be used to test whether the mechanisms of statistical learning are in principle scalable to larger lexicons. We report data from a large-scale learning experiment that demonstrates that adults can learn words from unsegmented input in much larger languages than previously documented and that they retain the words they learn for years. These results suggest that statistical word segmentation could be scalable to the challenges of lexical acquisition in natural language learning.
Date issued
2013-01
URI
http://hdl.handle.net/1721.1/77211
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Frank, Michael C., Joshua B. Tenenbaum, and Edward Gibson. “Learning and Long-Term Retention of Large-Scale Artificial Languages.” Ed. Joel Snyder. PLoS ONE 8.1 (2013).
Version: Final published version
ISSN
1932-6203

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