Learning and Long-Term Retention of Large-Scale Artificial Languages
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 langua...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2013
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| Online Access: | http://hdl.handle.net/1721.1/77211 https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0002-5912-883X |
| _version_ | 1826212383870156800 |
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| author | Frank, Michael C. Tenenbaum, Joshua B. Gibson, Edward A. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Frank, Michael C. Tenenbaum, Joshua B. Gibson, Edward A. |
| author_sort | Frank, Michael C. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:20:30Z |
| format | Article |
| id | mit-1721.1/77211 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:20:30Z |
| publishDate | 2013 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/772112022-09-29T14:20:47Z Learning and Long-Term Retention of Large-Scale Artificial Languages Frank, Michael C. Tenenbaum, Joshua B. Gibson, Edward A. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. Gibson, Edward A. 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. National Science Foundation (U.S.) (NSF DDRIG #0746251) 2013-02-27T16:50:25Z 2013-02-27T16:50:25Z 2013-01 2012-08 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/77211 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). https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0002-5912-883X en_US http://dx.doi.org/10.1371/journal.pone.0052500 PLoS ONE Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Frank, Michael C. Tenenbaum, Joshua B. Gibson, Edward A. Learning and Long-Term Retention of Large-Scale Artificial Languages |
| title | Learning and Long-Term Retention of Large-Scale Artificial Languages |
| title_full | Learning and Long-Term Retention of Large-Scale Artificial Languages |
| title_fullStr | Learning and Long-Term Retention of Large-Scale Artificial Languages |
| title_full_unstemmed | Learning and Long-Term Retention of Large-Scale Artificial Languages |
| title_short | Learning and Long-Term Retention of Large-Scale Artificial Languages |
| title_sort | learning and long term retention of large scale artificial languages |
| url | http://hdl.handle.net/1721.1/77211 https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0002-5912-883X |
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