Modeling human performance in statistical word segmentation
The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the le...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier
2016
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| Online Access: | http://hdl.handle.net/1721.1/102506 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1826215338665050112 |
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| author | Frank, Michael C. Goldwater, Sharon Griffiths, Thomas L. Tenenbaum, Joshua B. |
| 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. Goldwater, Sharon Griffiths, Thomas L. Tenenbaum, Joshua B. |
| author_sort | Frank, Michael C. |
| collection | MIT |
| description | The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance. |
| first_indexed | 2024-09-23T16:24:28Z |
| format | Article |
| id | mit-1721.1/102506 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:24:28Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1025062022-09-29T19:49:16Z Modeling human performance in statistical word segmentation Frank, Michael C. Goldwater, Sharon Griffiths, Thomas L. Tenenbaum, Joshua B. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance. National Science Foundation (U.S.) (Grant BCS-0631518) 2016-05-16T13:17:52Z 2016-05-16T13:17:52Z 2010-07 2010-07 Article http://purl.org/eprint/type/JournalArticle 00100277 http://hdl.handle.net/1721.1/102506 Frank, Michael C., Sharon Goldwater, Thomas L. Griffiths, and Joshua B. Tenenbaum. “Modeling Human Performance in Statistical Word Segmentation.” Cognition 117, no. 2 (November 2010): 107–125. https://orcid.org/0000-0002-1925-2035 en_US http://dx.doi.org/10.1016/j.cognition.2010.07.005 Cognition Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Other univ. web domain |
| spellingShingle | Frank, Michael C. Goldwater, Sharon Griffiths, Thomas L. Tenenbaum, Joshua B. Modeling human performance in statistical word segmentation |
| title | Modeling human performance in statistical word segmentation |
| title_full | Modeling human performance in statistical word segmentation |
| title_fullStr | Modeling human performance in statistical word segmentation |
| title_full_unstemmed | Modeling human performance in statistical word segmentation |
| title_short | Modeling human performance in statistical word segmentation |
| title_sort | modeling human performance in statistical word segmentation |
| url | http://hdl.handle.net/1721.1/102506 https://orcid.org/0000-0002-1925-2035 |
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