The Pursuit of Word Meanings. Stevens, J. S., Gleitman, L. R., Trueswell, J. C., & Yang, C. Cognitive Science, 41(S4):638-676, 2017. doi abstract bibtex Abstract We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from naturalistic corpora of parent-child interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest.
@Article{Stevens2017,
author = {Stevens, Jon Scott and Gleitman, Lila R. and Trueswell, John C. and Yang, Charles},
title = {The Pursuit of Word Meanings},
journal = {Cognitive Science},
year = {2017},
volume = {41},
number = {S4},
pages = {638-676},
abstract = {Abstract We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from naturalistic corpora of parent-child interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest.},
doi = {10.1111/cogs.12416},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.12416},
}
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