The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning
Abstract
Word learning is a “chicken and egg” problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.
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Probabilistic models of cognition: exploring representations and inductive biases
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568
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Metadata
- Authors
- Frank, M. C., Goodman, N. D., & Tenenbaum, J. B.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2009-05
- Volume
- 20
- Issue
- 5
- Pages
- 578-585
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2141038596
- Cited by
- 384
- References
- 43
- FWCI
- 38.06
- Type
- journal article
- Language
- en
- Open access
- closed
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