The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Word learning as Bayesian inference.
Abstract
The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.
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Citations
Cites
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Self-citationLanguage Acquisition and Conceptual Development
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Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880 · cited 1×
“In principle, both of these questions can be addressed within a hierarchical Bayesian framework (Kemp, Perfors, & Tenenbaum, 2004, 2007; Tenenbaum et al., 2006), an extension of the approach we have developed here to include hypothesis spaces at multiple levels of abstraction, with probabilistic…”
BackgroundSelf-citationDistributional Information: A Powerful Cue for Acquiring Syntactic Categories
Redington, M., Chater, N., & Finch, S. (1998) · Cognitive Science · doi:10.1207/s15516709cog2204_2 · Cited by 535 · cited 1×
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Cited by
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817
Self-citationBuilding machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Self-citationHow to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Self-citationPredicting pragmatic reasoning in language games
Frank, M. C., & Goodman, N. D. (2012) · Science · doi:10.1126/science.1218633 · Cited by 868
Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880
Self-citation
Metadata
- Authors
- Xu, F., & Tenenbaum, J. B.
- Venue
- Psychological Review
- Published
- 2007
- Volume
- 114
- Issue
- 2
- Pages
- 245-272
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2016429292
- Cited by
- 1,033
- References
- 8
- FWCI
- 62.99
- Type
- journal article
- Language
- en
- Open access
- closed
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