Work

Modeling disambiguation in word learning via multiple probabilistic constraints

Year: 2013
Type: article
Abstract: Young children tend to map novel words to novel objects even in the presence of familiar competitors, a finding that has been dubbed the "disambiguation" effect.Theoretical accounts of this effect have debated whether it is due to initial constraints on children's lexicons (e.g. a principle of mutual exclusivity) or situation-specific pragmatic inferences.We suggest that both could be true.We present a hierarchical Bayesian model that implements both situation-level and hierarchical inference, and show that both can in principle contribute to disambiguation inferences with different levels of strength depending on differences in the situation and language experience of the learner.We additionally present data testing a novel prediction of this probabilistic view of disambiguation.
Institution: Stanford University
Language: English
Field: Psychology
Open Access status: green
OpenAlex ID: W2399323185
FWCI: 2.01
Cites: 18
Cited by: 7
Related to: 10