Modeling disambiguation in word learning via multiple probabilistic constraints


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 differ- ences in the situation and language experience of the learner. We additionally present data testing a novel prediction of this probabilistic view of disambiguation.

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