title: Inferring word meanings by assuming that speakers are informative.
abstract: Language comprehension is more than a process of decoding the literal meaning of a speaker's utterance. Instead, by making the assumption that speakers choose their words to be informative in context, listeners routinely make pragmatic inferences that go beyond the linguistic data. If language learners make these same assumptions, they should be able to infer word meanings in otherwise ambiguous situations. We use probabilistic tools to formalize these kinds of informativeness inferences-extending a model of pragmatic language comprehension to the acquisition setting-and present four experiments whose data suggest that preschool children can use informativeness to infer word meanings and that adult judgments track quantitatively with informativeness.
authors.affiliation: Department of Psychology, Stanford University, United States. Electronic address: mcfrank@stanford.edu.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Frank
authors.affiliation: Department of Psychology, Stanford University, United States.
authors.forename: Noah D
authors.initials: ND
authors.lastname: Goodman
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D002675:Child, Preschool; D032882:Comprehension; D006801:Humans; D007600:Judgment; D007804:Language Development; D008037:Linguistics; D008960:Models, Psychological; D013067:Speech Perception
keywords: Bayesian models; Language acquisition; Pragmatics; Word learning