Word learning as Bayesian inference.

TLDR

The theory explains how learners can generalize meaningfully from one or a few positive examples by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of observed examples, thereby addressing shortcomings of deductive hypothesis elimination and associative learning. The authors present a Bayesian framework to understand how adults and children learn the meanings of words. They test this framework by conducting three experiments with adults and children on learning words for object categories across multiple taxonomic levels. The experiments provide strong support for the Bayesian account over competing models, with superior quantitative fits and explanatory power, and highlight its broader potential through several extensions.

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.