Word Learning Under Infinite Uncertainty

Citation

Blythe R, Smith ADM & Smith K (2016) Word Learning Under Infinite Uncertainty. Cognition, 151, pp. 18-27. https://doi.org/10.1016/j.cognition.2016.02.017

Abstract
Language learners must learn the meanings of many thousands of words, de- spite those words occurring in complex environments in which infinitely many meanings might be inferred by the learner as a word’s true meaning. This problem of infinite referential uncertainty is often attributed to Willard Van Orman Quine. We provide a mathematical formalisation of an ideal cross- situational learner attempting to learn under infinite referential uncertainty, and identify conditions under which word learning is possible. As Quine’s intuitions suggest, learning under infinite uncertainty is in fact possible, pro- vided that learners have some means of ranking candidate word meanings in terms of their plausibility; furthermore, our analysis shows that this rank- ing could in fact be exceedingly weak, implying that constraints which allow learners to infer the plausibility of candidate word meanings could themselves be weak. This approach lifts the burden of explanation from ‘smart’ word learning constraints in learners, and suggests a programme of research into weak, unreliable, probabilistic constraints on the inference of word meaning in real word learners.

Keywords
word learning; cross-situational learning; Quine's Problem

Journal
Cognition: Volume 151

People

Dr Andrew Smith

Lecturer - Language Studies, English Studies