Skip to main content


Computer modeling examines word learning

Research Achievements

Computer modeling examines word learning

Inspired by recent experimental work on word learning (Trueswell et al., 2013), IGERT faculty Trueswell (Psychology), Gleitman (Linguistics & Psychology) and Yang (Computer Science and Linguistics) have embarked on a computer modeling enterprise that examines how learners (both children and adults) learn the meanings of words from the immediate situational context. When a child hears a novel word, it could refer to many possible things present in the environment (or even absent entities). These researchers have developed and tested experimentally a model in which learners need only maintain a single meaning for each word, seeking verification at the next occurrence of that word (propose-but-verify). This stands in contrast with popular word learning accounts in which vast numbers of possible word-meaning pairs are tracked simultaneously in a memory intensive fashion. This work benefited from numerous discussions that occurred at IGERT-funded graduate seminars.