title: Learning and long-term retention of large-scale artificial languages.
abstract: Recovering discrete words from continuous speech is one of the first challenges facing language learners. Infants and adults can make use of the statistical structure of utterances to learn the forms of words from unsegmented input, suggesting that this ability may be useful for bootstrapping language-specific cues to segmentation. It is unknown, however, whether performance shown in small-scale laboratory demonstrations of "statistical learning" can scale up to allow learning of the lexicons of natural languages, which are orders of magnitude larger. Artificial language experiments with adults can be used to test whether the mechanisms of statistical learning are in principle scalable to larger lexicons. We report data from a large-scale learning experiment that demonstrates that adults can learn words from unsegmented input in much larger languages than previously documented and that they retain the words they learn for years. These results suggest that statistical word segmentation could be scalable to the challenges of lexical acquisition in natural language learning.
authors.affiliation: Department of Psychology, Stanford University, Palo Alto, California, United States of America. mcfrank@stanford.edu
authors.forename: Michael C
authors.initials: MC
authors.lastname: Frank
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
authors.forename: Edward
authors.initials: E
authors.lastname: Gibson
mesh_terms: D000328:Adult; D006801:Humans; D007802:Language; D007804:Language Development; D015233:Models, Statistical; D015203:Reproducibility of Results; D013060:Speech; D013067:Speech Perception; D014706:Verbal Learning; D014825:Vocabulary