What mechanisms underlie people’s ability to use cross-situational statistics to learn the meanings of words? Here wepresent a large-scale evaluation of two major models of cross-situational learning: associative (Kachergis, Yu, & Shiffrin,2012a) and hypothesis testing (Trueswell, Medina, Hafri, &Gleitman, 2013). We fit each model individually to over 1500participants across seven experiments with a wide range ofconditions. We find that the associative model better capturesthe full range of individual differences and conditions whenlearning is cross-situational, although the hypothesis testingapproach outperforms it when there is no referential ambiguityduring training.
