Modeling individual performance in cross-situational word learning
- Yung Han Khoe, Radboud University, Nijmegen, Netherlands
- Amy Perfors, University of Melbourne, Melbourne, VIC, Australia
- Andrew Hendrickson, Cognitive Science & Artificial Intelligence, Tilburg University, Tilburg, Netherlands
AbstractWhat mechanisms underlie people’s ability to use cross- situational statistics to learn the meanings of words? Here we present 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 1500 participants across seven experiments with a wide range of conditions. We find that the associative model better captures the full range of individual differences and conditions when learning is cross-situational, although the hypothesis testing approach outperforms it when there is no referential ambiguity during training.