We present the Retention and Recognition model (R&R), aprobabilistic exemplar model that accounts for segmentationin Artificial Language Learning experiments. We show thatR&R provides an excellent fit to human responses in threesegmentation experiments with adults (Frank et al., 2010),outperforming existing models. Additionally, we analyze theresults of the simulations and propose alternative explanationsfor the experimental findings.
