Memory Constraints on Cross Situational Word Learning

A simple memory component is amended to local (“Pursuit”; Stevens, Gleitman, Trueswell, and Yang (2017)) and globa l(e.g., Yu and Smith (2007); Fazly, Alishahi, and Stevenson (2010)) models of cross-situational word learning. Only a finite (and small) number of words can be concurrently learned; successfully learned words are removed from the memory buffer and stored in the lexicon. The memory buffer improves the empirical coverage for both local and global learn-ing models. However, the complex task of homophone learning (Yurovsky & Yu, 2008) proves a more decisive advantage for the local model (dubbed Memory Bound Pursuit; MBP). Implications and limitations of these results are discussed.