Modeling online word segmentation performance in structured artificial languages

Lexical dependencies abound in natural language: words tend to follow particular words or word categories. However, arti-ficial language learning experiments exploring word segmen-tation have so far lacked such structure. In the present study, we explore whether simple inter-word dependencies influence the word segmentation performance of adult learners. We use a continuous testing paradigm instead of an experiment-final test battery to reveal the trajectory of learning and to al-low detailed comparison with three computational models of word segmentation. Adult performance on languages with de-pendencies is equal or lower to those without. Of the mod-els tested, all perform worse on languages with dependencies, though a novel particle filter-based lexical segmentation model produces learning curves most similar to human subjects