Modeling the Influence of Language Input Statistics on Children's Speech Production.

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism-backward transitional probability-is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific "chunks" from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus.

Semantic interference in picture naming during dual-task performance does not vary with reading ability.

Previous dual-task studies examining the locus of semantic interference of distractor words in picture naming have obtained diverging results. In these studies, participants manually responded to tones and named pictures while ignoring distractor words (picture-word interference, PWI) with varying stimulus onset asynchrony (SOA) between tone and PWI stimulus. Whereas some studies observed no semantic interference at short SOAs, other studies observed effects of similar magnitude at short and long SOAs.