Linking working memory and long-term memory: A computational model of the learning of novel sound patterns

The nonword repetition (NWR) test has been shown to be a good predictor of children’s vocabulary size. NWR performance has been explained using the working memory model and specifically the phonological loop, which is seen as being critical in the learning of sound patterns. However, no detailed link between long-term memory and incoming sound patterns has been proposed. A computational model of children’s vocabulary acquisition (EPAM-VOC) is presented that concretely specifies how working memory and long-term memory interact. In this model performance differences arise from differences in long-term knowledge. The model’s behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. It is found that EPAM-VOC showed a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAMVOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM-VOC represents a good approximation of the learning of novel sound patterns that specifies how working memory and long-term memory interact, using an account that indicates that capacity differences are not necessary to simulate developmental change.