Learning novel sound patterns

The acquisition of vocabulary represents a key phenomenon in language acquisition, yet it is still poorly understood. Gathercole and colleagues have recently provided a rigorous test of vocabulary knowledge (the nonword repetition test, Gathercole, Willis, Baddeley, & Emslie, 1994) and have adapted the phonological loop part of the working memory model (Baddeley & Hitch, 1974) to explain the nonword repetition findings (e.g. Gathercole & Baddeley, 1989). However, there are two major failings in their explanation: there is no description of how words are learned, and no definition of how the phonological loop interacts with long-term memory. We present an EPAM based computational model which overcomes these problems by combining the phonological loop approach with the EPAM/chunking approach (Feigenbaum & Simon, 1984). Trained on naturalistic phonemically coded speech (from mother’s utterances to 2-3 year old children), the model provides a good match to the nonword repetition data from 2-3 year old children. The model is also able to show the effect on nonword repetition when the model is trained using different sets of input. Implementing the phonological loop within EPAM represents a parsimonious approach to learning novel sound patterns and provides a more precise definition of how vocabulary acquisition may occur