All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

Alexa R. Romberg · Jenny R. Saffran

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Language Development and Disorders Neurobiology of Language and Bilingualism Categorization, perception, and language

Abstract

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants' knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition.

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References

1996Statistical Learning by 8-Month-Old Infants 1996Word Segmentation: The Role of Distributional Cues 2002Infant sensitivity to distributional information can affect phonetic discrimination 1999Rule Learning by Seven-Month-Old Infants 1998Computation of Conditional Probability Statistics by 8-Month-Old Infants 2002Variability and Detection of Invariant Structure 2006Implicit learning and statistical learning: one phenomenon, two approaches 2003Learning at a distance I. Statistical learning of non-adjacent dependencies

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