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All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language
Author(s) -
Romberg Alexa R.,
Saffran Jenny R.
Publication year - 2013
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12050
Subject(s) - computer science , artificial intelligence , natural language processing , probabilistic logic , language acquisition , context (archaeology) , natural language , implicit learning , linguistics , psychology , cognition , paleontology , philosophy , biology , neuroscience
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.