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van den Bos, Esther; Christiansen, Morten H.; Misyak, Jennifer B. – Journal of Memory and Language, 2012
Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements…
Descriptors: Artificial Languages, Learning, Probability, Cues
Perruchet, Pierre; Poulin-Charronnat, Benedicte – Journal of Memory and Language, 2012
Endress and Mehler (2009) reported that when adult subjects are exposed to an unsegmented artificial language composed from trisyllabic words such as ABX, YBC, and AZC, they are unable to distinguish between these words and what they coined as the "phantom-word" ABC in a subsequent test. This suggests that statistical learning generates knowledge…
Descriptors: Artificial Languages, Probability, Models, Simulation
Wonnacott, Elizabeth – Journal of Memory and Language, 2011
Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Such learning has been considered problematic for theories of acquisition: if learners generalize abstract patterns to new words, how do they learn lexically-based exceptions? One approach claims that learners use…
Descriptors: Child Language, Artificial Languages, Generalization, Inferences
Finley, Sara; Badecker, William – Journal of Memory and Language, 2009
Abstract representations such as subsegmental phonological features play such a vital role in explanations of phonological processes that many assume that these representations play an equally prominent role in the learning process. This assumption is tested in three artificial grammar experiments involving a mini language with morpho-phonological…
Descriptors: Play, Vowels, Phonology, Artificial Languages