Beyond Transitional Probabilities: Human Learners Impose a Parsimony Bias in Statistical Word Segmentation


Human infants and adults are able to segment coherent sequences from unsegmented strings of auditory stimuli after only a short exposure, an ability thought to be linked to early language acquisition. Although some research has hypothesized that learners succeed in these tasks by computing transitional probabilities between syllables, current experimental results do not differentiate between a range of models of different computations that learners could perform. We created a set of stimuli that was consistent with two different lexicons—one consisting of two-syllable words and one of three-syllable words—but where transition probabilities would not lead learners to segment sentences consistently according to either lexicon. Participants’ responses formed a distribution over possible segmentations that included consistent segmentations into both two- and three-syllable words, suggesting that learners do not use pure transitional probabilities to segment but instead impose a bias towards parsimony on the lexicons they learn.

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