Low Entropy Facilitates Word Segmentation in Adult Learners

AbstractDo language learners benefit from exposure to input that is more predictable and has lower entropy? Frequency is known to facilitate learning (more frequent words acquired earlier). However, frequency is only one measure of the distributional structure of the linguistic input. Here, we show that entropy also impacts language learning: adults show better word segmentation in an artificial language when the sequence has lower entropy (created by making one word more frequent). Segmentation improved both for the language as a whole, and for the less frequent words, despite appearing half the number of times. These results illustrate the facilitative effect of entropy reduction on language learning. Theoretically, they show that the effect of frequency is relative, not absolute, and that language learners are sensitive to more complex measures of the environment. Methodologically, they suggest that the prevalent use of uniform distributions in word segmentation studies may underestimate learners’ abilities.

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